Update faiss for batch distances calc & caching when updating

.github/workflows/build-reusable.yml

@@ -35,8 +35,8 @@ jobs:
     strategy:
       matrix:
         include:
-          # Note: Python 3.9 dropped - uses PEP 604 union syntax (str | None)
+          - os: ubuntu-22.04
-          # which requires Python 3.10+
+            python: '3.9'
           - os: ubuntu-22.04
             python: '3.10'
           - os: ubuntu-22.04
@@ -46,6 +46,8 @@ jobs:
           - os: ubuntu-22.04
             python: '3.13'
           # ARM64 Linux builds
+          - os: ubuntu-24.04-arm
+            python: '3.9'
           - os: ubuntu-24.04-arm
             python: '3.10'
           - os: ubuntu-24.04-arm
@@ -54,6 +56,8 @@ jobs:
             python: '3.12'
           - os: ubuntu-24.04-arm
             python: '3.13'
+          - os: macos-14
+            python: '3.9'
           - os: macos-14
             python: '3.10'
           - os: macos-14
@@ -62,6 +66,8 @@ jobs:
             python: '3.12'
           - os: macos-14
             python: '3.13'
+          - os: macos-15
+            python: '3.9'
           - os: macos-15
             python: '3.10'
           - os: macos-15
@@ -70,24 +76,16 @@ jobs:
             python: '3.12'
           - os: macos-15
             python: '3.13'
-          # Intel Mac builds (x86_64) - replaces deprecated macos-13
+          - os: macos-13
-          # Note: Python 3.13 excluded - PyTorch has no wheels for macOS x86_64 + Python 3.13
+            python: '3.9'
-          # (PyTorch <=2.4.1 lacks cp313, PyTorch >=2.5.0 dropped Intel Mac support)
+          - os: macos-13
-          - os: macos-15-intel
             python: '3.10'
-          - os: macos-15-intel
+          - os: macos-13
             python: '3.11'
-          - os: macos-15-intel
+          - os: macos-13
             python: '3.12'
-          # macOS 26 (beta) - arm64
+          # Note: macos-13 + Python 3.13 excluded due to PyTorch compatibility
-          - os: macos-26
+          # (PyTorch 2.5+ supports Python 3.13 but not Intel Mac x86_64)
-            python: '3.10'
-          - os: macos-26
-            python: '3.11'
-          - os: macos-26
-            python: '3.12'
-          - os: macos-26
-            python: '3.13'
     runs-on: ${{ matrix.os }}
 
     steps:
@@ -206,16 +204,13 @@ jobs:
             # Use system clang for better compatibility
             export CC=clang
             export CXX=clang++
-            # Set deployment target based on runner
+            # Homebrew libraries on each macOS version require matching minimum version
-            # macos-15-intel runs macOS 15, so target 15.0 (system libraries require it)
+            if [[ "${{ matrix.os }}" == "macos-13" ]]; then
-            if [[ "${{ matrix.os }}" == "macos-15-intel" ]]; then
+              export MACOSX_DEPLOYMENT_TARGET=13.0
-              export MACOSX_DEPLOYMENT_TARGET=15.0
+            elif [[ "${{ matrix.os }}" == "macos-14" ]]; then
-            elif [[ "${{ matrix.os }}" == macos-14* ]]; then
               export MACOSX_DEPLOYMENT_TARGET=14.0
-            elif [[ "${{ matrix.os }}" == macos-15* ]]; then
+            elif [[ "${{ matrix.os }}" == "macos-15" ]]; then
               export MACOSX_DEPLOYMENT_TARGET=15.0
-            elif [[ "${{ matrix.os }}" == macos-26* ]]; then
-              export MACOSX_DEPLOYMENT_TARGET=26.0
             fi
             uv build --wheel --python ${{ matrix.python }} --find-links ${GITHUB_WORKSPACE}/packages/leann-core/dist
           else
@@ -229,16 +224,14 @@ jobs:
             # Use system clang for better compatibility
             export CC=clang
             export CXX=clang++
-            # Set deployment target based on runner
+            # DiskANN requires macOS 13.3+ for sgesdd_ LAPACK function
-            # macos-15-intel runs macOS 15, so target 15.0 (system libraries require it)
+            # But Homebrew libraries on each macOS version require matching minimum version
-            if [[ "${{ matrix.os }}" == "macos-15-intel" ]]; then
+            if [[ "${{ matrix.os }}" == "macos-13" ]]; then
-              export MACOSX_DEPLOYMENT_TARGET=15.0
+              export MACOSX_DEPLOYMENT_TARGET=13.3
-            elif [[ "${{ matrix.os }}" == macos-14* ]]; then
+            elif [[ "${{ matrix.os }}" == "macos-14" ]]; then
               export MACOSX_DEPLOYMENT_TARGET=14.0
-            elif [[ "${{ matrix.os }}" == macos-15* ]]; then
+            elif [[ "${{ matrix.os }}" == "macos-15" ]]; then
               export MACOSX_DEPLOYMENT_TARGET=15.0
-            elif [[ "${{ matrix.os }}" == macos-26* ]]; then
-              export MACOSX_DEPLOYMENT_TARGET=26.0
             fi
             uv build --wheel --python ${{ matrix.python }} --find-links ${GITHUB_WORKSPACE}/packages/leann-core/dist
           else
@@ -276,19 +269,16 @@ jobs:
         if: runner.os == 'macOS'
         run: |
           # Determine deployment target based on runner OS
-          # macos-15-intel runs macOS 15, so target 15.0 (system libraries require it)
+          # Must match the Homebrew libraries for each macOS version
-          if [[ "${{ matrix.os }}" == "macos-15-intel" ]]; then
+          if [[ "${{ matrix.os }}" == "macos-13" ]]; then
-            HNSW_TARGET="15.0"
+            HNSW_TARGET="13.0"
-            DISKANN_TARGET="15.0"
+            DISKANN_TARGET="13.3"
-          elif [[ "${{ matrix.os }}" == macos-14* ]]; then
+          elif [[ "${{ matrix.os }}" == "macos-14" ]]; then
             HNSW_TARGET="14.0"
             DISKANN_TARGET="14.0"
-          elif [[ "${{ matrix.os }}" == macos-15* ]]; then
+          elif [[ "${{ matrix.os }}" == "macos-15" ]]; then
             HNSW_TARGET="15.0"
             DISKANN_TARGET="15.0"
-          elif [[ "${{ matrix.os }}" == macos-26* ]]; then
-            HNSW_TARGET="26.0"
-            DISKANN_TARGET="26.0"
           fi
 
           # Repair HNSW wheel
@@ -344,15 +334,12 @@ jobs:
           PY_TAG=$($UV_PY -c "import sys; print(f'cp{sys.version_info[0]}{sys.version_info[1]}')")
 
           if [[ "$RUNNER_OS" == "macOS" ]]; then
-            # macos-15-intel runs macOS 15, so target 15.0 (system libraries require it)
+            if [[ "${{ matrix.os }}" == "macos-13" ]]; then
-            if [[ "${{ matrix.os }}" == "macos-15-intel" ]]; then
+              export MACOSX_DEPLOYMENT_TARGET=13.3
-              export MACOSX_DEPLOYMENT_TARGET=15.0
+            elif [[ "${{ matrix.os }}" == "macos-14" ]]; then
-            elif [[ "${{ matrix.os }}" == macos-14* ]]; then
               export MACOSX_DEPLOYMENT_TARGET=14.0
-            elif [[ "${{ matrix.os }}" == macos-15* ]]; then
+            elif [[ "${{ matrix.os }}" == "macos-15" ]]; then
               export MACOSX_DEPLOYMENT_TARGET=15.0
-            elif [[ "${{ matrix.os }}" == macos-26* ]]; then
-              export MACOSX_DEPLOYMENT_TARGET=26.0
             fi
           fi
 

.github/workflows/link-check.yml

@@ -14,6 +14,6 @@ jobs:
       - uses: actions/checkout@v4
       - uses: lycheeverse/lychee-action@v2
         with:
-          args: --no-progress --insecure --user-agent 'curl/7.68.0' --exclude '.*api\.star-history\.com.*' --accept 200,201,202,203,204,205,206,207,208,226,300,301,302,303,304,305,306,307,308,503 README.md docs/ apps/ examples/ benchmarks/
+          args: --no-progress --insecure --user-agent 'curl/7.68.0' README.md docs/ apps/ examples/ benchmarks/
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.gitignore

@@ -91,8 +91,7 @@ packages/leann-backend-diskann/third_party/DiskANN/_deps/
 
 *.meta.json
 *.passages.json
-*.npy
+
-*.db
 batchtest.py
 tests/__pytest_cache__/
 tests/__pycache__/
@@ -106,6 +105,3 @@ apps/multimodal/vision-based-pdf-multi-vector/multi-vector-colpali-native-weavia
 # The following line used to force-add a large demo PDF; remove it to satisfy pre-commit:
 # !apps/multimodal/vision-based-pdf-multi-vector/pdfs/2004.12832v2.pdf
 !apps/multimodal/vision-based-pdf-multi-vector/fig/*
-
-# AUR build directory (Arch Linux)
-paru-bin/

README.md

@@ -8,35 +8,19 @@
   <img src="https://img.shields.io/badge/Platform-Ubuntu%20%26%20Arch%20%26%20WSL%20%7C%20macOS%20(ARM64%2FIntel)-lightgrey" alt="Platform">
   <img src="https://img.shields.io/badge/License-MIT-green.svg" alt="MIT License">
   <img src="https://img.shields.io/badge/MCP-Native%20Integration-blue" alt="MCP Integration">
-  <a href="https://join.slack.com/t/leann-e2u9779/shared_invite/zt-3ckd2f6w1-OX08~NN4gkWhh10PRVBj1Q">
+  <a href="https://join.slack.com/t/leann-e2u9779/shared_invite/zt-3ckd2f6w1-OX08~NN4gkWhh10PRVBj1Q"><img src="https://img.shields.io/badge/Slack-Join-4A154B?logo=slack&logoColor=white" alt="Join Slack">
-    <img src="https://img.shields.io/badge/Slack-Join-4A154B?logo=slack&logoColor=white" alt="Join Slack">
+  <a href="assets/wechat_user_group.JPG" title="Join WeChat group"><img src="https://img.shields.io/badge/WeChat-Join-2DC100?logo=wechat&logoColor=white" alt="Join WeChat group"></a>
-  </a>
-  <a href="assets/wechat_user_group.JPG" title="Join WeChat group">
-    <img src="https://img.shields.io/badge/WeChat-Join-2DC100?logo=wechat&logoColor=white" alt="Join WeChat group">
-  </a>
 </p>
 
-<div align="center">
-  <a href="https://forms.gle/rDbZf864gMNxhpTq8">
-    <img src="https://img.shields.io/badge/📣_Community_Survey-Help_Shape_v0.4-007ec6?style=for-the-badge&logo=google-forms&logoColor=white" alt="Take Survey">
-  </a>
-  <p>
-    We track <b>zero telemetry</b>. This survey is the ONLY way to tell us if you want <br>
-    <b>GPU Acceleration</b> or <b>More Integrations</b> next.<br>
-    👉 <a href="https://forms.gle/rDbZf864gMNxhpTq8"><b>Click here to cast your vote (2 mins)</b></a>
-  </p>
-</div>
-
 <h2 align="center" tabindex="-1" class="heading-element" dir="auto">
     The smallest vector index in the world. RAG Everything with LEANN!
 </h2>
 
 LEANN is an innovative vector database that democratizes personal AI. Transform your laptop into a powerful RAG system that can index and search through millions of documents while using **97% less storage** than traditional solutions **without accuracy loss**.
 
-
 LEANN achieves this through *graph-based selective recomputation* with *high-degree preserving pruning*, computing embeddings on-demand instead of storing them all. [Illustration Fig →](#️-architecture--how-it-works) | [Paper →](https://arxiv.org/abs/2506.08276)
 
-**Ready to RAG Everything?** Transform your laptop into a personal AI assistant that can semantic search your **[file system](#-personal-data-manager-process-any-documents-pdf-txt-md)**, **[emails](#-your-personal-email-secretary-rag-on-apple-mail)**, **[browser history](#-time-machine-for-the-web-rag-your-entire-browser-history)**, **[chat history](#-wechat-detective-unlock-your-golden-memories)** ([WeChat](#-wechat-detective-unlock-your-golden-memories), [iMessage](#-imessage-history-your-personal-conversation-archive)), **[agent memory](#-chatgpt-chat-history-your-personal-ai-conversation-archive)** ([ChatGPT](#-chatgpt-chat-history-your-personal-ai-conversation-archive), [Claude](#-claude-chat-history-your-personal-ai-conversation-archive)), **[live data](#mcp-integration-rag-on-live-data-from-any-platform)** ([Slack](#slack-messages-search-your-team-conversations), [Twitter](#-twitter-bookmarks-your-personal-tweet-library)), **[codebase](#-claude-code-integration-transform-your-development-workflow)**\* , or external knowledge bases (i.e., 60M documents) - all on your laptop, with zero cloud costs and complete privacy.
+**Ready to RAG Everything?** Transform your laptop into a personal AI assistant that can semantic search your **[file system](#-personal-data-manager-process-any-documents-pdf-txt-md)**, **[emails](#-your-personal-email-secretary-rag-on-apple-mail)**, **[browser history](#-time-machine-for-the-web-rag-your-entire-browser-history)**, **[chat history](#-wechat-detective-unlock-your-golden-memories)**, **[codebase](#-claude-code-integration-transform-your-development-workflow)**\* , or external knowledge bases (i.e., 60M documents) - all on your laptop, with zero cloud costs and complete privacy.
 
 
 \* Claude Code only supports basic `grep`-style keyword search. **LEANN** is a drop-in **semantic search MCP service fully compatible with Claude Code**, unlocking intelligent retrieval without changing your workflow. 🔥 Check out [the easy setup →](packages/leann-mcp/README.md)
@@ -88,9 +72,8 @@ uv venv
 source .venv/bin/activate
 uv pip install leann
 ```
-
 <!--
-> Low-resource? See "Low-resource setups" in the [Configuration Guide](docs/configuration-guide.md#low-resource-setups). -->
+> Low-resource? See “Low-resource setups” in the [Configuration Guide](docs/configuration-guide.md#low-resource-setups). -->
 
 <details>
 <summary>
@@ -193,7 +176,7 @@ response = chat.ask("How much storage does LEANN save?", top_k=1)
 
 ## RAG on Everything!
 
-LEANN supports RAG on various data sources including documents (`.pdf`, `.txt`, `.md`), Apple Mail, Google Search History, WeChat, ChatGPT conversations, Claude conversations, iMessage conversations, and **live data from any platform through MCP (Model Context Protocol) servers** - including Slack, Twitter, and more.
+LEANN supports RAG on various data sources including documents (`.pdf`, `.txt`, `.md`), Apple Mail, Google Search History, WeChat, and more.
 
 
 
@@ -201,7 +184,7 @@ LEANN supports RAG on various data sources including documents (`.pdf`, `.txt`,
 
 #### LLM Backend
 
-LEANN supports many LLM providers for text generation (HuggingFace, Ollama, Anthropic, and Any OpenAI compatible API).
+LEANN supports many LLM providers for text generation (HuggingFace, Ollama, and Any OpenAI compatible API).
 
 
 <details>
@@ -269,7 +252,6 @@ Below is a list of base URLs for common providers to get you started.
 | **SiliconFlow** | `https://api.siliconflow.cn/v1`                            |
 | **Zhipu (BigModel)** | `https://open.bigmodel.cn/api/paas/v4/`                |
 | **Mistral AI** | `https://api.mistral.ai/v1`                                |
-| **Anthropic** | `https://api.anthropic.com/v1`                                |
 
 
 
@@ -329,7 +311,7 @@ All RAG examples share these common parameters. **Interactive mode** is availabl
 --embedding-mode MODE        # sentence-transformers, openai, mlx, or ollama
 
 # LLM Parameters (Text generation models)
---llm TYPE                   # LLM backend: openai, ollama, hf, or anthropic (default: openai)
+--llm TYPE                   # LLM backend: openai, ollama, or hf (default: openai)
 --llm-model MODEL            # Model name (default: gpt-4o) e.g., gpt-4o-mini, llama3.2:1b, Qwen/Qwen2.5-1.5B-Instruct
 --thinking-budget LEVEL      # Thinking budget for reasoning models: low/medium/high (supported by o3, o3-mini, GPT-Oss:20b, and other reasoning models)
 
@@ -392,54 +374,6 @@ python -m apps.code_rag --repo-dir "./my_codebase" --query "How does authenticat
 
 </details>
 
-### 🎨 ColQwen: Multimodal PDF Retrieval with Vision-Language Models
-
-Search through PDFs using both text and visual understanding with ColQwen2/ColPali models. Perfect for research papers, technical documents, and any PDFs with complex layouts, figures, or diagrams.
-
-> **🍎 Mac Users**: ColQwen is optimized for Apple Silicon with MPS acceleration for faster inference!
-
-```bash
-# Build index from PDFs
-python -m apps.colqwen_rag build --pdfs ./my_papers/ --index research_papers
-
-# Search with text queries
-python -m apps.colqwen_rag search research_papers "How does attention mechanism work?"
-
-# Interactive Q&A
-python -m apps.colqwen_rag ask research_papers --interactive
-```
-
-<details>
-<summary><strong>📋 Click to expand: ColQwen Setup & Usage</strong></summary>
-
-#### Prerequisites
-```bash
-# Install dependencies
-uv pip install colpali_engine pdf2image pillow matplotlib qwen_vl_utils einops seaborn
-brew install poppler  # macOS only, for PDF processing
-```
-
-#### Build Index
-```bash
-python -m apps.colqwen_rag build \
-  --pdfs ./pdf_directory/ \
-  --index my_index \
-  --model colqwen2  # or colpali
-```
-
-#### Search
-```bash
-python -m apps.colqwen_rag search my_index "your question here" --top-k 5
-```
-
-#### Models
-- **ColQwen2** (`colqwen2`): Latest vision-language model with improved performance
-- **ColPali** (`colpali`): Proven multimodal retriever
-
-For detailed usage, see the [ColQwen Guide](docs/COLQWEN_GUIDE.md).
-
-</details>
-
 ### 📧 Your Personal Email Secretary: RAG on Apple Mail!
 
 > **Note:** The examples below currently support macOS only. Windows support coming soon.
@@ -608,386 +542,10 @@ Once the index is built, you can ask questions like:
 
 </details>
 
-### 🤖 ChatGPT Chat History: Your Personal AI Conversation Archive!
-
-Transform your ChatGPT conversations into a searchable knowledge base! Search through all your ChatGPT discussions about coding, research, brainstorming, and more.
-
-```bash
-python -m apps.chatgpt_rag --export-path chatgpt_export.html --query "How do I create a list in Python?"
-```
-
-**Unlock your AI conversation history.** Never lose track of valuable insights from your ChatGPT discussions again.
-
-<details>
-<summary><strong>📋 Click to expand: How to Export ChatGPT Data</strong></summary>
-
-**Step-by-step export process:**
-
-1. **Sign in to ChatGPT**
-2. **Click your profile icon** in the top right corner
-3. **Navigate to Settings** → **Data Controls**
-4. **Click "Export"** under Export Data
-5. **Confirm the export** request
-6. **Download the ZIP file** from the email link (expires in 24 hours)
-7. **Extract or use directly** with LEANN
-
-**Supported formats:**
-- `.html` files from ChatGPT exports
-- `.zip` archives from ChatGPT
-- Directories with multiple export files
-
-</details>
-
-<details>
-<summary><strong>📋 Click to expand: ChatGPT-Specific Arguments</strong></summary>
-
-#### Parameters
-```bash
---export-path PATH           # Path to ChatGPT export file (.html/.zip) or directory (default: ./chatgpt_export)
---separate-messages         # Process each message separately instead of concatenated conversations
---chunk-size N              # Text chunk size (default: 512)
---chunk-overlap N           # Overlap between chunks (default: 128)
-```
-
-#### Example Commands
-```bash
-# Basic usage with HTML export
-python -m apps.chatgpt_rag --export-path conversations.html
-
-# Process ZIP archive from ChatGPT
-python -m apps.chatgpt_rag --export-path chatgpt_export.zip
-
-# Search with specific query
-python -m apps.chatgpt_rag --export-path chatgpt_data.html --query "Python programming help"
-
-# Process individual messages for fine-grained search
-python -m apps.chatgpt_rag --separate-messages --export-path chatgpt_export.html
-
-# Process directory containing multiple exports
-python -m apps.chatgpt_rag --export-path ./chatgpt_exports/ --max-items 1000
-```
-
-</details>
-
-<details>
-<summary><strong>💡 Click to expand: Example queries you can try</strong></summary>
-
-Once your ChatGPT conversations are indexed, you can search with queries like:
-- "What did I ask ChatGPT about Python programming?"
-- "Show me conversations about machine learning algorithms"
-- "Find discussions about web development frameworks"
-- "What coding advice did ChatGPT give me?"
-- "Search for conversations about debugging techniques"
-- "Find ChatGPT's recommendations for learning resources"
-
-</details>
-
-### 🤖 Claude Chat History: Your Personal AI Conversation Archive!
-
-Transform your Claude conversations into a searchable knowledge base! Search through all your Claude discussions about coding, research, brainstorming, and more.
-
-```bash
-python -m apps.claude_rag --export-path claude_export.json --query "What did I ask about Python dictionaries?"
-```
-
-**Unlock your AI conversation history.** Never lose track of valuable insights from your Claude discussions again.
-
-<details>
-<summary><strong>📋 Click to expand: How to Export Claude Data</strong></summary>
-
-**Step-by-step export process:**
-
-1. **Open Claude** in your browser
-2. **Navigate to Settings** (look for gear icon or settings menu)
-3. **Find Export/Download** options in your account settings
-4. **Download conversation data** (usually in JSON format)
-5. **Place the file** in your project directory
-
-*Note: Claude export methods may vary depending on the interface you're using. Check Claude's help documentation for the most current export instructions.*
-
-**Supported formats:**
-- `.json` files (recommended)
-- `.zip` archives containing JSON data
-- Directories with multiple export files
-
-</details>
-
-<details>
-<summary><strong>📋 Click to expand: Claude-Specific Arguments</strong></summary>
-
-#### Parameters
-```bash
---export-path PATH           # Path to Claude export file (.json/.zip) or directory (default: ./claude_export)
---separate-messages         # Process each message separately instead of concatenated conversations
---chunk-size N              # Text chunk size (default: 512)
---chunk-overlap N           # Overlap between chunks (default: 128)
-```
-
-#### Example Commands
-```bash
-# Basic usage with JSON export
-python -m apps.claude_rag --export-path my_claude_conversations.json
-
-# Process ZIP archive from Claude
-python -m apps.claude_rag --export-path claude_export.zip
-
-# Search with specific query
-python -m apps.claude_rag --export-path claude_data.json --query "machine learning advice"
-
-# Process individual messages for fine-grained search
-python -m apps.claude_rag --separate-messages --export-path claude_export.json
-
-# Process directory containing multiple exports
-python -m apps.claude_rag --export-path ./claude_exports/ --max-items 1000
-```
-
-</details>
-
-<details>
-<summary><strong>💡 Click to expand: Example queries you can try</strong></summary>
-
-Once your Claude conversations are indexed, you can search with queries like:
-- "What did I ask Claude about Python programming?"
-- "Show me conversations about machine learning algorithms"
-- "Find discussions about software architecture patterns"
-- "What debugging advice did Claude give me?"
-- "Search for conversations about data structures"
-- "Find Claude's recommendations for learning resources"
-
-</details>
-
-### 💬 iMessage History: Your Personal Conversation Archive!
-
-Transform your iMessage conversations into a searchable knowledge base! Search through all your text messages, group chats, and conversations with friends, family, and colleagues.
-
-```bash
-python -m apps.imessage_rag --query "What did we discuss about the weekend plans?"
-```
-
-**Unlock your message history.** Never lose track of important conversations, shared links, or memorable moments from your iMessage history.
-
-<details>
-<summary><strong>📋 Click to expand: How to Access iMessage Data</strong></summary>
-
-**iMessage data location:**
-
-iMessage conversations are stored in a SQLite database on your Mac at:
-```
-~/Library/Messages/chat.db
-```
-
-**Important setup requirements:**
-
-1. **Grant Full Disk Access** to your terminal or IDE:
-   - Open **System Preferences** → **Security & Privacy** → **Privacy**
-   - Select **Full Disk Access** from the left sidebar
-   - Click the **+** button and add your terminal app (Terminal, iTerm2) or IDE (VS Code, etc.)
-   - Restart your terminal/IDE after granting access
-
-2. **Alternative: Use a backup database**
-   - If you have Time Machine backups or manual copies of the database
-   - Use `--db-path` to specify a custom location
-
-**Supported formats:**
-- Direct access to `~/Library/Messages/chat.db` (default)
-- Custom database path with `--db-path`
-- Works with backup copies of the database
-
-</details>
-
-<details>
-<summary><strong>📋 Click to expand: iMessage-Specific Arguments</strong></summary>
-
-#### Parameters
-```bash
---db-path PATH                    # Path to chat.db file (default: ~/Library/Messages/chat.db)
---concatenate-conversations       # Group messages by conversation (default: True)
---no-concatenate-conversations    # Process each message individually
---chunk-size N                    # Text chunk size (default: 1000)
---chunk-overlap N                 # Overlap between chunks (default: 200)
-```
-
-#### Example Commands
-```bash
-# Basic usage (requires Full Disk Access)
-python -m apps.imessage_rag
-
-# Search with specific query
-python -m apps.imessage_rag --query "family dinner plans"
-
-# Use custom database path
-python -m apps.imessage_rag --db-path /path/to/backup/chat.db
-
-# Process individual messages instead of conversations
-python -m apps.imessage_rag --no-concatenate-conversations
-
-# Limit processing for testing
-python -m apps.imessage_rag --max-items 100 --query "weekend"
-```
-
-</details>
-
-<details>
-<summary><strong>💡 Click to expand: Example queries you can try</strong></summary>
-
-Once your iMessage conversations are indexed, you can search with queries like:
-- "What did we discuss about vacation plans?"
-- "Find messages about restaurant recommendations"
-- "Show me conversations with John about the project"
-- "Search for shared links about technology"
-- "Find group chat discussions about weekend events"
-- "What did mom say about the family gathering?"
-
-</details>
-
-### MCP Integration: RAG on Live Data from Any Platform
-
-Connect to live data sources through the Model Context Protocol (MCP). LEANN now supports real-time RAG on platforms like Slack, Twitter, and more through standardized MCP servers.
-
-**Key Benefits:**
-- **Live Data Access**: Fetch real-time data without manual exports
-- **Standardized Protocol**: Use any MCP-compatible server
-- **Easy Extension**: Add new platforms with minimal code
-- **Secure Access**: MCP servers handle authentication
-
-#### 💬 Slack Messages: Search Your Team Conversations
-
-Transform your Slack workspace into a searchable knowledge base! Find discussions, decisions, and shared knowledge across all your channels.
-
-```bash
-# Test MCP server connection
-python -m apps.slack_rag --mcp-server "slack-mcp-server" --test-connection
-
-# Index and search Slack messages
-python -m apps.slack_rag \
-  --mcp-server "slack-mcp-server" \
-  --workspace-name "my-team" \
-  --channels general dev-team random \
-  --query "What did we decide about the product launch?"
-```
-
-**📖 Comprehensive Setup Guide**: For detailed setup instructions, troubleshooting common issues (like "users cache is not ready yet"), and advanced configuration options, see our [**Slack Setup Guide**](docs/slack-setup-guide.md).
-
-**Quick Setup:**
-1. Install a Slack MCP server (e.g., `npm install -g slack-mcp-server`)
-2. Create a Slack App and get API credentials (see detailed guide above)
-3. Set environment variables:
-   ```bash
-   export SLACK_BOT_TOKEN="xoxb-your-bot-token"
-   export SLACK_APP_TOKEN="xapp-your-app-token"  # Optional
-   ```
-4. Test connection with `--test-connection` flag
-
-**Arguments:**
-- `--mcp-server`: Command to start the Slack MCP server
-- `--workspace-name`: Slack workspace name for organization
-- `--channels`: Specific channels to index (optional)
-- `--concatenate-conversations`: Group messages by channel (default: true)
-- `--max-messages-per-channel`: Limit messages per channel (default: 100)
-- `--max-retries`: Maximum retries for cache sync issues (default: 5)
-- `--retry-delay`: Initial delay between retries in seconds (default: 2.0)
-
-#### 🐦 Twitter Bookmarks: Your Personal Tweet Library
-
-Search through your Twitter bookmarks! Find that perfect article, thread, or insight you saved for later.
-
-```bash
-# Test MCP server connection
-python -m apps.twitter_rag --mcp-server "twitter-mcp-server" --test-connection
-
-# Index and search Twitter bookmarks
-python -m apps.twitter_rag \
-  --mcp-server "twitter-mcp-server" \
-  --max-bookmarks 1000 \
-  --query "What AI articles did I bookmark about machine learning?"
-```
-
-**Setup Requirements:**
-1. Install a Twitter MCP server (e.g., `npm install -g twitter-mcp-server`)
-2. Get Twitter API credentials:
-   - Apply for a Twitter Developer Account at [developer.twitter.com](https://developer.twitter.com)
-   - Create a new app in the Twitter Developer Portal
-   - Generate API keys and access tokens with "Read" permissions
-   - For bookmarks access, you may need Twitter API v2 with appropriate scopes
-   ```bash
-   export TWITTER_API_KEY="your-api-key"
-   export TWITTER_API_SECRET="your-api-secret"
-   export TWITTER_ACCESS_TOKEN="your-access-token"
-   export TWITTER_ACCESS_TOKEN_SECRET="your-access-token-secret"
-   ```
-3. Test connection with `--test-connection` flag
-
-**Arguments:**
-- `--mcp-server`: Command to start the Twitter MCP server
-- `--username`: Filter bookmarks by username (optional)
-- `--max-bookmarks`: Maximum bookmarks to fetch (default: 1000)
-- `--no-tweet-content`: Exclude tweet content, only metadata
-- `--no-metadata`: Exclude engagement metadata
-
-</details>
-
-<details>
-<summary><strong>💡 Click to expand: Example queries you can try</strong></summary>
-
-**Slack Queries:**
-- "What did the team discuss about the project deadline?"
-- "Find messages about the new feature launch"
-- "Show me conversations about budget planning"
-- "What decisions were made in the dev-team channel?"
-
-**Twitter Queries:**
-- "What AI articles did I bookmark last month?"
-- "Find tweets about machine learning techniques"
-- "Show me bookmarked threads about startup advice"
-- "What Python tutorials did I save?"
-
-</details>
-<summary><strong>🔧 Using MCP with CLI Commands</strong></summary>
-
-**Want to use MCP data with regular LEANN CLI?** You can combine MCP apps with CLI commands:
-
-```bash
-# Step 1: Use MCP app to fetch and index data
-python -m apps.slack_rag --mcp-server "slack-mcp-server" --workspace-name "my-team"
-
-# Step 2: The data is now indexed and available via CLI
-leann search slack_messages "project deadline"
-leann ask slack_messages "What decisions were made about the product launch?"
-
-# Same for Twitter bookmarks
-python -m apps.twitter_rag --mcp-server "twitter-mcp-server"
-leann search twitter_bookmarks "machine learning articles"
-```
-
-**MCP vs Manual Export:**
-- **MCP**: Live data, automatic updates, requires server setup
-- **Manual Export**: One-time setup, works offline, requires manual data export
-
-</details>
-
-<details>
-<summary><strong>🔧 Adding New MCP Platforms</strong></summary>
-
-Want to add support for other platforms? LEANN's MCP integration is designed for easy extension:
-
-1. **Find or create an MCP server** for your platform
-2. **Create a reader class** following the pattern in `apps/slack_data/slack_mcp_reader.py`
-3. **Create a RAG application** following the pattern in `apps/slack_rag.py`
-4. **Test and contribute** back to the community!
-
-**Popular MCP servers to explore:**
-- GitHub repositories and issues
-- Discord messages
-- Notion pages
-- Google Drive documents
-- And many more in the MCP ecosystem!
-
-</details>
-
 ### 🚀 Claude Code Integration: Transform Your Development Workflow!
 
 <details>
-<summary><strong>AST‑Aware Code Chunking</strong></summary>
+<summary><strong>NEW!! AST‑Aware Code Chunking</strong></summary>
 
 LEANN features intelligent code chunking that preserves semantic boundaries (functions, classes, methods) for Python, Java, C#, and TypeScript, improving code understanding compared to text-based chunking.
 
@@ -1015,7 +573,7 @@ Try our fully agentic pipeline with auto query rewriting, semantic search planni
 
 **🔥 Ready to supercharge your coding?** [Complete Setup Guide →](packages/leann-mcp/README.md)
 
-## Command Line Interface
+## 🖥️ Command Line Interface
 
 LEANN includes a powerful CLI for document processing and search. Perfect for quick document indexing and interactive chat.
 
@@ -1106,7 +664,7 @@ Options:
 leann ask INDEX_NAME [OPTIONS]
 
 Options:
-  --llm {ollama,openai,hf,anthropic}    LLM provider (default: ollama)
+  --llm {ollama,openai,hf}    LLM provider (default: ollama)
   --model MODEL               Model name (default: qwen3:8b)
   --interactive              Interactive chat mode
   --top-k N                  Retrieval count (default: 20)
@@ -1257,7 +815,7 @@ MIT License - see [LICENSE](LICENSE) for details.
 
 Core Contributors: [Yichuan Wang](https://yichuan-w.github.io/) & [Zhifei Li](https://github.com/andylizf).
 
-Active Contributors: [Gabriel Dehan](https://github.com/gabriel-dehan), [Aakash Suresh](https://github.com/ASuresh0524)
+Active Contributors: [Gabriel Dehan](https://github.com/gabriel-dehan)
 
 
 We welcome more contributors! Feel free to open issues or submit PRs.
@@ -1274,7 +832,3 @@ This work is done at [**Berkeley Sky Computing Lab**](https://sky.cs.berkeley.ed
 <p align="center">
   Made with ❤️ by the Leann team
 </p>
-
-## 🤖 Explore LEANN with AI
-
-LEANN is indexed on [DeepWiki](https://deepwiki.com/yichuan-w/LEANN), so you can ask questions to LLMs using Deep Research to explore the codebase and get help to add new features.

apps/base_rag_example.py

@@ -6,43 +6,12 @@ Provides common parameters and functionality for all RAG examples.
 import argparse
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Any, Union
+from typing import Any
 
 import dotenv
 from leann.api import LeannBuilder, LeannChat
-
-# Optional import: older PyPI builds may not include interactive_utils
-try:
-    from leann.interactive_utils import create_rag_session
-except ImportError:
-
-    def create_rag_session(app_name: str, data_description: str):
-        class _SimpleSession:
-            def run_interactive_loop(self, handler):
-                print(f"Interactive session for {app_name}: {data_description}")
-                print("Interactive mode not available in this build")
-
-        return _SimpleSession()
-
-
 from leann.registry import register_project_directory
-
+from leann.settings import resolve_ollama_host, resolve_openai_api_key, resolve_openai_base_url
-# Optional import: older PyPI builds may not include settings
-try:
-    from leann.settings import resolve_ollama_host, resolve_openai_api_key, resolve_openai_base_url
-except ImportError:
-    # Minimal fallbacks if settings helpers are unavailable
-    import os
-
-    def resolve_ollama_host(value: str | None) -> str | None:
-        return value or os.getenv("LEANN_OLLAMA_HOST") or os.getenv("OLLAMA_HOST")
-
-    def resolve_openai_api_key(value: str | None) -> str | None:
-        return value or os.getenv("OPENAI_API_KEY")
-
-    def resolve_openai_base_url(value: str | None) -> str | None:
-        return value or os.getenv("OPENAI_BASE_URL")
-
 
 dotenv.load_dotenv()
 
@@ -180,14 +149,14 @@ class BaseRAGExample(ABC):
         ast_group.add_argument(
             "--ast-chunk-size",
             type=int,
-            default=300,
+            default=512,
-            help="Maximum CHARACTERS per AST chunk (default: 300). Final chunks may be larger due to overlap. For 512 token models: recommended 300 chars",
+            help="Maximum characters per AST chunk (default: 512)",
         )
         ast_group.add_argument(
             "--ast-chunk-overlap",
             type=int,
             default=64,
-            help="Overlap between AST chunks in CHARACTERS (default: 64). Added to chunk size, not included in it",
+            help="Overlap between AST chunks (default: 64)",
         )
         ast_group.add_argument(
             "--code-file-extensions",
@@ -257,8 +226,8 @@ class BaseRAGExample(ABC):
         pass
 
     @abstractmethod
-    async def load_data(self, args) -> list[Union[str, dict[str, Any]]]:
+    async def load_data(self, args) -> list[str]:
-        """Load data from the source. Returns list of text chunks (strings or dicts with 'text' key)."""
+        """Load data from the source. Returns list of text chunks."""
         pass
 
     def get_llm_config(self, args) -> dict[str, Any]:
@@ -282,8 +251,8 @@ class BaseRAGExample(ABC):
 
         return config
 
-    async def build_index(self, args, texts: list[Union[str, dict[str, Any]]]) -> str:
+    async def build_index(self, args, texts: list[str]) -> str:
-        """Build LEANN index from texts (accepts strings or dicts with 'text' key)."""
+        """Build LEANN index from texts."""
         index_path = str(Path(args.index_dir) / f"{self.default_index_name}.leann")
 
         print(f"\n[Building Index] Creating {self.name} index...")
@@ -314,14 +283,8 @@ class BaseRAGExample(ABC):
         batch_size = 1000
         for i in range(0, len(texts), batch_size):
             batch = texts[i : i + batch_size]
-            for item in batch:
+            for text in batch:
-                # Handle both dict format (from create_text_chunks) and plain strings
+                builder.add_text(text)
-                if isinstance(item, dict):
-                    text = item.get("text", "")
-                    metadata = item.get("metadata")
-                    builder.add_text(text, metadata)
-                else:
-                    builder.add_text(item)
             print(f"Added {min(i + batch_size, len(texts))}/{len(texts)} texts...")
 
         print("Building index structure...")
@@ -344,12 +307,19 @@ class BaseRAGExample(ABC):
             complexity=args.search_complexity,
         )
 
-        # Create interactive session
+        print(f"\n[Interactive Mode] Chat with your {self.name} data!")
-        session = create_rag_session(
+        print("Type 'quit' or 'exit' to stop.\n")
-            app_name=self.name.lower().replace(" ", "_"), data_description=self.name
+
-        )
+        while True:
+            try:
+                query = input("You: ").strip()
+                if query.lower() in ["quit", "exit", "q"]:
+                    print("Goodbye!")
+                    break
+
+                if not query:
+                    continue
 
-        def handle_query(query: str):
                 # Prepare LLM kwargs with thinking budget if specified
                 llm_kwargs = {}
                 if hasattr(args, "thinking_budget") and args.thinking_budget:
@@ -363,7 +333,11 @@ class BaseRAGExample(ABC):
                 )
                 print(f"\nAssistant: {response}\n")
 
-        session.run_interactive_loop(handle_query)
+            except KeyboardInterrupt:
+                print("\nGoodbye!")
+                break
+            except Exception as e:
+                print(f"Error: {e}")
 
     async def run_single_query(self, args, index_path: str, query: str):
         """Run a single query against the index."""

apps/chatgpt_data/chatgpt_reader.py

@@ -1,413 +0,0 @@
-"""
-ChatGPT export data reader.
-
-Reads and processes ChatGPT export data from chat.html files.
-"""
-
-import re
-from pathlib import Path
-from typing import Any
-from zipfile import ZipFile
-
-from bs4 import BeautifulSoup
-from llama_index.core import Document
-from llama_index.core.readers.base import BaseReader
-
-
-class ChatGPTReader(BaseReader):
-    """
-    ChatGPT export data reader.
-
-    Reads ChatGPT conversation data from exported chat.html files or zip archives.
-    Processes conversations into structured documents with metadata.
-    """
-
-    def __init__(self, concatenate_conversations: bool = True) -> None:
-        """
-        Initialize.
-
-        Args:
-            concatenate_conversations: Whether to concatenate messages within conversations for better context
-        """
-        try:
-            from bs4 import BeautifulSoup  # noqa
-        except ImportError:
-            raise ImportError("`beautifulsoup4` package not found: `pip install beautifulsoup4`")
-
-        self.concatenate_conversations = concatenate_conversations
-
-    def _extract_html_from_zip(self, zip_path: Path) -> str | None:
-        """
-        Extract chat.html from ChatGPT export zip file.
-
-        Args:
-            zip_path: Path to the ChatGPT export zip file
-
-        Returns:
-            HTML content as string, or None if not found
-        """
-        try:
-            with ZipFile(zip_path, "r") as zip_file:
-                # Look for chat.html or conversations.html
-                html_files = [
-                    f
-                    for f in zip_file.namelist()
-                    if f.endswith(".html") and ("chat" in f.lower() or "conversation" in f.lower())
-                ]
-
-                if not html_files:
-                    print(f"No HTML chat file found in {zip_path}")
-                    return None
-
-                # Use the first HTML file found
-                html_file = html_files[0]
-                print(f"Found HTML file: {html_file}")
-
-                with zip_file.open(html_file) as f:
-                    return f.read().decode("utf-8", errors="ignore")
-
-        except Exception as e:
-            print(f"Error extracting HTML from zip {zip_path}: {e}")
-            return None
-
-    def _parse_chatgpt_html(self, html_content: str) -> list[dict]:
-        """
-        Parse ChatGPT HTML export to extract conversations.
-
-        Args:
-            html_content: HTML content from ChatGPT export
-
-        Returns:
-            List of conversation dictionaries
-        """
-        soup = BeautifulSoup(html_content, "html.parser")
-        conversations = []
-
-        # Try different possible structures for ChatGPT exports
-        # Structure 1: Look for conversation containers
-        conversation_containers = soup.find_all(
-            ["div", "section"], class_=re.compile(r"conversation|chat", re.I)
-        )
-
-        if not conversation_containers:
-            # Structure 2: Look for message containers directly
-            conversation_containers = [soup]  # Use the entire document as one conversation
-
-        for container in conversation_containers:
-            conversation = self._extract_conversation_from_container(container)
-            if conversation and conversation.get("messages"):
-                conversations.append(conversation)
-
-        # If no structured conversations found, try to extract all text as one conversation
-        if not conversations:
-            all_text = soup.get_text(separator="\n", strip=True)
-            if all_text:
-                conversations.append(
-                    {
-                        "title": "ChatGPT Conversation",
-                        "messages": [{"role": "mixed", "content": all_text, "timestamp": None}],
-                        "timestamp": None,
-                    }
-                )
-
-        return conversations
-
-    def _extract_conversation_from_container(self, container) -> dict | None:
-        """
-        Extract conversation data from a container element.
-
-        Args:
-            container: BeautifulSoup element containing conversation
-
-        Returns:
-            Dictionary with conversation data or None
-        """
-        messages = []
-
-        # Look for message elements with various possible structures
-        message_selectors = ['[class*="message"]', '[class*="chat"]', "[data-message]", "p", "div"]
-
-        for selector in message_selectors:
-            message_elements = container.select(selector)
-            if message_elements:
-                break
-        else:
-            message_elements = []
-
-        # If no structured messages found, treat the entire container as one message
-        if not message_elements:
-            text_content = container.get_text(separator="\n", strip=True)
-            if text_content:
-                messages.append({"role": "mixed", "content": text_content, "timestamp": None})
-        else:
-            for element in message_elements:
-                message = self._extract_message_from_element(element)
-                if message:
-                    messages.append(message)
-
-        if not messages:
-            return None
-
-        # Try to extract conversation title
-        title_element = container.find(["h1", "h2", "h3", "title"])
-        title = title_element.get_text(strip=True) if title_element else "ChatGPT Conversation"
-
-        # Try to extract timestamp from various possible locations
-        timestamp = self._extract_timestamp_from_container(container)
-
-        return {"title": title, "messages": messages, "timestamp": timestamp}
-
-    def _extract_message_from_element(self, element) -> dict | None:
-        """
-        Extract message data from an element.
-
-        Args:
-            element: BeautifulSoup element containing message
-
-        Returns:
-            Dictionary with message data or None
-        """
-        text_content = element.get_text(separator=" ", strip=True)
-
-        # Skip empty or very short messages
-        if not text_content or len(text_content.strip()) < 3:
-            return None
-
-        # Try to determine role (user/assistant) from class names or content
-        role = "mixed"  # Default role
-
-        class_names = " ".join(element.get("class", [])).lower()
-        if "user" in class_names or "human" in class_names:
-            role = "user"
-        elif "assistant" in class_names or "ai" in class_names or "gpt" in class_names:
-            role = "assistant"
-        elif text_content.lower().startswith(("you:", "user:", "me:")):
-            role = "user"
-            text_content = re.sub(r"^(you|user|me):\s*", "", text_content, flags=re.IGNORECASE)
-        elif text_content.lower().startswith(("chatgpt:", "assistant:", "ai:")):
-            role = "assistant"
-            text_content = re.sub(
-                r"^(chatgpt|assistant|ai):\s*", "", text_content, flags=re.IGNORECASE
-            )
-
-        # Try to extract timestamp
-        timestamp = self._extract_timestamp_from_element(element)
-
-        return {"role": role, "content": text_content, "timestamp": timestamp}
-
-    def _extract_timestamp_from_element(self, element) -> str | None:
-        """Extract timestamp from element."""
-        # Look for timestamp in various attributes and child elements
-        timestamp_attrs = ["data-timestamp", "timestamp", "datetime"]
-        for attr in timestamp_attrs:
-            if element.get(attr):
-                return element.get(attr)
-
-        # Look for time elements
-        time_element = element.find("time")
-        if time_element:
-            return time_element.get("datetime") or time_element.get_text(strip=True)
-
-        # Look for date-like text patterns
-        text = element.get_text()
-        date_patterns = [r"\d{4}-\d{2}-\d{2}", r"\d{1,2}/\d{1,2}/\d{4}", r"\w+ \d{1,2}, \d{4}"]
-
-        for pattern in date_patterns:
-            match = re.search(pattern, text)
-            if match:
-                return match.group()
-
-        return None
-
-    def _extract_timestamp_from_container(self, container) -> str | None:
-        """Extract timestamp from conversation container."""
-        return self._extract_timestamp_from_element(container)
-
-    def _create_concatenated_content(self, conversation: dict) -> str:
-        """
-        Create concatenated content from conversation messages.
-
-        Args:
-            conversation: Dictionary containing conversation data
-
-        Returns:
-            Formatted concatenated content
-        """
-        title = conversation.get("title", "ChatGPT Conversation")
-        messages = conversation.get("messages", [])
-        timestamp = conversation.get("timestamp", "Unknown")
-
-        # Build message content
-        message_parts = []
-        for message in messages:
-            role = message.get("role", "mixed")
-            content = message.get("content", "")
-            msg_timestamp = message.get("timestamp", "")
-
-            if role == "user":
-                prefix = "[You]"
-            elif role == "assistant":
-                prefix = "[ChatGPT]"
-            else:
-                prefix = "[Message]"
-
-            # Add timestamp if available
-            if msg_timestamp:
-                prefix += f" ({msg_timestamp})"
-
-            message_parts.append(f"{prefix}: {content}")
-
-        concatenated_text = "\n\n".join(message_parts)
-
-        # Create final document content
-        doc_content = f"""Conversation: {title}
-Date: {timestamp}
-Messages ({len(messages)} messages):
-
-{concatenated_text}
-"""
-        return doc_content
-
-    def load_data(self, input_dir: str | None = None, **load_kwargs: Any) -> list[Document]:
-        """
-        Load ChatGPT export data.
-
-        Args:
-            input_dir: Directory containing ChatGPT export files or path to specific file
-            **load_kwargs:
-                max_count (int): Maximum number of conversations to process
-                chatgpt_export_path (str): Specific path to ChatGPT export file/directory
-                include_metadata (bool): Whether to include metadata in documents
-        """
-        docs: list[Document] = []
-        max_count = load_kwargs.get("max_count", -1)
-        chatgpt_export_path = load_kwargs.get("chatgpt_export_path", input_dir)
-        include_metadata = load_kwargs.get("include_metadata", True)
-
-        if not chatgpt_export_path:
-            print("No ChatGPT export path provided")
-            return docs
-
-        export_path = Path(chatgpt_export_path)
-
-        if not export_path.exists():
-            print(f"ChatGPT export path not found: {export_path}")
-            return docs
-
-        html_content = None
-
-        # Handle different input types
-        if export_path.is_file():
-            if export_path.suffix.lower() == ".zip":
-                # Extract HTML from zip file
-                html_content = self._extract_html_from_zip(export_path)
-            elif export_path.suffix.lower() == ".html":
-                # Read HTML file directly
-                try:
-                    with open(export_path, encoding="utf-8", errors="ignore") as f:
-                        html_content = f.read()
-                except Exception as e:
-                    print(f"Error reading HTML file {export_path}: {e}")
-                    return docs
-            else:
-                print(f"Unsupported file type: {export_path.suffix}")
-                return docs
-
-        elif export_path.is_dir():
-            # Look for HTML files in directory
-            html_files = list(export_path.glob("*.html"))
-            zip_files = list(export_path.glob("*.zip"))
-
-            if html_files:
-                # Use first HTML file found
-                html_file = html_files[0]
-                print(f"Found HTML file: {html_file}")
-                try:
-                    with open(html_file, encoding="utf-8", errors="ignore") as f:
-                        html_content = f.read()
-                except Exception as e:
-                    print(f"Error reading HTML file {html_file}: {e}")
-                    return docs
-
-            elif zip_files:
-                # Use first zip file found
-                zip_file = zip_files[0]
-                print(f"Found zip file: {zip_file}")
-                html_content = self._extract_html_from_zip(zip_file)
-
-            else:
-                print(f"No HTML or zip files found in {export_path}")
-                return docs
-
-        if not html_content:
-            print("No HTML content found to process")
-            return docs
-
-        # Parse conversations from HTML
-        print("Parsing ChatGPT conversations from HTML...")
-        conversations = self._parse_chatgpt_html(html_content)
-
-        if not conversations:
-            print("No conversations found in HTML content")
-            return docs
-
-        print(f"Found {len(conversations)} conversations")
-
-        # Process conversations into documents
-        count = 0
-        for conversation in conversations:
-            if max_count > 0 and count >= max_count:
-                break
-
-            if self.concatenate_conversations:
-                # Create one document per conversation with concatenated messages
-                doc_content = self._create_concatenated_content(conversation)
-
-                metadata = {}
-                if include_metadata:
-                    metadata = {
-                        "title": conversation.get("title", "ChatGPT Conversation"),
-                        "timestamp": conversation.get("timestamp", "Unknown"),
-                        "message_count": len(conversation.get("messages", [])),
-                        "source": "ChatGPT Export",
-                    }
-
-                doc = Document(text=doc_content, metadata=metadata)
-                docs.append(doc)
-                count += 1
-
-            else:
-                # Create separate documents for each message
-                for message in conversation.get("messages", []):
-                    if max_count > 0 and count >= max_count:
-                        break
-
-                    role = message.get("role", "mixed")
-                    content = message.get("content", "")
-                    msg_timestamp = message.get("timestamp", "")
-
-                    if not content.strip():
-                        continue
-
-                    # Create document content with context
-                    doc_content = f"""Conversation: {conversation.get("title", "ChatGPT Conversation")}
-Role: {role}
-Timestamp: {msg_timestamp or conversation.get("timestamp", "Unknown")}
-Message: {content}
-"""
-
-                    metadata = {}
-                    if include_metadata:
-                        metadata = {
-                            "conversation_title": conversation.get("title", "ChatGPT Conversation"),
-                            "role": role,
-                            "timestamp": msg_timestamp or conversation.get("timestamp", "Unknown"),
-                            "source": "ChatGPT Export",
-                        }
-
-                    doc = Document(text=doc_content, metadata=metadata)
-                    docs.append(doc)
-                    count += 1
-
-        print(f"Created {len(docs)} documents from ChatGPT export")
-        return docs

apps/chatgpt_rag.py

@@ -1,186 +0,0 @@
-"""
-ChatGPT RAG example using the unified interface.
-Supports ChatGPT export data from chat.html files.
-"""
-
-import sys
-from pathlib import Path
-
-# Add parent directory to path for imports
-sys.path.insert(0, str(Path(__file__).parent))
-
-from base_rag_example import BaseRAGExample
-from chunking import create_text_chunks
-
-from .chatgpt_data.chatgpt_reader import ChatGPTReader
-
-
-class ChatGPTRAG(BaseRAGExample):
-    """RAG example for ChatGPT conversation data."""
-
-    def __init__(self):
-        # Set default values BEFORE calling super().__init__
-        self.max_items_default = -1  # Process all conversations by default
-        self.embedding_model_default = (
-            "sentence-transformers/all-MiniLM-L6-v2"  # Fast 384-dim model
-        )
-
-        super().__init__(
-            name="ChatGPT",
-            description="Process and query ChatGPT conversation exports with LEANN",
-            default_index_name="chatgpt_conversations_index",
-        )
-
-    def _add_specific_arguments(self, parser):
-        """Add ChatGPT-specific arguments."""
-        chatgpt_group = parser.add_argument_group("ChatGPT Parameters")
-        chatgpt_group.add_argument(
-            "--export-path",
-            type=str,
-            default="./chatgpt_export",
-            help="Path to ChatGPT export file (.zip or .html) or directory containing exports (default: ./chatgpt_export)",
-        )
-        chatgpt_group.add_argument(
-            "--concatenate-conversations",
-            action="store_true",
-            default=True,
-            help="Concatenate messages within conversations for better context (default: True)",
-        )
-        chatgpt_group.add_argument(
-            "--separate-messages",
-            action="store_true",
-            help="Process each message as a separate document (overrides --concatenate-conversations)",
-        )
-        chatgpt_group.add_argument(
-            "--chunk-size", type=int, default=512, help="Text chunk size (default: 512)"
-        )
-        chatgpt_group.add_argument(
-            "--chunk-overlap", type=int, default=128, help="Text chunk overlap (default: 128)"
-        )
-
-    def _find_chatgpt_exports(self, export_path: Path) -> list[Path]:
-        """
-        Find ChatGPT export files in the given path.
-
-        Args:
-            export_path: Path to search for exports
-
-        Returns:
-            List of paths to ChatGPT export files
-        """
-        export_files = []
-
-        if export_path.is_file():
-            if export_path.suffix.lower() in [".zip", ".html"]:
-                export_files.append(export_path)
-        elif export_path.is_dir():
-            # Look for zip and html files
-            export_files.extend(export_path.glob("*.zip"))
-            export_files.extend(export_path.glob("*.html"))
-
-        return export_files
-
-    async def load_data(self, args) -> list[str]:
-        """Load ChatGPT export data and convert to text chunks."""
-        export_path = Path(args.export_path)
-
-        if not export_path.exists():
-            print(f"ChatGPT export path not found: {export_path}")
-            print(
-                "Please ensure you have exported your ChatGPT data and placed it in the correct location."
-            )
-            print("\nTo export your ChatGPT data:")
-            print("1. Sign in to ChatGPT")
-            print("2. Click on your profile icon → Settings → Data Controls")
-            print("3. Click 'Export' under Export Data")
-            print("4. Download the zip file from the email link")
-            print("5. Extract or place the file/directory at the specified path")
-            return []
-
-        # Find export files
-        export_files = self._find_chatgpt_exports(export_path)
-
-        if not export_files:
-            print(f"No ChatGPT export files (.zip or .html) found in: {export_path}")
-            return []
-
-        print(f"Found {len(export_files)} ChatGPT export files")
-
-        # Create reader with appropriate settings
-        concatenate = args.concatenate_conversations and not args.separate_messages
-        reader = ChatGPTReader(concatenate_conversations=concatenate)
-
-        # Process each export file
-        all_documents = []
-        total_processed = 0
-
-        for i, export_file in enumerate(export_files):
-            print(f"\nProcessing export file {i + 1}/{len(export_files)}: {export_file.name}")
-
-            try:
-                # Apply max_items limit per file
-                max_per_file = -1
-                if args.max_items > 0:
-                    remaining = args.max_items - total_processed
-                    if remaining <= 0:
-                        break
-                    max_per_file = remaining
-
-                # Load conversations
-                documents = reader.load_data(
-                    chatgpt_export_path=str(export_file),
-                    max_count=max_per_file,
-                    include_metadata=True,
-                )
-
-                if documents:
-                    all_documents.extend(documents)
-                    total_processed += len(documents)
-                    print(f"Processed {len(documents)} conversations from this file")
-                else:
-                    print(f"No conversations loaded from {export_file}")
-
-            except Exception as e:
-                print(f"Error processing {export_file}: {e}")
-                continue
-
-        if not all_documents:
-            print("No conversations found to process!")
-            print("\nTroubleshooting:")
-            print("- Ensure the export file is a valid ChatGPT export")
-            print("- Check that the HTML file contains conversation data")
-            print("- Try extracting the zip file and pointing to the HTML file directly")
-            return []
-
-        print(f"\nTotal conversations processed: {len(all_documents)}")
-        print("Now starting to split into text chunks... this may take some time")
-
-        # Convert to text chunks
-        all_texts = create_text_chunks(
-            all_documents, chunk_size=args.chunk_size, chunk_overlap=args.chunk_overlap
-        )
-
-        print(f"Created {len(all_texts)} text chunks from {len(all_documents)} conversations")
-        return all_texts
-
-
-if __name__ == "__main__":
-    import asyncio
-
-    # Example queries for ChatGPT RAG
-    print("\n🤖 ChatGPT RAG Example")
-    print("=" * 50)
-    print("\nExample queries you can try:")
-    print("- 'What did I ask about Python programming?'")
-    print("- 'Show me conversations about machine learning'")
-    print("- 'Find discussions about travel planning'")
-    print("- 'What advice did ChatGPT give me about career development?'")
-    print("- 'Search for conversations about cooking recipes'")
-    print("\nTo get started:")
-    print("1. Export your ChatGPT data from Settings → Data Controls → Export")
-    print("2. Place the downloaded zip file or extracted HTML in ./chatgpt_export/")
-    print("3. Run this script to build your personal ChatGPT knowledge base!")
-    print("\nOr run without --query for interactive mode\n")
-
-    rag = ChatGPTRAG()
-    asyncio.run(rag.run())

apps/chunking/__init__.py

@@ -12,7 +12,6 @@ from pathlib import Path
 try:
     from leann.chunking_utils import (
         CODE_EXTENSIONS,
-        _traditional_chunks_as_dicts,
         create_ast_chunks,
         create_text_chunks,
         create_traditional_chunks,
@@ -26,7 +25,6 @@ except Exception:  # pragma: no cover - best-effort fallback for dev environment
         sys.path.insert(0, str(leann_src))
         from leann.chunking_utils import (
             CODE_EXTENSIONS,
-            _traditional_chunks_as_dicts,
             create_ast_chunks,
             create_text_chunks,
             create_traditional_chunks,
@@ -38,7 +36,6 @@ except Exception:  # pragma: no cover - best-effort fallback for dev environment
 
 __all__ = [
     "CODE_EXTENSIONS",
-    "_traditional_chunks_as_dicts",
     "create_ast_chunks",
     "create_text_chunks",
     "create_traditional_chunks",

apps/claude_data/claude_reader.py

@@ -1,420 +0,0 @@
-"""
-Claude export data reader.
-
-Reads and processes Claude conversation data from exported JSON files.
-"""
-
-import json
-from pathlib import Path
-from typing import Any
-from zipfile import ZipFile
-
-from llama_index.core import Document
-from llama_index.core.readers.base import BaseReader
-
-
-class ClaudeReader(BaseReader):
-    """
-    Claude export data reader.
-
-    Reads Claude conversation data from exported JSON files or zip archives.
-    Processes conversations into structured documents with metadata.
-    """
-
-    def __init__(self, concatenate_conversations: bool = True) -> None:
-        """
-        Initialize.
-
-        Args:
-            concatenate_conversations: Whether to concatenate messages within conversations for better context
-        """
-        self.concatenate_conversations = concatenate_conversations
-
-    def _extract_json_from_zip(self, zip_path: Path) -> list[str]:
-        """
-        Extract JSON files from Claude export zip file.
-
-        Args:
-            zip_path: Path to the Claude export zip file
-
-        Returns:
-            List of JSON content strings, or empty list if not found
-        """
-        json_contents = []
-        try:
-            with ZipFile(zip_path, "r") as zip_file:
-                # Look for JSON files
-                json_files = [f for f in zip_file.namelist() if f.endswith(".json")]
-
-                if not json_files:
-                    print(f"No JSON files found in {zip_path}")
-                    return []
-
-                print(f"Found {len(json_files)} JSON files in archive")
-
-                for json_file in json_files:
-                    with zip_file.open(json_file) as f:
-                        content = f.read().decode("utf-8", errors="ignore")
-                        json_contents.append(content)
-
-        except Exception as e:
-            print(f"Error extracting JSON from zip {zip_path}: {e}")
-
-        return json_contents
-
-    def _parse_claude_json(self, json_content: str) -> list[dict]:
-        """
-        Parse Claude JSON export to extract conversations.
-
-        Args:
-            json_content: JSON content from Claude export
-
-        Returns:
-            List of conversation dictionaries
-        """
-        try:
-            data = json.loads(json_content)
-        except json.JSONDecodeError as e:
-            print(f"Error parsing JSON: {e}")
-            return []
-
-        conversations = []
-
-        # Handle different possible JSON structures
-        if isinstance(data, list):
-            # If data is a list of conversations
-            for item in data:
-                conversation = self._extract_conversation_from_json(item)
-                if conversation:
-                    conversations.append(conversation)
-        elif isinstance(data, dict):
-            # Check for common structures
-            if "conversations" in data:
-                # Structure: {"conversations": [...]}
-                for item in data["conversations"]:
-                    conversation = self._extract_conversation_from_json(item)
-                    if conversation:
-                        conversations.append(conversation)
-            elif "messages" in data:
-                # Single conversation with messages
-                conversation = self._extract_conversation_from_json(data)
-                if conversation:
-                    conversations.append(conversation)
-            else:
-                # Try to treat the whole object as a conversation
-                conversation = self._extract_conversation_from_json(data)
-                if conversation:
-                    conversations.append(conversation)
-
-        return conversations
-
-    def _extract_conversation_from_json(self, conv_data: dict) -> dict | None:
-        """
-        Extract conversation data from a JSON object.
-
-        Args:
-            conv_data: Dictionary containing conversation data
-
-        Returns:
-            Dictionary with conversation data or None
-        """
-        if not isinstance(conv_data, dict):
-            return None
-
-        messages = []
-
-        # Look for messages in various possible structures
-        message_sources = []
-        if "messages" in conv_data:
-            message_sources = conv_data["messages"]
-        elif "chat" in conv_data:
-            message_sources = conv_data["chat"]
-        elif "conversation" in conv_data:
-            message_sources = conv_data["conversation"]
-        else:
-            # If no clear message structure, try to extract from the object itself
-            if "content" in conv_data and "role" in conv_data:
-                message_sources = [conv_data]
-
-        for msg_data in message_sources:
-            message = self._extract_message_from_json(msg_data)
-            if message:
-                messages.append(message)
-
-        if not messages:
-            return None
-
-        # Extract conversation metadata
-        title = self._extract_title_from_conversation(conv_data, messages)
-        timestamp = self._extract_timestamp_from_conversation(conv_data)
-
-        return {"title": title, "messages": messages, "timestamp": timestamp}
-
-    def _extract_message_from_json(self, msg_data: dict) -> dict | None:
-        """
-        Extract message data from a JSON message object.
-
-        Args:
-            msg_data: Dictionary containing message data
-
-        Returns:
-            Dictionary with message data or None
-        """
-        if not isinstance(msg_data, dict):
-            return None
-
-        # Extract content from various possible fields
-        content = ""
-        content_fields = ["content", "text", "message", "body"]
-        for field in content_fields:
-            if msg_data.get(field):
-                content = str(msg_data[field])
-                break
-
-        if not content or len(content.strip()) < 3:
-            return None
-
-        # Extract role (user/assistant/human/ai/claude)
-        role = "mixed"  # Default role
-        role_fields = ["role", "sender", "from", "author", "type"]
-        for field in role_fields:
-            if msg_data.get(field):
-                role_value = str(msg_data[field]).lower()
-                if role_value in ["user", "human", "person"]:
-                    role = "user"
-                elif role_value in ["assistant", "ai", "claude", "bot"]:
-                    role = "assistant"
-                break
-
-        # Extract timestamp
-        timestamp = self._extract_timestamp_from_message(msg_data)
-
-        return {"role": role, "content": content, "timestamp": timestamp}
-
-    def _extract_timestamp_from_message(self, msg_data: dict) -> str | None:
-        """Extract timestamp from message data."""
-        timestamp_fields = ["timestamp", "created_at", "date", "time"]
-        for field in timestamp_fields:
-            if msg_data.get(field):
-                return str(msg_data[field])
-        return None
-
-    def _extract_timestamp_from_conversation(self, conv_data: dict) -> str | None:
-        """Extract timestamp from conversation data."""
-        timestamp_fields = ["timestamp", "created_at", "date", "updated_at", "last_updated"]
-        for field in timestamp_fields:
-            if conv_data.get(field):
-                return str(conv_data[field])
-        return None
-
-    def _extract_title_from_conversation(self, conv_data: dict, messages: list) -> str:
-        """Extract or generate title for conversation."""
-        # Try to find explicit title
-        title_fields = ["title", "name", "subject", "topic"]
-        for field in title_fields:
-            if conv_data.get(field):
-                return str(conv_data[field])
-
-        # Generate title from first user message
-        for message in messages:
-            if message.get("role") == "user":
-                content = message.get("content", "")
-                if content:
-                    # Use first 50 characters as title
-                    title = content[:50].strip()
-                    if len(content) > 50:
-                        title += "..."
-                    return title
-
-        return "Claude Conversation"
-
-    def _create_concatenated_content(self, conversation: dict) -> str:
-        """
-        Create concatenated content from conversation messages.
-
-        Args:
-            conversation: Dictionary containing conversation data
-
-        Returns:
-            Formatted concatenated content
-        """
-        title = conversation.get("title", "Claude Conversation")
-        messages = conversation.get("messages", [])
-        timestamp = conversation.get("timestamp", "Unknown")
-
-        # Build message content
-        message_parts = []
-        for message in messages:
-            role = message.get("role", "mixed")
-            content = message.get("content", "")
-            msg_timestamp = message.get("timestamp", "")
-
-            if role == "user":
-                prefix = "[You]"
-            elif role == "assistant":
-                prefix = "[Claude]"
-            else:
-                prefix = "[Message]"
-
-            # Add timestamp if available
-            if msg_timestamp:
-                prefix += f" ({msg_timestamp})"
-
-            message_parts.append(f"{prefix}: {content}")
-
-        concatenated_text = "\n\n".join(message_parts)
-
-        # Create final document content
-        doc_content = f"""Conversation: {title}
-Date: {timestamp}
-Messages ({len(messages)} messages):
-
-{concatenated_text}
-"""
-        return doc_content
-
-    def load_data(self, input_dir: str | None = None, **load_kwargs: Any) -> list[Document]:
-        """
-        Load Claude export data.
-
-        Args:
-            input_dir: Directory containing Claude export files or path to specific file
-            **load_kwargs:
-                max_count (int): Maximum number of conversations to process
-                claude_export_path (str): Specific path to Claude export file/directory
-                include_metadata (bool): Whether to include metadata in documents
-        """
-        docs: list[Document] = []
-        max_count = load_kwargs.get("max_count", -1)
-        claude_export_path = load_kwargs.get("claude_export_path", input_dir)
-        include_metadata = load_kwargs.get("include_metadata", True)
-
-        if not claude_export_path:
-            print("No Claude export path provided")
-            return docs
-
-        export_path = Path(claude_export_path)
-
-        if not export_path.exists():
-            print(f"Claude export path not found: {export_path}")
-            return docs
-
-        json_contents = []
-
-        # Handle different input types
-        if export_path.is_file():
-            if export_path.suffix.lower() == ".zip":
-                # Extract JSON from zip file
-                json_contents = self._extract_json_from_zip(export_path)
-            elif export_path.suffix.lower() == ".json":
-                # Read JSON file directly
-                try:
-                    with open(export_path, encoding="utf-8", errors="ignore") as f:
-                        json_contents.append(f.read())
-                except Exception as e:
-                    print(f"Error reading JSON file {export_path}: {e}")
-                    return docs
-            else:
-                print(f"Unsupported file type: {export_path.suffix}")
-                return docs
-
-        elif export_path.is_dir():
-            # Look for JSON files in directory
-            json_files = list(export_path.glob("*.json"))
-            zip_files = list(export_path.glob("*.zip"))
-
-            if json_files:
-                print(f"Found {len(json_files)} JSON files in directory")
-                for json_file in json_files:
-                    try:
-                        with open(json_file, encoding="utf-8", errors="ignore") as f:
-                            json_contents.append(f.read())
-                    except Exception as e:
-                        print(f"Error reading JSON file {json_file}: {e}")
-                        continue
-
-            if zip_files:
-                print(f"Found {len(zip_files)} ZIP files in directory")
-                for zip_file in zip_files:
-                    zip_contents = self._extract_json_from_zip(zip_file)
-                    json_contents.extend(zip_contents)
-
-            if not json_files and not zip_files:
-                print(f"No JSON or ZIP files found in {export_path}")
-                return docs
-
-        if not json_contents:
-            print("No JSON content found to process")
-            return docs
-
-        # Parse conversations from JSON content
-        print("Parsing Claude conversations from JSON...")
-        all_conversations = []
-        for json_content in json_contents:
-            conversations = self._parse_claude_json(json_content)
-            all_conversations.extend(conversations)
-
-        if not all_conversations:
-            print("No conversations found in JSON content")
-            return docs
-
-        print(f"Found {len(all_conversations)} conversations")
-
-        # Process conversations into documents
-        count = 0
-        for conversation in all_conversations:
-            if max_count > 0 and count >= max_count:
-                break
-
-            if self.concatenate_conversations:
-                # Create one document per conversation with concatenated messages
-                doc_content = self._create_concatenated_content(conversation)
-
-                metadata = {}
-                if include_metadata:
-                    metadata = {
-                        "title": conversation.get("title", "Claude Conversation"),
-                        "timestamp": conversation.get("timestamp", "Unknown"),
-                        "message_count": len(conversation.get("messages", [])),
-                        "source": "Claude Export",
-                    }
-
-                doc = Document(text=doc_content, metadata=metadata)
-                docs.append(doc)
-                count += 1
-
-            else:
-                # Create separate documents for each message
-                for message in conversation.get("messages", []):
-                    if max_count > 0 and count >= max_count:
-                        break
-
-                    role = message.get("role", "mixed")
-                    content = message.get("content", "")
-                    msg_timestamp = message.get("timestamp", "")
-
-                    if not content.strip():
-                        continue
-
-                    # Create document content with context
-                    doc_content = f"""Conversation: {conversation.get("title", "Claude Conversation")}
-Role: {role}
-Timestamp: {msg_timestamp or conversation.get("timestamp", "Unknown")}
-Message: {content}
-"""
-
-                    metadata = {}
-                    if include_metadata:
-                        metadata = {
-                            "conversation_title": conversation.get("title", "Claude Conversation"),
-                            "role": role,
-                            "timestamp": msg_timestamp or conversation.get("timestamp", "Unknown"),
-                            "source": "Claude Export",
-                        }
-
-                    doc = Document(text=doc_content, metadata=metadata)
-                    docs.append(doc)
-                    count += 1
-
-        print(f"Created {len(docs)} documents from Claude export")
-        return docs

apps/claude_rag.py

@@ -1,189 +0,0 @@
-"""
-Claude RAG example using the unified interface.
-Supports Claude export data from JSON files.
-"""
-
-import sys
-from pathlib import Path
-
-# Add parent directory to path for imports
-sys.path.insert(0, str(Path(__file__).parent))
-
-from base_rag_example import BaseRAGExample
-from chunking import create_text_chunks
-
-from .claude_data.claude_reader import ClaudeReader
-
-
-class ClaudeRAG(BaseRAGExample):
-    """RAG example for Claude conversation data."""
-
-    def __init__(self):
-        # Set default values BEFORE calling super().__init__
-        self.max_items_default = -1  # Process all conversations by default
-        self.embedding_model_default = (
-            "sentence-transformers/all-MiniLM-L6-v2"  # Fast 384-dim model
-        )
-
-        super().__init__(
-            name="Claude",
-            description="Process and query Claude conversation exports with LEANN",
-            default_index_name="claude_conversations_index",
-        )
-
-    def _add_specific_arguments(self, parser):
-        """Add Claude-specific arguments."""
-        claude_group = parser.add_argument_group("Claude Parameters")
-        claude_group.add_argument(
-            "--export-path",
-            type=str,
-            default="./claude_export",
-            help="Path to Claude export file (.json or .zip) or directory containing exports (default: ./claude_export)",
-        )
-        claude_group.add_argument(
-            "--concatenate-conversations",
-            action="store_true",
-            default=True,
-            help="Concatenate messages within conversations for better context (default: True)",
-        )
-        claude_group.add_argument(
-            "--separate-messages",
-            action="store_true",
-            help="Process each message as a separate document (overrides --concatenate-conversations)",
-        )
-        claude_group.add_argument(
-            "--chunk-size", type=int, default=512, help="Text chunk size (default: 512)"
-        )
-        claude_group.add_argument(
-            "--chunk-overlap", type=int, default=128, help="Text chunk overlap (default: 128)"
-        )
-
-    def _find_claude_exports(self, export_path: Path) -> list[Path]:
-        """
-        Find Claude export files in the given path.
-
-        Args:
-            export_path: Path to search for exports
-
-        Returns:
-            List of paths to Claude export files
-        """
-        export_files = []
-
-        if export_path.is_file():
-            if export_path.suffix.lower() in [".zip", ".json"]:
-                export_files.append(export_path)
-        elif export_path.is_dir():
-            # Look for zip and json files
-            export_files.extend(export_path.glob("*.zip"))
-            export_files.extend(export_path.glob("*.json"))
-
-        return export_files
-
-    async def load_data(self, args) -> list[str]:
-        """Load Claude export data and convert to text chunks."""
-        export_path = Path(args.export_path)
-
-        if not export_path.exists():
-            print(f"Claude export path not found: {export_path}")
-            print(
-                "Please ensure you have exported your Claude data and placed it in the correct location."
-            )
-            print("\nTo export your Claude data:")
-            print("1. Open Claude in your browser")
-            print("2. Look for export/download options in settings or conversation menu")
-            print("3. Download the conversation data (usually in JSON format)")
-            print("4. Place the file/directory at the specified path")
-            print(
-                "\nNote: Claude export methods may vary. Check Claude's help documentation for current instructions."
-            )
-            return []
-
-        # Find export files
-        export_files = self._find_claude_exports(export_path)
-
-        if not export_files:
-            print(f"No Claude export files (.json or .zip) found in: {export_path}")
-            return []
-
-        print(f"Found {len(export_files)} Claude export files")
-
-        # Create reader with appropriate settings
-        concatenate = args.concatenate_conversations and not args.separate_messages
-        reader = ClaudeReader(concatenate_conversations=concatenate)
-
-        # Process each export file
-        all_documents = []
-        total_processed = 0
-
-        for i, export_file in enumerate(export_files):
-            print(f"\nProcessing export file {i + 1}/{len(export_files)}: {export_file.name}")
-
-            try:
-                # Apply max_items limit per file
-                max_per_file = -1
-                if args.max_items > 0:
-                    remaining = args.max_items - total_processed
-                    if remaining <= 0:
-                        break
-                    max_per_file = remaining
-
-                # Load conversations
-                documents = reader.load_data(
-                    claude_export_path=str(export_file),
-                    max_count=max_per_file,
-                    include_metadata=True,
-                )
-
-                if documents:
-                    all_documents.extend(documents)
-                    total_processed += len(documents)
-                    print(f"Processed {len(documents)} conversations from this file")
-                else:
-                    print(f"No conversations loaded from {export_file}")
-
-            except Exception as e:
-                print(f"Error processing {export_file}: {e}")
-                continue
-
-        if not all_documents:
-            print("No conversations found to process!")
-            print("\nTroubleshooting:")
-            print("- Ensure the export file is a valid Claude export")
-            print("- Check that the JSON file contains conversation data")
-            print("- Try using a different export format or method")
-            print("- Check Claude's documentation for current export procedures")
-            return []
-
-        print(f"\nTotal conversations processed: {len(all_documents)}")
-        print("Now starting to split into text chunks... this may take some time")
-
-        # Convert to text chunks
-        all_texts = create_text_chunks(
-            all_documents, chunk_size=args.chunk_size, chunk_overlap=args.chunk_overlap
-        )
-
-        print(f"Created {len(all_texts)} text chunks from {len(all_documents)} conversations")
-        return all_texts
-
-
-if __name__ == "__main__":
-    import asyncio
-
-    # Example queries for Claude RAG
-    print("\n🤖 Claude RAG Example")
-    print("=" * 50)
-    print("\nExample queries you can try:")
-    print("- 'What did I ask Claude about Python programming?'")
-    print("- 'Show me conversations about machine learning'")
-    print("- 'Find discussions about code optimization'")
-    print("- 'What advice did Claude give me about software design?'")
-    print("- 'Search for conversations about debugging techniques'")
-    print("\nTo get started:")
-    print("1. Export your Claude conversation data")
-    print("2. Place the JSON/ZIP file in ./claude_export/")
-    print("3. Run this script to build your personal Claude knowledge base!")
-    print("\nOr run without --query for interactive mode\n")
-
-    rag = ClaudeRAG()
-    asyncio.run(rag.run())

apps/colqwen_rag.py

@@ -1,364 +0,0 @@
-#!/usr/bin/env python3
-"""
-ColQwen RAG - Easy-to-use multimodal PDF retrieval with ColQwen2/ColPali
-
-Usage:
-    python -m apps.colqwen_rag build --pdfs ./my_pdfs/ --index my_index
-    python -m apps.colqwen_rag search my_index "How does attention work?"
-    python -m apps.colqwen_rag ask my_index --interactive
-"""
-
-import argparse
-import os
-import sys
-from pathlib import Path
-from typing import Optional, cast
-
-# Add LEANN packages to path
-_repo_root = Path(__file__).resolve().parents[1]
-_leann_core_src = _repo_root / "packages" / "leann-core" / "src"
-_leann_hnsw_pkg = _repo_root / "packages" / "leann-backend-hnsw"
-if str(_leann_core_src) not in sys.path:
-    sys.path.append(str(_leann_core_src))
-if str(_leann_hnsw_pkg) not in sys.path:
-    sys.path.append(str(_leann_hnsw_pkg))
-
-import torch  # noqa: E402
-from colpali_engine import ColPali, ColPaliProcessor, ColQwen2, ColQwen2Processor  # noqa: E402
-from colpali_engine.utils.torch_utils import ListDataset  # noqa: E402
-from pdf2image import convert_from_path  # noqa: E402
-from PIL import Image  # noqa: E402
-from torch.utils.data import DataLoader  # noqa: E402
-from tqdm import tqdm  # noqa: E402
-
-# Import the existing multi-vector implementation
-sys.path.append(str(_repo_root / "apps" / "multimodal" / "vision-based-pdf-multi-vector"))
-from leann_multi_vector import LeannMultiVector  # noqa: E402
-
-
-class ColQwenRAG:
-    """Easy-to-use ColQwen RAG system for multimodal PDF retrieval."""
-
-    def __init__(self, model_type: str = "colpali"):
-        """
-        Initialize ColQwen RAG system.
-
-        Args:
-            model_type: "colqwen2" or "colpali"
-        """
-        self.model_type = model_type
-        self.device = self._get_device()
-        # Use float32 on MPS to avoid memory issues, float16 on CUDA, bfloat16 on CPU
-        if self.device.type == "mps":
-            self.dtype = torch.float32
-        elif self.device.type == "cuda":
-            self.dtype = torch.float16
-        else:
-            self.dtype = torch.bfloat16
-
-        print(f"🚀 Initializing {model_type.upper()} on {self.device} with {self.dtype}")
-
-        # Load model and processor with MPS-optimized settings
-        try:
-            if model_type == "colqwen2":
-                self.model_name = "vidore/colqwen2-v1.0"
-                if self.device.type == "mps":
-                    # For MPS, load on CPU first then move to avoid memory allocation issues
-                    self.model = ColQwen2.from_pretrained(
-                        self.model_name,
-                        torch_dtype=self.dtype,
-                        device_map="cpu",
-                        low_cpu_mem_usage=True,
-                    ).eval()
-                    self.model = self.model.to(self.device)
-                else:
-                    self.model = ColQwen2.from_pretrained(
-                        self.model_name,
-                        torch_dtype=self.dtype,
-                        device_map=self.device,
-                        low_cpu_mem_usage=True,
-                    ).eval()
-                self.processor = ColQwen2Processor.from_pretrained(self.model_name)
-            else:  # colpali
-                self.model_name = "vidore/colpali-v1.2"
-                if self.device.type == "mps":
-                    # For MPS, load on CPU first then move to avoid memory allocation issues
-                    self.model = ColPali.from_pretrained(
-                        self.model_name,
-                        torch_dtype=self.dtype,
-                        device_map="cpu",
-                        low_cpu_mem_usage=True,
-                    ).eval()
-                    self.model = self.model.to(self.device)
-                else:
-                    self.model = ColPali.from_pretrained(
-                        self.model_name,
-                        torch_dtype=self.dtype,
-                        device_map=self.device,
-                        low_cpu_mem_usage=True,
-                    ).eval()
-                self.processor = ColPaliProcessor.from_pretrained(self.model_name)
-        except Exception as e:
-            if "memory" in str(e).lower() or "offload" in str(e).lower():
-                print(f"⚠️  Memory constraint on {self.device}, using CPU with optimizations...")
-                self.device = torch.device("cpu")
-                self.dtype = torch.float32
-
-                if model_type == "colqwen2":
-                    self.model = ColQwen2.from_pretrained(
-                        self.model_name,
-                        torch_dtype=self.dtype,
-                        device_map="cpu",
-                        low_cpu_mem_usage=True,
-                    ).eval()
-                else:
-                    self.model = ColPali.from_pretrained(
-                        self.model_name,
-                        torch_dtype=self.dtype,
-                        device_map="cpu",
-                        low_cpu_mem_usage=True,
-                    ).eval()
-            else:
-                raise
-
-    def _get_device(self):
-        """Auto-select best available device."""
-        if torch.cuda.is_available():
-            return torch.device("cuda")
-        elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
-            return torch.device("mps")
-        else:
-            return torch.device("cpu")
-
-    def build_index(self, pdf_paths: list[str], index_name: str, pages_dir: Optional[str] = None):
-        """
-        Build multimodal index from PDF files.
-
-        Args:
-            pdf_paths: List of PDF file paths
-            index_name: Name for the index
-            pages_dir: Directory to save page images (optional)
-        """
-        print(f"Building index '{index_name}' from {len(pdf_paths)} PDFs...")
-
-        # Convert PDFs to images
-        all_images = []
-        all_metadata = []
-
-        if pages_dir:
-            os.makedirs(pages_dir, exist_ok=True)
-
-        for pdf_path in tqdm(pdf_paths, desc="Converting PDFs"):
-            try:
-                images = convert_from_path(pdf_path, dpi=150)
-                pdf_name = Path(pdf_path).stem
-
-                for i, image in enumerate(images):
-                    # Save image if pages_dir specified
-                    if pages_dir:
-                        image_path = Path(pages_dir) / f"{pdf_name}_page_{i + 1}.png"
-                        image.save(image_path)
-
-                    all_images.append(image)
-                    all_metadata.append(
-                        {
-                            "pdf_path": pdf_path,
-                            "pdf_name": pdf_name,
-                            "page_number": i + 1,
-                            "image_path": str(image_path) if pages_dir else None,
-                        }
-                    )
-
-            except Exception as e:
-                print(f"❌ Error processing {pdf_path}: {e}")
-                continue
-
-        print(f"📄 Converted {len(all_images)} pages from {len(pdf_paths)} PDFs")
-        print(f"All metadata: {all_metadata}")
-
-        # Generate embeddings
-        print("🧠 Generating embeddings...")
-        embeddings = self._embed_images(all_images)
-
-        # Build LEANN index
-        print("🔍 Building LEANN index...")
-        leann_mv = LeannMultiVector(
-            index_path=index_name,
-            dim=embeddings.shape[-1],
-            embedding_model_name=self.model_type,
-        )
-
-        # Create collection and insert data
-        leann_mv.create_collection()
-        for i, (embedding, metadata) in enumerate(zip(embeddings, all_metadata)):
-            data = {
-                "doc_id": i,
-                "filepath": metadata.get("image_path", ""),
-                "colbert_vecs": embedding.numpy(),  # Convert tensor to numpy
-            }
-            leann_mv.insert(data)
-
-        # Build the index
-        leann_mv.create_index()
-        print(f"✅ Index '{index_name}' built successfully!")
-
-        return leann_mv
-
-    def search(self, index_name: str, query: str, top_k: int = 5):
-        """
-        Search the index with a text query.
-
-        Args:
-            index_name: Name of the index to search
-            query: Text query
-            top_k: Number of results to return
-        """
-        print(f"🔍 Searching '{index_name}' for: '{query}'")
-
-        # Load index
-        leann_mv = LeannMultiVector(
-            index_path=index_name,
-            dim=128,  # Will be updated when loading
-            embedding_model_name=self.model_type,
-        )
-
-        # Generate query embedding
-        query_embedding = self._embed_query(query)
-
-        # Search (returns list of (score, doc_id) tuples)
-        search_results = leann_mv.search(query_embedding.numpy(), topk=top_k)
-
-        # Display results
-        print(f"\n📋 Top {len(search_results)} results:")
-        for i, (score, doc_id) in enumerate(search_results, 1):
-            # Get metadata for this doc_id (we need to load the metadata)
-            print(f"{i}. Score: {score:.3f} | Doc ID: {doc_id}")
-
-        return search_results
-
-    def ask(self, index_name: str, interactive: bool = False):
-        """
-        Interactive Q&A with the indexed documents.
-
-        Args:
-            index_name: Name of the index to query
-            interactive: Whether to run in interactive mode
-        """
-        print(f"💬 ColQwen Chat with '{index_name}'")
-
-        if interactive:
-            print("Type 'quit' to exit, 'help' for commands")
-            while True:
-                try:
-                    query = input("\n🤔 Your question: ").strip()
-                    if query.lower() in ["quit", "exit", "q"]:
-                        break
-                    elif query.lower() == "help":
-                        print("Commands: quit/exit/q (exit), help (this message)")
-                        continue
-                    elif not query:
-                        continue
-
-                    self.search(index_name, query, top_k=3)
-
-                    # TODO: Add answer generation with Qwen-VL
-                    print("\n💡 For detailed answers, we can integrate Qwen-VL here!")
-
-                except KeyboardInterrupt:
-                    print("\n👋 Goodbye!")
-                    break
-        else:
-            query = input("🤔 Your question: ").strip()
-            if query:
-                self.search(index_name, query)
-
-    def _embed_images(self, images: list[Image.Image]) -> torch.Tensor:
-        """Generate embeddings for a list of images."""
-        dataset = ListDataset(images)
-        dataloader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=lambda x: x)
-
-        embeddings = []
-        with torch.no_grad():
-            for batch in tqdm(dataloader, desc="Embedding images"):
-                batch_images = cast(list, batch)
-                batch_inputs = self.processor.process_images(batch_images).to(self.device)
-                batch_embeddings = self.model(**batch_inputs)
-                embeddings.append(batch_embeddings.cpu())
-
-        return torch.cat(embeddings, dim=0)
-
-    def _embed_query(self, query: str) -> torch.Tensor:
-        """Generate embedding for a text query."""
-        with torch.no_grad():
-            query_inputs = self.processor.process_queries([query]).to(self.device)
-            query_embedding = self.model(**query_inputs)
-            return query_embedding.cpu()
-
-
-def main():
-    parser = argparse.ArgumentParser(description="ColQwen RAG - Easy multimodal PDF retrieval")
-    subparsers = parser.add_subparsers(dest="command", help="Available commands")
-
-    # Build command
-    build_parser = subparsers.add_parser("build", help="Build index from PDFs")
-    build_parser.add_argument("--pdfs", required=True, help="Directory containing PDF files")
-    build_parser.add_argument("--index", required=True, help="Index name")
-    build_parser.add_argument(
-        "--model", choices=["colqwen2", "colpali"], default="colqwen2", help="Model to use"
-    )
-    build_parser.add_argument("--pages-dir", help="Directory to save page images")
-
-    # Search command
-    search_parser = subparsers.add_parser("search", help="Search the index")
-    search_parser.add_argument("index", help="Index name")
-    search_parser.add_argument("query", help="Search query")
-    search_parser.add_argument("--top-k", type=int, default=5, help="Number of results")
-    search_parser.add_argument(
-        "--model", choices=["colqwen2", "colpali"], default="colqwen2", help="Model to use"
-    )
-
-    # Ask command
-    ask_parser = subparsers.add_parser("ask", help="Interactive Q&A")
-    ask_parser.add_argument("index", help="Index name")
-    ask_parser.add_argument("--interactive", action="store_true", help="Interactive mode")
-    ask_parser.add_argument(
-        "--model", choices=["colqwen2", "colpali"], default="colqwen2", help="Model to use"
-    )
-
-    args = parser.parse_args()
-
-    if not args.command:
-        parser.print_help()
-        return
-
-    # Initialize ColQwen RAG
-    if args.command == "build":
-        colqwen = ColQwenRAG(args.model)
-
-        # Get PDF files
-        pdf_dir = Path(args.pdfs)
-        if pdf_dir.is_file() and pdf_dir.suffix.lower() == ".pdf":
-            pdf_paths = [str(pdf_dir)]
-        elif pdf_dir.is_dir():
-            pdf_paths = [str(p) for p in pdf_dir.glob("*.pdf")]
-        else:
-            print(f"❌ Invalid PDF path: {args.pdfs}")
-            return
-
-        if not pdf_paths:
-            print(f"❌ No PDF files found in {args.pdfs}")
-            return
-
-        colqwen.build_index(pdf_paths, args.index, args.pages_dir)
-
-    elif args.command == "search":
-        colqwen = ColQwenRAG(args.model)
-        colqwen.search(args.index, args.query, args.top_k)
-
-    elif args.command == "ask":
-        colqwen = ColQwenRAG(args.model)
-        colqwen.ask(args.index, args.interactive)
-
-
-if __name__ == "__main__":
-    main()

apps/document_rag.py

@@ -5,7 +5,6 @@ Supports PDF, TXT, MD, and other document formats.
 
 import sys
 from pathlib import Path
-from typing import Any, Union
 
 # Add parent directory to path for imports
 sys.path.insert(0, str(Path(__file__).parent))
@@ -52,7 +51,7 @@ class DocumentRAG(BaseRAGExample):
             help="Enable AST-aware chunking for code files in the data directory",
         )
 
-    async def load_data(self, args) -> list[Union[str, dict[str, Any]]]:
+    async def load_data(self, args) -> list[str]:
         """Load documents and convert to text chunks."""
         print(f"Loading documents from: {args.data_dir}")
         if args.file_types:

apps/image_rag.py

@@ -1,218 +0,0 @@
-#!/usr/bin/env python3
-"""
-CLIP Image RAG Application
-
-This application enables RAG (Retrieval-Augmented Generation) on images using CLIP embeddings.
-You can index a directory of images and search them using text queries.
-
-Usage:
-    python -m apps.image_rag --image-dir ./my_images/ --query "a sunset over mountains"
-    python -m apps.image_rag --image-dir ./my_images/ --interactive
-"""
-
-import argparse
-import pickle
-import tempfile
-from pathlib import Path
-
-import numpy as np
-from PIL import Image
-from sentence_transformers import SentenceTransformer
-from tqdm import tqdm
-
-from apps.base_rag_example import BaseRAGExample
-
-
-class ImageRAG(BaseRAGExample):
-    """
-    RAG application for images using CLIP embeddings.
-
-    This class provides a complete RAG pipeline for image data, including
-    CLIP embedding generation, indexing, and text-based image search.
-    """
-
-    def __init__(self):
-        super().__init__(
-            name="Image RAG",
-            description="RAG application for images using CLIP embeddings",
-            default_index_name="image_index",
-        )
-        # Override default embedding model to use CLIP
-        self.embedding_model_default = "clip-ViT-L-14"
-        self.embedding_mode_default = "sentence-transformers"
-        self._image_data: list[dict] = []
-
-    def _add_specific_arguments(self, parser: argparse.ArgumentParser):
-        """Add image-specific arguments."""
-        image_group = parser.add_argument_group("Image Parameters")
-        image_group.add_argument(
-            "--image-dir",
-            type=str,
-            required=True,
-            help="Directory containing images to index",
-        )
-        image_group.add_argument(
-            "--image-extensions",
-            type=str,
-            nargs="+",
-            default=[".jpg", ".jpeg", ".png", ".gif", ".bmp", ".webp"],
-            help="Image file extensions to process (default: .jpg .jpeg .png .gif .bmp .webp)",
-        )
-        image_group.add_argument(
-            "--batch-size",
-            type=int,
-            default=32,
-            help="Batch size for CLIP embedding generation (default: 32)",
-        )
-
-    async def load_data(self, args) -> list[str]:
-        """Load images, generate CLIP embeddings, and return text descriptions."""
-        self._image_data = self._load_images_and_embeddings(args)
-        return [entry["text"] for entry in self._image_data]
-
-    def _load_images_and_embeddings(self, args) -> list[dict]:
-        """Helper to process images and produce embeddings/metadata."""
-        image_dir = Path(args.image_dir)
-        if not image_dir.exists():
-            raise ValueError(f"Image directory does not exist: {image_dir}")
-
-        print(f"📸 Loading images from {image_dir}...")
-
-        # Find all image files
-        image_files = []
-        for ext in args.image_extensions:
-            image_files.extend(image_dir.rglob(f"*{ext}"))
-            image_files.extend(image_dir.rglob(f"*{ext.upper()}"))
-
-        if not image_files:
-            raise ValueError(
-                f"No images found in {image_dir} with extensions {args.image_extensions}"
-            )
-
-        print(f"✅ Found {len(image_files)} images")
-
-        # Limit if max_items is set
-        if args.max_items > 0:
-            image_files = image_files[: args.max_items]
-            print(f"📊 Processing {len(image_files)} images (limited by --max-items)")
-
-        # Load CLIP model
-        print("🔍 Loading CLIP model...")
-        model = SentenceTransformer(self.embedding_model_default)
-
-        # Process images and generate embeddings
-        print("🖼️  Processing images and generating embeddings...")
-        image_data = []
-        batch_images = []
-        batch_paths = []
-
-        for image_path in tqdm(image_files, desc="Processing images"):
-            try:
-                image = Image.open(image_path).convert("RGB")
-                batch_images.append(image)
-                batch_paths.append(image_path)
-
-                # Process in batches
-                if len(batch_images) >= args.batch_size:
-                    embeddings = model.encode(
-                        batch_images,
-                        convert_to_numpy=True,
-                        normalize_embeddings=True,
-                        batch_size=args.batch_size,
-                        show_progress_bar=False,
-                    )
-
-                    for img_path, embedding in zip(batch_paths, embeddings):
-                        image_data.append(
-                            {
-                                "text": f"Image: {img_path.name}\nPath: {img_path}",
-                                "metadata": {
-                                    "image_path": str(img_path),
-                                    "image_name": img_path.name,
-                                    "image_dir": str(image_dir),
-                                },
-                                "embedding": embedding.astype(np.float32),
-                            }
-                        )
-
-                    batch_images = []
-                    batch_paths = []
-
-            except Exception as e:
-                print(f"⚠️  Failed to process {image_path}: {e}")
-                continue
-
-        # Process remaining images
-        if batch_images:
-            embeddings = model.encode(
-                batch_images,
-                convert_to_numpy=True,
-                normalize_embeddings=True,
-                batch_size=len(batch_images),
-                show_progress_bar=False,
-            )
-
-            for img_path, embedding in zip(batch_paths, embeddings):
-                image_data.append(
-                    {
-                        "text": f"Image: {img_path.name}\nPath: {img_path}",
-                        "metadata": {
-                            "image_path": str(img_path),
-                            "image_name": img_path.name,
-                            "image_dir": str(image_dir),
-                        },
-                        "embedding": embedding.astype(np.float32),
-                    }
-                )
-
-        print(f"✅ Processed {len(image_data)} images")
-        return image_data
-
-    async def build_index(self, args, texts: list[str]) -> str:
-        """Build index using pre-computed CLIP embeddings."""
-        from leann.api import LeannBuilder
-
-        if not self._image_data or len(self._image_data) != len(texts):
-            raise RuntimeError("No image data found. Make sure load_data() ran successfully.")
-
-        print("🔨 Building LEANN index with CLIP embeddings...")
-        builder = LeannBuilder(
-            backend_name=args.backend_name,
-            embedding_model=self.embedding_model_default,
-            embedding_mode=self.embedding_mode_default,
-            is_recompute=False,
-            distance_metric="cosine",
-            graph_degree=args.graph_degree,
-            build_complexity=args.build_complexity,
-            is_compact=not args.no_compact,
-        )
-
-        for text, data in zip(texts, self._image_data):
-            builder.add_text(text=text, metadata=data["metadata"])
-
-        ids = [str(i) for i in range(len(self._image_data))]
-        embeddings = np.array([data["embedding"] for data in self._image_data], dtype=np.float32)
-
-        with tempfile.NamedTemporaryFile(mode="wb", suffix=".pkl", delete=False) as f:
-            pickle.dump((ids, embeddings), f)
-            pkl_path = f.name
-
-        try:
-            index_path = str(Path(args.index_dir) / f"{self.default_index_name}.leann")
-            builder.build_index_from_embeddings(index_path, pkl_path)
-            print(f"✅ Index built successfully at {index_path}")
-            return index_path
-        finally:
-            Path(pkl_path).unlink()
-
-
-def main():
-    """Main entry point for the image RAG application."""
-    import asyncio
-
-    app = ImageRAG()
-    asyncio.run(app.run())
-
-
-if __name__ == "__main__":
-    main()

apps/imessage_data/__init__.py

@@ -1 +0,0 @@
-"""iMessage data processing module."""

apps/imessage_data/imessage_reader.py

@@ -1,342 +0,0 @@
-"""
-iMessage data reader.
-
-Reads and processes iMessage conversation data from the macOS Messages database.
-"""
-
-import sqlite3
-from datetime import datetime
-from pathlib import Path
-from typing import Any
-
-from llama_index.core import Document
-from llama_index.core.readers.base import BaseReader
-
-
-class IMessageReader(BaseReader):
-    """
-    iMessage data reader.
-
-    Reads iMessage conversation data from the macOS Messages database (chat.db).
-    Processes conversations into structured documents with metadata.
-    """
-
-    def __init__(self, concatenate_conversations: bool = True) -> None:
-        """
-        Initialize.
-
-        Args:
-            concatenate_conversations: Whether to concatenate messages within conversations for better context
-        """
-        self.concatenate_conversations = concatenate_conversations
-
-    def _get_default_chat_db_path(self) -> Path:
-        """
-        Get the default path to the iMessage chat database.
-
-        Returns:
-            Path to the chat.db file
-        """
-        home = Path.home()
-        return home / "Library" / "Messages" / "chat.db"
-
-    def _convert_cocoa_timestamp(self, cocoa_timestamp: int) -> str:
-        """
-        Convert Cocoa timestamp to readable format.
-
-        Args:
-            cocoa_timestamp: Timestamp in Cocoa format (nanoseconds since 2001-01-01)
-
-        Returns:
-            Formatted timestamp string
-        """
-        if cocoa_timestamp == 0:
-            return "Unknown"
-
-        try:
-            # Cocoa timestamp is nanoseconds since 2001-01-01 00:00:00 UTC
-            # Convert to seconds and add to Unix epoch
-            cocoa_epoch = datetime(2001, 1, 1)
-            unix_timestamp = cocoa_timestamp / 1_000_000_000  # Convert nanoseconds to seconds
-            message_time = cocoa_epoch.timestamp() + unix_timestamp
-            return datetime.fromtimestamp(message_time).strftime("%Y-%m-%d %H:%M:%S")
-        except (ValueError, OSError):
-            return "Unknown"
-
-    def _get_contact_name(self, handle_id: str) -> str:
-        """
-        Get a readable contact name from handle ID.
-
-        Args:
-            handle_id: The handle ID (phone number or email)
-
-        Returns:
-            Formatted contact name
-        """
-        if not handle_id:
-            return "Unknown"
-
-        # Clean up phone numbers and emails for display
-        if "@" in handle_id:
-            return handle_id  # Email address
-        elif handle_id.startswith("+"):
-            return handle_id  # International phone number
-        else:
-            # Try to format as phone number
-            digits = "".join(filter(str.isdigit, handle_id))
-            if len(digits) == 10:
-                return f"({digits[:3]}) {digits[3:6]}-{digits[6:]}"
-            elif len(digits) == 11 and digits[0] == "1":
-                return f"+1 ({digits[1:4]}) {digits[4:7]}-{digits[7:]}"
-            else:
-                return handle_id
-
-    def _read_messages_from_db(self, db_path: Path) -> list[dict]:
-        """
-        Read messages from the iMessage database.
-
-        Args:
-            db_path: Path to the chat.db file
-
-        Returns:
-            List of message dictionaries
-        """
-        if not db_path.exists():
-            print(f"iMessage database not found at: {db_path}")
-            return []
-
-        try:
-            # Connect to the database
-            conn = sqlite3.connect(str(db_path))
-            cursor = conn.cursor()
-
-            # Query to get messages with chat and handle information
-            query = """
-            SELECT
-                m.ROWID as message_id,
-                m.text,
-                m.date,
-                m.is_from_me,
-                m.service,
-                c.chat_identifier,
-                c.display_name as chat_display_name,
-                h.id as handle_id,
-                c.ROWID as chat_id
-            FROM message m
-            LEFT JOIN chat_message_join cmj ON m.ROWID = cmj.message_id
-            LEFT JOIN chat c ON cmj.chat_id = c.ROWID
-            LEFT JOIN handle h ON m.handle_id = h.ROWID
-            WHERE m.text IS NOT NULL AND m.text != ''
-            ORDER BY c.ROWID, m.date
-            """
-
-            cursor.execute(query)
-            rows = cursor.fetchall()
-
-            messages = []
-            for row in rows:
-                (
-                    message_id,
-                    text,
-                    date,
-                    is_from_me,
-                    service,
-                    chat_identifier,
-                    chat_display_name,
-                    handle_id,
-                    chat_id,
-                ) = row
-
-                message = {
-                    "message_id": message_id,
-                    "text": text,
-                    "timestamp": self._convert_cocoa_timestamp(date),
-                    "is_from_me": bool(is_from_me),
-                    "service": service or "iMessage",
-                    "chat_identifier": chat_identifier or "Unknown",
-                    "chat_display_name": chat_display_name or "Unknown Chat",
-                    "handle_id": handle_id or "Unknown",
-                    "contact_name": self._get_contact_name(handle_id or ""),
-                    "chat_id": chat_id,
-                }
-                messages.append(message)
-
-            conn.close()
-            print(f"Found {len(messages)} messages in database")
-            return messages
-
-        except sqlite3.Error as e:
-            print(f"Error reading iMessage database: {e}")
-            return []
-        except Exception as e:
-            print(f"Unexpected error reading iMessage database: {e}")
-            return []
-
-    def _group_messages_by_chat(self, messages: list[dict]) -> dict[int, list[dict]]:
-        """
-        Group messages by chat ID.
-
-        Args:
-            messages: List of message dictionaries
-
-        Returns:
-            Dictionary mapping chat_id to list of messages
-        """
-        chats = {}
-        for message in messages:
-            chat_id = message["chat_id"]
-            if chat_id not in chats:
-                chats[chat_id] = []
-            chats[chat_id].append(message)
-
-        return chats
-
-    def _create_concatenated_content(self, chat_id: int, messages: list[dict]) -> str:
-        """
-        Create concatenated content from chat messages.
-
-        Args:
-            chat_id: The chat ID
-            messages: List of messages in the chat
-
-        Returns:
-            Concatenated text content
-        """
-        if not messages:
-            return ""
-
-        # Get chat info from first message
-        first_msg = messages[0]
-        chat_name = first_msg["chat_display_name"]
-        chat_identifier = first_msg["chat_identifier"]
-
-        # Build message content
-        message_parts = []
-        for message in messages:
-            timestamp = message["timestamp"]
-            is_from_me = message["is_from_me"]
-            text = message["text"]
-            contact_name = message["contact_name"]
-
-            if is_from_me:
-                prefix = "[You]"
-            else:
-                prefix = f"[{contact_name}]"
-
-            if timestamp != "Unknown":
-                prefix += f" ({timestamp})"
-
-            message_parts.append(f"{prefix}: {text}")
-
-        concatenated_text = "\n\n".join(message_parts)
-
-        doc_content = f"""Chat: {chat_name}
-Identifier: {chat_identifier}
-Messages ({len(messages)} messages):
-
-{concatenated_text}
-"""
-        return doc_content
-
-    def _create_individual_content(self, message: dict) -> str:
-        """
-        Create content for individual message.
-
-        Args:
-            message: Message dictionary
-
-        Returns:
-            Formatted message content
-        """
-        timestamp = message["timestamp"]
-        is_from_me = message["is_from_me"]
-        text = message["text"]
-        contact_name = message["contact_name"]
-        chat_name = message["chat_display_name"]
-
-        sender = "You" if is_from_me else contact_name
-
-        return f"""Message from {sender} in chat "{chat_name}"
-Time: {timestamp}
-Content: {text}
-"""
-
-    def load_data(self, input_dir: str | None = None, **load_kwargs: Any) -> list[Document]:
-        """
-        Load iMessage data and return as documents.
-
-        Args:
-            input_dir: Optional path to directory containing chat.db file.
-                      If not provided, uses default macOS location.
-            **load_kwargs: Additional arguments (unused)
-
-        Returns:
-            List of Document objects containing iMessage data
-        """
-        docs = []
-
-        # Determine database path
-        if input_dir:
-            db_path = Path(input_dir) / "chat.db"
-        else:
-            db_path = self._get_default_chat_db_path()
-
-        print(f"Reading iMessage database from: {db_path}")
-
-        # Read messages from database
-        messages = self._read_messages_from_db(db_path)
-        if not messages:
-            return docs
-
-        if self.concatenate_conversations:
-            # Group messages by chat and create concatenated documents
-            chats = self._group_messages_by_chat(messages)
-
-            for chat_id, chat_messages in chats.items():
-                if not chat_messages:
-                    continue
-
-                content = self._create_concatenated_content(chat_id, chat_messages)
-
-                # Create metadata
-                first_msg = chat_messages[0]
-                last_msg = chat_messages[-1]
-
-                metadata = {
-                    "source": "iMessage",
-                    "chat_id": chat_id,
-                    "chat_name": first_msg["chat_display_name"],
-                    "chat_identifier": first_msg["chat_identifier"],
-                    "message_count": len(chat_messages),
-                    "first_message_date": first_msg["timestamp"],
-                    "last_message_date": last_msg["timestamp"],
-                    "participants": list(
-                        {msg["contact_name"] for msg in chat_messages if not msg["is_from_me"]}
-                    ),
-                }
-
-                doc = Document(text=content, metadata=metadata)
-                docs.append(doc)
-
-        else:
-            # Create individual documents for each message
-            for message in messages:
-                content = self._create_individual_content(message)
-
-                metadata = {
-                    "source": "iMessage",
-                    "message_id": message["message_id"],
-                    "chat_id": message["chat_id"],
-                    "chat_name": message["chat_display_name"],
-                    "chat_identifier": message["chat_identifier"],
-                    "timestamp": message["timestamp"],
-                    "is_from_me": message["is_from_me"],
-                    "contact_name": message["contact_name"],
-                    "service": message["service"],
-                }
-
-                doc = Document(text=content, metadata=metadata)
-                docs.append(doc)
-
-        print(f"Created {len(docs)} documents from iMessage data")
-        return docs

apps/imessage_rag.py

@@ -1,125 +0,0 @@
-"""
-iMessage RAG Example.
-
-This example demonstrates how to build a RAG system on your iMessage conversation history.
-"""
-
-import asyncio
-from pathlib import Path
-
-from leann.chunking_utils import create_text_chunks
-
-from apps.base_rag_example import BaseRAGExample
-from apps.imessage_data.imessage_reader import IMessageReader
-
-
-class IMessageRAG(BaseRAGExample):
-    """RAG example for iMessage conversation history."""
-
-    def __init__(self):
-        super().__init__(
-            name="iMessage",
-            description="RAG on your iMessage conversation history",
-            default_index_name="imessage_index",
-        )
-
-    def _add_specific_arguments(self, parser):
-        """Add iMessage-specific arguments."""
-        imessage_group = parser.add_argument_group("iMessage Parameters")
-        imessage_group.add_argument(
-            "--db-path",
-            type=str,
-            default=None,
-            help="Path to iMessage chat.db file (default: ~/Library/Messages/chat.db)",
-        )
-        imessage_group.add_argument(
-            "--concatenate-conversations",
-            action="store_true",
-            default=True,
-            help="Concatenate messages within conversations for better context (default: True)",
-        )
-        imessage_group.add_argument(
-            "--no-concatenate-conversations",
-            action="store_true",
-            help="Process each message individually instead of concatenating by conversation",
-        )
-        imessage_group.add_argument(
-            "--chunk-size",
-            type=int,
-            default=1000,
-            help="Maximum characters per text chunk (default: 1000)",
-        )
-        imessage_group.add_argument(
-            "--chunk-overlap",
-            type=int,
-            default=200,
-            help="Overlap between text chunks (default: 200)",
-        )
-
-    async def load_data(self, args) -> list[str]:
-        """Load iMessage history and convert to text chunks."""
-        print("Loading iMessage conversation history...")
-
-        # Determine concatenation setting
-        concatenate = args.concatenate_conversations and not args.no_concatenate_conversations
-
-        # Initialize iMessage reader
-        reader = IMessageReader(concatenate_conversations=concatenate)
-
-        # Load documents
-        try:
-            if args.db_path:
-                # Use custom database path
-                db_dir = str(Path(args.db_path).parent)
-                documents = reader.load_data(input_dir=db_dir)
-            else:
-                # Use default macOS location
-                documents = reader.load_data()
-
-        except Exception as e:
-            print(f"Error loading iMessage data: {e}")
-            print("\nTroubleshooting tips:")
-            print("1. Make sure you have granted Full Disk Access to your terminal/IDE")
-            print("2. Check that the iMessage database exists at ~/Library/Messages/chat.db")
-            print("3. Try specifying a custom path with --db-path if you have a backup")
-            return []
-
-        if not documents:
-            print("No iMessage conversations found!")
-            return []
-
-        print(f"Loaded {len(documents)} iMessage documents")
-
-        # Show some statistics
-        total_messages = sum(doc.metadata.get("message_count", 1) for doc in documents)
-        print(f"Total messages: {total_messages}")
-
-        if concatenate:
-            # Show chat statistics
-            chat_names = [doc.metadata.get("chat_name", "Unknown") for doc in documents]
-            unique_chats = len(set(chat_names))
-            print(f"Unique conversations: {unique_chats}")
-
-        # Convert to text chunks
-        all_texts = create_text_chunks(
-            documents,
-            chunk_size=args.chunk_size,
-            chunk_overlap=args.chunk_overlap,
-        )
-
-        # Apply max_items limit if specified
-        if args.max_items > 0:
-            all_texts = all_texts[: args.max_items]
-            print(f"Limited to {len(all_texts)} text chunks (max_items={args.max_items})")
-
-        return all_texts
-
-
-async def main():
-    """Main entry point."""
-    app = IMessageRAG()
-    await app.run()
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

apps/multimodal/vision-based-pdf-multi-vector/colqwen_forward.py

@@ -1,132 +0,0 @@
-#!/usr/bin/env python3
-"""Simple test script to test colqwen2 forward pass with a single image."""
-
-import os
-import sys
-from pathlib import Path
-
-# Add the current directory to path to import leann_multi_vector
-sys.path.insert(0, str(Path(__file__).parent))
-
-import torch
-from leann_multi_vector import _embed_images, _ensure_repo_paths_importable, _load_colvision
-from PIL import Image
-
-# Ensure repo paths are importable
-_ensure_repo_paths_importable(__file__)
-
-# Set environment variable
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-
-def create_test_image():
-    """Create a simple test image."""
-    # Create a simple RGB image (800x600)
-    img = Image.new("RGB", (800, 600), color="white")
-    return img
-
-
-def load_test_image_from_file():
-    """Try to load an image from the indexes directory if available."""
-    # Try to find an existing image in the indexes directory
-    indexes_dir = Path(__file__).parent / "indexes"
-
-    # Look for images in common locations
-    possible_paths = [
-        indexes_dir / "vidore_fastplaid" / "images",
-        indexes_dir / "colvision_large.leann.images",
-        indexes_dir / "colvision.leann.images",
-    ]
-
-    for img_dir in possible_paths:
-        if img_dir.exists():
-            # Find first image file
-            for ext in [".png", ".jpg", ".jpeg"]:
-                for img_file in img_dir.glob(f"*{ext}"):
-                    print(f"Loading test image from: {img_file}")
-                    return Image.open(img_file)
-
-    return None
-
-
-def main():
-    print("=" * 60)
-    print("Testing ColQwen2 Forward Pass")
-    print("=" * 60)
-
-    # Step 1: Load or create test image
-    print("\n[Step 1] Loading test image...")
-    test_image = load_test_image_from_file()
-    if test_image is None:
-        print("No existing image found, creating a simple test image...")
-        test_image = create_test_image()
-    else:
-        print(f"✓ Loaded image: {test_image.size} ({test_image.mode})")
-
-    # Convert to RGB if needed
-    if test_image.mode != "RGB":
-        test_image = test_image.convert("RGB")
-        print(f"✓ Converted to RGB: {test_image.size}")
-
-    # Step 2: Load model
-    print("\n[Step 2] Loading ColQwen2 model...")
-    try:
-        model_name, model, processor, device_str, device, dtype = _load_colvision("colqwen2")
-        print(f"✓ Model loaded: {model_name}")
-        print(f"✓ Device: {device_str}, dtype: {dtype}")
-
-        # Print model info
-        if hasattr(model, "device"):
-            print(f"✓ Model device: {model.device}")
-        if hasattr(model, "dtype"):
-            print(f"✓ Model dtype: {model.dtype}")
-
-    except Exception as e:
-        print(f"✗ Error loading model: {e}")
-        import traceback
-
-        traceback.print_exc()
-        return
-
-    # Step 3: Test forward pass
-    print("\n[Step 3] Running forward pass...")
-    try:
-        # Use the _embed_images function which handles batching and forward pass
-        images = [test_image]
-        print(f"Processing {len(images)} image(s)...")
-
-        doc_vecs = _embed_images(model, processor, images)
-
-        print("✓ Forward pass completed!")
-        print(f"✓ Number of embeddings: {len(doc_vecs)}")
-
-        if len(doc_vecs) > 0:
-            emb = doc_vecs[0]
-            print(f"✓ Embedding shape: {emb.shape}")
-            print(f"✓ Embedding dtype: {emb.dtype}")
-            print("✓ Embedding stats:")
-            print(f"    - Min: {emb.min().item():.4f}")
-            print(f"    - Max: {emb.max().item():.4f}")
-            print(f"    - Mean: {emb.mean().item():.4f}")
-            print(f"    - Std: {emb.std().item():.4f}")
-
-            # Check for NaN or Inf
-            if torch.isnan(emb).any():
-                print("⚠ Warning: Embedding contains NaN values!")
-            if torch.isinf(emb).any():
-                print("⚠ Warning: Embedding contains Inf values!")
-
-    except Exception as e:
-        print(f"✗ Error during forward pass: {e}")
-        import traceback
-
-        traceback.print_exc()
-        return
-
-    print("\n" + "=" * 60)
-    print("Test completed successfully!")
-    print("=" * 60)
-
-
-if __name__ == "__main__":
-    main()

apps/multimodal/vision-based-pdf-multi-vector/vidore_v1_benchmark.py

@@ -1,448 +0,0 @@
-#!/usr/bin/env python3
-"""
-Modular script to reproduce NDCG results for ViDoRe v1 benchmark.
-
-This script uses the interface from leann_multi_vector.py to:
-1. Download ViDoRe v1 datasets
-2. Build indexes (LEANN or Fast-Plaid)
-3. Perform retrieval
-4. Evaluate using NDCG metrics
-
-Usage:
-    # Evaluate all ViDoRe v1 tasks
-    python vidore_v1_benchmark.py --model colqwen2 --tasks all
-
-    # Evaluate specific task
-    python vidore_v1_benchmark.py --model colqwen2 --task VidoreArxivQARetrieval
-
-    # Use Fast-Plaid index
-    python vidore_v1_benchmark.py --model colqwen2 --use-fast-plaid --fast-plaid-index-path ./indexes/vidore_fastplaid
-
-    # Rebuild index
-    python vidore_v1_benchmark.py --model colqwen2 --rebuild-index
-"""
-
-import argparse
-import json
-import os
-from typing import Optional
-
-from datasets import load_dataset
-from leann_multi_vector import (
-    ViDoReBenchmarkEvaluator,
-    _ensure_repo_paths_importable,
-)
-
-_ensure_repo_paths_importable(__file__)
-
-# ViDoRe v1 task configurations
-# Prompts match MTEB task metadata prompts
-VIDORE_V1_TASKS = {
-    "VidoreArxivQARetrieval": {
-        "dataset_path": "vidore/arxivqa_test_subsampled_beir",
-        "revision": "7d94d570960eac2408d3baa7a33f9de4822ae3e4",
-        "prompt": {"query": "Find a screenshot that relevant to the user's question."},
-    },
-    "VidoreDocVQARetrieval": {
-        "dataset_path": "vidore/docvqa_test_subsampled_beir",
-        "revision": "162ba2fc1a8437eda8b6c37b240bc1c0f0deb092",
-        "prompt": {"query": "Find a screenshot that relevant to the user's question."},
-    },
-    "VidoreInfoVQARetrieval": {
-        "dataset_path": "vidore/infovqa_test_subsampled_beir",
-        "revision": "b802cc5fd6c605df2d673a963667d74881d2c9a4",
-        "prompt": {"query": "Find a screenshot that relevant to the user's question."},
-    },
-    "VidoreTabfquadRetrieval": {
-        "dataset_path": "vidore/tabfquad_test_subsampled_beir",
-        "revision": "61a2224bcd29b7b261a4892ff4c8bea353527a31",
-        "prompt": {"query": "Find a screenshot that relevant to the user's question."},
-    },
-    "VidoreTatdqaRetrieval": {
-        "dataset_path": "vidore/tatdqa_test_beir",
-        "revision": "5feb5630fdff4d8d189ffedb2dba56862fdd45c0",
-        "prompt": {"query": "Find a screenshot that relevant to the user's question."},
-    },
-    "VidoreShiftProjectRetrieval": {
-        "dataset_path": "vidore/shiftproject_test_beir",
-        "revision": "84a382e05c4473fed9cff2bbae95fe2379416117",
-        "prompt": {"query": "Find a screenshot that relevant to the user's question."},
-    },
-    "VidoreSyntheticDocQAAIRetrieval": {
-        "dataset_path": "vidore/syntheticDocQA_artificial_intelligence_test_beir",
-        "revision": "2d9ebea5a1c6e9ef4a3b902a612f605dca11261c",
-        "prompt": {"query": "Find a screenshot that relevant to the user's question."},
-    },
-    "VidoreSyntheticDocQAEnergyRetrieval": {
-        "dataset_path": "vidore/syntheticDocQA_energy_test_beir",
-        "revision": "9935aadbad5c8deec30910489db1b2c7133ae7a7",
-        "prompt": {"query": "Find a screenshot that relevant to the user's question."},
-    },
-    "VidoreSyntheticDocQAGovernmentReportsRetrieval": {
-        "dataset_path": "vidore/syntheticDocQA_government_reports_test_beir",
-        "revision": "b4909afa930f81282fd20601e860668073ad02aa",
-        "prompt": {"query": "Find a screenshot that relevant to the user's question."},
-    },
-    "VidoreSyntheticDocQAHealthcareIndustryRetrieval": {
-        "dataset_path": "vidore/syntheticDocQA_healthcare_industry_test_beir",
-        "revision": "f9e25d5b6e13e1ad9f5c3cce202565031b3ab164",
-        "prompt": {"query": "Find a screenshot that relevant to the user's question."},
-    },
-}
-
-# Task name aliases (short names -> full names)
-TASK_ALIASES = {
-    "arxivqa": "VidoreArxivQARetrieval",
-    "docvqa": "VidoreDocVQARetrieval",
-    "infovqa": "VidoreInfoVQARetrieval",
-    "tabfquad": "VidoreTabfquadRetrieval",
-    "tatdqa": "VidoreTatdqaRetrieval",
-    "shiftproject": "VidoreShiftProjectRetrieval",
-    "syntheticdocqa_ai": "VidoreSyntheticDocQAAIRetrieval",
-    "syntheticdocqa_energy": "VidoreSyntheticDocQAEnergyRetrieval",
-    "syntheticdocqa_government": "VidoreSyntheticDocQAGovernmentReportsRetrieval",
-    "syntheticdocqa_healthcare": "VidoreSyntheticDocQAHealthcareIndustryRetrieval",
-}
-
-
-def normalize_task_name(task_name: str) -> str:
-    """Normalize task name (handle aliases)."""
-    task_name_lower = task_name.lower()
-    if task_name in VIDORE_V1_TASKS:
-        return task_name
-    if task_name_lower in TASK_ALIASES:
-        return TASK_ALIASES[task_name_lower]
-    # Try partial match
-    for alias, full_name in TASK_ALIASES.items():
-        if alias in task_name_lower or task_name_lower in alias:
-            return full_name
-    return task_name
-
-
-def get_safe_model_name(model_name: str) -> str:
-    """Get a safe model name for use in file paths."""
-    import hashlib
-    import os
-
-    # If it's a path, use basename or hash
-    if os.path.exists(model_name) and os.path.isdir(model_name):
-        # Use basename if it's reasonable, otherwise use hash
-        basename = os.path.basename(model_name.rstrip("/"))
-        if basename and len(basename) < 100 and not basename.startswith("."):
-            return basename
-        # Use hash for very long or problematic paths
-        return hashlib.md5(model_name.encode()).hexdigest()[:16]
-    # For HuggingFace model names, replace / with _
-    return model_name.replace("/", "_").replace(":", "_")
-
-
-def load_vidore_v1_data(
-    dataset_path: str,
-    revision: Optional[str] = None,
-    split: str = "test",
-):
-    """
-    Load ViDoRe v1 dataset.
-
-    Returns:
-        corpus: dict mapping corpus_id to PIL Image
-        queries: dict mapping query_id to query text
-        qrels: dict mapping query_id to dict of {corpus_id: relevance_score}
-    """
-    print(f"Loading dataset: {dataset_path} (split={split})")
-
-    # Load queries
-    query_ds = load_dataset(dataset_path, "queries", split=split, revision=revision)
-
-    queries = {}
-    for row in query_ds:
-        query_id = f"query-{split}-{row['query-id']}"
-        queries[query_id] = row["query"]
-
-    # Load corpus (images)
-    corpus_ds = load_dataset(dataset_path, "corpus", split=split, revision=revision)
-
-    corpus = {}
-    for row in corpus_ds:
-        corpus_id = f"corpus-{split}-{row['corpus-id']}"
-        # Extract image from the dataset row
-        if "image" in row:
-            corpus[corpus_id] = row["image"]
-        elif "page_image" in row:
-            corpus[corpus_id] = row["page_image"]
-        else:
-            raise ValueError(
-                f"No image field found in corpus. Available fields: {list(row.keys())}"
-            )
-
-    # Load qrels (relevance judgments)
-    qrels_ds = load_dataset(dataset_path, "qrels", split=split, revision=revision)
-
-    qrels = {}
-    for row in qrels_ds:
-        query_id = f"query-{split}-{row['query-id']}"
-        corpus_id = f"corpus-{split}-{row['corpus-id']}"
-        if query_id not in qrels:
-            qrels[query_id] = {}
-        qrels[query_id][corpus_id] = int(row["score"])
-
-    print(
-        f"Loaded {len(queries)} queries, {len(corpus)} corpus items, {len(qrels)} query-relevance mappings"
-    )
-
-    # Filter qrels to only include queries that exist
-    qrels = {qid: rel_docs for qid, rel_docs in qrels.items() if qid in queries}
-
-    # Filter out queries without any relevant documents (matching MTEB behavior)
-    # This is important for correct NDCG calculation
-    qrels_filtered = {qid: rel_docs for qid, rel_docs in qrels.items() if len(rel_docs) > 0}
-    queries_filtered = {
-        qid: query_text for qid, query_text in queries.items() if qid in qrels_filtered
-    }
-
-    print(
-        f"After filtering queries without positives: {len(queries_filtered)} queries, {len(qrels_filtered)} query-relevance mappings"
-    )
-
-    return corpus, queries_filtered, qrels_filtered
-
-
-def evaluate_task(
-    task_name: str,
-    model_name: str,
-    index_path: str,
-    use_fast_plaid: bool = False,
-    fast_plaid_index_path: Optional[str] = None,
-    rebuild_index: bool = False,
-    top_k: int = 1000,
-    first_stage_k: int = 500,
-    k_values: Optional[list[int]] = None,
-    output_dir: Optional[str] = None,
-):
-    """
-    Evaluate a single ViDoRe v1 task.
-    """
-    print(f"\n{'=' * 80}")
-    print(f"Evaluating task: {task_name}")
-    print(f"{'=' * 80}")
-
-    # Normalize task name (handle aliases)
-    task_name = normalize_task_name(task_name)
-
-    # Get task config
-    if task_name not in VIDORE_V1_TASKS:
-        raise ValueError(f"Unknown task: {task_name}. Available: {list(VIDORE_V1_TASKS.keys())}")
-
-    task_config = VIDORE_V1_TASKS[task_name]
-    dataset_path = task_config["dataset_path"]
-    revision = task_config["revision"]
-
-    # Load data
-    corpus, queries, qrels = load_vidore_v1_data(
-        dataset_path=dataset_path,
-        revision=revision,
-        split="test",
-    )
-
-    # Initialize k_values if not provided
-    if k_values is None:
-        k_values = [1, 3, 5, 10, 20, 100, 1000]
-
-    # Check if we have any queries
-    if len(queries) == 0:
-        print(f"\nWarning: No queries found for task {task_name}. Skipping evaluation.")
-        # Return zero scores
-        scores = {}
-        for k in k_values:
-            scores[f"ndcg_at_{k}"] = 0.0
-            scores[f"map_at_{k}"] = 0.0
-            scores[f"recall_at_{k}"] = 0.0
-            scores[f"precision_at_{k}"] = 0.0
-            scores[f"mrr_at_{k}"] = 0.0
-        return scores
-
-    # Initialize evaluator
-    evaluator = ViDoReBenchmarkEvaluator(
-        model_name=model_name,
-        use_fast_plaid=use_fast_plaid,
-        top_k=top_k,
-        first_stage_k=first_stage_k,
-        k_values=k_values,
-    )
-
-    # Build or load index
-    # Use safe model name for index path (different models need different indexes)
-    safe_model_name = get_safe_model_name(model_name)
-    index_path_full = index_path if not use_fast_plaid else fast_plaid_index_path
-    if index_path_full is None:
-        index_path_full = f"./indexes/{task_name}_{safe_model_name}"
-        if use_fast_plaid:
-            index_path_full = f"./indexes/{task_name}_{safe_model_name}_fastplaid"
-
-    index_or_retriever, corpus_ids_ordered = evaluator.build_index_from_corpus(
-        corpus=corpus,
-        index_path=index_path_full,
-        rebuild=rebuild_index,
-    )
-
-    # Search queries
-    task_prompt = task_config.get("prompt")
-    results = evaluator.search_queries(
-        queries=queries,
-        corpus_ids=corpus_ids_ordered,
-        index_or_retriever=index_or_retriever,
-        fast_plaid_index_path=fast_plaid_index_path,
-        task_prompt=task_prompt,
-    )
-
-    # Evaluate
-    scores = evaluator.evaluate_results(results, qrels, k_values=k_values)
-
-    # Print results
-    print(f"\n{'=' * 80}")
-    print(f"Results for {task_name}:")
-    print(f"{'=' * 80}")
-    for metric, value in scores.items():
-        if isinstance(value, (int, float)):
-            print(f"  {metric}: {value:.5f}")
-
-    # Save results
-    if output_dir:
-        os.makedirs(output_dir, exist_ok=True)
-        results_file = os.path.join(output_dir, f"{task_name}_results.json")
-        scores_file = os.path.join(output_dir, f"{task_name}_scores.json")
-
-        with open(results_file, "w") as f:
-            json.dump(results, f, indent=2)
-        print(f"\nSaved results to: {results_file}")
-
-        with open(scores_file, "w") as f:
-            json.dump(scores, f, indent=2)
-        print(f"Saved scores to: {scores_file}")
-
-    return scores
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Evaluate ViDoRe v1 benchmark using LEANN/Fast-Plaid indexing"
-    )
-    parser.add_argument(
-        "--model",
-        type=str,
-        default="colqwen2",
-        help="Model to use: 'colqwen2', 'colpali', or path to a model directory (supports LoRA adapters)",
-    )
-    parser.add_argument(
-        "--task",
-        type=str,
-        default=None,
-        help="Specific task to evaluate (or 'all' for all tasks)",
-    )
-    parser.add_argument(
-        "--tasks",
-        type=str,
-        default="all",
-        help="Tasks to evaluate: 'all' or comma-separated list",
-    )
-    parser.add_argument(
-        "--index-path",
-        type=str,
-        default=None,
-        help="Path to LEANN index (auto-generated if not provided)",
-    )
-    parser.add_argument(
-        "--use-fast-plaid",
-        action="store_true",
-        help="Use Fast-Plaid instead of LEANN",
-    )
-    parser.add_argument(
-        "--fast-plaid-index-path",
-        type=str,
-        default=None,
-        help="Path to Fast-Plaid index (auto-generated if not provided)",
-    )
-    parser.add_argument(
-        "--rebuild-index",
-        action="store_true",
-        help="Rebuild index even if it exists",
-    )
-    parser.add_argument(
-        "--top-k",
-        type=int,
-        default=1000,
-        help="Top-k results to retrieve (MTEB default is max(k_values)=1000)",
-    )
-    parser.add_argument(
-        "--first-stage-k",
-        type=int,
-        default=500,
-        help="First stage k for LEANN search",
-    )
-    parser.add_argument(
-        "--k-values",
-        type=str,
-        default="1,3,5,10,20,100,1000",
-        help="Comma-separated k values for evaluation (e.g., '1,3,5,10,100')",
-    )
-    parser.add_argument(
-        "--output-dir",
-        type=str,
-        default="./vidore_v1_results",
-        help="Output directory for results",
-    )
-
-    args = parser.parse_args()
-
-    # Parse k_values
-    k_values = [int(k.strip()) for k in args.k_values.split(",")]
-
-    # Determine tasks to evaluate
-    if args.task:
-        tasks_to_eval = [normalize_task_name(args.task)]
-    elif args.tasks.lower() == "all":
-        tasks_to_eval = list(VIDORE_V1_TASKS.keys())
-    else:
-        tasks_to_eval = [normalize_task_name(t.strip()) for t in args.tasks.split(",")]
-
-    print(f"Tasks to evaluate: {tasks_to_eval}")
-
-    # Evaluate each task
-    all_scores = {}
-    for task_name in tasks_to_eval:
-        try:
-            scores = evaluate_task(
-                task_name=task_name,
-                model_name=args.model,
-                index_path=args.index_path,
-                use_fast_plaid=args.use_fast_plaid,
-                fast_plaid_index_path=args.fast_plaid_index_path,
-                rebuild_index=args.rebuild_index,
-                top_k=args.top_k,
-                first_stage_k=args.first_stage_k,
-                k_values=k_values,
-                output_dir=args.output_dir,
-            )
-            all_scores[task_name] = scores
-        except Exception as e:
-            print(f"\nError evaluating {task_name}: {e}")
-            import traceback
-
-            traceback.print_exc()
-            continue
-
-    # Print summary
-    if all_scores:
-        print(f"\n{'=' * 80}")
-        print("SUMMARY")
-        print(f"{'=' * 80}")
-        for task_name, scores in all_scores.items():
-            print(f"\n{task_name}:")
-            # Print main metrics
-            for metric in ["ndcg_at_5", "ndcg_at_10", "ndcg_at_100", "map_at_10", "recall_at_10"]:
-                if metric in scores:
-                    print(f"  {metric}: {scores[metric]:.5f}")
-
-
-if __name__ == "__main__":
-    main()

apps/multimodal/vision-based-pdf-multi-vector/vidore_v2_benchmark.py

@@ -1,439 +0,0 @@
-#!/usr/bin/env python3
-"""
-Modular script to reproduce NDCG results for ViDoRe v2 benchmark.
-
-This script uses the interface from leann_multi_vector.py to:
-1. Download ViDoRe v2 datasets
-2. Build indexes (LEANN or Fast-Plaid)
-3. Perform retrieval
-4. Evaluate using NDCG metrics
-
-Usage:
-    # Evaluate all ViDoRe v2 tasks
-    python vidore_v2_benchmark.py --model colqwen2 --tasks all
-
-    # Evaluate specific task
-    python vidore_v2_benchmark.py --model colqwen2 --task Vidore2ESGReportsRetrieval
-
-    # Use Fast-Plaid index
-    python vidore_v2_benchmark.py --model colqwen2 --use-fast-plaid --fast-plaid-index-path ./indexes/vidore_fastplaid
-
-    # Rebuild index
-    python vidore_v2_benchmark.py --model colqwen2 --rebuild-index
-"""
-
-import argparse
-import json
-import os
-from typing import Optional
-
-from datasets import load_dataset
-from leann_multi_vector import (
-    ViDoReBenchmarkEvaluator,
-    _ensure_repo_paths_importable,
-)
-
-_ensure_repo_paths_importable(__file__)
-
-# Language name to dataset language field value mapping
-# Dataset uses ISO 639-3 + ISO 15924 format (e.g., "eng-Latn")
-LANGUAGE_MAPPING = {
-    "english": "eng-Latn",
-    "french": "fra-Latn",
-    "spanish": "spa-Latn",
-    "german": "deu-Latn",
-}
-
-# ViDoRe v2 task configurations
-# Prompts match MTEB task metadata prompts
-VIDORE_V2_TASKS = {
-    "Vidore2ESGReportsRetrieval": {
-        "dataset_path": "vidore/esg_reports_v2",
-        "revision": "0542c0d03da0ec1c8cbc517c8d78e7e95c75d3d3",
-        "languages": ["french", "spanish", "english", "german"],
-        "prompt": {"query": "Find a screenshot that relevant to the user's question."},
-    },
-    "Vidore2EconomicsReportsRetrieval": {
-        "dataset_path": "vidore/economics_reports_v2",
-        "revision": "b3e3a04b07fbbaffe79be49dabf92f691fbca252",
-        "languages": ["french", "spanish", "english", "german"],
-        "prompt": {"query": "Find a screenshot that relevant to the user's question."},
-    },
-    "Vidore2BioMedicalLecturesRetrieval": {
-        "dataset_path": "vidore/biomedical_lectures_v2",
-        "revision": "a29202f0da409034d651614d87cd8938d254e2ea",
-        "languages": ["french", "spanish", "english", "german"],
-        "prompt": {"query": "Find a screenshot that relevant to the user's question."},
-    },
-    "Vidore2ESGReportsHLRetrieval": {
-        "dataset_path": "vidore/esg_reports_human_labeled_v2",
-        "revision": "6d467dedb09a75144ede1421747e47cf036857dd",
-        # Note: This dataset doesn't have language filtering - all queries are English
-        "languages": None,  # No language filtering needed
-        "prompt": {"query": "Find a screenshot that relevant to the user's question."},
-    },
-}
-
-
-def load_vidore_v2_data(
-    dataset_path: str,
-    revision: Optional[str] = None,
-    split: str = "test",
-    language: Optional[str] = None,
-):
-    """
-    Load ViDoRe v2 dataset.
-
-    Returns:
-        corpus: dict mapping corpus_id to PIL Image
-        queries: dict mapping query_id to query text
-        qrels: dict mapping query_id to dict of {corpus_id: relevance_score}
-    """
-    print(f"Loading dataset: {dataset_path} (split={split}, language={language})")
-
-    # Load queries
-    query_ds = load_dataset(dataset_path, "queries", split=split, revision=revision)
-
-    # Check if dataset has language field before filtering
-    has_language_field = len(query_ds) > 0 and "language" in query_ds.column_names
-
-    if language and has_language_field:
-        # Map language name to dataset language field value (e.g., "english" -> "eng-Latn")
-        dataset_language = LANGUAGE_MAPPING.get(language, language)
-        query_ds_filtered = query_ds.filter(lambda x: x.get("language") == dataset_language)
-        # Check if filtering resulted in empty dataset
-        if len(query_ds_filtered) == 0:
-            print(
-                f"Warning: No queries found after filtering by language '{language}' (mapped to '{dataset_language}')."
-            )
-            # Try with original language value (dataset might use simple names like 'english')
-            print(f"Trying with original language value '{language}'...")
-            query_ds_filtered = query_ds.filter(lambda x: x.get("language") == language)
-            if len(query_ds_filtered) == 0:
-                # Try to get a sample to see actual language values
-                try:
-                    sample_ds = load_dataset(
-                        dataset_path, "queries", split=split, revision=revision
-                    )
-                    if len(sample_ds) > 0 and "language" in sample_ds.column_names:
-                        sample_langs = set(sample_ds["language"])
-                        print(f"Available language values in dataset: {sample_langs}")
-                except Exception:
-                    pass
-            else:
-                print(
-                    f"Found {len(query_ds_filtered)} queries using original language value '{language}'"
-                )
-        query_ds = query_ds_filtered
-
-    queries = {}
-    for row in query_ds:
-        query_id = f"query-{split}-{row['query-id']}"
-        queries[query_id] = row["query"]
-
-    # Load corpus (images)
-    corpus_ds = load_dataset(dataset_path, "corpus", split=split, revision=revision)
-
-    corpus = {}
-    for row in corpus_ds:
-        corpus_id = f"corpus-{split}-{row['corpus-id']}"
-        # Extract image from the dataset row
-        if "image" in row:
-            corpus[corpus_id] = row["image"]
-        elif "page_image" in row:
-            corpus[corpus_id] = row["page_image"]
-        else:
-            raise ValueError(
-                f"No image field found in corpus. Available fields: {list(row.keys())}"
-            )
-
-    # Load qrels (relevance judgments)
-    qrels_ds = load_dataset(dataset_path, "qrels", split=split, revision=revision)
-
-    qrels = {}
-    for row in qrels_ds:
-        query_id = f"query-{split}-{row['query-id']}"
-        corpus_id = f"corpus-{split}-{row['corpus-id']}"
-        if query_id not in qrels:
-            qrels[query_id] = {}
-        qrels[query_id][corpus_id] = int(row["score"])
-
-    print(
-        f"Loaded {len(queries)} queries, {len(corpus)} corpus items, {len(qrels)} query-relevance mappings"
-    )
-
-    # Filter qrels to only include queries that exist
-    qrels = {qid: rel_docs for qid, rel_docs in qrels.items() if qid in queries}
-
-    # Filter out queries without any relevant documents (matching MTEB behavior)
-    # This is important for correct NDCG calculation
-    qrels_filtered = {qid: rel_docs for qid, rel_docs in qrels.items() if len(rel_docs) > 0}
-    queries_filtered = {
-        qid: query_text for qid, query_text in queries.items() if qid in qrels_filtered
-    }
-
-    print(
-        f"After filtering queries without positives: {len(queries_filtered)} queries, {len(qrels_filtered)} query-relevance mappings"
-    )
-
-    return corpus, queries_filtered, qrels_filtered
-
-
-def evaluate_task(
-    task_name: str,
-    model_name: str,
-    index_path: str,
-    use_fast_plaid: bool = False,
-    fast_plaid_index_path: Optional[str] = None,
-    language: Optional[str] = None,
-    rebuild_index: bool = False,
-    top_k: int = 100,
-    first_stage_k: int = 500,
-    k_values: Optional[list[int]] = None,
-    output_dir: Optional[str] = None,
-):
-    """
-    Evaluate a single ViDoRe v2 task.
-    """
-    print(f"\n{'=' * 80}")
-    print(f"Evaluating task: {task_name}")
-    print(f"{'=' * 80}")
-
-    # Get task config
-    if task_name not in VIDORE_V2_TASKS:
-        raise ValueError(f"Unknown task: {task_name}. Available: {list(VIDORE_V2_TASKS.keys())}")
-
-    task_config = VIDORE_V2_TASKS[task_name]
-    dataset_path = task_config["dataset_path"]
-    revision = task_config["revision"]
-
-    # Determine language
-    if language is None:
-        # Use first language if multiple available
-        languages = task_config.get("languages")
-        if languages is None:
-            # Task doesn't support language filtering (e.g., Vidore2ESGReportsHLRetrieval)
-            language = None
-        elif len(languages) == 1:
-            language = languages[0]
-        else:
-            language = None
-
-    # Initialize k_values if not provided
-    if k_values is None:
-        k_values = [1, 3, 5, 10, 100]
-
-    # Load data
-    corpus, queries, qrels = load_vidore_v2_data(
-        dataset_path=dataset_path,
-        revision=revision,
-        split="test",
-        language=language,
-    )
-
-    # Check if we have any queries
-    if len(queries) == 0:
-        print(
-            f"\nWarning: No queries found for task {task_name} with language {language}. Skipping evaluation."
-        )
-        # Return zero scores
-        scores = {}
-        for k in k_values:
-            scores[f"ndcg_at_{k}"] = 0.0
-            scores[f"map_at_{k}"] = 0.0
-            scores[f"recall_at_{k}"] = 0.0
-            scores[f"precision_at_{k}"] = 0.0
-            scores[f"mrr_at_{k}"] = 0.0
-        return scores
-
-    # Initialize evaluator
-    evaluator = ViDoReBenchmarkEvaluator(
-        model_name=model_name,
-        use_fast_plaid=use_fast_plaid,
-        top_k=top_k,
-        first_stage_k=first_stage_k,
-        k_values=k_values,
-    )
-
-    # Build or load index
-    index_path_full = index_path if not use_fast_plaid else fast_plaid_index_path
-    if index_path_full is None:
-        index_path_full = f"./indexes/{task_name}_{model_name}"
-        if use_fast_plaid:
-            index_path_full = f"./indexes/{task_name}_{model_name}_fastplaid"
-
-    index_or_retriever, corpus_ids_ordered = evaluator.build_index_from_corpus(
-        corpus=corpus,
-        index_path=index_path_full,
-        rebuild=rebuild_index,
-    )
-
-    # Search queries
-    task_prompt = task_config.get("prompt")
-    results = evaluator.search_queries(
-        queries=queries,
-        corpus_ids=corpus_ids_ordered,
-        index_or_retriever=index_or_retriever,
-        fast_plaid_index_path=fast_plaid_index_path,
-        task_prompt=task_prompt,
-    )
-
-    # Evaluate
-    scores = evaluator.evaluate_results(results, qrels, k_values=k_values)
-
-    # Print results
-    print(f"\n{'=' * 80}")
-    print(f"Results for {task_name}:")
-    print(f"{'=' * 80}")
-    for metric, value in scores.items():
-        if isinstance(value, (int, float)):
-            print(f"  {metric}: {value:.5f}")
-
-    # Save results
-    if output_dir:
-        os.makedirs(output_dir, exist_ok=True)
-        results_file = os.path.join(output_dir, f"{task_name}_results.json")
-        scores_file = os.path.join(output_dir, f"{task_name}_scores.json")
-
-        with open(results_file, "w") as f:
-            json.dump(results, f, indent=2)
-        print(f"\nSaved results to: {results_file}")
-
-        with open(scores_file, "w") as f:
-            json.dump(scores, f, indent=2)
-        print(f"Saved scores to: {scores_file}")
-
-    return scores
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Evaluate ViDoRe v2 benchmark using LEANN/Fast-Plaid indexing"
-    )
-    parser.add_argument(
-        "--model",
-        type=str,
-        default="colqwen2",
-        choices=["colqwen2", "colpali"],
-        help="Model to use",
-    )
-    parser.add_argument(
-        "--task",
-        type=str,
-        default=None,
-        help="Specific task to evaluate (or 'all' for all tasks)",
-    )
-    parser.add_argument(
-        "--tasks",
-        type=str,
-        default="all",
-        help="Tasks to evaluate: 'all' or comma-separated list",
-    )
-    parser.add_argument(
-        "--index-path",
-        type=str,
-        default=None,
-        help="Path to LEANN index (auto-generated if not provided)",
-    )
-    parser.add_argument(
-        "--use-fast-plaid",
-        action="store_true",
-        help="Use Fast-Plaid instead of LEANN",
-    )
-    parser.add_argument(
-        "--fast-plaid-index-path",
-        type=str,
-        default=None,
-        help="Path to Fast-Plaid index (auto-generated if not provided)",
-    )
-    parser.add_argument(
-        "--rebuild-index",
-        action="store_true",
-        help="Rebuild index even if it exists",
-    )
-    parser.add_argument(
-        "--language",
-        type=str,
-        default=None,
-        help="Language to evaluate (default: first available)",
-    )
-    parser.add_argument(
-        "--top-k",
-        type=int,
-        default=100,
-        help="Top-k results to retrieve",
-    )
-    parser.add_argument(
-        "--first-stage-k",
-        type=int,
-        default=500,
-        help="First stage k for LEANN search",
-    )
-    parser.add_argument(
-        "--k-values",
-        type=str,
-        default="1,3,5,10,100",
-        help="Comma-separated k values for evaluation (e.g., '1,3,5,10,100')",
-    )
-    parser.add_argument(
-        "--output-dir",
-        type=str,
-        default="./vidore_v2_results",
-        help="Output directory for results",
-    )
-
-    args = parser.parse_args()
-
-    # Parse k_values
-    k_values = [int(k.strip()) for k in args.k_values.split(",")]
-
-    # Determine tasks to evaluate
-    if args.task:
-        tasks_to_eval = [args.task]
-    elif args.tasks.lower() == "all":
-        tasks_to_eval = list(VIDORE_V2_TASKS.keys())
-    else:
-        tasks_to_eval = [t.strip() for t in args.tasks.split(",")]
-
-    print(f"Tasks to evaluate: {tasks_to_eval}")
-
-    # Evaluate each task
-    all_scores = {}
-    for task_name in tasks_to_eval:
-        try:
-            scores = evaluate_task(
-                task_name=task_name,
-                model_name=args.model,
-                index_path=args.index_path,
-                use_fast_plaid=args.use_fast_plaid,
-                fast_plaid_index_path=args.fast_plaid_index_path,
-                language=args.language,
-                rebuild_index=args.rebuild_index,
-                top_k=args.top_k,
-                first_stage_k=args.first_stage_k,
-                k_values=k_values,
-                output_dir=args.output_dir,
-            )
-            all_scores[task_name] = scores
-        except Exception as e:
-            print(f"\nError evaluating {task_name}: {e}")
-            import traceback
-
-            traceback.print_exc()
-            continue
-
-    # Print summary
-    if all_scores:
-        print(f"\n{'=' * 80}")
-        print("SUMMARY")
-        print(f"{'=' * 80}")
-        for task_name, scores in all_scores.items():
-            print(f"\n{task_name}:")
-            # Print main metrics
-            for metric in ["ndcg_at_5", "ndcg_at_10", "ndcg_at_100", "map_at_10", "recall_at_10"]:
-                if metric in scores:
-                    print(f"  {metric}: {scores[metric]:.5f}")
-
-
-if __name__ == "__main__":
-    main()

apps/semantic_file_search/leann-plus-temporal-search.py

@@ -1,183 +0,0 @@
-#!/usr/bin/env python3
-import re
-import sys
-from datetime import datetime, timedelta
-from pathlib import Path
-
-from leann import LeannSearcher
-
-INDEX_PATH = str(Path("./").resolve() / "demo.leann")
-
-
-class TimeParser:
-    def __init__(self):
-        # Main pattern: captures optional fuzzy modifier, number, unit, and optional "ago"
-        self.pattern = r"(?:(around|about|roughly|approximately)\s+)?(\d+)\s+(hour|day|week|month|year)s?(?:\s+ago)?"
-
-        # Compile for performance
-        self.regex = re.compile(self.pattern, re.IGNORECASE)
-
-        # Stop words to remove before regex parsing
-        self.stop_words = {
-            "in",
-            "at",
-            "of",
-            "by",
-            "as",
-            "me",
-            "the",
-            "a",
-            "an",
-            "and",
-            "any",
-            "find",
-            "search",
-            "list",
-            "ago",
-            "back",
-            "past",
-            "earlier",
-        }
-
-    def clean_text(self, text):
-        """Remove stop words from text"""
-        words = text.split()
-        cleaned = " ".join(word for word in words if word.lower() not in self.stop_words)
-        return cleaned
-
-    def parse(self, text):
-        """Extract all time expressions from text"""
-        # Clean text first
-        cleaned_text = self.clean_text(text)
-
-        matches = []
-        for match in self.regex.finditer(cleaned_text):
-            fuzzy = match.group(1)  # "around", "about", etc.
-            number = int(match.group(2))
-            unit = match.group(3).lower()
-
-            matches.append(
-                {
-                    "full_match": match.group(0),
-                    "fuzzy": bool(fuzzy),
-                    "number": number,
-                    "unit": unit,
-                    "range": self.calculate_range(number, unit, bool(fuzzy)),
-                }
-            )
-
-        return matches
-
-    def calculate_range(self, number, unit, is_fuzzy):
-        """Convert to actual datetime range and return ISO format strings"""
-        units = {
-            "hour": timedelta(hours=number),
-            "day": timedelta(days=number),
-            "week": timedelta(weeks=number),
-            "month": timedelta(days=number * 30),
-            "year": timedelta(days=number * 365),
-        }
-
-        delta = units[unit]
-        now = datetime.now()
-        target = now - delta
-
-        if is_fuzzy:
-            buffer = delta * 0.2  # 20% buffer for fuzzy
-            start = (target - buffer).isoformat()
-            end = (target + buffer).isoformat()
-        else:
-            start = target.isoformat()
-            end = now.isoformat()
-
-        return (start, end)
-
-
-def search_files(query, top_k=15):
-    """Search the index and return results"""
-    # Parse time expressions
-    parser = TimeParser()
-    time_matches = parser.parse(query)
-
-    # Remove time expressions from query for semantic search
-    clean_query = query
-    if time_matches:
-        for match in time_matches:
-            clean_query = clean_query.replace(match["full_match"], "").strip()
-
-    # Check if clean_query is less than 4 characters
-    if len(clean_query) < 4:
-        print("Error: add more input for accurate results.")
-        return
-
-    # Single query to vector DB
-    searcher = LeannSearcher(INDEX_PATH)
-    results = searcher.search(
-        clean_query if clean_query else query, top_k=top_k, recompute_embeddings=False
-    )
-
-    # Filter by time if time expression found
-    if time_matches:
-        time_range = time_matches[0]["range"]  # Use first time expression
-        start_time, end_time = time_range
-
-        filtered_results = []
-        for result in results:
-            # Access metadata attribute directly (not .get())
-            metadata = result.metadata if hasattr(result, "metadata") else {}
-
-            if metadata:
-                # Check modification date first, fall back to creation date
-                date_str = metadata.get("modification_date") or metadata.get("creation_date")
-
-                if date_str:
-                    # Convert strings to datetime objects for proper comparison
-                    try:
-                        file_date = datetime.fromisoformat(date_str)
-                        start_dt = datetime.fromisoformat(start_time)
-                        end_dt = datetime.fromisoformat(end_time)
-
-                        # Compare dates properly
-                        if start_dt <= file_date <= end_dt:
-                            filtered_results.append(result)
-                    except (ValueError, TypeError):
-                        # Handle invalid date formats
-                        print(f"Warning: Invalid date format in metadata: {date_str}")
-                        continue
-
-        results = filtered_results
-
-    # Print results
-    print(f"\nSearch results for: '{query}'")
-    if time_matches:
-        print(
-            f"Time filter: {time_matches[0]['number']} {time_matches[0]['unit']}(s) {'(fuzzy)' if time_matches[0]['fuzzy'] else ''}"
-        )
-        print(
-            f"Date range: {time_matches[0]['range'][0][:10]} to {time_matches[0]['range'][1][:10]}"
-        )
-    print("-" * 80)
-
-    for i, result in enumerate(results, 1):
-        print(f"\n[{i}] Score: {result.score:.4f}")
-        print(f"Content: {result.text}")
-
-        # Show metadata if present
-        metadata = result.metadata if hasattr(result, "metadata") else None
-        if metadata:
-            if "creation_date" in metadata:
-                print(f"Created: {metadata['creation_date']}")
-            if "modification_date" in metadata:
-                print(f"Modified: {metadata['modification_date']}")
-        print("-" * 80)
-
-
-if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        print('Usage: python search_index.py "<search query>" [top_k]')
-        sys.exit(1)
-
-    query = sys.argv[1]
-    top_k = int(sys.argv[2]) if len(sys.argv) > 2 else 15
-
-    search_files(query, top_k)

apps/semantic_file_search/leann_index_builder.py

@@ -1,82 +0,0 @@
-#!/usr/bin/env python3
-import json
-import sys
-from pathlib import Path
-
-from leann import LeannBuilder
-
-
-def process_json_items(json_file_path):
-    """Load and process JSON file with metadata items"""
-
-    with open(json_file_path, encoding="utf-8") as f:
-        items = json.load(f)
-
-    # Guard against empty JSON
-    if not items:
-        print("⚠️  No items found in the JSON file. Exiting gracefully.")
-        return
-
-    INDEX_PATH = str(Path("./").resolve() / "demo.leann")
-    builder = LeannBuilder(backend_name="hnsw", is_recompute=False)
-
-    total_items = len(items)
-    items_added = 0
-    print(f"Processing {total_items} items...")
-
-    for idx, item in enumerate(items):
-        try:
-            # Create embedding text sentence
-            embedding_text = f"{item.get('Name', 'unknown')} located at {item.get('Path', 'unknown')} and size {item.get('Size', 'unknown')} bytes with content type {item.get('ContentType', 'unknown')} and kind {item.get('Kind', 'unknown')}"
-
-            # Prepare metadata with dates
-            metadata = {}
-            if "CreationDate" in item:
-                metadata["creation_date"] = item["CreationDate"]
-            if "ContentChangeDate" in item:
-                metadata["modification_date"] = item["ContentChangeDate"]
-
-            # Add to builder
-            builder.add_text(embedding_text, metadata=metadata)
-            items_added += 1
-
-        except Exception as e:
-            print(f"\n⚠️  Warning: Failed to process item {idx}: {e}")
-            continue
-
-        # Show progress
-        progress = (idx + 1) / total_items * 100
-        sys.stdout.write(f"\rProgress: {idx + 1}/{total_items} ({progress:.1f}%)")
-        sys.stdout.flush()
-
-    print()  # New line after progress
-
-    # Guard against no successfully added items
-    if items_added == 0:
-        print("⚠️  No items were successfully added to the index. Exiting gracefully.")
-        return
-
-    print(f"\n✅ Successfully processed {items_added}/{total_items} items")
-    print("Building index...")
-
-    try:
-        builder.build_index(INDEX_PATH)
-        print(f"✓ Index saved to {INDEX_PATH}")
-    except ValueError as e:
-        if "No chunks added" in str(e):
-            print("⚠️  No chunks were added to the builder. Index not created.")
-        else:
-            raise
-
-
-if __name__ == "__main__":
-    if len(sys.argv) != 2:
-        print("Usage: python build_index.py <json_file>")
-        sys.exit(1)
-
-    json_file = sys.argv[1]
-    if not Path(json_file).exists():
-        print(f"Error: File {json_file} not found")
-        sys.exit(1)
-
-    process_json_items(json_file)

apps/semantic_file_search/spotlight_index_dump.py

@@ -1,265 +0,0 @@
-#!/usr/bin/env python3
-"""
-Spotlight Metadata Dumper for Vector DB
-Extracts only essential metadata for semantic search embeddings
-Output is optimized for vector database storage with minimal fields
-"""
-
-import json
-import sys
-from datetime import datetime
-
-# Check platform before importing macOS-specific modules
-if sys.platform != "darwin":
-    print("This script requires macOS (uses Spotlight)")
-    sys.exit(1)
-
-from Foundation import NSDate, NSMetadataQuery, NSPredicate, NSRunLoop
-
-# EDIT THIS LIST: Add or remove folders to search
-# Can be either:
-# - Folder names relative to home directory (e.g., "Desktop", "Downloads")
-# - Absolute paths (e.g., "/Applications", "/System/Library")
-SEARCH_FOLDERS = [
-    "Desktop",
-    "Downloads",
-    "Documents",
-    "Music",
-    "Pictures",
-    "Movies",
-    # "Library",  # Uncomment to include
-    # "/Applications",  # Absolute path example
-    # "Code/Projects",  # Subfolder example
-    # Add any other folders here
-]
-
-
-def convert_to_serializable(obj):
-    """Convert NS objects to Python serializable types"""
-    if obj is None:
-        return None
-
-    # Handle NSDate
-    if hasattr(obj, "timeIntervalSince1970"):
-        return datetime.fromtimestamp(obj.timeIntervalSince1970()).isoformat()
-
-    # Handle NSArray
-    if hasattr(obj, "count") and hasattr(obj, "objectAtIndex_"):
-        return [convert_to_serializable(obj.objectAtIndex_(i)) for i in range(obj.count())]
-
-    # Convert to string
-    try:
-        return str(obj)
-    except Exception:
-        return repr(obj)
-
-
-def dump_spotlight_data(max_items=10, output_file="spotlight_dump.json"):
-    """
-    Dump Spotlight data using public.item predicate
-    """
-    # Build full paths from SEARCH_FOLDERS
-    import os
-
-    home_dir = os.path.expanduser("~")
-    search_paths = []
-
-    print("Search locations:")
-    for folder in SEARCH_FOLDERS:
-        # Check if it's an absolute path or relative
-        if folder.startswith("/"):
-            full_path = folder
-        else:
-            full_path = os.path.join(home_dir, folder)
-
-        if os.path.exists(full_path):
-            search_paths.append(full_path)
-            print(f"  ✓ {full_path}")
-        else:
-            print(f"  ✗ {full_path} (not found)")
-
-    if not search_paths:
-        print("No valid search paths found!")
-        return []
-
-    print(f"\nDumping {max_items} items from Spotlight (public.item)...")
-
-    # Create query with public.item predicate
-    query = NSMetadataQuery.alloc().init()
-    predicate = NSPredicate.predicateWithFormat_("kMDItemContentTypeTree CONTAINS 'public.item'")
-    query.setPredicate_(predicate)
-
-    # Set search scopes to our specific folders
-    query.setSearchScopes_(search_paths)
-
-    print("Starting query...")
-    query.startQuery()
-
-    # Wait for gathering to complete
-    run_loop = NSRunLoop.currentRunLoop()
-    print("Gathering results...")
-
-    # Let it gather for a few seconds
-    for i in range(50):  # 5 seconds max
-        run_loop.runMode_beforeDate_(
-            "NSDefaultRunLoopMode", NSDate.dateWithTimeIntervalSinceNow_(0.1)
-        )
-        # Check gathering status periodically
-        if i % 10 == 0:
-            current_count = query.resultCount()
-            if current_count > 0:
-                print(f"  Found {current_count} items so far...")
-
-    # Continue while still gathering (up to 2 more seconds)
-    timeout = NSDate.dateWithTimeIntervalSinceNow_(2.0)
-    while query.isGathering() and timeout.timeIntervalSinceNow() > 0:
-        run_loop.runMode_beforeDate_(
-            "NSDefaultRunLoopMode", NSDate.dateWithTimeIntervalSinceNow_(0.1)
-        )
-
-    query.stopQuery()
-
-    total_results = query.resultCount()
-    print(f"Found {total_results} total items")
-
-    if total_results == 0:
-        print("No results found")
-        return []
-
-    # Process items
-    items_to_process = min(total_results, max_items)
-    results = []
-
-    # ONLY relevant attributes for vector embeddings
-    # These provide essential context for semantic search without bloat
-    attributes = [
-        "kMDItemPath",  # Full path for file retrieval
-        "kMDItemFSName",  # Filename for display & embedding
-        "kMDItemFSSize",  # Size for filtering/ranking
-        "kMDItemContentType",  # File type for categorization
-        "kMDItemKind",  # Human-readable type for embedding
-        "kMDItemFSCreationDate",  # Temporal context
-        "kMDItemFSContentChangeDate",  # Recency for ranking
-    ]
-
-    print(f"Processing {items_to_process} items...")
-
-    for i in range(items_to_process):
-        try:
-            item = query.resultAtIndex_(i)
-            metadata = {}
-
-            # Extract ONLY the relevant attributes
-            for attr in attributes:
-                try:
-                    value = item.valueForAttribute_(attr)
-                    if value is not None:
-                        # Keep the attribute name clean (remove kMDItem prefix for cleaner JSON)
-                        clean_key = attr.replace("kMDItem", "").replace("FS", "")
-                        metadata[clean_key] = convert_to_serializable(value)
-                except (AttributeError, ValueError, TypeError):
-                    continue
-
-            # Only add if we have at least a path
-            if metadata.get("Path"):
-                results.append(metadata)
-
-        except Exception as e:
-            print(f"Error processing item {i}: {e}")
-            continue
-
-    # Save to JSON
-    with open(output_file, "w", encoding="utf-8") as f:
-        json.dump(results, f, indent=2, ensure_ascii=False)
-
-    print(f"\n✓ Saved {len(results)} items to {output_file}")
-
-    # Show summary
-    print("\nSample items:")
-    import os
-
-    home_dir = os.path.expanduser("~")
-
-    for i, item in enumerate(results[:3]):
-        print(f"\n[Item {i + 1}]")
-        print(f"  Path: {item.get('Path', 'N/A')}")
-        print(f"  Name: {item.get('Name', 'N/A')}")
-        print(f"  Type: {item.get('ContentType', 'N/A')}")
-        print(f"  Kind: {item.get('Kind', 'N/A')}")
-
-        # Handle size properly
-        size = item.get("Size")
-        if size:
-            try:
-                size_int = int(size)
-                if size_int > 1024 * 1024:
-                    print(f"  Size: {size_int / (1024 * 1024):.2f} MB")
-                elif size_int > 1024:
-                    print(f"  Size: {size_int / 1024:.2f} KB")
-                else:
-                    print(f"  Size: {size_int} bytes")
-            except (ValueError, TypeError):
-                print(f"  Size: {size}")
-
-        # Show dates
-        if "CreationDate" in item:
-            print(f"  Created: {item['CreationDate']}")
-        if "ContentChangeDate" in item:
-            print(f"  Modified: {item['ContentChangeDate']}")
-
-    # Count by type
-    type_counts = {}
-    for item in results:
-        content_type = item.get("ContentType", "unknown")
-        type_counts[content_type] = type_counts.get(content_type, 0) + 1
-
-    print(f"\nTotal items saved: {len(results)}")
-
-    if type_counts:
-        print("\nTop content types:")
-        for ct, count in sorted(type_counts.items(), key=lambda x: x[1], reverse=True)[:5]:
-            print(f"  {ct}: {count} items")
-
-    # Count by folder
-    folder_counts = {}
-    for item in results:
-        path = item.get("Path", "")
-        for folder in SEARCH_FOLDERS:
-            # Build the full folder path
-            if folder.startswith("/"):
-                folder_path = folder
-            else:
-                folder_path = os.path.join(home_dir, folder)
-
-            if path.startswith(folder_path):
-                folder_counts[folder] = folder_counts.get(folder, 0) + 1
-                break
-
-    if folder_counts:
-        print("\nItems by location:")
-        for folder, count in sorted(folder_counts.items(), key=lambda x: x[1], reverse=True):
-            print(f"  {folder}: {count} items")
-
-    return results
-
-
-def main():
-    # Parse arguments
-    if len(sys.argv) > 1:
-        try:
-            max_items = int(sys.argv[1])
-        except ValueError:
-            print("Usage: python spot.py [number_of_items]")
-            print("Default: 10 items")
-            sys.exit(1)
-    else:
-        max_items = 10
-
-    output_file = sys.argv[2] if len(sys.argv) > 2 else "spotlight_dump.json"
-
-    # Run dump
-    dump_spotlight_data(max_items=max_items, output_file=output_file)
-
-
-if __name__ == "__main__":
-    main()

apps/slack_data/__init__.py

@@ -1 +0,0 @@
-# Slack MCP data integration for LEANN

apps/slack_data/slack_mcp_reader.py

@@ -1,511 +0,0 @@
-#!/usr/bin/env python3
-"""
-Slack MCP Reader for LEANN
-
-This module provides functionality to connect to Slack MCP servers and fetch message data
-for indexing in LEANN. It supports various Slack MCP server implementations and provides
-flexible message processing options.
-"""
-
-import ast
-import asyncio
-import json
-import logging
-from typing import Any, Optional
-
-logger = logging.getLogger(__name__)
-
-
-class SlackMCPReader:
-    """
-    Reader for Slack data via MCP (Model Context Protocol) servers.
-
-    This class connects to Slack MCP servers to fetch message data and convert it
-    into a format suitable for LEANN indexing.
-    """
-
-    def __init__(
-        self,
-        mcp_server_command: str,
-        workspace_name: Optional[str] = None,
-        concatenate_conversations: bool = True,
-        max_messages_per_conversation: int = 100,
-        max_retries: int = 5,
-        retry_delay: float = 2.0,
-    ):
-        """
-        Initialize the Slack MCP Reader.
-
-        Args:
-            mcp_server_command: Command to start the MCP server (e.g., 'slack-mcp-server')
-            workspace_name: Optional workspace name to filter messages
-            concatenate_conversations: Whether to group messages by channel/thread
-            max_messages_per_conversation: Maximum messages to include per conversation
-            max_retries: Maximum number of retries for failed operations
-            retry_delay: Initial delay between retries in seconds
-        """
-        self.mcp_server_command = mcp_server_command
-        self.workspace_name = workspace_name
-        self.concatenate_conversations = concatenate_conversations
-        self.max_messages_per_conversation = max_messages_per_conversation
-        self.max_retries = max_retries
-        self.retry_delay = retry_delay
-        self.mcp_process = None
-
-    async def start_mcp_server(self):
-        """Start the MCP server process."""
-        try:
-            self.mcp_process = await asyncio.create_subprocess_exec(
-                *self.mcp_server_command.split(),
-                stdin=asyncio.subprocess.PIPE,
-                stdout=asyncio.subprocess.PIPE,
-                stderr=asyncio.subprocess.PIPE,
-            )
-            logger.info(f"Started MCP server: {self.mcp_server_command}")
-        except Exception as e:
-            logger.error(f"Failed to start MCP server: {e}")
-            raise
-
-    async def stop_mcp_server(self):
-        """Stop the MCP server process."""
-        if self.mcp_process:
-            self.mcp_process.terminate()
-            await self.mcp_process.wait()
-            logger.info("Stopped MCP server")
-
-    async def send_mcp_request(self, request: dict[str, Any]) -> dict[str, Any]:
-        """Send a request to the MCP server and get response."""
-        if not self.mcp_process:
-            raise RuntimeError("MCP server not started")
-
-        request_json = json.dumps(request) + "\n"
-        self.mcp_process.stdin.write(request_json.encode())
-        await self.mcp_process.stdin.drain()
-
-        response_line = await self.mcp_process.stdout.readline()
-        if not response_line:
-            raise RuntimeError("No response from MCP server")
-
-        return json.loads(response_line.decode().strip())
-
-    async def initialize_mcp_connection(self):
-        """Initialize the MCP connection."""
-        init_request = {
-            "jsonrpc": "2.0",
-            "id": 1,
-            "method": "initialize",
-            "params": {
-                "protocolVersion": "2024-11-05",
-                "capabilities": {},
-                "clientInfo": {"name": "leann-slack-reader", "version": "1.0.0"},
-            },
-        }
-
-        response = await self.send_mcp_request(init_request)
-        if "error" in response:
-            raise RuntimeError(f"MCP initialization failed: {response['error']}")
-
-        logger.info("MCP connection initialized successfully")
-
-    async def list_available_tools(self) -> list[dict[str, Any]]:
-        """List available tools from the MCP server."""
-        list_request = {"jsonrpc": "2.0", "id": 2, "method": "tools/list", "params": {}}
-
-        response = await self.send_mcp_request(list_request)
-        if "error" in response:
-            raise RuntimeError(f"Failed to list tools: {response['error']}")
-
-        return response.get("result", {}).get("tools", [])
-
-    def _is_cache_sync_error(self, error: dict) -> bool:
-        """Check if the error is related to users cache not being ready."""
-        if isinstance(error, dict):
-            message = error.get("message", "").lower()
-            return (
-                "users cache is not ready" in message or "sync process is still running" in message
-            )
-        return False
-
-    async def _retry_with_backoff(self, func, *args, **kwargs):
-        """Retry a function with exponential backoff, especially for cache sync issues."""
-        last_exception = None
-
-        for attempt in range(self.max_retries + 1):
-            try:
-                return await func(*args, **kwargs)
-            except Exception as e:
-                last_exception = e
-
-                # Check if this is a cache sync error
-                error_dict = {}
-                if hasattr(e, "args") and e.args and isinstance(e.args[0], dict):
-                    error_dict = e.args[0]
-                elif "Failed to fetch messages" in str(e):
-                    # Try to extract error from the exception message
-                    import re
-
-                    match = re.search(r"'error':\s*(\{[^}]+\})", str(e))
-                    if match:
-                        try:
-                            error_dict = ast.literal_eval(match.group(1))
-                        except (ValueError, SyntaxError):
-                            pass
-                    else:
-                        # Try alternative format
-                        match = re.search(r"Failed to fetch messages:\s*(\{[^}]+\})", str(e))
-                        if match:
-                            try:
-                                error_dict = ast.literal_eval(match.group(1))
-                            except (ValueError, SyntaxError):
-                                pass
-
-                if self._is_cache_sync_error(error_dict):
-                    if attempt < self.max_retries:
-                        delay = self.retry_delay * (2**attempt)  # Exponential backoff
-                        logger.info(
-                            f"Cache sync not ready, waiting {delay:.1f}s before retry {attempt + 1}/{self.max_retries}"
-                        )
-                        await asyncio.sleep(delay)
-                        continue
-                    else:
-                        logger.warning(
-                            f"Cache sync still not ready after {self.max_retries} retries, giving up"
-                        )
-                        break
-                else:
-                    # Not a cache sync error, don't retry
-                    break
-
-        # If we get here, all retries failed or it's not a retryable error
-        raise last_exception
-
-    async def fetch_slack_messages(
-        self, channel: Optional[str] = None, limit: int = 100
-    ) -> list[dict[str, Any]]:
-        """
-        Fetch Slack messages using MCP tools with retry logic for cache sync issues.
-
-        Args:
-            channel: Optional channel name to filter messages
-            limit: Maximum number of messages to fetch
-
-        Returns:
-            List of message dictionaries
-        """
-        return await self._retry_with_backoff(self._fetch_slack_messages_impl, channel, limit)
-
-    async def _fetch_slack_messages_impl(
-        self, channel: Optional[str] = None, limit: int = 100
-    ) -> list[dict[str, Any]]:
-        """
-        Internal implementation of fetch_slack_messages without retry logic.
-        """
-        # This is a generic implementation - specific MCP servers may have different tool names
-        # Common tool names might be: 'get_messages', 'list_messages', 'fetch_channel_history'
-
-        tools = await self.list_available_tools()
-        logger.info(f"Available tools: {[tool.get('name') for tool in tools]}")
-        message_tool = None
-
-        # Look for a tool that can fetch messages - prioritize conversations_history
-        message_tool = None
-
-        # First, try to find conversations_history specifically
-        for tool in tools:
-            tool_name = tool.get("name", "").lower()
-            if "conversations_history" in tool_name:
-                message_tool = tool
-                logger.info(f"Found conversations_history tool: {tool}")
-                break
-
-        # If not found, look for other message-fetching tools
-        if not message_tool:
-            for tool in tools:
-                tool_name = tool.get("name", "").lower()
-                if any(
-                    keyword in tool_name
-                    for keyword in ["conversations_search", "message", "history"]
-                ):
-                    message_tool = tool
-                    break
-
-        if not message_tool:
-            raise RuntimeError("No message fetching tool found in MCP server")
-
-        # Prepare tool call parameters
-        tool_params = {"limit": "180d"}  # Use 180 days to get older messages
-        if channel:
-            # For conversations_history, use channel_id parameter
-            if message_tool["name"] == "conversations_history":
-                tool_params["channel_id"] = channel
-            else:
-                # Try common parameter names for channel specification
-                for param_name in ["channel", "channel_id", "channel_name"]:
-                    tool_params[param_name] = channel
-                    break
-
-        logger.info(f"Tool parameters: {tool_params}")
-
-        fetch_request = {
-            "jsonrpc": "2.0",
-            "id": 3,
-            "method": "tools/call",
-            "params": {"name": message_tool["name"], "arguments": tool_params},
-        }
-
-        response = await self.send_mcp_request(fetch_request)
-        if "error" in response:
-            raise RuntimeError(f"Failed to fetch messages: {response['error']}")
-
-        # Extract messages from response - format may vary by MCP server
-        result = response.get("result", {})
-        if "content" in result and isinstance(result["content"], list):
-            # Some MCP servers return content as a list
-            content = result["content"][0] if result["content"] else {}
-            if "text" in content:
-                try:
-                    messages = json.loads(content["text"])
-                except json.JSONDecodeError:
-                    # If not JSON, try to parse as CSV format (Slack MCP server format)
-                    messages = self._parse_csv_messages(content["text"], channel)
-            else:
-                messages = result["content"]
-        else:
-            # Direct message format
-            messages = result.get("messages", [result])
-
-        return messages if isinstance(messages, list) else [messages]
-
-    def _parse_csv_messages(self, csv_text: str, channel: str) -> list[dict[str, Any]]:
-        """Parse CSV format messages from Slack MCP server."""
-        import csv
-        import io
-
-        messages = []
-        try:
-            # Split by lines and process each line as a CSV row
-            lines = csv_text.strip().split("\n")
-            if not lines:
-                return messages
-
-            # Skip header line if it exists
-            start_idx = 0
-            if lines[0].startswith("MsgID,UserID,UserName"):
-                start_idx = 1
-
-            for line in lines[start_idx:]:
-                if not line.strip():
-                    continue
-
-                # Parse CSV line
-                reader = csv.reader(io.StringIO(line))
-                try:
-                    row = next(reader)
-                    if len(row) >= 7:  # Ensure we have enough columns
-                        message = {
-                            "ts": row[0],
-                            "user": row[1],
-                            "username": row[2],
-                            "real_name": row[3],
-                            "channel": row[4],
-                            "thread_ts": row[5],
-                            "text": row[6],
-                            "time": row[7] if len(row) > 7 else "",
-                            "reactions": row[8] if len(row) > 8 else "",
-                            "cursor": row[9] if len(row) > 9 else "",
-                        }
-                        messages.append(message)
-                except Exception as e:
-                    logger.warning(f"Failed to parse CSV line: {line[:100]}... Error: {e}")
-                    continue
-
-        except Exception as e:
-            logger.warning(f"Failed to parse CSV messages: {e}")
-            # Fallback: treat entire text as one message
-            messages = [{"text": csv_text, "channel": channel or "unknown"}]
-
-        return messages
-
-    def _format_message(self, message: dict[str, Any]) -> str:
-        """Format a single message for indexing."""
-        text = message.get("text", "")
-        user = message.get("user", message.get("username", "Unknown"))
-        channel = message.get("channel", message.get("channel_name", "Unknown"))
-        timestamp = message.get("ts", message.get("timestamp", ""))
-
-        # Format timestamp if available
-        formatted_time = ""
-        if timestamp:
-            try:
-                import datetime
-
-                if isinstance(timestamp, str) and "." in timestamp:
-                    dt = datetime.datetime.fromtimestamp(float(timestamp))
-                    formatted_time = dt.strftime("%Y-%m-%d %H:%M:%S")
-                elif isinstance(timestamp, (int, float)):
-                    dt = datetime.datetime.fromtimestamp(timestamp)
-                    formatted_time = dt.strftime("%Y-%m-%d %H:%M:%S")
-                else:
-                    formatted_time = str(timestamp)
-            except (ValueError, TypeError):
-                formatted_time = str(timestamp)
-
-        # Build formatted message
-        parts = []
-        if channel:
-            parts.append(f"Channel: #{channel}")
-        if user:
-            parts.append(f"User: {user}")
-        if formatted_time:
-            parts.append(f"Time: {formatted_time}")
-        if text:
-            parts.append(f"Message: {text}")
-
-        return "\n".join(parts)
-
-    def _create_concatenated_content(self, messages: list[dict[str, Any]], channel: str) -> str:
-        """Create concatenated content from multiple messages in a channel."""
-        if not messages:
-            return ""
-
-        # Sort messages by timestamp if available
-        try:
-            messages.sort(key=lambda x: float(x.get("ts", x.get("timestamp", 0))))
-        except (ValueError, TypeError):
-            pass  # Keep original order if timestamps aren't numeric
-
-        # Limit messages per conversation
-        if len(messages) > self.max_messages_per_conversation:
-            messages = messages[-self.max_messages_per_conversation :]
-
-        # Create header
-        content_parts = [
-            f"Slack Channel: #{channel}",
-            f"Message Count: {len(messages)}",
-            f"Workspace: {self.workspace_name or 'Unknown'}",
-            "=" * 50,
-            "",
-        ]
-
-        # Add messages
-        for message in messages:
-            formatted_msg = self._format_message(message)
-            if formatted_msg.strip():
-                content_parts.append(formatted_msg)
-                content_parts.append("-" * 30)
-                content_parts.append("")
-
-        return "\n".join(content_parts)
-
-    async def get_all_channels(self) -> list[str]:
-        """Get list of all available channels."""
-        try:
-            channels_list_request = {
-                "jsonrpc": "2.0",
-                "id": 4,
-                "method": "tools/call",
-                "params": {"name": "channels_list", "arguments": {}},
-            }
-            channels_response = await self.send_mcp_request(channels_list_request)
-            if "result" in channels_response:
-                result = channels_response["result"]
-                if "content" in result and isinstance(result["content"], list):
-                    content = result["content"][0] if result["content"] else {}
-                    if "text" in content:
-                        # Parse the channels from the response
-                        channels = []
-                        lines = content["text"].split("\n")
-                        for line in lines:
-                            if line.strip() and ("#" in line or "C" in line[:10]):
-                                # Extract channel ID or name
-                                parts = line.split()
-                                for part in parts:
-                                    if part.startswith("C") and len(part) > 5:
-                                        channels.append(part)
-                                    elif part.startswith("#"):
-                                        channels.append(part[1:])  # Remove #
-                        logger.info(f"Found {len(channels)} channels: {channels}")
-                        return channels
-            return []
-        except Exception as e:
-            logger.warning(f"Failed to get channels list: {e}")
-            return []
-
-    async def read_slack_data(self, channels: Optional[list[str]] = None) -> list[str]:
-        """
-        Read Slack data and return formatted text chunks.
-
-        Args:
-            channels: Optional list of channel names to fetch. If None, fetches from all available channels.
-
-        Returns:
-            List of formatted text chunks ready for LEANN indexing
-        """
-        try:
-            await self.start_mcp_server()
-            await self.initialize_mcp_connection()
-
-            all_texts = []
-
-            if channels:
-                # Fetch specific channels
-                for channel in channels:
-                    try:
-                        messages = await self.fetch_slack_messages(channel=channel, limit=1000)
-                        if messages:
-                            if self.concatenate_conversations:
-                                text_content = self._create_concatenated_content(messages, channel)
-                                if text_content.strip():
-                                    all_texts.append(text_content)
-                            else:
-                                # Process individual messages
-                                for message in messages:
-                                    formatted_msg = self._format_message(message)
-                                    if formatted_msg.strip():
-                                        all_texts.append(formatted_msg)
-                    except Exception as e:
-                        logger.warning(f"Failed to fetch messages from channel {channel}: {e}")
-                        continue
-            else:
-                # Fetch from all available channels
-                logger.info("Fetching from all available channels...")
-                all_channels = await self.get_all_channels()
-
-                if not all_channels:
-                    # Fallback to common channel names if we can't get the list
-                    all_channels = ["general", "random", "announcements", "C0GN5BX0F"]
-                    logger.info(f"Using fallback channels: {all_channels}")
-
-                for channel in all_channels:
-                    try:
-                        logger.info(f"Searching channel: {channel}")
-                        messages = await self.fetch_slack_messages(channel=channel, limit=1000)
-                        if messages:
-                            if self.concatenate_conversations:
-                                text_content = self._create_concatenated_content(messages, channel)
-                                if text_content.strip():
-                                    all_texts.append(text_content)
-                            else:
-                                # Process individual messages
-                                for message in messages:
-                                    formatted_msg = self._format_message(message)
-                                    if formatted_msg.strip():
-                                        all_texts.append(formatted_msg)
-                    except Exception as e:
-                        logger.warning(f"Failed to fetch messages from channel {channel}: {e}")
-                        continue
-
-            return all_texts
-
-        finally:
-            await self.stop_mcp_server()
-
-    async def __aenter__(self):
-        """Async context manager entry."""
-        await self.start_mcp_server()
-        await self.initialize_mcp_connection()
-        return self
-
-    async def __aexit__(self, exc_type, exc_val, exc_tb):
-        """Async context manager exit."""
-        await self.stop_mcp_server()

apps/slack_rag.py

@@ -1,227 +0,0 @@
-#!/usr/bin/env python3
-"""
-Slack RAG Application with MCP Support
-
-This application enables RAG (Retrieval-Augmented Generation) on Slack messages
-by connecting to Slack MCP servers to fetch live data and index it in LEANN.
-
-Usage:
-    python -m apps.slack_rag --mcp-server "slack-mcp-server" --query "What did the team discuss about the project?"
-"""
-
-import argparse
-import asyncio
-
-from apps.base_rag_example import BaseRAGExample
-from apps.slack_data.slack_mcp_reader import SlackMCPReader
-
-
-class SlackMCPRAG(BaseRAGExample):
-    """
-    RAG application for Slack messages via MCP servers.
-
-    This class provides a complete RAG pipeline for Slack data, including
-    MCP server connection, data fetching, indexing, and interactive chat.
-    """
-
-    def __init__(self):
-        super().__init__(
-            name="Slack MCP RAG",
-            description="RAG application for Slack messages via MCP servers",
-            default_index_name="slack_messages",
-        )
-
-    def _add_specific_arguments(self, parser: argparse.ArgumentParser):
-        """Add Slack MCP-specific arguments."""
-        parser.add_argument(
-            "--mcp-server",
-            type=str,
-            required=True,
-            help="Command to start the Slack MCP server (e.g., 'slack-mcp-server' or 'npx slack-mcp-server')",
-        )
-
-        parser.add_argument(
-            "--workspace-name",
-            type=str,
-            help="Slack workspace name for better organization and filtering",
-        )
-
-        parser.add_argument(
-            "--channels",
-            nargs="+",
-            help="Specific Slack channels to index (e.g., general random). If not specified, fetches from all available channels",
-        )
-
-        parser.add_argument(
-            "--concatenate-conversations",
-            action="store_true",
-            default=True,
-            help="Group messages by channel/thread for better context (default: True)",
-        )
-
-        parser.add_argument(
-            "--no-concatenate-conversations",
-            action="store_true",
-            help="Process individual messages instead of grouping by channel",
-        )
-
-        parser.add_argument(
-            "--max-messages-per-channel",
-            type=int,
-            default=100,
-            help="Maximum number of messages to include per channel (default: 100)",
-        )
-
-        parser.add_argument(
-            "--test-connection",
-            action="store_true",
-            help="Test MCP server connection and list available tools without indexing",
-        )
-
-        parser.add_argument(
-            "--max-retries",
-            type=int,
-            default=5,
-            help="Maximum number of retries for failed operations (default: 5)",
-        )
-
-        parser.add_argument(
-            "--retry-delay",
-            type=float,
-            default=2.0,
-            help="Initial delay between retries in seconds (default: 2.0)",
-        )
-
-    async def test_mcp_connection(self, args) -> bool:
-        """Test the MCP server connection and display available tools."""
-        print(f"Testing connection to MCP server: {args.mcp_server}")
-
-        try:
-            reader = SlackMCPReader(
-                mcp_server_command=args.mcp_server,
-                workspace_name=args.workspace_name,
-                concatenate_conversations=not args.no_concatenate_conversations,
-                max_messages_per_conversation=args.max_messages_per_channel,
-                max_retries=args.max_retries,
-                retry_delay=args.retry_delay,
-            )
-
-            async with reader:
-                tools = await reader.list_available_tools()
-
-                print("Successfully connected to MCP server!")
-                print(f"Available tools ({len(tools)}):")
-
-                for i, tool in enumerate(tools, 1):
-                    name = tool.get("name", "Unknown")
-                    description = tool.get("description", "No description available")
-                    print(f"\n{i}. {name}")
-                    print(
-                        f"   Description: {description[:100]}{'...' if len(description) > 100 else ''}"
-                    )
-
-                    # Show input schema if available
-                    schema = tool.get("inputSchema", {})
-                    if schema.get("properties"):
-                        props = list(schema["properties"].keys())[:3]  # Show first 3 properties
-                        print(
-                            f"   Parameters: {', '.join(props)}{'...' if len(schema['properties']) > 3 else ''}"
-                        )
-
-                return True
-
-        except Exception as e:
-            print(f"Failed to connect to MCP server: {e}")
-            print("\nTroubleshooting tips:")
-            print("1. Make sure the MCP server is installed and accessible")
-            print("2. Check if the server command is correct")
-            print("3. Ensure you have proper authentication/credentials configured")
-            print("4. Try running the MCP server command directly to test it")
-            return False
-
-    async def load_data(self, args) -> list[str]:
-        """Load Slack messages via MCP server."""
-        print(f"Connecting to Slack MCP server: {args.mcp_server}")
-
-        if args.workspace_name:
-            print(f"Workspace: {args.workspace_name}")
-
-        # Filter out empty strings from channels
-        channels = [ch for ch in args.channels if ch.strip()] if args.channels else None
-
-        if channels:
-            print(f"Channels: {', '.join(channels)}")
-        else:
-            print("Fetching from all available channels")
-
-        concatenate = not args.no_concatenate_conversations
-        print(
-            f"Processing mode: {'Concatenated conversations' if concatenate else 'Individual messages'}"
-        )
-
-        try:
-            reader = SlackMCPReader(
-                mcp_server_command=args.mcp_server,
-                workspace_name=args.workspace_name,
-                concatenate_conversations=concatenate,
-                max_messages_per_conversation=args.max_messages_per_channel,
-                max_retries=args.max_retries,
-                retry_delay=args.retry_delay,
-            )
-
-            texts = await reader.read_slack_data(channels=channels)
-
-            if not texts:
-                print("No messages found! This could mean:")
-                print("- The MCP server couldn't fetch messages")
-                print("- The specified channels don't exist or are empty")
-                print("- Authentication issues with the Slack workspace")
-                return []
-
-            print(f"Successfully loaded {len(texts)} text chunks from Slack")
-
-            # Show sample of what was loaded
-            if texts:
-                sample_text = texts[0][:200] + "..." if len(texts[0]) > 200 else texts[0]
-                print("\nSample content:")
-                print("-" * 40)
-                print(sample_text)
-                print("-" * 40)
-
-            return texts
-
-        except Exception as e:
-            print(f"Error loading Slack data: {e}")
-            print("\nThis might be due to:")
-            print("- MCP server connection issues")
-            print("- Authentication problems")
-            print("- Network connectivity issues")
-            print("- Incorrect channel names")
-            raise
-
-    async def run(self):
-        """Main entry point with MCP connection testing."""
-        args = self.parser.parse_args()
-
-        # Test connection if requested
-        if args.test_connection:
-            success = await self.test_mcp_connection(args)
-            if not success:
-                return
-            print(
-                "MCP server is working! You can now run without --test-connection to start indexing."
-            )
-            return
-
-        # Run the standard RAG pipeline
-        await super().run()
-
-
-async def main():
-    """Main entry point for the Slack MCP RAG application."""
-    app = SlackMCPRAG()
-    await app.run()
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

apps/twitter_data/__init__.py

@@ -1 +0,0 @@
-# Twitter MCP data integration for LEANN

apps/twitter_data/twitter_mcp_reader.py

@@ -1,295 +0,0 @@
-#!/usr/bin/env python3
-"""
-Twitter MCP Reader for LEANN
-
-This module provides functionality to connect to Twitter MCP servers and fetch bookmark data
-for indexing in LEANN. It supports various Twitter MCP server implementations and provides
-flexible bookmark processing options.
-"""
-
-import asyncio
-import json
-import logging
-from typing import Any, Optional
-
-logger = logging.getLogger(__name__)
-
-
-class TwitterMCPReader:
-    """
-    Reader for Twitter bookmark data via MCP (Model Context Protocol) servers.
-
-    This class connects to Twitter MCP servers to fetch bookmark data and convert it
-    into a format suitable for LEANN indexing.
-    """
-
-    def __init__(
-        self,
-        mcp_server_command: str,
-        username: Optional[str] = None,
-        include_tweet_content: bool = True,
-        include_metadata: bool = True,
-        max_bookmarks: int = 1000,
-    ):
-        """
-        Initialize the Twitter MCP Reader.
-
-        Args:
-            mcp_server_command: Command to start the MCP server (e.g., 'twitter-mcp-server')
-            username: Optional Twitter username to filter bookmarks
-            include_tweet_content: Whether to include full tweet content
-            include_metadata: Whether to include tweet metadata (likes, retweets, etc.)
-            max_bookmarks: Maximum number of bookmarks to fetch
-        """
-        self.mcp_server_command = mcp_server_command
-        self.username = username
-        self.include_tweet_content = include_tweet_content
-        self.include_metadata = include_metadata
-        self.max_bookmarks = max_bookmarks
-        self.mcp_process = None
-
-    async def start_mcp_server(self):
-        """Start the MCP server process."""
-        try:
-            self.mcp_process = await asyncio.create_subprocess_exec(
-                *self.mcp_server_command.split(),
-                stdin=asyncio.subprocess.PIPE,
-                stdout=asyncio.subprocess.PIPE,
-                stderr=asyncio.subprocess.PIPE,
-            )
-            logger.info(f"Started MCP server: {self.mcp_server_command}")
-        except Exception as e:
-            logger.error(f"Failed to start MCP server: {e}")
-            raise
-
-    async def stop_mcp_server(self):
-        """Stop the MCP server process."""
-        if self.mcp_process:
-            self.mcp_process.terminate()
-            await self.mcp_process.wait()
-            logger.info("Stopped MCP server")
-
-    async def send_mcp_request(self, request: dict[str, Any]) -> dict[str, Any]:
-        """Send a request to the MCP server and get response."""
-        if not self.mcp_process:
-            raise RuntimeError("MCP server not started")
-
-        request_json = json.dumps(request) + "\n"
-        self.mcp_process.stdin.write(request_json.encode())
-        await self.mcp_process.stdin.drain()
-
-        response_line = await self.mcp_process.stdout.readline()
-        if not response_line:
-            raise RuntimeError("No response from MCP server")
-
-        return json.loads(response_line.decode().strip())
-
-    async def initialize_mcp_connection(self):
-        """Initialize the MCP connection."""
-        init_request = {
-            "jsonrpc": "2.0",
-            "id": 1,
-            "method": "initialize",
-            "params": {
-                "protocolVersion": "2024-11-05",
-                "capabilities": {},
-                "clientInfo": {"name": "leann-twitter-reader", "version": "1.0.0"},
-            },
-        }
-
-        response = await self.send_mcp_request(init_request)
-        if "error" in response:
-            raise RuntimeError(f"MCP initialization failed: {response['error']}")
-
-        logger.info("MCP connection initialized successfully")
-
-    async def list_available_tools(self) -> list[dict[str, Any]]:
-        """List available tools from the MCP server."""
-        list_request = {"jsonrpc": "2.0", "id": 2, "method": "tools/list", "params": {}}
-
-        response = await self.send_mcp_request(list_request)
-        if "error" in response:
-            raise RuntimeError(f"Failed to list tools: {response['error']}")
-
-        return response.get("result", {}).get("tools", [])
-
-    async def fetch_twitter_bookmarks(self, limit: Optional[int] = None) -> list[dict[str, Any]]:
-        """
-        Fetch Twitter bookmarks using MCP tools.
-
-        Args:
-            limit: Maximum number of bookmarks to fetch
-
-        Returns:
-            List of bookmark dictionaries
-        """
-        tools = await self.list_available_tools()
-        bookmark_tool = None
-
-        # Look for a tool that can fetch bookmarks
-        for tool in tools:
-            tool_name = tool.get("name", "").lower()
-            if any(keyword in tool_name for keyword in ["bookmark", "saved", "favorite"]):
-                bookmark_tool = tool
-                break
-
-        if not bookmark_tool:
-            raise RuntimeError("No bookmark fetching tool found in MCP server")
-
-        # Prepare tool call parameters
-        tool_params = {}
-        if limit or self.max_bookmarks:
-            tool_params["limit"] = limit or self.max_bookmarks
-        if self.username:
-            tool_params["username"] = self.username
-
-        fetch_request = {
-            "jsonrpc": "2.0",
-            "id": 3,
-            "method": "tools/call",
-            "params": {"name": bookmark_tool["name"], "arguments": tool_params},
-        }
-
-        response = await self.send_mcp_request(fetch_request)
-        if "error" in response:
-            raise RuntimeError(f"Failed to fetch bookmarks: {response['error']}")
-
-        # Extract bookmarks from response
-        result = response.get("result", {})
-        if "content" in result and isinstance(result["content"], list):
-            content = result["content"][0] if result["content"] else {}
-            if "text" in content:
-                try:
-                    bookmarks = json.loads(content["text"])
-                except json.JSONDecodeError:
-                    # If not JSON, treat as plain text
-                    bookmarks = [{"text": content["text"], "source": "twitter"}]
-            else:
-                bookmarks = result["content"]
-        else:
-            bookmarks = result.get("bookmarks", result.get("tweets", [result]))
-
-        return bookmarks if isinstance(bookmarks, list) else [bookmarks]
-
-    def _format_bookmark(self, bookmark: dict[str, Any]) -> str:
-        """Format a single bookmark for indexing."""
-        # Extract tweet information
-        text = bookmark.get("text", bookmark.get("content", ""))
-        author = bookmark.get(
-            "author", bookmark.get("username", bookmark.get("user", {}).get("username", "Unknown"))
-        )
-        timestamp = bookmark.get("created_at", bookmark.get("timestamp", ""))
-        url = bookmark.get("url", bookmark.get("tweet_url", ""))
-
-        # Extract metadata if available
-        likes = bookmark.get("likes", bookmark.get("favorite_count", 0))
-        retweets = bookmark.get("retweets", bookmark.get("retweet_count", 0))
-        replies = bookmark.get("replies", bookmark.get("reply_count", 0))
-
-        # Build formatted bookmark
-        parts = []
-
-        # Header
-        parts.append("=== Twitter Bookmark ===")
-
-        if author:
-            parts.append(f"Author: @{author}")
-
-        if timestamp:
-            # Format timestamp if it's a standard format
-            try:
-                import datetime
-
-                if "T" in str(timestamp):  # ISO format
-                    dt = datetime.datetime.fromisoformat(timestamp.replace("Z", "+00:00"))
-                    formatted_time = dt.strftime("%Y-%m-%d %H:%M:%S")
-                else:
-                    formatted_time = str(timestamp)
-                parts.append(f"Date: {formatted_time}")
-            except (ValueError, TypeError):
-                parts.append(f"Date: {timestamp}")
-
-        if url:
-            parts.append(f"URL: {url}")
-
-        # Tweet content
-        if text and self.include_tweet_content:
-            parts.append("")
-            parts.append("Content:")
-            parts.append(text)
-
-        # Metadata
-        if self.include_metadata and any([likes, retweets, replies]):
-            parts.append("")
-            parts.append("Engagement:")
-            if likes:
-                parts.append(f"  Likes: {likes}")
-            if retweets:
-                parts.append(f"  Retweets: {retweets}")
-            if replies:
-                parts.append(f"  Replies: {replies}")
-
-        # Extract hashtags and mentions if available
-        hashtags = bookmark.get("hashtags", [])
-        mentions = bookmark.get("mentions", [])
-
-        if hashtags or mentions:
-            parts.append("")
-            if hashtags:
-                parts.append(f"Hashtags: {', '.join(hashtags)}")
-            if mentions:
-                parts.append(f"Mentions: {', '.join(mentions)}")
-
-        return "\n".join(parts)
-
-    async def read_twitter_bookmarks(self) -> list[str]:
-        """
-        Read Twitter bookmark data and return formatted text chunks.
-
-        Returns:
-            List of formatted text chunks ready for LEANN indexing
-        """
-        try:
-            await self.start_mcp_server()
-            await self.initialize_mcp_connection()
-
-            print(f"Fetching up to {self.max_bookmarks} bookmarks...")
-            if self.username:
-                print(f"Filtering for user: @{self.username}")
-
-            bookmarks = await self.fetch_twitter_bookmarks()
-
-            if not bookmarks:
-                print("No bookmarks found")
-                return []
-
-            print(f"Processing {len(bookmarks)} bookmarks...")
-
-            all_texts = []
-            processed_count = 0
-
-            for bookmark in bookmarks:
-                try:
-                    formatted_bookmark = self._format_bookmark(bookmark)
-                    if formatted_bookmark.strip():
-                        all_texts.append(formatted_bookmark)
-                        processed_count += 1
-                except Exception as e:
-                    logger.warning(f"Failed to format bookmark: {e}")
-                    continue
-
-            print(f"Successfully processed {processed_count} bookmarks")
-            return all_texts
-
-        finally:
-            await self.stop_mcp_server()
-
-    async def __aenter__(self):
-        """Async context manager entry."""
-        await self.start_mcp_server()
-        await self.initialize_mcp_connection()
-        return self
-
-    async def __aexit__(self, exc_type, exc_val, exc_tb):
-        """Async context manager exit."""
-        await self.stop_mcp_server()

apps/twitter_rag.py

@@ -1,195 +0,0 @@
-#!/usr/bin/env python3
-"""
-Twitter RAG Application with MCP Support
-
-This application enables RAG (Retrieval-Augmented Generation) on Twitter bookmarks
-by connecting to Twitter MCP servers to fetch live data and index it in LEANN.
-
-Usage:
-    python -m apps.twitter_rag --mcp-server "twitter-mcp-server" --query "What articles did I bookmark about AI?"
-"""
-
-import argparse
-import asyncio
-
-from apps.base_rag_example import BaseRAGExample
-from apps.twitter_data.twitter_mcp_reader import TwitterMCPReader
-
-
-class TwitterMCPRAG(BaseRAGExample):
-    """
-    RAG application for Twitter bookmarks via MCP servers.
-
-    This class provides a complete RAG pipeline for Twitter bookmark data, including
-    MCP server connection, data fetching, indexing, and interactive chat.
-    """
-
-    def __init__(self):
-        super().__init__(
-            name="Twitter MCP RAG",
-            description="RAG application for Twitter bookmarks via MCP servers",
-            default_index_name="twitter_bookmarks",
-        )
-
-    def _add_specific_arguments(self, parser: argparse.ArgumentParser):
-        """Add Twitter MCP-specific arguments."""
-        parser.add_argument(
-            "--mcp-server",
-            type=str,
-            required=True,
-            help="Command to start the Twitter MCP server (e.g., 'twitter-mcp-server' or 'npx twitter-mcp-server')",
-        )
-
-        parser.add_argument(
-            "--username", type=str, help="Twitter username to filter bookmarks (without @)"
-        )
-
-        parser.add_argument(
-            "--max-bookmarks",
-            type=int,
-            default=1000,
-            help="Maximum number of bookmarks to fetch (default: 1000)",
-        )
-
-        parser.add_argument(
-            "--no-tweet-content",
-            action="store_true",
-            help="Exclude tweet content, only include metadata",
-        )
-
-        parser.add_argument(
-            "--no-metadata",
-            action="store_true",
-            help="Exclude engagement metadata (likes, retweets, etc.)",
-        )
-
-        parser.add_argument(
-            "--test-connection",
-            action="store_true",
-            help="Test MCP server connection and list available tools without indexing",
-        )
-
-    async def test_mcp_connection(self, args) -> bool:
-        """Test the MCP server connection and display available tools."""
-        print(f"Testing connection to MCP server: {args.mcp_server}")
-
-        try:
-            reader = TwitterMCPReader(
-                mcp_server_command=args.mcp_server,
-                username=args.username,
-                include_tweet_content=not args.no_tweet_content,
-                include_metadata=not args.no_metadata,
-                max_bookmarks=args.max_bookmarks,
-            )
-
-            async with reader:
-                tools = await reader.list_available_tools()
-
-                print("\n✅ Successfully connected to MCP server!")
-                print(f"Available tools ({len(tools)}):")
-
-                for i, tool in enumerate(tools, 1):
-                    name = tool.get("name", "Unknown")
-                    description = tool.get("description", "No description available")
-                    print(f"\n{i}. {name}")
-                    print(
-                        f"   Description: {description[:100]}{'...' if len(description) > 100 else ''}"
-                    )
-
-                    # Show input schema if available
-                    schema = tool.get("inputSchema", {})
-                    if schema.get("properties"):
-                        props = list(schema["properties"].keys())[:3]  # Show first 3 properties
-                        print(
-                            f"   Parameters: {', '.join(props)}{'...' if len(schema['properties']) > 3 else ''}"
-                        )
-
-                return True
-
-        except Exception as e:
-            print(f"\n❌ Failed to connect to MCP server: {e}")
-            print("\nTroubleshooting tips:")
-            print("1. Make sure the Twitter MCP server is installed and accessible")
-            print("2. Check if the server command is correct")
-            print("3. Ensure you have proper Twitter API credentials configured")
-            print("4. Verify your Twitter account has bookmarks to fetch")
-            print("5. Try running the MCP server command directly to test it")
-            return False
-
-    async def load_data(self, args) -> list[str]:
-        """Load Twitter bookmarks via MCP server."""
-        print(f"Connecting to Twitter MCP server: {args.mcp_server}")
-
-        if args.username:
-            print(f"Username filter: @{args.username}")
-
-        print(f"Max bookmarks: {args.max_bookmarks}")
-        print(f"Include tweet content: {not args.no_tweet_content}")
-        print(f"Include metadata: {not args.no_metadata}")
-
-        try:
-            reader = TwitterMCPReader(
-                mcp_server_command=args.mcp_server,
-                username=args.username,
-                include_tweet_content=not args.no_tweet_content,
-                include_metadata=not args.no_metadata,
-                max_bookmarks=args.max_bookmarks,
-            )
-
-            texts = await reader.read_twitter_bookmarks()
-
-            if not texts:
-                print("❌ No bookmarks found! This could mean:")
-                print("- You don't have any bookmarks on Twitter")
-                print("- The MCP server couldn't access your bookmarks")
-                print("- Authentication issues with Twitter API")
-                print("- The username filter didn't match any bookmarks")
-                return []
-
-            print(f"✅ Successfully loaded {len(texts)} bookmarks from Twitter")
-
-            # Show sample of what was loaded
-            if texts:
-                sample_text = texts[0][:300] + "..." if len(texts[0]) > 300 else texts[0]
-                print("\nSample bookmark:")
-                print("-" * 50)
-                print(sample_text)
-                print("-" * 50)
-
-            return texts
-
-        except Exception as e:
-            print(f"❌ Error loading Twitter bookmarks: {e}")
-            print("\nThis might be due to:")
-            print("- MCP server connection issues")
-            print("- Twitter API authentication problems")
-            print("- Network connectivity issues")
-            print("- Rate limiting from Twitter API")
-            raise
-
-    async def run(self):
-        """Main entry point with MCP connection testing."""
-        args = self.parser.parse_args()
-
-        # Test connection if requested
-        if args.test_connection:
-            success = await self.test_mcp_connection(args)
-            if not success:
-                return
-            print(
-                "\n🎉 MCP server is working! You can now run without --test-connection to start indexing."
-            )
-            return
-
-        # Run the standard RAG pipeline
-        await super().run()
-
-
-async def main():
-    """Main entry point for the Twitter MCP RAG application."""
-    app = TwitterMCPRAG()
-    await app.run()
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

benchmarks/llm_utils.py

@@ -54,51 +54,29 @@ def extract_thinking_answer(response):
     return response.strip()
 
 
-def load_hf_model(model_name="Qwen/Qwen3-8B", trust_remote_code=False):
+def load_hf_model(model_name="Qwen/Qwen3-8B"):
-    """Load HuggingFace model
+    """Load HuggingFace model"""
-
-    Args:
-        model_name (str): Name of the model to load
-        trust_remote_code (bool): Whether to allow execution of code from the model repository.
-            Defaults to False for security. Only enable for trusted models.
-    """
     if not HF_AVAILABLE:
         raise ImportError("transformers not available")
 
-    if trust_remote_code:
-        print(
-            "⚠️  WARNING: Loading model with trust_remote_code=True. This can execute arbitrary code."
-        )
-
     print(f"Loading HF: {model_name}")
-    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=trust_remote_code)
+    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
     model = AutoModelForCausalLM.from_pretrained(
         model_name,
         torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
         device_map="auto",
-        trust_remote_code=trust_remote_code,
+        trust_remote_code=True,
     )
     return tokenizer, model
 
 
-def load_vllm_model(model_name="Qwen/Qwen3-8B", trust_remote_code=False):
+def load_vllm_model(model_name="Qwen/Qwen3-8B"):
-    """Load vLLM model
+    """Load vLLM model"""
-
-    Args:
-        model_name (str): Name of the model to load
-        trust_remote_code (bool): Whether to allow execution of code from the model repository.
-            Defaults to False for security. Only enable for trusted models.
-    """
     if not VLLM_AVAILABLE:
         raise ImportError("vllm not available")
 
-    if trust_remote_code:
-        print(
-            "⚠️  WARNING: Loading model with trust_remote_code=True. This can execute arbitrary code."
-        )
-
     print(f"Loading vLLM: {model_name}")
-    llm = LLM(model=model_name, trust_remote_code=trust_remote_code)
+    llm = LLM(model=model_name, trust_remote_code=True)
 
     # Qwen3 specific config
     if is_qwen3_model(model_name):
@@ -200,33 +178,19 @@ def evaluate_rag(searcher, llm_func, queries, domain="default", top_k=3, complex
     }
 
 
-def load_qwen_vl_model(model_name="Qwen/Qwen2.5-VL-7B-Instruct", trust_remote_code=False):
+def load_qwen_vl_model(model_name="Qwen/Qwen2.5-VL-7B-Instruct"):
-    """Load Qwen2.5-VL multimodal model
+    """Load Qwen2.5-VL multimodal model"""
-
-    Args:
-        model_name (str): Name of the model to load
-        trust_remote_code (bool): Whether to allow execution of code from the model repository.
-            Defaults to False for security. Only enable for trusted models.
-    """
     if not HF_AVAILABLE:
         raise ImportError("transformers not available")
 
-    if trust_remote_code:
-        print(
-            "⚠️  WARNING: Loading model with trust_remote_code=True. This can execute arbitrary code."
-        )
-
     print(f"Loading Qwen2.5-VL: {model_name}")
 
     try:
         from transformers import AutoModelForVision2Seq, AutoProcessor
 
-        processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=trust_remote_code)
+        processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
         model = AutoModelForVision2Seq.from_pretrained(
-            model_name,
+            model_name, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
-            torch_dtype=torch.bfloat16,
-            device_map="auto",
-            trust_remote_code=trust_remote_code,
         )
 
         return processor, model
@@ -238,14 +202,9 @@ def load_qwen_vl_model(model_name="Qwen/Qwen2.5-VL-7B-Instruct", trust_remote_co
         try:
             from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
 
-            processor = AutoProcessor.from_pretrained(
+            processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
-                model_name, trust_remote_code=trust_remote_code
-            )
             model = Qwen2VLForConditionalGeneration.from_pretrained(
-                model_name,
+                model_name, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
-                torch_dtype=torch.bfloat16,
-                device_map="auto",
-                trust_remote_code=trust_remote_code,
             )
 
             return processor, model

benchmarks/update/README.md

@@ -1,143 +0,0 @@
-# Update Benchmarks
-
-This directory hosts two benchmark suites that exercise LEANN’s HNSW “update +
-search” pipeline under different assumptions:
-
-1. **RNG recompute latency** – measure how random-neighbour pruning and cache
-   settings influence incremental `add()` latency when embeddings are fetched
-   over the ZMQ embedding server.
-2. **Update strategy comparison** – compare a fully sequential update pipeline
-   against an offline approach that keeps the graph static and fuses results.
-
-Both suites build a non-compact, `is_recompute=True` index so that new
-embeddings are pulled from the embedding server. Benchmark outputs are written
-under `.leann/bench/` by default and appended to CSV files for later plotting.
-
-## Benchmarks
-
-### 1. HNSW RNG Recompute Benchmark
-
-`bench_hnsw_rng_recompute.py` evaluates incremental update latency under four
-random-neighbour (RNG) configurations. Each scenario uses the same dataset but
-changes the forward / reverse RNG pruning flags and whether the embedding cache
-is enabled:
-
-| Scenario name                      | Forward RNG | Reverse RNG | ZMQ embedding cache |
-| ---------------------------------- | ----------- | ----------- | ------------------- |
-| `baseline`                         | Enabled     | Enabled     | Enabled             |
-| `no_cache_baseline`                | Enabled     | Enabled     | **Disabled**        |
-| `disable_forward_rng`              | **Disabled**| Enabled     | Enabled             |
-| `disable_forward_and_reverse_rng`  | **Disabled**| **Disabled**| Enabled             |
-
-For each scenario the script:
-1. (Re)builds a `is_recompute=True` index and writes it to `.leann/bench/`.
-2. Starts `leann_backend_hnsw.hnsw_embedding_server` for remote embeddings.
-3. Appends the requested updates using the scenario’s RNG flags.
-4. Records total time, latency per passage, ZMQ fetch counts, and stage-level
-   timings before appending a row to the CSV output.
-
-**Run:**
-```bash
-LEANN_HNSW_LOG_PATH=.leann/bench/hnsw_server.log \
-LEANN_LOG_LEVEL=INFO \
-uv run -m benchmarks.update.bench_hnsw_rng_recompute \
-  --runs 1 \
-  --index-path .leann/bench/test.leann \
-  --initial-files data/PrideandPrejudice.txt \
-  --update-files data/huawei_pangu.md \
-  --max-initial 300 \
-  --max-updates 1 \
-  --add-timeout 120
-```
-
-**Output:**
-- `benchmarks/update/bench_results.csv` – per-scenario timing statistics
-  (including ms/passage) for each run.
-- `.leann/bench/hnsw_server.log` – detailed ZMQ/server logs (path controlled by
-  `LEANN_HNSW_LOG_PATH`).
-  _The reference CSVs checked into this branch were generated on a workstation with an NVIDIA RTX 4090 GPU; throughput numbers will differ on other hardware._
-
-### 2. Sequential vs. Offline Update Benchmark
-
-`bench_update_vs_offline_search.py` compares two end-to-end strategies on the
-same dataset:
-
-- **Scenario A – Sequential Update**
-  - Start an embedding server.
-  - Sequentially call `index.add()`; each call fetches embeddings via ZMQ and
-    mutates the HNSW graph.
-  - After all inserts, run a search on the updated graph.
-  - Metrics recorded: update time (`add_total_s`), post-update search time
-    (`search_time_s`), combined total (`total_time_s`), and per-passage
-    latency.
-
-- **Scenario B – Offline Embedding + Concurrent Search**
-  - Stop Scenario A’s server and start a fresh embedding server.
-  - Spawn two threads: one generates embeddings for the new passages offline
-    (graph unchanged); the other computes the query embedding and searches the
-    existing graph.
-  - Merge offline similarities with the graph search results to emulate late
-    fusion, then report the merged top‑k preview.
-  - Metrics recorded: embedding time (`emb_time_s`), search time
-    (`search_time_s`), concurrent makespan (`makespan_s`), and scenario total.
-
-**Run (both scenarios):**
-```bash
-uv run -m benchmarks.update.bench_update_vs_offline_search \
-  --index-path .leann/bench/offline_vs_update.leann \
-  --max-initial 300 \
-  --num-updates 1
-```
-
-You can pass `--only A` or `--only B` to run a single scenario. The script will
-print timing summaries to stdout and append the results to CSV.
-
-**Output:**
-- `benchmarks/update/offline_vs_update.csv` – per-scenario timing statistics for
-  Scenario A and B.
-- Console output includes Scenario B’s merged top‑k preview for quick sanity
-  checks.
-  _The sample results committed here come from runs on an RTX 4090-equipped machine; expect variations if you benchmark on different GPUs._
-
-### 3. Visualisation
-
-`plot_bench_results.py` combines the RNG benchmark and the update strategy
-benchmark into a single two-panel plot.
-
-**Run:**
-```bash
-uv run -m benchmarks.update.plot_bench_results \
-  --csv benchmarks/update/bench_results.csv \
-  --csv-right benchmarks/update/offline_vs_update.csv \
-  --out benchmarks/update/bench_latency_from_csv.png
-```
-
-**Options:**
-- `--broken-y` – Enable a broken Y-axis (default: true when appropriate).
-- `--csv` – RNG benchmark results CSV (left panel).
-- `--csv-right` – Update strategy results CSV (right panel).
-- `--out` – Output image path (PNG/PDF supported).
-
-**Output:**
-- `benchmarks/update/bench_latency_from_csv.png` – visual comparison of the two
-  suites.
-- `benchmarks/update/bench_latency_from_csv.pdf` – PDF version, suitable for
-  slides/papers.
-
-## Parameters & Environment
-
-### Common CLI Flags
-- `--max-initial` – Number of initial passages used to seed the index.
-- `--max-updates` / `--num-updates` – Number of passages to treat as updates.
-- `--index-path` – Base path (without extension) where the LEANN index is stored.
-- `--runs` – Number of repetitions (RNG benchmark only).
-
-### Environment Variables
-- `LEANN_HNSW_LOG_PATH` – File to receive embedding-server logs (optional).
-- `LEANN_LOG_LEVEL` – Logging verbosity (DEBUG/INFO/WARNING/ERROR).
-- `CUDA_VISIBLE_DEVICES` – Set to empty string if you want to force CPU
-  execution of the embedding model.
-
-With these scripts you can easily replicate LEANN’s update benchmarks, compare
-multiple RNG strategies, and evaluate whether sequential updates or offline
-fusion better match your latency/accuracy trade-offs.

benchmarks/update/__init__.py

@@ -1,16 +0,0 @@
-"""Benchmarks for LEANN update workflows."""
-
-# Expose helper to locate repository root for other modules that need it.
-from pathlib import Path
-
-
-def find_repo_root() -> Path:
-    """Return the project root containing pyproject.toml."""
-    current = Path(__file__).resolve()
-    for parent in current.parents:
-        if (parent / "pyproject.toml").exists():
-            return parent
-    return current.parents[1]
-
-
-__all__ = ["find_repo_root"]

benchmarks/update/bench_hnsw_rng_recompute.py

@@ -1,804 +0,0 @@
-"""Benchmark incremental HNSW add() under different RNG pruning modes with real
-embedding recomputation.
-
-This script clones the structure of ``examples/dynamic_update_no_recompute.py``
-so that we build a non-compact ``is_recompute=True`` index, spin up the
-standard HNSW embedding server, and measure how long incremental ``add`` takes
-when RNG pruning is fully enabled vs. partially/fully disabled.
-
-Example usage (run from the repo root; downloads the model on first run)::
-
-    uv run -m benchmarks.update.bench_hnsw_rng_recompute \
-        --index-path .leann/bench/leann-demo.leann \
-        --runs 1
-
-You can tweak the input documents with ``--initial-files`` / ``--update-files``
-if you want a larger or different workload, and change the embedding model via
-``--model-name``.
-"""
-
-import argparse
-import json
-import logging
-import os
-import pickle
-import re
-import sys
-import time
-from pathlib import Path
-from typing import Any
-
-import msgpack
-import numpy as np
-import zmq
-from leann.api import LeannBuilder
-
-if os.environ.get("LEANN_FORCE_CPU", "").lower() in ("1", "true", "yes"):
-    os.environ.setdefault("CUDA_VISIBLE_DEVICES", "")
-
-from leann.embedding_compute import compute_embeddings
-from leann.embedding_server_manager import EmbeddingServerManager
-from leann.registry import register_project_directory
-from leann_backend_hnsw import faiss  # type: ignore
-from leann_backend_hnsw.convert_to_csr import prune_hnsw_embeddings_inplace
-
-logger = logging.getLogger(__name__)
-if not logging.getLogger().handlers:
-    logging.basicConfig(level=logging.INFO)
-
-
-def _find_repo_root() -> Path:
-    """Locate project root by walking up until pyproject.toml is found."""
-    current = Path(__file__).resolve()
-    for parent in current.parents:
-        if (parent / "pyproject.toml").exists():
-            return parent
-    # Fallback: assume repo is two levels up (../..)
-    return current.parents[2]
-
-
-REPO_ROOT = _find_repo_root()
-if str(REPO_ROOT) not in sys.path:
-    sys.path.insert(0, str(REPO_ROOT))
-
-from apps.chunking import create_text_chunks  # noqa: E402
-
-DEFAULT_INITIAL_FILES = [
-    REPO_ROOT / "data" / "2501.14312v1 (1).pdf",
-    REPO_ROOT / "data" / "huawei_pangu.md",
-]
-DEFAULT_UPDATE_FILES = [REPO_ROOT / "data" / "2506.08276v1.pdf"]
-
-DEFAULT_HNSW_LOG = Path(".leann/bench/hnsw_server.log")
-
-
-def load_chunks_from_files(paths: list[Path], limit: int | None = None) -> list[str]:
-    from llama_index.core import SimpleDirectoryReader
-
-    documents = []
-    for path in paths:
-        p = path.expanduser().resolve()
-        if not p.exists():
-            raise FileNotFoundError(f"Input path not found: {p}")
-        if p.is_dir():
-            reader = SimpleDirectoryReader(str(p), recursive=False)
-            documents.extend(reader.load_data(show_progress=True))
-        else:
-            reader = SimpleDirectoryReader(input_files=[str(p)])
-            documents.extend(reader.load_data(show_progress=True))
-
-    if not documents:
-        return []
-
-    chunks = create_text_chunks(
-        documents,
-        chunk_size=512,
-        chunk_overlap=128,
-        use_ast_chunking=False,
-    )
-    cleaned = [c for c in chunks if isinstance(c, str) and c.strip()]
-    if limit is not None:
-        cleaned = cleaned[:limit]
-    return cleaned
-
-
-def ensure_index_dir(index_path: Path) -> None:
-    index_path.parent.mkdir(parents=True, exist_ok=True)
-
-
-def cleanup_index_files(index_path: Path) -> None:
-    parent = index_path.parent
-    if not parent.exists():
-        return
-    stem = index_path.stem
-    for file in parent.glob(f"{stem}*"):
-        if file.is_file():
-            file.unlink()
-
-
-def build_initial_index(
-    index_path: Path,
-    paragraphs: list[str],
-    model_name: str,
-    embedding_mode: str,
-    distance_metric: str,
-    ef_construction: int,
-) -> None:
-    builder = LeannBuilder(
-        backend_name="hnsw",
-        embedding_model=model_name,
-        embedding_mode=embedding_mode,
-        is_compact=False,
-        is_recompute=True,
-        distance_metric=distance_metric,
-        backend_kwargs={
-            "distance_metric": distance_metric,
-            "is_compact": False,
-            "is_recompute": True,
-            "efConstruction": ef_construction,
-        },
-    )
-    for idx, passage in enumerate(paragraphs):
-        builder.add_text(passage, metadata={"id": str(idx)})
-    builder.build_index(str(index_path))
-
-
-def prepare_new_chunks(paragraphs: list[str]) -> list[dict[str, Any]]:
-    return [{"text": text, "metadata": {}} for text in paragraphs]
-
-
-def benchmark_update_with_mode(
-    index_path: Path,
-    new_chunks: list[dict[str, Any]],
-    model_name: str,
-    embedding_mode: str,
-    distance_metric: str,
-    disable_forward_rng: bool,
-    disable_reverse_rng: bool,
-    server_port: int,
-    add_timeout: int,
-    ef_construction: int,
-) -> tuple[float, float]:
-    meta_path = index_path.parent / f"{index_path.name}.meta.json"
-    passages_file = index_path.parent / f"{index_path.name}.passages.jsonl"
-    offset_file = index_path.parent / f"{index_path.name}.passages.idx"
-    index_file = index_path.parent / f"{index_path.stem}.index"
-
-    with open(meta_path, encoding="utf-8") as f:
-        meta = json.load(f)
-
-    with open(offset_file, "rb") as f:
-        offset_map: dict[str, int] = pickle.load(f)
-    existing_ids = set(offset_map.keys())
-
-    valid_chunks: list[dict[str, Any]] = []
-    for chunk in new_chunks:
-        text = chunk.get("text", "")
-        if not isinstance(text, str) or not text.strip():
-            continue
-        metadata = chunk.setdefault("metadata", {})
-        passage_id = chunk.get("id") or metadata.get("id")
-        if passage_id and passage_id in existing_ids:
-            raise ValueError(f"Passage ID '{passage_id}' already exists in the index.")
-        valid_chunks.append(chunk)
-
-    if not valid_chunks:
-        raise ValueError("No valid chunks to append.")
-
-    texts_to_embed = [chunk["text"] for chunk in valid_chunks]
-    embeddings = compute_embeddings(
-        texts_to_embed,
-        model_name,
-        mode=embedding_mode,
-        is_build=False,
-        batch_size=16,
-    )
-
-    embeddings = np.ascontiguousarray(embeddings, dtype=np.float32)
-    if distance_metric == "cosine":
-        norms = np.linalg.norm(embeddings, axis=1, keepdims=True)
-        norms[norms == 0] = 1
-        embeddings = embeddings / norms
-
-    index = faiss.read_index(str(index_file))
-    index.is_recompute = True
-    if getattr(index, "storage", None) is None:
-        if index.metric_type == faiss.METRIC_INNER_PRODUCT:
-            storage_index = faiss.IndexFlatIP(index.d)
-        else:
-            storage_index = faiss.IndexFlatL2(index.d)
-        index.storage = storage_index
-        index.own_fields = True
-        try:
-            storage_index.ntotal = index.ntotal
-        except AttributeError:
-            pass
-    try:
-        index.hnsw.set_disable_rng_during_add(disable_forward_rng)
-        index.hnsw.set_disable_reverse_prune(disable_reverse_rng)
-        if ef_construction is not None:
-            index.hnsw.efConstruction = ef_construction
-    except AttributeError:
-        pass
-
-    applied_forward = getattr(index.hnsw, "disable_rng_during_add", None)
-    applied_reverse = getattr(index.hnsw, "disable_reverse_prune", None)
-    logger.info(
-        "HNSW RNG config -> requested forward=%s, reverse=%s | applied forward=%s, reverse=%s",
-        disable_forward_rng,
-        disable_reverse_rng,
-        applied_forward,
-        applied_reverse,
-    )
-
-    base_id = index.ntotal
-    for offset, chunk in enumerate(valid_chunks):
-        new_id = str(base_id + offset)
-        chunk.setdefault("metadata", {})["id"] = new_id
-        chunk["id"] = new_id
-
-    rollback_size = passages_file.stat().st_size if passages_file.exists() else 0
-    offset_map_backup = offset_map.copy()
-
-    try:
-        with open(passages_file, "a", encoding="utf-8") as f:
-            for chunk in valid_chunks:
-                offset = f.tell()
-                json.dump(
-                    {
-                        "id": chunk["id"],
-                        "text": chunk["text"],
-                        "metadata": chunk.get("metadata", {}),
-                    },
-                    f,
-                    ensure_ascii=False,
-                )
-                f.write("\n")
-                offset_map[chunk["id"]] = offset
-
-        with open(offset_file, "wb") as f:
-            pickle.dump(offset_map, f)
-
-        server_manager = EmbeddingServerManager(
-            backend_module_name="leann_backend_hnsw.hnsw_embedding_server"
-        )
-        server_started, actual_port = server_manager.start_server(
-            port=server_port,
-            model_name=model_name,
-            embedding_mode=embedding_mode,
-            passages_file=str(meta_path),
-            distance_metric=distance_metric,
-        )
-        if not server_started:
-            raise RuntimeError("Failed to start embedding server.")
-
-        if hasattr(index.hnsw, "set_zmq_port"):
-            index.hnsw.set_zmq_port(actual_port)
-        elif hasattr(index, "set_zmq_port"):
-            index.set_zmq_port(actual_port)
-
-        _warmup_embedding_server(actual_port)
-
-        total_start = time.time()
-        add_elapsed = 0.0
-
-        try:
-            import signal
-
-            def _timeout_handler(signum, frame):
-                raise TimeoutError("incremental add timed out")
-
-            if add_timeout > 0:
-                signal.signal(signal.SIGALRM, _timeout_handler)
-                signal.alarm(add_timeout)
-
-            add_start = time.time()
-            for i in range(embeddings.shape[0]):
-                index.add(1, faiss.swig_ptr(embeddings[i : i + 1]))
-            add_elapsed = time.time() - add_start
-            if add_timeout > 0:
-                signal.alarm(0)
-            faiss.write_index(index, str(index_file))
-        finally:
-            server_manager.stop_server()
-
-    except TimeoutError:
-        raise
-    except Exception:
-        if passages_file.exists():
-            with open(passages_file, "rb+") as f:
-                f.truncate(rollback_size)
-        with open(offset_file, "wb") as f:
-            pickle.dump(offset_map_backup, f)
-        raise
-
-    prune_hnsw_embeddings_inplace(str(index_file))
-
-    meta["total_passages"] = len(offset_map)
-    with open(meta_path, "w", encoding="utf-8") as f:
-        json.dump(meta, f, indent=2)
-
-    # Reset toggles so the index on disk returns to baseline behaviour.
-    try:
-        index.hnsw.set_disable_rng_during_add(False)
-        index.hnsw.set_disable_reverse_prune(False)
-    except AttributeError:
-        pass
-    faiss.write_index(index, str(index_file))
-
-    total_elapsed = time.time() - total_start
-
-    return total_elapsed, add_elapsed
-
-
-def _total_zmq_nodes(log_path: Path) -> int:
-    if not log_path.exists():
-        return 0
-    with log_path.open("r", encoding="utf-8") as log_file:
-        text = log_file.read()
-    return sum(int(match) for match in re.findall(r"ZMQ received (\d+) node IDs", text))
-
-
-def _warmup_embedding_server(port: int) -> None:
-    """Send a dummy REQ so the embedding server loads its model."""
-    ctx = zmq.Context()
-    try:
-        sock = ctx.socket(zmq.REQ)
-        sock.setsockopt(zmq.LINGER, 0)
-        sock.setsockopt(zmq.RCVTIMEO, 5000)
-        sock.setsockopt(zmq.SNDTIMEO, 5000)
-        sock.connect(f"tcp://127.0.0.1:{port}")
-        payload = msgpack.packb(["__WARMUP__"], use_bin_type=True)
-        sock.send(payload)
-        try:
-            sock.recv()
-        except zmq.error.Again:
-            pass
-    finally:
-        sock.close()
-        ctx.term()
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument(
-        "--index-path",
-        type=Path,
-        default=Path(".leann/bench/leann-demo.leann"),
-        help="Output index base path (without extension).",
-    )
-    parser.add_argument(
-        "--initial-files",
-        nargs="*",
-        type=Path,
-        default=DEFAULT_INITIAL_FILES,
-        help="Files used to build the initial index.",
-    )
-    parser.add_argument(
-        "--update-files",
-        nargs="*",
-        type=Path,
-        default=DEFAULT_UPDATE_FILES,
-        help="Files appended during the benchmark.",
-    )
-    parser.add_argument(
-        "--runs", type=int, default=1, help="How many times to repeat each scenario."
-    )
-    parser.add_argument(
-        "--model-name",
-        default="sentence-transformers/all-MiniLM-L6-v2",
-        help="Embedding model used for build/update.",
-    )
-    parser.add_argument(
-        "--embedding-mode",
-        default="sentence-transformers",
-        help="Embedding mode passed to LeannBuilder/embedding server.",
-    )
-    parser.add_argument(
-        "--distance-metric",
-        default="mips",
-        choices=["mips", "l2", "cosine"],
-        help="Distance metric for HNSW backend.",
-    )
-    parser.add_argument(
-        "--ef-construction",
-        type=int,
-        default=200,
-        help="efConstruction setting for initial build.",
-    )
-    parser.add_argument(
-        "--server-port",
-        type=int,
-        default=5557,
-        help="Port for the real embedding server.",
-    )
-    parser.add_argument(
-        "--max-initial",
-        type=int,
-        default=300,
-        help="Optional cap on initial passages (after chunking).",
-    )
-    parser.add_argument(
-        "--max-updates",
-        type=int,
-        default=1,
-        help="Optional cap on update passages (after chunking).",
-    )
-    parser.add_argument(
-        "--add-timeout",
-        type=int,
-        default=900,
-        help="Timeout in seconds for the incremental add loop (0 = no timeout).",
-    )
-    parser.add_argument(
-        "--plot-path",
-        type=Path,
-        default=Path("bench_latency.png"),
-        help="Where to save the latency bar plot.",
-    )
-    parser.add_argument(
-        "--cap-y",
-        type=float,
-        default=None,
-        help="Cap Y-axis (ms). Bars above are hatched and annotated.",
-    )
-    parser.add_argument(
-        "--broken-y",
-        action="store_true",
-        help="Use broken Y-axis (two stacked axes with gap). Overrides --cap-y unless both provided.",
-    )
-    parser.add_argument(
-        "--lower-cap-y",
-        type=float,
-        default=None,
-        help="Lower axes upper bound for broken Y (ms). Default=1.1x second-highest.",
-    )
-    parser.add_argument(
-        "--upper-start-y",
-        type=float,
-        default=None,
-        help="Upper axes lower bound for broken Y (ms). Default=1.2x second-highest.",
-    )
-    parser.add_argument(
-        "--csv-path",
-        type=Path,
-        default=Path("benchmarks/update/bench_results.csv"),
-        help="Where to append per-scenario results as CSV.",
-    )
-
-    args = parser.parse_args()
-
-    register_project_directory(REPO_ROOT)
-
-    initial_paragraphs = load_chunks_from_files(args.initial_files, args.max_initial)
-    update_paragraphs = load_chunks_from_files(args.update_files, args.max_updates)
-    if not update_paragraphs:
-        raise ValueError("No update passages found; please provide --update-files with content.")
-
-    update_chunks = prepare_new_chunks(update_paragraphs)
-    ensure_index_dir(args.index_path)
-
-    scenarios = [
-        ("baseline", False, False, True),
-        ("no_cache_baseline", False, False, False),
-        ("disable_forward_rng", True, False, True),
-        ("disable_forward_and_reverse_rng", True, True, True),
-    ]
-
-    log_path = Path(os.environ.get("LEANN_HNSW_LOG_PATH", DEFAULT_HNSW_LOG))
-    log_path.parent.mkdir(parents=True, exist_ok=True)
-    os.environ["LEANN_HNSW_LOG_PATH"] = str(log_path.resolve())
-    os.environ.setdefault("LEANN_LOG_LEVEL", "INFO")
-
-    results_total: dict[str, list[float]] = {name: [] for name, *_ in scenarios}
-    results_add: dict[str, list[float]] = {name: [] for name, *_ in scenarios}
-    results_zmq: dict[str, list[int]] = {name: [] for name, *_ in scenarios}
-    results_stageA: dict[str, list[float]] = {name: [] for name, *_ in scenarios}
-    results_stageBC: dict[str, list[float]] = {name: [] for name, *_ in scenarios}
-    results_ms_per_passage: dict[str, list[float]] = {name: [] for name, *_ in scenarios}
-
-    # CSV setup
-    import csv
-
-    run_id = time.strftime("%Y%m%d-%H%M%S")
-    csv_fields = [
-        "run_id",
-        "scenario",
-        "cache_enabled",
-        "ef_construction",
-        "max_initial",
-        "max_updates",
-        "total_time_s",
-        "add_only_s",
-        "latency_ms_per_passage",
-        "zmq_nodes",
-        "stageA_time_s",
-        "stageBC_time_s",
-        "model_name",
-        "embedding_mode",
-        "distance_metric",
-    ]
-    # Create CSV with header if missing
-    if args.csv_path:
-        args.csv_path.parent.mkdir(parents=True, exist_ok=True)
-        if not args.csv_path.exists() or args.csv_path.stat().st_size == 0:
-            with args.csv_path.open("w", newline="", encoding="utf-8") as f:
-                writer = csv.DictWriter(f, fieldnames=csv_fields)
-                writer.writeheader()
-
-    for run in range(args.runs):
-        print(f"\n=== Benchmark run {run + 1}/{args.runs} ===")
-        for name, disable_forward, disable_reverse, cache_enabled in scenarios:
-            print(f"\nScenario: {name}")
-            cleanup_index_files(args.index_path)
-            if log_path.exists():
-                try:
-                    log_path.unlink()
-                except OSError:
-                    pass
-            os.environ["LEANN_ZMQ_EMBED_CACHE"] = "1" if cache_enabled else "0"
-            build_initial_index(
-                args.index_path,
-                initial_paragraphs,
-                args.model_name,
-                args.embedding_mode,
-                args.distance_metric,
-                args.ef_construction,
-            )
-
-            prev_size = log_path.stat().st_size if log_path.exists() else 0
-
-            try:
-                total_elapsed, add_elapsed = benchmark_update_with_mode(
-                    args.index_path,
-                    update_chunks,
-                    args.model_name,
-                    args.embedding_mode,
-                    args.distance_metric,
-                    disable_forward,
-                    disable_reverse,
-                    args.server_port,
-                    args.add_timeout,
-                    args.ef_construction,
-                )
-            except TimeoutError as exc:
-                print(f"Scenario {name} timed out: {exc}")
-                continue
-
-            curr_size = log_path.stat().st_size if log_path.exists() else 0
-            if curr_size < prev_size:
-                prev_size = 0
-            zmq_count = 0
-            if log_path.exists():
-                with log_path.open("r", encoding="utf-8") as log_file:
-                    log_file.seek(prev_size)
-                    new_entries = log_file.read()
-                zmq_count = sum(
-                    int(match) for match in re.findall(r"ZMQ received (\d+) node IDs", new_entries)
-                )
-                stageA = sum(
-                    float(x)
-                    for x in re.findall(r"Distance calculation E2E time: ([0-9.]+)s", new_entries)
-                )
-                stageBC = sum(
-                    float(x) for x in re.findall(r"ZMQ E2E time: ([0-9.]+)s", new_entries)
-                )
-            else:
-                stageA = 0.0
-                stageBC = 0.0
-
-            per_chunk = add_elapsed / len(update_chunks)
-            print(
-                f"Total time: {total_elapsed:.3f} s | add-only: {add_elapsed:.3f} s "
-                f"for {len(update_chunks)} passages => {per_chunk * 1e3:.3f} ms/passage"
-            )
-            print(f"ZMQ node fetch total: {zmq_count}")
-            results_total[name].append(total_elapsed)
-            results_add[name].append(add_elapsed)
-            results_zmq[name].append(zmq_count)
-            results_ms_per_passage[name].append(per_chunk * 1e3)
-            results_stageA[name].append(stageA)
-            results_stageBC[name].append(stageBC)
-
-            # Append row to CSV
-            if args.csv_path:
-                row = {
-                    "run_id": run_id,
-                    "scenario": name,
-                    "cache_enabled": 1 if cache_enabled else 0,
-                    "ef_construction": args.ef_construction,
-                    "max_initial": args.max_initial,
-                    "max_updates": args.max_updates,
-                    "total_time_s": round(total_elapsed, 6),
-                    "add_only_s": round(add_elapsed, 6),
-                    "latency_ms_per_passage": round(per_chunk * 1e3, 6),
-                    "zmq_nodes": int(zmq_count),
-                    "stageA_time_s": round(stageA, 6),
-                    "stageBC_time_s": round(stageBC, 6),
-                    "model_name": args.model_name,
-                    "embedding_mode": args.embedding_mode,
-                    "distance_metric": args.distance_metric,
-                }
-                with args.csv_path.open("a", newline="", encoding="utf-8") as f:
-                    writer = csv.DictWriter(f, fieldnames=csv_fields)
-                    writer.writerow(row)
-
-    print("\n=== Summary ===")
-    for name in results_add:
-        add_values = results_add[name]
-        total_values = results_total[name]
-        zmq_values = results_zmq[name]
-        latency_values = results_ms_per_passage[name]
-        if not add_values:
-            print(f"{name}: no successful runs")
-            continue
-        avg_add = sum(add_values) / len(add_values)
-        avg_total = sum(total_values) / len(total_values)
-        avg_zmq = sum(zmq_values) / len(zmq_values) if zmq_values else 0.0
-        avg_latency = sum(latency_values) / len(latency_values) if latency_values else 0.0
-        runs = len(add_values)
-        print(
-            f"{name}: add-only avg {avg_add:.3f} s | total avg {avg_total:.3f} s "
-            f"| ZMQ avg {avg_zmq:.1f} node fetches | latency {avg_latency:.2f} ms/passage over {runs} run(s)"
-        )
-
-    if args.plot_path:
-        try:
-            import matplotlib.pyplot as plt
-
-            labels = [name for name, *_ in scenarios]
-            values = [
-                sum(results_ms_per_passage[name]) / len(results_ms_per_passage[name])
-                if results_ms_per_passage[name]
-                else 0.0
-                for name in labels
-            ]
-
-            def _auto_cap(vals: list[float]) -> float | None:
-                s = sorted(vals, reverse=True)
-                if len(s) < 2:
-                    return None
-                if s[1] > 0 and s[0] >= 2.5 * s[1]:
-                    return s[1] * 1.1
-                return None
-
-            def _fmt_ms(v: float) -> str:
-                return f"{v / 1000:.1f}k" if v >= 1000 else f"{v:.1f}"
-
-            colors = ["#4e79a7", "#f28e2c", "#e15759", "#76b7b2"]
-
-            if args.broken_y:
-                s = sorted(values, reverse=True)
-                second = s[1] if len(s) >= 2 else (s[0] if s else 0.0)
-                lower_cap = args.lower_cap_y if args.lower_cap_y is not None else second * 1.1
-                upper_start = (
-                    args.upper_start_y
-                    if args.upper_start_y is not None
-                    else max(second * 1.2, lower_cap * 1.02)
-                )
-                ymax = max(values) * 1.10 if values else 1.0
-                fig, (ax_top, ax_bottom) = plt.subplots(
-                    2,
-                    1,
-                    sharex=True,
-                    figsize=(7.4, 5.0),
-                    gridspec_kw={"height_ratios": [1, 3], "hspace": 0.05},
-                )
-                x = list(range(len(labels)))
-                ax_bottom.bar(x, values, color=colors[: len(labels)], width=0.8)
-                ax_top.bar(x, values, color=colors[: len(labels)], width=0.8)
-                ax_bottom.set_ylim(0, lower_cap)
-                ax_top.set_ylim(upper_start, ymax)
-                for i, v in enumerate(values):
-                    if v <= lower_cap:
-                        ax_bottom.text(
-                            i,
-                            v + lower_cap * 0.02,
-                            _fmt_ms(v),
-                            ha="center",
-                            va="bottom",
-                            fontsize=9,
-                        )
-                    else:
-                        ax_top.text(i, v, _fmt_ms(v), ha="center", va="bottom", fontsize=9)
-                ax_top.spines["bottom"].set_visible(False)
-                ax_bottom.spines["top"].set_visible(False)
-                ax_top.tick_params(labeltop=False)
-                ax_bottom.xaxis.tick_bottom()
-                d = 0.015
-                kwargs = {"transform": ax_top.transAxes, "color": "k", "clip_on": False}
-                ax_top.plot((-d, +d), (-d, +d), **kwargs)
-                ax_top.plot((1 - d, 1 + d), (-d, +d), **kwargs)
-                kwargs.update({"transform": ax_bottom.transAxes})
-                ax_bottom.plot((-d, +d), (1 - d, 1 + d), **kwargs)
-                ax_bottom.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs)
-                ax_bottom.set_xticks(range(len(labels)))
-                ax_bottom.set_xticklabels(labels)
-                ax = ax_bottom
-            else:
-                cap = args.cap_y or _auto_cap(values)
-                plt.figure(figsize=(7.2, 4.2))
-                ax = plt.gca()
-                if cap is not None:
-                    show_vals = [min(v, cap) for v in values]
-                    bars = []
-                    for i, (v, show) in enumerate(zip(values, show_vals)):
-                        b = ax.bar(i, show, color=colors[i], width=0.8)
-                        bars.append(b[0])
-                        if v > cap:
-                            bars[-1].set_hatch("//")
-                            ax.text(i, cap * 1.02, _fmt_ms(v), ha="center", va="bottom", fontsize=9)
-                        else:
-                            ax.text(
-                                i,
-                                show + max(1.0, 0.01 * (cap or show)),
-                                _fmt_ms(v),
-                                ha="center",
-                                va="bottom",
-                                fontsize=9,
-                            )
-                    ax.set_ylim(0, cap * 1.10)
-                    ax.plot(
-                        [0.02 - 0.02, 0.02 + 0.02],
-                        [0.98 + 0.02, 0.98 - 0.02],
-                        transform=ax.transAxes,
-                        color="k",
-                        lw=1,
-                    )
-                    ax.plot(
-                        [0.98 - 0.02, 0.98 + 0.02],
-                        [0.98 + 0.02, 0.98 - 0.02],
-                        transform=ax.transAxes,
-                        color="k",
-                        lw=1,
-                    )
-                    if any(v > cap for v in values):
-                        ax.legend(
-                            [bars[0]], ["capped"], fontsize=8, frameon=False, loc="upper right"
-                        )
-                    ax.set_xticks(range(len(labels)))
-                    ax.set_xticklabels(labels)
-                else:
-                    ax.bar(labels, values, color=colors[: len(labels)])
-                    for idx, val in enumerate(values):
-                        ax.text(idx, val + 1.0, f"{val:.1f}", ha="center", va="bottom")
-
-            plt.ylabel("Average add latency (ms per passage)")
-            plt.title(f"Initial passages {args.max_initial}, updates {args.max_updates}")
-            plt.tight_layout()
-            plt.savefig(args.plot_path)
-            print(f"Saved latency bar plot to {args.plot_path}")
-            # ZMQ time split (Stage A vs B/C)
-            try:
-                plt.figure(figsize=(6, 4))
-                a_vals = [sum(results_stageA[n]) / max(1, len(results_stageA[n])) for n in labels]
-                bc_vals = [
-                    sum(results_stageBC[n]) / max(1, len(results_stageBC[n])) for n in labels
-                ]
-                ind = range(len(labels))
-                plt.bar(ind, a_vals, color="#4e79a7", label="Stage A distance (s)")
-                plt.bar(
-                    ind, bc_vals, bottom=a_vals, color="#e15759", label="Stage B/C embed-by-id (s)"
-                )
-                plt.xticks(list(ind), labels, rotation=10)
-                plt.ylabel("Server ZMQ time (s)")
-                plt.title(
-                    f"ZMQ time split (initial {args.max_initial}, updates {args.max_updates})"
-                )
-                plt.legend()
-                out2 = args.plot_path.with_name(
-                    args.plot_path.stem + "_zmq_split" + args.plot_path.suffix
-                )
-                plt.tight_layout()
-                plt.savefig(out2)
-                print(f"Saved ZMQ time split plot to {out2}")
-            except Exception as e:
-                print("Failed to plot ZMQ split:", e)
-        except ImportError:
-            print("matplotlib not available; skipping plot generation")
-
-    # leave the last build on disk for inspection
-
-
-if __name__ == "__main__":
-    main()

benchmarks/update/bench_results.csv

@@ -1,5 +0,0 @@
-run_id,scenario,cache_enabled,ef_construction,max_initial,max_updates,total_time_s,add_only_s,latency_ms_per_passage,zmq_nodes,stageA_time_s,stageBC_time_s,model_name,embedding_mode,distance_metric
-20251024-133101,baseline,1,200,300,1,3.391856,1.120359,1120.359421,126,0.507821,0.601608,sentence-transformers/all-MiniLM-L6-v2,sentence-transformers,mips
-20251024-133101,no_cache_baseline,0,200,300,1,34.941514,32.91376,32913.760185,4033,0.506933,32.159928,sentence-transformers/all-MiniLM-L6-v2,sentence-transformers,mips
-20251024-133101,disable_forward_rng,1,200,300,1,2.746756,0.8202,820.200443,66,0.474354,0.338454,sentence-transformers/all-MiniLM-L6-v2,sentence-transformers,mips
-20251024-133101,disable_forward_and_reverse_rng,1,200,300,1,2.396566,0.521478,521.478415,1,0.508973,0.006938,sentence-transformers/all-MiniLM-L6-v2,sentence-transformers,mips

benchmarks/update/bench_update_vs_offline_search.py

@@ -1,704 +0,0 @@
-"""
-Compare two latency models for small incremental updates vs. search:
-
-Scenario A (sequential update then search):
-  - Build initial HNSW (is_recompute=True)
-  - Start embedding server (ZMQ) for recompute
-  - Add N passages one-by-one (each triggers recompute over ZMQ)
-  - Then run a search query on the updated index
-  - Report total time = sum(add_i) + search_time, with breakdowns
-
-Scenario B (offline embeds + concurrent search; no graph updates):
-  - Do NOT insert the N passages into the graph
-  - In parallel: (1) compute embeddings for the N passages; (2) compute query
-    embedding and run a search on the existing index
-  - After both finish, compute similarity between the query embedding and the N
-    new passage embeddings, merge with the index search results by score, and
-    report time = max(embed_time, search_time) (i.e., no blocking on updates)
-
-This script reuses the model/data loading conventions of
-examples/bench_hnsw_rng_recompute.py but focuses on end-to-end latency
-comparison for the two execution strategies above.
-
-Example (from the repository root):
-  uv run -m benchmarks.update.bench_update_vs_offline_search \
-    --index-path .leann/bench/offline_vs_update.leann \
-    --max-initial 300 --num-updates 5 --k 10
-"""
-
-import argparse
-import csv
-import json
-import logging
-import os
-import pickle
-import sys
-import threading
-import time
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Any
-
-import numpy as np
-import psutil  # type: ignore
-from leann.api import LeannBuilder
-
-if os.environ.get("LEANN_FORCE_CPU", "").lower() in ("1", "true", "yes"):
-    os.environ.setdefault("CUDA_VISIBLE_DEVICES", "")
-
-from leann.embedding_compute import compute_embeddings
-from leann.embedding_server_manager import EmbeddingServerManager
-from leann.registry import register_project_directory
-from leann_backend_hnsw import faiss  # type: ignore
-
-logger = logging.getLogger(__name__)
-if not logging.getLogger().handlers:
-    logging.basicConfig(level=logging.INFO)
-
-
-def _find_repo_root() -> Path:
-    """Locate project root by walking up until pyproject.toml is found."""
-    current = Path(__file__).resolve()
-    for parent in current.parents:
-        if (parent / "pyproject.toml").exists():
-            return parent
-    # Fallback: assume repo is two levels up (../..)
-    return current.parents[2]
-
-
-REPO_ROOT = _find_repo_root()
-if str(REPO_ROOT) not in sys.path:
-    sys.path.insert(0, str(REPO_ROOT))
-
-from apps.chunking import create_text_chunks  # noqa: E402
-
-DEFAULT_INITIAL_FILES = [
-    REPO_ROOT / "data" / "2501.14312v1 (1).pdf",
-    REPO_ROOT / "data" / "huawei_pangu.md",
-]
-DEFAULT_UPDATE_FILES = [REPO_ROOT / "data" / "2506.08276v1.pdf"]
-
-
-def load_chunks_from_files(paths: list[Path], limit: int | None = None) -> list[str]:
-    from llama_index.core import SimpleDirectoryReader
-
-    documents = []
-    for path in paths:
-        p = path.expanduser().resolve()
-        if not p.exists():
-            raise FileNotFoundError(f"Input path not found: {p}")
-        if p.is_dir():
-            reader = SimpleDirectoryReader(str(p), recursive=False)
-            documents.extend(reader.load_data(show_progress=True))
-        else:
-            reader = SimpleDirectoryReader(input_files=[str(p)])
-            documents.extend(reader.load_data(show_progress=True))
-
-    if not documents:
-        return []
-
-    chunks = create_text_chunks(
-        documents,
-        chunk_size=512,
-        chunk_overlap=128,
-        use_ast_chunking=False,
-    )
-    cleaned = [c for c in chunks if isinstance(c, str) and c.strip()]
-    if limit is not None:
-        cleaned = cleaned[:limit]
-    return cleaned
-
-
-def ensure_index_dir(index_path: Path) -> None:
-    index_path.parent.mkdir(parents=True, exist_ok=True)
-
-
-def cleanup_index_files(index_path: Path) -> None:
-    parent = index_path.parent
-    if not parent.exists():
-        return
-    stem = index_path.stem
-    for file in parent.glob(f"{stem}*"):
-        if file.is_file():
-            file.unlink()
-
-
-def build_initial_index(
-    index_path: Path,
-    paragraphs: list[str],
-    model_name: str,
-    embedding_mode: str,
-    distance_metric: str,
-    ef_construction: int,
-) -> None:
-    builder = LeannBuilder(
-        backend_name="hnsw",
-        embedding_model=model_name,
-        embedding_mode=embedding_mode,
-        is_compact=False,
-        is_recompute=True,
-        distance_metric=distance_metric,
-        backend_kwargs={
-            "distance_metric": distance_metric,
-            "is_compact": False,
-            "is_recompute": True,
-            "efConstruction": ef_construction,
-        },
-    )
-    for idx, passage in enumerate(paragraphs):
-        builder.add_text(passage, metadata={"id": str(idx)})
-    builder.build_index(str(index_path))
-
-
-def _maybe_norm_cosine(vecs: np.ndarray, metric: str) -> np.ndarray:
-    if metric == "cosine":
-        vecs = np.ascontiguousarray(vecs, dtype=np.float32)
-        norms = np.linalg.norm(vecs, axis=1, keepdims=True)
-        norms[norms == 0] = 1
-        vecs = vecs / norms
-    return vecs
-
-
-def _read_index_for_search(index_path: Path) -> Any:
-    index_file = index_path.parent / f"{index_path.stem}.index"
-    # Force-disable experimental disk cache when loading the index so that
-    # incremental benchmarks don't pick up stale top-degree bitmaps.
-    cfg = faiss.HNSWIndexConfig()
-    cfg.is_recompute = True
-    if hasattr(cfg, "disk_cache_ratio"):
-        cfg.disk_cache_ratio = 0.0
-    if hasattr(cfg, "external_storage_path"):
-        cfg.external_storage_path = None
-    io_flags = getattr(faiss, "IO_FLAG_MMAP", 0)
-    index = faiss.read_index(str(index_file), io_flags, cfg)
-    # ensure recompute mode persists after reload
-    try:
-        index.is_recompute = True
-    except AttributeError:
-        pass
-    try:
-        actual_ntotal = index.hnsw.levels.size()
-    except AttributeError:
-        actual_ntotal = index.ntotal
-    if actual_ntotal != index.ntotal:
-        print(
-            f"[bench_update_vs_offline_search] Correcting ntotal from {index.ntotal} to {actual_ntotal}",
-            flush=True,
-        )
-        index.ntotal = actual_ntotal
-    if getattr(index, "storage", None) is None:
-        if index.metric_type == faiss.METRIC_INNER_PRODUCT:
-            storage_index = faiss.IndexFlatIP(index.d)
-        else:
-            storage_index = faiss.IndexFlatL2(index.d)
-        index.storage = storage_index
-        index.own_fields = True
-    return index
-
-
-def _append_passages_for_updates(
-    meta_path: Path,
-    start_id: int,
-    texts: list[str],
-) -> list[str]:
-    """Append update passages so the embedding server can serve recompute fetches."""
-
-    if not texts:
-        return []
-
-    index_dir = meta_path.parent
-    meta_name = meta_path.name
-    if not meta_name.endswith(".meta.json"):
-        raise ValueError(f"Unexpected meta filename: {meta_path}")
-    index_base = meta_name[: -len(".meta.json")]
-
-    passages_file = index_dir / f"{index_base}.passages.jsonl"
-    offsets_file = index_dir / f"{index_base}.passages.idx"
-
-    if not passages_file.exists() or not offsets_file.exists():
-        raise FileNotFoundError(
-            "Passage store missing; cannot register update passages for recompute mode."
-        )
-
-    with open(offsets_file, "rb") as f:
-        offset_map: dict[str, int] = pickle.load(f)
-
-    assigned_ids: list[str] = []
-    with open(passages_file, "a", encoding="utf-8") as f:
-        for i, text in enumerate(texts):
-            passage_id = str(start_id + i)
-            offset = f.tell()
-            json.dump({"id": passage_id, "text": text, "metadata": {}}, f, ensure_ascii=False)
-            f.write("\n")
-            offset_map[passage_id] = offset
-            assigned_ids.append(passage_id)
-
-    with open(offsets_file, "wb") as f:
-        pickle.dump(offset_map, f)
-
-    try:
-        with open(meta_path, encoding="utf-8") as f:
-            meta = json.load(f)
-    except json.JSONDecodeError:
-        meta = {}
-    meta["total_passages"] = len(offset_map)
-    with open(meta_path, "w", encoding="utf-8") as f:
-        json.dump(meta, f, indent=2)
-
-    return assigned_ids
-
-
-def _search(index: Any, q: np.ndarray, k: int) -> tuple[np.ndarray, np.ndarray]:
-    q = np.ascontiguousarray(q, dtype=np.float32)
-    distances = np.zeros((1, k), dtype=np.float32)
-    indices = np.zeros((1, k), dtype=np.int64)
-    index.search(
-        1,
-        faiss.swig_ptr(q),
-        k,
-        faiss.swig_ptr(distances),
-        faiss.swig_ptr(indices),
-    )
-    return distances[0], indices[0]
-
-
-def _score_for_metric(dist: float, metric: str) -> float:
-    # Convert FAISS distance to a "higher is better" score
-    if metric in ("mips", "cosine"):
-        return float(dist)
-    # l2 distance (smaller better) -> negative distance as score
-    return -float(dist)
-
-
-def _merge_results(
-    index_results: tuple[np.ndarray, np.ndarray],
-    offline_scores: list[tuple[int, float]],
-    k: int,
-    metric: str,
-) -> list[tuple[str, float]]:
-    distances, indices = index_results
-    merged: list[tuple[str, float]] = []
-    for distance, idx in zip(distances.tolist(), indices.tolist()):
-        merged.append((f"idx:{idx}", _score_for_metric(distance, metric)))
-    for j, s in offline_scores:
-        merged.append((f"offline:{j}", s))
-    merged.sort(key=lambda x: x[1], reverse=True)
-    return merged[:k]
-
-
-@dataclass
-class ScenarioResult:
-    name: str
-    update_total_s: float
-    search_s: float
-    overall_s: float
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument(
-        "--index-path",
-        type=Path,
-        default=Path(".leann/bench/offline-vs-update.leann"),
-    )
-    parser.add_argument(
-        "--initial-files",
-        nargs="*",
-        type=Path,
-        default=DEFAULT_INITIAL_FILES,
-    )
-    parser.add_argument(
-        "--update-files",
-        nargs="*",
-        type=Path,
-        default=DEFAULT_UPDATE_FILES,
-    )
-    parser.add_argument("--max-initial", type=int, default=300)
-    parser.add_argument("--num-updates", type=int, default=5)
-    parser.add_argument("--k", type=int, default=10, help="Top-k for search/merge")
-    parser.add_argument(
-        "--query",
-        type=str,
-        default="neural network",
-        help="Query text used for the search benchmark.",
-    )
-    parser.add_argument("--server-port", type=int, default=5557)
-    parser.add_argument("--add-timeout", type=int, default=600)
-    parser.add_argument("--model-name", default="sentence-transformers/all-MiniLM-L6-v2")
-    parser.add_argument("--embedding-mode", default="sentence-transformers")
-    parser.add_argument(
-        "--distance-metric",
-        default="mips",
-        choices=["mips", "l2", "cosine"],
-    )
-    parser.add_argument("--ef-construction", type=int, default=200)
-    parser.add_argument(
-        "--only",
-        choices=["A", "B", "both"],
-        default="both",
-        help="Run only Scenario A, Scenario B, or both",
-    )
-    parser.add_argument(
-        "--csv-path",
-        type=Path,
-        default=Path("benchmarks/update/offline_vs_update.csv"),
-        help="Where to append results (CSV).",
-    )
-
-    args = parser.parse_args()
-
-    register_project_directory(REPO_ROOT)
-
-    # Load data
-    initial_paragraphs = load_chunks_from_files(args.initial_files, args.max_initial)
-    update_paragraphs = load_chunks_from_files(args.update_files, None)
-    if not update_paragraphs:
-        raise ValueError("No update passages loaded from --update-files")
-    update_paragraphs = update_paragraphs[: args.num_updates]
-    if len(update_paragraphs) < args.num_updates:
-        raise ValueError(
-            f"Not enough update passages ({len(update_paragraphs)}) for --num-updates={args.num_updates}"
-        )
-
-    ensure_index_dir(args.index_path)
-    cleanup_index_files(args.index_path)
-
-    # Build initial index
-    build_initial_index(
-        args.index_path,
-        initial_paragraphs,
-        args.model_name,
-        args.embedding_mode,
-        args.distance_metric,
-        args.ef_construction,
-    )
-
-    # Prepare index object and meta
-    meta_path = args.index_path.parent / f"{args.index_path.name}.meta.json"
-    index = _read_index_for_search(args.index_path)
-
-    # CSV setup
-    run_id = time.strftime("%Y%m%d-%H%M%S")
-    if args.csv_path:
-        args.csv_path.parent.mkdir(parents=True, exist_ok=True)
-        csv_fields = [
-            "run_id",
-            "scenario",
-            "max_initial",
-            "num_updates",
-            "k",
-            "total_time_s",
-            "add_total_s",
-            "search_time_s",
-            "emb_time_s",
-            "makespan_s",
-            "model_name",
-            "embedding_mode",
-            "distance_metric",
-        ]
-        if not args.csv_path.exists() or args.csv_path.stat().st_size == 0:
-            with args.csv_path.open("w", newline="", encoding="utf-8") as f:
-                writer = csv.DictWriter(f, fieldnames=csv_fields)
-                writer.writeheader()
-
-    # Debug: list existing HNSW server PIDs before starting
-    try:
-        existing = [
-            p
-            for p in psutil.process_iter(attrs=["pid", "cmdline"])
-            if any(
-                isinstance(arg, str) and "leann_backend_hnsw.hnsw_embedding_server" in arg
-                for arg in (p.info.get("cmdline") or [])
-            )
-        ]
-        if existing:
-            print("[debug] Found existing hnsw_embedding_server processes before run:")
-            for p in existing:
-                print(f"[debug]  PID={p.info['pid']} cmd={' '.join(p.info.get('cmdline') or [])}")
-    except Exception as _e:
-        pass
-
-    add_total = 0.0
-    search_after_add = 0.0
-    total_seq = 0.0
-    port_a = None
-    if args.only in ("A", "both"):
-        # Scenario A: sequential update then search
-        start_id = index.ntotal
-        assigned_ids = _append_passages_for_updates(meta_path, start_id, update_paragraphs)
-        if assigned_ids:
-            logger.debug(
-                "Registered %d update passages starting at id %s",
-                len(assigned_ids),
-                assigned_ids[0],
-            )
-        server_manager = EmbeddingServerManager(
-            backend_module_name="leann_backend_hnsw.hnsw_embedding_server"
-        )
-        ok, port = server_manager.start_server(
-            port=args.server_port,
-            model_name=args.model_name,
-            embedding_mode=args.embedding_mode,
-            passages_file=str(meta_path),
-            distance_metric=args.distance_metric,
-        )
-        if not ok:
-            raise RuntimeError("Failed to start embedding server")
-        try:
-            # Set ZMQ port for recompute mode
-            if hasattr(index.hnsw, "set_zmq_port"):
-                index.hnsw.set_zmq_port(port)
-            elif hasattr(index, "set_zmq_port"):
-                index.set_zmq_port(port)
-
-            # Start A overall timer BEFORE computing update embeddings
-            t0 = time.time()
-
-            # Compute embeddings for updates (counted into A's overall)
-            t_emb0 = time.time()
-            upd_embs = compute_embeddings(
-                update_paragraphs,
-                args.model_name,
-                mode=args.embedding_mode,
-                is_build=False,
-                batch_size=16,
-            )
-            emb_time_updates = time.time() - t_emb0
-            upd_embs = np.asarray(upd_embs, dtype=np.float32)
-            upd_embs = _maybe_norm_cosine(upd_embs, args.distance_metric)
-
-            # Perform sequential adds
-            for i in range(upd_embs.shape[0]):
-                t_add0 = time.time()
-                index.add(1, faiss.swig_ptr(upd_embs[i : i + 1]))
-                add_total += time.time() - t_add0
-            # Don't persist index after adds to avoid contaminating Scenario B
-            # index_file = args.index_path.parent / f"{args.index_path.stem}.index"
-            # faiss.write_index(index, str(index_file))
-
-            # Search after updates
-            q_emb = compute_embeddings(
-                [args.query], args.model_name, mode=args.embedding_mode, is_build=False
-            )
-            q_emb = np.asarray(q_emb, dtype=np.float32)
-            q_emb = _maybe_norm_cosine(q_emb, args.distance_metric)
-
-            # Warm up search with a dummy query first
-            print("[DEBUG] Warming up search...")
-            _ = _search(index, q_emb, 1)
-
-            t_s0 = time.time()
-            D_upd, I_upd = _search(index, q_emb, args.k)
-            search_after_add = time.time() - t_s0
-            total_seq = time.time() - t0
-        finally:
-            server_manager.stop_server()
-        port_a = port
-
-        print("\n=== Scenario A: update->search (sequential) ===")
-        # emb_time_updates is defined only when A runs
-        try:
-            _emb_a = emb_time_updates
-        except NameError:
-            _emb_a = 0.0
-        print(
-            f"Adds: {args.num_updates} passages; embeds={_emb_a:.3f}s; add_total={add_total:.3f}s; "
-            f"search={search_after_add:.3f}s; overall={total_seq:.3f}s"
-        )
-        # CSV row for A
-        if args.csv_path:
-            row_a = {
-                "run_id": run_id,
-                "scenario": "A",
-                "max_initial": args.max_initial,
-                "num_updates": args.num_updates,
-                "k": args.k,
-                "total_time_s": round(total_seq, 6),
-                "add_total_s": round(add_total, 6),
-                "search_time_s": round(search_after_add, 6),
-                "emb_time_s": round(_emb_a, 6),
-                "makespan_s": 0.0,
-                "model_name": args.model_name,
-                "embedding_mode": args.embedding_mode,
-                "distance_metric": args.distance_metric,
-            }
-            with args.csv_path.open("a", newline="", encoding="utf-8") as f:
-                writer = csv.DictWriter(f, fieldnames=csv_fields)
-                writer.writerow(row_a)
-
-        # Verify server cleanup
-        try:
-            # short sleep to allow signal handling to finish
-            time.sleep(0.5)
-            leftovers = [
-                p
-                for p in psutil.process_iter(attrs=["pid", "cmdline"])
-                if any(
-                    isinstance(arg, str) and "leann_backend_hnsw.hnsw_embedding_server" in arg
-                    for arg in (p.info.get("cmdline") or [])
-                )
-            ]
-            if leftovers:
-                print("[warn] hnsw_embedding_server process(es) still alive after A-stop:")
-                for p in leftovers:
-                    print(
-                        f"[warn]  PID={p.info['pid']} cmd={' '.join(p.info.get('cmdline') or [])}"
-                    )
-            else:
-                print("[debug] server cleanup confirmed: no hnsw_embedding_server found")
-        except Exception:
-            pass
-
-    # Scenario B: offline embeds + concurrent search (no graph updates)
-    if args.only in ("B", "both"):
-        # ensure a server is available for recompute search
-        server_manager_b = EmbeddingServerManager(
-            backend_module_name="leann_backend_hnsw.hnsw_embedding_server"
-        )
-        requested_port = args.server_port if port_a is None else port_a
-        ok_b, port_b = server_manager_b.start_server(
-            port=requested_port,
-            model_name=args.model_name,
-            embedding_mode=args.embedding_mode,
-            passages_file=str(meta_path),
-            distance_metric=args.distance_metric,
-        )
-        if not ok_b:
-            raise RuntimeError("Failed to start embedding server for Scenario B")
-
-        # Wait for server to fully initialize
-        print("[DEBUG] Waiting 2s for embedding server to fully initialize...")
-        time.sleep(2)
-
-        try:
-            # Read the index first
-            index_no_update = _read_index_for_search(args.index_path)  # unchanged index
-
-            # Then configure ZMQ port on the correct index object
-            if hasattr(index_no_update.hnsw, "set_zmq_port"):
-                index_no_update.hnsw.set_zmq_port(port_b)
-            elif hasattr(index_no_update, "set_zmq_port"):
-                index_no_update.set_zmq_port(port_b)
-
-            # Warmup the embedding model before benchmarking (do this for both --only B and --only both)
-            # This ensures fair comparison as Scenario A has warmed up the model during update embeddings
-            logger.info("Warming up embedding model for Scenario B...")
-            _ = compute_embeddings(
-                ["warmup text"], args.model_name, mode=args.embedding_mode, is_build=False
-            )
-
-            # Prepare worker A: compute embeddings for the same N passages
-            emb_time = 0.0
-            updates_embs_offline: np.ndarray | None = None
-
-            def _worker_emb():
-                nonlocal emb_time, updates_embs_offline
-                t = time.time()
-                updates_embs_offline = compute_embeddings(
-                    update_paragraphs,
-                    args.model_name,
-                    mode=args.embedding_mode,
-                    is_build=False,
-                    batch_size=16,
-                )
-                emb_time = time.time() - t
-
-            # Pre-compute query embedding and warm up search outside of timed section.
-            q_vec = compute_embeddings(
-                [args.query], args.model_name, mode=args.embedding_mode, is_build=False
-            )
-            q_vec = np.asarray(q_vec, dtype=np.float32)
-            q_vec = _maybe_norm_cosine(q_vec, args.distance_metric)
-            print("[DEBUG B] Warming up search...")
-            _ = _search(index_no_update, q_vec, 1)
-
-            # Worker B: timed search on the warmed index
-            search_time = 0.0
-            offline_elapsed = 0.0
-            index_results: tuple[np.ndarray, np.ndarray] | None = None
-
-            def _worker_search():
-                nonlocal search_time, index_results
-                t = time.time()
-                distances, indices = _search(index_no_update, q_vec, args.k)
-                search_time = time.time() - t
-                index_results = (distances, indices)
-
-            # Run two workers concurrently
-            t0 = time.time()
-            th1 = threading.Thread(target=_worker_emb)
-            th2 = threading.Thread(target=_worker_search)
-            th1.start()
-            th2.start()
-            th1.join()
-            th2.join()
-            offline_elapsed = time.time() - t0
-
-            # For mixing: compute query vs. offline update similarities (pure client-side)
-            offline_scores: list[tuple[int, float]] = []
-            if updates_embs_offline is not None:
-                upd2 = np.asarray(updates_embs_offline, dtype=np.float32)
-                upd2 = _maybe_norm_cosine(upd2, args.distance_metric)
-                # For mips/cosine, score = dot; for l2, score = -||x-y||^2
-                for j in range(upd2.shape[0]):
-                    if args.distance_metric in ("mips", "cosine"):
-                        s = float(np.dot(q_vec[0], upd2[j]))
-                    else:
-                        diff = q_vec[0] - upd2[j]
-                        s = -float(np.dot(diff, diff))
-                    offline_scores.append((j, s))
-
-            merged_topk = (
-                _merge_results(index_results, offline_scores, args.k, args.distance_metric)
-                if index_results
-                else []
-            )
-
-            print("\n=== Scenario B: offline embeds + concurrent search (no add) ===")
-            print(
-                f"embeddings({args.num_updates})={emb_time:.3f}s; search={search_time:.3f}s; makespan≈{offline_elapsed:.3f}s (≈max)"
-            )
-            if merged_topk:
-                preview = ", ".join([f"{lab}:{score:.3f}" for lab, score in merged_topk[:5]])
-                print(f"Merged top-5 preview: {preview}")
-            # CSV row for B
-            if args.csv_path:
-                row_b = {
-                    "run_id": run_id,
-                    "scenario": "B",
-                    "max_initial": args.max_initial,
-                    "num_updates": args.num_updates,
-                    "k": args.k,
-                    "total_time_s": 0.0,
-                    "add_total_s": 0.0,
-                    "search_time_s": round(search_time, 6),
-                    "emb_time_s": round(emb_time, 6),
-                    "makespan_s": round(offline_elapsed, 6),
-                    "model_name": args.model_name,
-                    "embedding_mode": args.embedding_mode,
-                    "distance_metric": args.distance_metric,
-                }
-                with args.csv_path.open("a", newline="", encoding="utf-8") as f:
-                    writer = csv.DictWriter(f, fieldnames=csv_fields)
-                    writer.writerow(row_b)
-
-        finally:
-            server_manager_b.stop_server()
-
-    # Summary
-    print("\n=== Summary ===")
-    msg_a = (
-        f"A: seq-add+search overall={total_seq:.3f}s (adds={add_total:.3f}s, search={search_after_add:.3f}s)"
-        if args.only in ("A", "both")
-        else "A: skipped"
-    )
-    msg_b = (
-        f"B: offline+concurrent overall≈{offline_elapsed:.3f}s (emb={emb_time:.3f}s, search={search_time:.3f}s)"
-        if args.only in ("B", "both")
-        else "B: skipped"
-    )
-    print(msg_a + "\n" + msg_b)
-
-
-if __name__ == "__main__":
-    main()

benchmarks/update/offline_vs_update.csv

@@ -1,5 +0,0 @@
-run_id,scenario,max_initial,num_updates,k,total_time_s,add_total_s,search_time_s,emb_time_s,makespan_s,model_name,embedding_mode,distance_metric
-20251024-141607,A,300,1,10,3.273957,3.050168,0.097825,0.017339,0.0,sentence-transformers/all-MiniLM-L6-v2,sentence-transformers,mips
-20251024-141607,B,300,1,10,0.0,0.0,0.111892,0.007869,0.112635,sentence-transformers/all-MiniLM-L6-v2,sentence-transformers,mips
-20251025-160652,A,300,5,10,5.061945,4.805962,0.123271,0.015008,0.0,sentence-transformers/all-MiniLM-L6-v2,sentence-transformers,mips
-20251025-160652,B,300,5,10,0.0,0.0,0.101809,0.008817,0.102447,sentence-transformers/all-MiniLM-L6-v2,sentence-transformers,mips

benchmarks/update/plot_bench_results.py

@@ -1,645 +0,0 @@
-#!/usr/bin/env python3
-"""
-Plot latency bars from the benchmark CSV produced by
-benchmarks/update/bench_hnsw_rng_recompute.py.
-
-If you also provide an offline_vs_update.csv via --csv-right
-(from benchmarks/update/bench_update_vs_offline_search.py), this script will
-output a side-by-side figure:
-- Left: ms/passage bars (four RNG scenarios).
-- Right: seconds bars (Scenario A seq add+search vs Scenario B offline+search).
-
-Usage:
-  uv run python benchmarks/update/plot_bench_results.py \
-      --csv benchmarks/update/bench_results.csv \
-      --out benchmarks/update/bench_latency_from_csv.png
-
-The script selects the latest run_id in the CSV and plots four bars for
-the default scenarios:
-  - baseline
-  - no_cache_baseline
-  - disable_forward_rng
-  - disable_forward_and_reverse_rng
-
-If multiple rows exist per scenario for that run_id, the script averages
-their latency_ms_per_passage values.
-"""
-
-import argparse
-import csv
-from collections import defaultdict
-from pathlib import Path
-
-DEFAULT_SCENARIOS = [
-    "no_cache_baseline",
-    "baseline",
-    "disable_forward_rng",
-    "disable_forward_and_reverse_rng",
-]
-
-SCENARIO_LABELS = {
-    "baseline": "+ Cache",
-    "no_cache_baseline": "Naive \n Recompute",
-    "disable_forward_rng": "+ w/o \n Fwd RNG",
-    "disable_forward_and_reverse_rng": "+ w/o \n Bwd RNG",
-}
-
-# Paper-style colors and hatches for scenarios
-SCENARIO_STYLES = {
-    "no_cache_baseline": {"edgecolor": "dimgrey", "hatch": "/////"},
-    "baseline": {"edgecolor": "#63B8B6", "hatch": "xxxxx"},
-    "disable_forward_rng": {"edgecolor": "green", "hatch": "....."},
-    "disable_forward_and_reverse_rng": {"edgecolor": "tomato", "hatch": "\\\\\\\\\\"},
-}
-
-
-def load_latest_run(csv_path: Path):
-    rows = []
-    with csv_path.open("r", encoding="utf-8") as f:
-        reader = csv.DictReader(f)
-        for row in reader:
-            rows.append(row)
-    if not rows:
-        raise SystemExit("CSV is empty: no rows to plot")
-    # Choose latest run_id lexicographically (YYYYMMDD-HHMMSS)
-    run_ids = [r.get("run_id", "") for r in rows]
-    latest = max(run_ids)
-    latest_rows = [r for r in rows if r.get("run_id", "") == latest]
-    if not latest_rows:
-        # Fallback: take last 4 rows
-        latest_rows = rows[-4:]
-        latest = latest_rows[-1].get("run_id", "unknown")
-    return latest, latest_rows
-
-
-def aggregate_latency(rows):
-    acc = defaultdict(list)
-    for r in rows:
-        sc = r.get("scenario", "")
-        try:
-            val = float(r.get("latency_ms_per_passage", "nan"))
-        except ValueError:
-            continue
-        acc[sc].append(val)
-    avg = {k: (sum(v) / len(v) if v else 0.0) for k, v in acc.items()}
-    return avg
-
-
-def _auto_cap(values: list[float]) -> float | None:
-    if not values:
-        return None
-    sorted_vals = sorted(values, reverse=True)
-    if len(sorted_vals) < 2:
-        return None
-    max_v, second = sorted_vals[0], sorted_vals[1]
-    if second <= 0:
-        return None
-    # If the tallest bar dwarfs the second by 2.5x+, cap near the second
-    if max_v >= 2.5 * second:
-        return second * 1.1
-    return None
-
-
-def _add_break_marker(ax, y, rel_x0=0.02, rel_x1=0.98, size=0.02):
-    # Draw small diagonal ticks near left/right to signal cap
-    x0, x1 = rel_x0, rel_x1
-    ax.plot([x0 - size, x0 + size], [y + size, y - size], transform=ax.transAxes, color="k", lw=1)
-    ax.plot([x1 - size, x1 + size], [y + size, y - size], transform=ax.transAxes, color="k", lw=1)
-
-
-def _fmt_ms(v: float) -> str:
-    if v >= 1000:
-        return f"{v / 1000:.1f}k"
-    return f"{v:.1f}"
-
-
-def main():
-    # Set LaTeX style for paper figures (matching paper_fig.py)
-    import matplotlib.pyplot as plt
-
-    plt.rcParams["font.family"] = "Helvetica"
-    plt.rcParams["ytick.direction"] = "in"
-    plt.rcParams["hatch.linewidth"] = 1.5
-    plt.rcParams["font.weight"] = "bold"
-    plt.rcParams["axes.labelweight"] = "bold"
-    plt.rcParams["text.usetex"] = True
-
-    ap = argparse.ArgumentParser(description=__doc__)
-    ap.add_argument(
-        "--csv",
-        type=Path,
-        default=Path("benchmarks/update/bench_results.csv"),
-        help="Path to results CSV (defaults to bench_results.csv)",
-    )
-    ap.add_argument(
-        "--out",
-        type=Path,
-        default=Path("add_ablation.pdf"),
-        help="Output image path",
-    )
-    ap.add_argument(
-        "--csv-right",
-        type=Path,
-        default=Path("benchmarks/update/offline_vs_update.csv"),
-        help="Optional: offline_vs_update.csv to render right subplot (A vs B)",
-    )
-    ap.add_argument(
-        "--cap-y",
-        type=float,
-        default=None,
-        help="Cap Y-axis at this ms value; bars above are hatched and annotated.",
-    )
-    ap.add_argument(
-        "--no-auto-cap",
-        action="store_true",
-        help="Disable auto-cap heuristic when --cap-y is not provided.",
-    )
-    ap.add_argument(
-        "--broken-y",
-        action="store_true",
-        default=True,
-        help="Use a broken Y-axis (two stacked axes with a gap). Overrides --cap-y unless both provided.",
-    )
-    ap.add_argument(
-        "--lower-cap-y",
-        type=float,
-        default=None,
-        help="Lower axes upper bound for broken Y (ms). Default = 1.1x second-highest.",
-    )
-    ap.add_argument(
-        "--upper-start-y",
-        type=float,
-        default=None,
-        help="Upper axes lower bound for broken Y (ms). Default = 1.2x second-highest.",
-    )
-    args = ap.parse_args()
-
-    latest_run, latest_rows = load_latest_run(args.csv)
-    avg = aggregate_latency(latest_rows)
-
-    try:
-        import matplotlib.pyplot as plt
-    except Exception as e:
-        raise SystemExit(f"matplotlib not available: {e}")
-
-    scenarios = DEFAULT_SCENARIOS
-    values = [avg.get(name, 0.0) for name in scenarios]
-    labels = [SCENARIO_LABELS.get(name, name) for name in scenarios]
-    colors = ["#4e79a7", "#f28e2c", "#e15759", "#76b7b2"]
-
-    # If right CSV is provided, build side-by-side figure
-    if args.csv_right is not None:
-        try:
-            right_rows_all = []
-            with args.csv_right.open("r", encoding="utf-8") as f:
-                rreader = csv.DictReader(f)
-                right_rows_all = list(rreader)
-            if right_rows_all:
-                r_latest = max(r.get("run_id", "") for r in right_rows_all)
-                right_rows = [r for r in right_rows_all if r.get("run_id", "") == r_latest]
-            else:
-                r_latest = None
-                right_rows = []
-        except Exception:
-            r_latest = None
-            right_rows = []
-
-        a_total = 0.0
-        b_makespan = 0.0
-        for r in right_rows:
-            sc = (r.get("scenario", "") or "").strip().upper()
-            if sc == "A":
-                try:
-                    a_total = float(r.get("total_time_s", 0.0))
-                except Exception:
-                    pass
-            elif sc == "B":
-                try:
-                    b_makespan = float(r.get("makespan_s", 0.0))
-                except Exception:
-                    pass
-
-        import matplotlib.pyplot as plt
-        from matplotlib import gridspec
-
-        # Left subplot (reuse current style, with optional cap)
-        cap = args.cap_y
-        if cap is None and not args.no_auto_cap:
-            cap = _auto_cap(values)
-        x = list(range(len(labels)))
-
-        if args.broken_y:
-            # Use broken axis for left subplot
-            # Auto-adjust width ratios: left has 4 bars, right has 2 bars
-            fig = plt.figure(figsize=(4.8, 1.8))  # Scaled down to 80%
-            gs = gridspec.GridSpec(
-                2, 2, height_ratios=[1, 3], width_ratios=[1.5, 1], hspace=0.08, wspace=0.35
-            )
-            ax_left_top = fig.add_subplot(gs[0, 0])
-            ax_left_bottom = fig.add_subplot(gs[1, 0], sharex=ax_left_top)
-            ax_right = fig.add_subplot(gs[:, 1])
-
-            # Determine break points
-            s = sorted(values, reverse=True)
-            second = s[1] if len(s) >= 2 else (s[0] if s else 0.0)
-            lower_cap = (
-                args.lower_cap_y if args.lower_cap_y is not None else second * 1.4
-            )  # Increased to show more range
-            upper_start = (
-                args.upper_start_y
-                if args.upper_start_y is not None
-                else max(second * 1.5, lower_cap * 1.02)
-            )
-            ymax = (
-                max(values) * 1.90 if values else 1.0
-            )  # Increase headroom to 1.90 for text label and tick range
-
-            # Draw bars on both axes
-            ax_left_bottom.bar(x, values, color=colors[: len(labels)], width=0.8)
-            ax_left_top.bar(x, values, color=colors[: len(labels)], width=0.8)
-
-            # Set limits
-            ax_left_bottom.set_ylim(0, lower_cap)
-            ax_left_top.set_ylim(upper_start, ymax)
-
-            # Annotate values (convert ms to s)
-            values_s = [v / 1000.0 for v in values]
-            lower_cap_s = lower_cap / 1000.0
-            upper_start_s = upper_start / 1000.0
-            ymax_s = ymax / 1000.0
-
-            ax_left_bottom.set_ylim(0, lower_cap_s)
-            ax_left_top.set_ylim(upper_start_s, ymax_s)
-
-            # Redraw bars with s values (paper style: white fill + colored edge + hatch)
-            ax_left_bottom.clear()
-            ax_left_top.clear()
-            bar_width = 0.50  # Reduced for wider spacing between bars
-            for i, (scenario_name, v) in enumerate(zip(scenarios, values_s)):
-                style = SCENARIO_STYLES.get(scenario_name, {"edgecolor": "black", "hatch": ""})
-                # Draw in bottom axis for all bars
-                ax_left_bottom.bar(
-                    i,
-                    v,
-                    width=bar_width,
-                    color="white",
-                    edgecolor=style["edgecolor"],
-                    hatch=style["hatch"],
-                    linewidth=1.2,
-                )
-                # Only draw in top axis if the bar is tall enough to reach the upper range
-                if v > upper_start_s:
-                    ax_left_top.bar(
-                        i,
-                        v,
-                        width=bar_width,
-                        color="white",
-                        edgecolor=style["edgecolor"],
-                        hatch=style["hatch"],
-                        linewidth=1.2,
-                    )
-            ax_left_bottom.set_ylim(0, lower_cap_s)
-            ax_left_top.set_ylim(upper_start_s, ymax_s)
-
-            for i, v in enumerate(values_s):
-                if v <= lower_cap_s:
-                    ax_left_bottom.text(
-                        i,
-                        v + lower_cap_s * 0.02,
-                        f"{v:.2f}",
-                        ha="center",
-                        va="bottom",
-                        fontsize=8,
-                        fontweight="bold",
-                    )
-                else:
-                    ax_left_top.text(
-                        i,
-                        v + (ymax_s - upper_start_s) * 0.02,
-                        f"{v:.2f}",
-                        ha="center",
-                        va="bottom",
-                        fontsize=8,
-                        fontweight="bold",
-                    )
-
-            # Hide spines between axes
-            ax_left_top.spines["bottom"].set_visible(False)
-            ax_left_bottom.spines["top"].set_visible(False)
-            ax_left_top.tick_params(
-                labeltop=False, labelbottom=False, bottom=False
-            )  # Hide tick marks
-            ax_left_bottom.xaxis.tick_bottom()
-            ax_left_bottom.tick_params(top=False)  # Hide top tick marks
-
-            # Draw break marks (matching paper_fig.py style)
-            d = 0.015
-            kwargs = {
-                "transform": ax_left_top.transAxes,
-                "color": "k",
-                "clip_on": False,
-                "linewidth": 0.8,
-                "zorder": 10,
-            }
-            ax_left_top.plot((-d, +d), (-d, +d), **kwargs)
-            ax_left_top.plot((1 - d, 1 + d), (-d, +d), **kwargs)
-            kwargs.update({"transform": ax_left_bottom.transAxes})
-            ax_left_bottom.plot((-d, +d), (1 - d, 1 + d), **kwargs)
-            ax_left_bottom.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs)
-
-            ax_left_bottom.set_xticks(x)
-            ax_left_bottom.set_xticklabels(labels, rotation=0, fontsize=7)
-            # Don't set ylabel here - will use fig.text for alignment
-            ax_left_bottom.tick_params(axis="y", labelsize=10)
-            ax_left_top.tick_params(axis="y", labelsize=10)
-            # Add subtle grid for better readability
-            ax_left_bottom.grid(axis="y", alpha=0.3, linestyle="--", linewidth=0.5)
-            ax_left_top.grid(axis="y", alpha=0.3, linestyle="--", linewidth=0.5)
-            ax_left_top.set_title("Single Add Operation", fontsize=11, pad=10, fontweight="bold")
-
-            # Set x-axis limits to match bar width with right subplot
-            ax_left_bottom.set_xlim(-0.6, 3.6)
-            ax_left_top.set_xlim(-0.6, 3.6)
-
-            ax_left = ax_left_bottom  # for compatibility
-        else:
-            # Regular side-by-side layout
-            fig, (ax_left, ax_right) = plt.subplots(1, 2, figsize=(8.4, 3.15))
-
-            if cap is not None:
-                show_vals = [min(v, cap) for v in values]
-                bars = ax_left.bar(x, show_vals, color=colors[: len(labels)], width=0.8)
-                for i, (val, show) in enumerate(zip(values, show_vals)):
-                    if val > cap:
-                        bars[i].set_hatch("//")
-                        ax_left.text(
-                            i, cap * 1.02, _fmt_ms(val), ha="center", va="bottom", fontsize=9
-                        )
-                    else:
-                        ax_left.text(
-                            i,
-                            show + max(1.0, 0.01 * (cap or show)),
-                            _fmt_ms(val),
-                            ha="center",
-                            va="bottom",
-                            fontsize=9,
-                        )
-                ax_left.set_ylim(0, cap * 1.10)
-                _add_break_marker(ax_left, y=0.98)
-                ax_left.set_xticks(x)
-                ax_left.set_xticklabels(labels, rotation=0, fontsize=10)
-            else:
-                ax_left.bar(x, values, color=colors[: len(labels)], width=0.8)
-                for i, v in enumerate(values):
-                    ax_left.text(i, v + 1.0, _fmt_ms(v), ha="center", va="bottom", fontsize=9)
-                ax_left.set_xticks(x)
-                ax_left.set_xticklabels(labels, rotation=0, fontsize=10)
-            ax_left.set_ylabel("Latency (ms per passage)")
-            max_initial = latest_rows[0].get("max_initial", "?")
-            max_updates = latest_rows[0].get("max_updates", "?")
-            ax_left.set_title(
-                f"HNSW RNG (run {latest_run}) | init={max_initial}, upd={max_updates}"
-            )
-
-        # Right subplot (A vs B, seconds) - paper style
-        r_labels = ["Sequential", "Delayed \n Add+Search"]
-        r_values = [a_total or 0.0, b_makespan or 0.0]
-        r_styles = [
-            {"edgecolor": "#59a14f", "hatch": "xxxxx"},
-            {"edgecolor": "#edc948", "hatch": "/////"},
-        ]
-        # 2 bars, centered with proper spacing
-        xr = [0, 1]
-        bar_width = 0.50  # Reduced for wider spacing between bars
-        for i, (v, style) in enumerate(zip(r_values, r_styles)):
-            ax_right.bar(
-                xr[i],
-                v,
-                width=bar_width,
-                color="white",
-                edgecolor=style["edgecolor"],
-                hatch=style["hatch"],
-                linewidth=1.2,
-            )
-        for i, v in enumerate(r_values):
-            max_v = max(r_values) if r_values else 1.0
-            offset = max(0.0002, 0.02 * max_v)
-            ax_right.text(
-                xr[i],
-                v + offset,
-                f"{v:.2f}",
-                ha="center",
-                va="bottom",
-                fontsize=8,
-                fontweight="bold",
-            )
-        ax_right.set_xticks(xr)
-        ax_right.set_xticklabels(r_labels, rotation=0, fontsize=7)
-        # Don't set ylabel here - will use fig.text for alignment
-        ax_right.tick_params(axis="y", labelsize=10)
-        # Add subtle grid for better readability
-        ax_right.grid(axis="y", alpha=0.3, linestyle="--", linewidth=0.5)
-        ax_right.set_title("Batched Add Operation", fontsize=11, pad=10, fontweight="bold")
-
-        # Set x-axis limits to match left subplot's bar width visually
-        # Accounting for width_ratios=[1.5, 1]:
-        # Left: 4 bars, xlim(-0.6, 3.6), range=4.2, physical_width=1.5*unit
-        # bar_width_visual = 0.72 * (1.5*unit / 4.2)
-        # Right: 2 bars, need same visual width
-        # 0.72 * (1.0*unit / range_right) = 0.72 * (1.5*unit / 4.2)
-        # range_right = 4.2 / 1.5 = 2.8
-        # For bars at 0, 1: padding = (2.8 - 1) / 2 = 0.9
-        ax_right.set_xlim(-0.9, 1.9)
-
-        # Set y-axis limit with headroom for text labels
-        if r_values:
-            max_v = max(r_values)
-            ax_right.set_ylim(0, max_v * 1.15)
-
-        # Format y-axis to avoid scientific notation
-        ax_right.ticklabel_format(style="plain", axis="y")
-
-        plt.tight_layout()
-
-        # Add aligned ylabels using fig.text (after tight_layout)
-        # Get the vertical center of the entire figure
-        fig_center_y = 0.5
-        # Left ylabel - closer to left plot
-        left_x = 0.05
-        fig.text(
-            left_x,
-            fig_center_y,
-            "Latency (s)",
-            va="center",
-            rotation="vertical",
-            fontsize=11,
-            fontweight="bold",
-        )
-        # Right ylabel - closer to right plot
-        right_bbox = ax_right.get_position()
-        right_x = right_bbox.x0 - 0.07
-        fig.text(
-            right_x,
-            fig_center_y,
-            "Latency (s)",
-            va="center",
-            rotation="vertical",
-            fontsize=11,
-            fontweight="bold",
-        )
-
-        plt.savefig(args.out, bbox_inches="tight", pad_inches=0.05)
-        # Also save PDF for paper
-        pdf_out = args.out.with_suffix(".pdf")
-        plt.savefig(pdf_out, bbox_inches="tight", pad_inches=0.05)
-        print(f"Saved: {args.out}")
-        print(f"Saved: {pdf_out}")
-        return
-
-    # Broken-Y mode
-    if args.broken_y:
-        import matplotlib.pyplot as plt
-
-        fig, (ax_top, ax_bottom) = plt.subplots(
-            2,
-            1,
-            sharex=True,
-            figsize=(7.5, 6.75),
-            gridspec_kw={"height_ratios": [1, 3], "hspace": 0.08},
-        )
-
-        # Determine default breaks from second-highest
-        s = sorted(values, reverse=True)
-        second = s[1] if len(s) >= 2 else (s[0] if s else 0.0)
-        lower_cap = args.lower_cap_y if args.lower_cap_y is not None else second * 1.1
-        upper_start = (
-            args.upper_start_y
-            if args.upper_start_y is not None
-            else max(second * 1.2, lower_cap * 1.02)
-        )
-        ymax = max(values) * 1.10 if values else 1.0
-
-        x = list(range(len(labels)))
-        ax_bottom.bar(x, values, color=colors[: len(labels)], width=0.8)
-        ax_top.bar(x, values, color=colors[: len(labels)], width=0.8)
-
-        # Limits
-        ax_bottom.set_ylim(0, lower_cap)
-        ax_top.set_ylim(upper_start, ymax)
-
-        # Annotate values
-        for i, v in enumerate(values):
-            if v <= lower_cap:
-                ax_bottom.text(
-                    i, v + lower_cap * 0.02, _fmt_ms(v), ha="center", va="bottom", fontsize=9
-                )
-            else:
-                ax_top.text(i, v, _fmt_ms(v), ha="center", va="bottom", fontsize=9)
-
-        # Hide spines between axes and draw diagonal break marks
-        ax_top.spines["bottom"].set_visible(False)
-        ax_bottom.spines["top"].set_visible(False)
-        ax_top.tick_params(labeltop=False)  # don't put tick labels at the top
-        ax_bottom.xaxis.tick_bottom()
-
-        # Diagonal lines at the break (matching paper_fig.py style)
-        d = 0.015
-        kwargs = {
-            "transform": ax_top.transAxes,
-            "color": "k",
-            "clip_on": False,
-            "linewidth": 0.8,
-            "zorder": 10,
-        }
-        ax_top.plot((-d, +d), (-d, +d), **kwargs)  # top-left diagonal
-        ax_top.plot((1 - d, 1 + d), (-d, +d), **kwargs)  # top-right diagonal
-        kwargs.update({"transform": ax_bottom.transAxes})
-        ax_bottom.plot((-d, +d), (1 - d, 1 + d), **kwargs)  # bottom-left diagonal
-        ax_bottom.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs)  # bottom-right diagonal
-
-        ax_bottom.set_xticks(x)
-        ax_bottom.set_xticklabels(labels, rotation=0, fontsize=10)
-        ax = ax_bottom  # for labeling below
-    else:
-        cap = args.cap_y
-        if cap is None and not args.no_auto_cap:
-            cap = _auto_cap(values)
-
-        plt.figure(figsize=(5.4, 3.15))
-        ax = plt.gca()
-
-        if cap is not None:
-            show_vals = [min(v, cap) for v in values]
-            bars = []
-            for i, (_label, val, show) in enumerate(zip(labels, values, show_vals)):
-                bar = ax.bar(i, show, color=colors[i], width=0.8)
-                bars.append(bar[0])
-                # Hatch and annotate when capped
-                if val > cap:
-                    bars[-1].set_hatch("//")
-                    ax.text(i, cap * 1.02, f"{_fmt_ms(val)}", ha="center", va="bottom", fontsize=9)
-                else:
-                    ax.text(
-                        i,
-                        show + max(1.0, 0.01 * (cap or show)),
-                        f"{_fmt_ms(val)}",
-                        ha="center",
-                        va="bottom",
-                        fontsize=9,
-                    )
-            ax.set_ylim(0, cap * 1.10)
-            _add_break_marker(ax, y=0.98)
-            ax.legend([bars[1]], ["capped"], fontsize=8, frameon=False, loc="upper right") if any(
-                v > cap for v in values
-            ) else None
-            ax.set_xticks(range(len(labels)))
-            ax.set_xticklabels(labels, fontsize=11, fontweight="bold")
-        else:
-            ax.bar(labels, values, color=colors[: len(labels)])
-            for idx, val in enumerate(values):
-                ax.text(
-                    idx,
-                    val + 1.0,
-                    f"{_fmt_ms(val)}",
-                    ha="center",
-                    va="bottom",
-                    fontsize=10,
-                    fontweight="bold",
-                )
-            ax.set_xticklabels(labels, fontsize=11, fontweight="bold")
-    # Try to extract some context for title
-    max_initial = latest_rows[0].get("max_initial", "?")
-    max_updates = latest_rows[0].get("max_updates", "?")
-
-    if args.broken_y:
-        fig.text(
-            0.02,
-            0.5,
-            "Latency (s)",
-            va="center",
-            rotation="vertical",
-            fontsize=11,
-            fontweight="bold",
-        )
-        fig.suptitle(
-            "Add Operation Latency",
-            fontsize=11,
-            y=0.98,
-            fontweight="bold",
-        )
-        plt.tight_layout(rect=(0.03, 0.04, 1, 0.96))
-    else:
-        plt.ylabel("Latency (s)", fontsize=11, fontweight="bold")
-        plt.title("Add Operation Latency", fontsize=11, fontweight="bold")
-        plt.tight_layout()
-
-    plt.savefig(args.out, bbox_inches="tight", pad_inches=0.05)
-    # Also save PDF for paper
-    pdf_out = args.out.with_suffix(".pdf")
-    plt.savefig(pdf_out, bbox_inches="tight", pad_inches=0.05)
-    print(f"Saved: {args.out}")
-    print(f"Saved: {pdf_out}")
-
-
-if __name__ == "__main__":
-    main()

docs/COLQWEN_GUIDE.md

@@ -1,200 +0,0 @@
-# ColQwen Integration Guide
-
-Easy-to-use multimodal PDF retrieval with ColQwen2/ColPali models.
-
-## Quick Start
-
-> **🍎 Mac Users**: ColQwen is optimized for Apple Silicon with MPS acceleration for faster inference!
-
-### 1. Install Dependencies
-```bash
-uv pip install colpali_engine pdf2image pillow matplotlib qwen_vl_utils einops seaborn
-brew install poppler  # macOS only, for PDF processing
-```
-
-### 2. Basic Usage
-```bash
-# Build index from PDFs
-python -m apps.colqwen_rag build --pdfs ./my_papers/ --index research_papers
-
-# Search with text queries
-python -m apps.colqwen_rag search research_papers "How does attention mechanism work?"
-
-# Interactive Q&A
-python -m apps.colqwen_rag ask research_papers --interactive
-```
-
-## Commands
-
-### Build Index
-```bash
-python -m apps.colqwen_rag build \
-  --pdfs ./pdf_directory/ \
-  --index my_index \
-  --model colqwen2 \
-  --pages-dir ./page_images/  # Optional: save page images
-```
-
-**Options:**
-- `--pdfs`: Directory containing PDF files (or single PDF path)
-- `--index`: Name for the index (required)
-- `--model`: `colqwen2` (default) or `colpali`
-- `--pages-dir`: Directory to save page images (optional)
-
-### Search Index
-```bash
-python -m apps.colqwen_rag search my_index "your question here" --top-k 5
-```
-
-**Options:**
-- `--top-k`: Number of results to return (default: 5)
-- `--model`: Model used for search (should match build model)
-
-### Interactive Q&A
-```bash
-python -m apps.colqwen_rag ask my_index --interactive
-```
-
-**Commands in interactive mode:**
-- Type your questions naturally
-- `help`: Show available commands
-- `quit`/`exit`/`q`: Exit interactive mode
-
-## 🧪 Test & Reproduce Results
-
-Run the reproduction test for issue #119:
-```bash
-python test_colqwen_reproduction.py
-```
-
-This will:
-1. ✅ Check dependencies
-2. 📥 Download sample PDF (Attention Is All You Need paper)
-3. 🏗️ Build test index
-4. 🔍 Run sample queries
-5. 📊 Show how to generate similarity maps
-
-## 🎨 Advanced: Similarity Maps
-
-For visual similarity analysis, use the existing advanced script:
-```bash
-cd apps/multimodal/vision-based-pdf-multi-vector/
-python multi-vector-leann-similarity-map.py
-```
-
-Edit the script to customize:
-- `QUERY`: Your question
-- `MODEL`: "colqwen2" or "colpali"
-- `USE_HF_DATASET`: Use HuggingFace dataset or local PDFs
-- `SIMILARITY_MAP`: Generate heatmaps
-- `ANSWER`: Enable Qwen-VL answer generation
-
-## 🔧 How It Works
-
-### ColQwen2 vs ColPali
-- **ColQwen2** (`vidore/colqwen2-v1.0`): Latest vision-language model
-- **ColPali** (`vidore/colpali-v1.2`): Proven multimodal retriever
-
-### Architecture
-1. **PDF → Images**: Convert PDF pages to images (150 DPI)
-2. **Vision Encoding**: Process images with ColQwen2/ColPali
-3. **Multi-Vector Index**: Build LEANN HNSW index with multiple embeddings per page
-4. **Query Processing**: Encode text queries with same model
-5. **Similarity Search**: Find most relevant pages/regions
-6. **Visual Maps**: Generate attention heatmaps (optional)
-
-### Device Support
-- **CUDA**: Best performance with GPU acceleration
-- **MPS**: Apple Silicon Mac support
-- **CPU**: Fallback for any system (slower)
-
-Auto-detection: CUDA > MPS > CPU
-
-## 📊 Performance Tips
-
-### For Best Performance:
-```bash
-# Use ColQwen2 for latest features
---model colqwen2
-
-# Save page images for reuse
---pages-dir ./cached_pages/
-
-# Adjust batch size based on GPU memory
-# (automatically handled)
-```
-
-### For Large Document Sets:
-- Process PDFs in batches
-- Use SSD storage for index files
-- Consider using CUDA if available
-
-## 🔗 Related Resources
-
-- **Fast-PLAID**: https://github.com/lightonai/fast-plaid
-- **Pylate**: https://github.com/lightonai/pylate
-- **ColBERT**: https://github.com/stanford-futuredata/ColBERT
-- **ColPali Paper**: Vision-Language Models for Document Retrieval
-- **Issue #119**: https://github.com/yichuan-w/LEANN/issues/119
-
-## 🐛 Troubleshooting
-
-### PDF Conversion Issues (macOS)
-```bash
-# Install poppler
-brew install poppler
-which pdfinfo && pdfinfo -v
-```
-
-### Memory Issues
-- Reduce batch size (automatically handled)
-- Use CPU instead of GPU: `export CUDA_VISIBLE_DEVICES=""`
-- Process fewer PDFs at once
-
-### Model Download Issues
-- Ensure internet connection for first run
-- Models are cached after first download
-- Use HuggingFace mirrors if needed
-
-### Import Errors
-```bash
-# Ensure all dependencies installed
-uv pip install colpali_engine pdf2image pillow matplotlib qwen_vl_utils einops seaborn
-
-# Check PyTorch installation
-python -c "import torch; print(torch.__version__)"
-```
-
-## 💡 Examples
-
-### Research Paper Analysis
-```bash
-# Index your research papers
-python -m apps.colqwen_rag build --pdfs ~/Papers/AI/ --index ai_papers
-
-# Ask research questions
-python -m apps.colqwen_rag search ai_papers "What are the limitations of transformer models?"
-python -m apps.colqwen_rag search ai_papers "How does BERT compare to GPT?"
-```
-
-### Document Q&A
-```bash
-# Index business documents
-python -m apps.colqwen_rag build --pdfs ~/Documents/Reports/ --index reports
-
-# Interactive analysis
-python -m apps.colqwen_rag ask reports --interactive
-```
-
-### Visual Analysis
-```bash
-# Generate similarity maps for specific queries
-cd apps/multimodal/vision-based-pdf-multi-vector/
-# Edit multi-vector-leann-similarity-map.py with your query
-python multi-vector-leann-similarity-map.py
-# Check ./figures/ for generated heatmaps
-```
-
----
-
-**🎯 This integration makes ColQwen as easy to use as other LEANN features while maintaining the full power of multimodal document understanding!**

docs/configuration-guide.md

@@ -158,95 +158,6 @@ builder.build_index("./indexes/my-notes", chunks)
 
 `embedding_options` is persisted to the index `meta.json`, so subsequent `LeannSearcher` or `LeannChat` sessions automatically reuse the same provider settings (the embedding server manager forwards them to the provider for you).
 
-## Optional Embedding Features
-
-### Task-Specific Prompt Templates
-
-Some embedding models are trained with task-specific prompts to differentiate between documents and queries. The most notable example is **Google's EmbeddingGemma**, which requires different prompts depending on the use case:
-
-- **Indexing documents**: `"title: none | text: "`
-- **Search queries**: `"task: search result | query: "`
-
-LEANN supports automatic prompt prepending via the `--embedding-prompt-template` flag:
-
-```bash
-# Build index with EmbeddingGemma (via LM Studio or Ollama)
-leann build my-docs \
-  --docs ./documents \
-  --embedding-mode openai \
-  --embedding-model text-embedding-embeddinggemma-300m-qat \
-  --embedding-api-base http://localhost:1234/v1 \
-  --embedding-prompt-template "title: none | text: " \
-  --force
-
-# Search with query-specific prompt
-leann search my-docs \
-  --query "What is quantum computing?" \
-  --embedding-prompt-template "task: search result | query: "
-```
-
-**Important Notes:**
-- **Only use with compatible models**: EmbeddingGemma and similar task-specific models
-- **NOT for regular models**: Adding prompts to models like `nomic-embed-text`, `text-embedding-3-small`, or `bge-base-en-v1.5` will corrupt embeddings
-- **Template is saved**: Build-time templates are saved to `.meta.json` for reference
-- **Flexible prompts**: You can use any prompt string, or leave it empty (`""`)
-
-**Python API:**
-```python
-from leann.api import LeannBuilder
-
-builder = LeannBuilder(
-    embedding_mode="openai",
-    embedding_model="text-embedding-embeddinggemma-300m-qat",
-    embedding_options={
-        "base_url": "http://localhost:1234/v1",
-        "api_key": "lm-studio",
-        "prompt_template": "title: none | text: ",
-    },
-)
-builder.build_index("./indexes/my-docs", chunks)
-```
-
-**References:**
-- [HuggingFace Blog: EmbeddingGemma](https://huggingface.co/blog/embeddinggemma) - Technical details
-
-### LM Studio Auto-Detection (Optional)
-
-When using LM Studio with the OpenAI-compatible API, LEANN can optionally auto-detect model context lengths via the LM Studio SDK. This eliminates manual configuration for token limits.
-
-**Prerequisites:**
-```bash
-# Install Node.js (if not already installed)
-# Then install the LM Studio SDK globally
-npm install -g @lmstudio/sdk
-```
-
-**How it works:**
-1. LEANN detects LM Studio URLs (`:1234`, `lmstudio` in URL)
-2. Queries model metadata via Node.js subprocess
-3. Automatically unloads model after query (respects your JIT auto-evict settings)
-4. Falls back to static registry if SDK unavailable
-
-**No configuration needed** - it works automatically when SDK is installed:
-
-```bash
-leann build my-docs \
-  --docs ./documents \
-  --embedding-mode openai \
-  --embedding-model text-embedding-nomic-embed-text-v1.5 \
-  --embedding-api-base http://localhost:1234/v1
-  # Context length auto-detected if SDK available
-  # Falls back to registry (2048) if not
-```
-
-**Benefits:**
-- ✅ Automatic token limit detection
-- ✅ Respects LM Studio JIT auto-evict settings
-- ✅ No manual registry maintenance
-- ✅ Graceful fallback if SDK unavailable
-
-**Note:** This is completely optional. LEANN works perfectly fine without the SDK using the built-in token limit registry.
-
 ## Index Selection: Matching Your Scale
 
 ### HNSW (Hierarchical Navigable Small World)
@@ -454,7 +365,7 @@ leann search my-index "your query" \
 
 ### 2) Run remote builds with SkyPilot (cloud GPU)
 
-Offload embedding generation and index building to a GPU VM using [SkyPilot](https://docs.skypilot.co/en/latest/docs/index.html). A template is provided at `sky/leann-build.yaml`.
+Offload embedding generation and index building to a GPU VM using [SkyPilot](https://skypilot.readthedocs.io/en/latest/). A template is provided at `sky/leann-build.yaml`.
 
 ```bash
 # One-time: install and configure SkyPilot
@@ -544,5 +455,5 @@ Conclusion:
 
 - [Lessons Learned Developing LEANN](https://yichuan-w.github.io/blog/lessons_learned_in_dev_leann/)
 - [LEANN Technical Paper](https://arxiv.org/abs/2506.08276)
-- [DiskANN Original Paper](https://suhasjs.github.io/files/diskann_neurips19.pdf)
+- [DiskANN Original Paper](https://papers.nips.cc/paper/2019/file/09853c7fb1d3f8ee67a61b6bf4a7f8e6-Paper.pdf)
 - [SSD-based Graph Partitioning](https://github.com/SonglinLife/SSD_BASED_PLAN)

docs/faq.md

@@ -8,51 +8,3 @@ You can speed up the process by using a lightweight embedding model. Add this to
 --embedding-model sentence-transformers/all-MiniLM-L6-v2
 ```
 **Model sizes:** `all-MiniLM-L6-v2` (30M parameters), `facebook/contriever` (~100M parameters), `Qwen3-0.6B` (600M parameters)
-
-## 2. When should I use prompt templates?
-
-**Use prompt templates ONLY with task-specific embedding models** like Google's EmbeddingGemma. These models are specially trained to use different prompts for documents vs queries.
-
-**DO NOT use with regular models** like `nomic-embed-text`, `text-embedding-3-small`, or `bge-base-en-v1.5` - adding prompts to these models will corrupt the embeddings.
-
-**Example usage with EmbeddingGemma:**
-```bash
-# Build with document prompt
-leann build my-docs --embedding-prompt-template "title: none | text: "
-
-# Search with query prompt
-leann search my-docs --query "your question" --embedding-prompt-template "task: search result | query: "
-```
-
-See the [Configuration Guide: Task-Specific Prompt Templates](configuration-guide.md#task-specific-prompt-templates) for detailed usage.
-
-## 3. Why is LM Studio loading multiple copies of my model?
-
-This was fixed in recent versions. LEANN now properly unloads models after querying metadata, respecting your LM Studio JIT auto-evict settings.
-
-**If you still see duplicates:**
-- Update to the latest LEANN version
-- Restart LM Studio to clear loaded models
-- Check that you have JIT auto-evict enabled in LM Studio settings
-
-**How it works now:**
-1. LEANN loads model temporarily to get context length
-2. Immediately unloads after query
-3. LM Studio JIT loads model on-demand for actual embeddings
-4. Auto-evicts per your settings
-
-## 4. Do I need Node.js and @lmstudio/sdk?
-
-**No, it's completely optional.** LEANN works perfectly fine without them using a built-in token limit registry.
-
-**Benefits if you install it:**
-- Automatic context length detection for LM Studio models
-- No manual registry maintenance
-- Always gets accurate token limits from the model itself
-
-**To install (optional):**
-```bash
-npm install -g @lmstudio/sdk
-```
-
-See [Configuration Guide: LM Studio Auto-Detection](configuration-guide.md#lm-studio-auto-detection-optional) for details.

docs/slack-setup-guide.md

@@ -1,395 +0,0 @@
-# Slack Integration Setup Guide
-
-This guide provides step-by-step instructions for setting up Slack integration with LEANN.
-
-## Overview
-
-LEANN's Slack integration uses MCP (Model Context Protocol) servers to fetch and index your Slack messages for RAG (Retrieval-Augmented Generation). This allows you to search through your Slack conversations using natural language queries.
-
-## Prerequisites
-
-1. **Slack Workspace Access**: You need admin or owner permissions in your Slack workspace to create apps and configure OAuth tokens.
-
-2. **Slack MCP Server**: Install a Slack MCP server (e.g., `slack-mcp-server` via npm)
-
-3. **LEANN**: Ensure you have LEANN installed and working
-
-## Step 1: Create a Slack App
-
-### 1.1 Go to Slack API Dashboard
-
-1. Visit [https://api.slack.com/apps](https://api.slack.com/apps)
-2. Click **"Create New App"**
-3. Choose **"From scratch"**
-4. Enter your app name (e.g., "LEANN Slack Integration")
-5. Select your workspace
-6. Click **"Create App"**
-
-### 1.2 Configure App Permissions
-
-#### Token Scopes
-
-1. In your app dashboard, go to **"OAuth & Permissions"** in the left sidebar
-2. Scroll down to **"Scopes"** section
-3. Under **"Bot Token Scopes & OAuth Scope"**, click **"Add an OAuth Scope"**
-4. Add the following scopes:
-   - `channels:read` - Read public channel information
-   - `channels:history` - Read messages in public channels
-   - `groups:read` - Read private channel information
-   - `groups:history` - Read messages in private channels
-   - `im:read` - Read direct message information
-   - `im:history` - Read direct messages
-   - `mpim:read` - Read group direct message information
-   - `mpim:history` - Read group direct messages
-   - `users:read` - Read user information
-   - `team:read` - Read workspace information
-
-#### App-Level Tokens (Optional)
-
-Some MCP servers may require app-level tokens:
-
-1. Go to **"Basic Information"** in the left sidebar
-2. Scroll down to **"App-Level Tokens"**
-3. Click **"Generate Token and Scopes"**
-4. Enter a name (e.g., "LEANN Integration")
-5. Add the `connections:write` scope
-6. Click **"Generate"**
-7. Copy the token (starts with `xapp-`)
-
-### 1.3 Install App to Workspace
-
-1. Go to **"OAuth & Permissions"** in the left sidebar
-2. Click **"Install to Workspace"**
-3. Review the permissions and click **"Allow"**
-4. Copy the **"Bot User OAuth Token"** (starts with `xoxb-`)
-5. Copy the **"User OAuth Token"** (starts with `xoxp-`)
-
-## Step 2: Install Slack MCP Server
-
-### Option A: Using npm (Recommended)
-
-```bash
-# Install globally
-npm install -g slack-mcp-server
-
-# Or install locally
-npm install slack-mcp-server
-```
-
-### Option B: Using npx (No installation required)
-
-```bash
-# Use directly without installation
-npx slack-mcp-server
-```
-
-## Step 3: Install and Configure Ollama (for Real LLM Responses)
-
-### 3.1 Install Ollama
-
-```bash
-# Install Ollama using Homebrew (macOS)
-brew install ollama
-
-# Or download from https://ollama.ai/
-```
-
-### 3.2 Start Ollama Service
-
-```bash
-# Start Ollama as a service
-brew services start ollama
-
-# Or start manually
-ollama serve
-```
-
-### 3.3 Pull a Model
-
-```bash
-# Pull a lightweight model for testing
-ollama pull llama3.2:1b
-
-# Verify the model is available
-ollama list
-```
-
-## Step 4: Configure Environment Variables
-
-Create a `.env` file or set environment variables:
-
-```bash
-# Required: User OAuth Token
-SLACK_OAUTH_TOKEN=xoxp-your-user-oauth-token-here
-
-# Optional: App-Level Token (if your MCP server requires it)
-SLACK_APP_TOKEN=xapp-your-app-token-here
-
-# Optional: Workspace-specific settings
-SLACK_WORKSPACE_ID=T1234567890  # Your workspace ID (optional)
-```
-
-## Step 5: Test the Setup
-
-### 5.1 Test MCP Server Connection
-
-```bash
-python -m apps.slack_rag \
-  --mcp-server "slack-mcp-server" \
-  --test-connection \
-  --workspace-name "Your Workspace Name"
-```
-
-This will test the connection and list available tools without indexing any data.
-
-### 5.2 Index a Specific Channel
-
-```bash
-python -m apps.slack_rag \
-  --mcp-server "slack-mcp-server" \
-  --workspace-name "Your Workspace Name" \
-  --channels general \
-  --query "What did we discuss about the project?"
-```
-
-### 5.3 Real RAG Query Examples
-
-This section demonstrates successful Slack RAG integration queries against the Sky Lab Computing workspace's "random" channel. The system successfully retrieves actual conversation messages and performs semantic search with high relevance scores, including finding specific research paper announcements and technical discussions.
-
-### Example 1: Advisor Models Query
-
-**Query:** "train black-box models to adopt to your personal data"
-
-This query demonstrates the system's ability to find specific research announcements about training black-box models for personal data adaptation.
-
-![Advisor Models Query - Command Setup](videos/slack_integration_1.1.png)
-
-![Advisor Models Query - Search Results](videos/slack_integration_1.2.png)
-
-![Advisor Models Query - LLM Response](videos/slack_integration_1.3.png)
-
-### Example 2: Barbarians at the Gate Query
-
-**Query:** "AI-driven research systems ADRS"
-
-This query demonstrates the system's ability to find specific research announcements about AI-driven research systems and algorithm discovery.
-
-![Barbarians Query - Command Setup](videos/slack_integration_2.1.png)
-
-![Barbarians Query - Search Results](videos/slack_integration_2.2.png)
-
-![Barbarians Query - LLM Response](videos/slack_integration_2.3.png)
-
-### Prerequisites
-
-- Bot is installed in the Sky Lab Computing workspace and invited to the target channel (run `/invite @YourBotName` in the channel if needed)
-- Bot token available and exported in the same terminal session
-
-### Commands
-
-1) Set the workspace token for this shell
-
-```bash
-export SLACK_MCP_XOXP_TOKEN="xoxp-***-redacted-***"
-```
-
-2) Run queries against the "random" channel by channel ID (C0GN5BX0F)
-
-**Advisor Models Query:**
-```bash
-python -m apps.slack_rag \
-  --mcp-server "slack-mcp-server" \
-  --workspace-name "Sky Lab Computing" \
-  --channels C0GN5BX0F \
-  --max-messages-per-channel 100000 \
-  --query "train black-box models to adopt to your personal data" \
-  --llm ollama \
-  --llm-model "llama3.2:1b" \
-  --llm-host "http://localhost:11434" \
-  --no-concatenate-conversations
-```
-
-**Barbarians at the Gate Query:**
-```bash
-python -m apps.slack_rag \
-  --mcp-server "slack-mcp-server" \
-  --workspace-name "Sky Lab Computing" \
-  --channels C0GN5BX0F \
-  --max-messages-per-channel 100000 \
-  --query "AI-driven research systems ADRS" \
-  --llm ollama \
-  --llm-model "llama3.2:1b" \
-  --llm-host "http://localhost:11434" \
-  --no-concatenate-conversations
-```
-
-These examples demonstrate the system's ability to find and retrieve specific research announcements and technical discussions from the conversation history, showcasing the power of semantic search in Slack data.
-
-3) Optional: Ask a broader question
-
-```bash
-python test_channel_by_id_or_name.py \
-  --channel-id C0GN5BX0F \
-  --workspace-name "Sky Lab Computing" \
-  --query "What is LEANN about?"
-```
-
-Notes:
-- If you see `not_in_channel`, invite the bot to the channel and re-run.
-- If you see `channel_not_found`, confirm the channel ID and workspace.
-- Deep search via server-side “search” tools may require additional Slack scopes; the example above performs client-side filtering over retrieved history.
-
-## Common Issues and Solutions
-
-### Issue 1: "users cache is not ready yet" Error
-
-**Problem**: You see this warning:
-```
-WARNING - Failed to fetch messages from channel random: Failed to fetch messages: {'code': -32603, 'message': 'users cache is not ready yet, sync process is still running... please wait'}
-```
-
-**Solution**: This is a common timing issue. The LEANN integration now includes automatic retry logic:
-
-1. **Wait and Retry**: The system will automatically retry with exponential backoff (2s, 4s, 8s, etc.)
-2. **Increase Retry Parameters**: If needed, you can customize retry behavior:
-   ```bash
-   python -m apps.slack_rag \
-     --mcp-server "slack-mcp-server" \
-     --max-retries 10 \
-     --retry-delay 3.0 \
-     --channels general \
-     --query "Your query here"
-   ```
-3. **Keep MCP Server Running**: Start the MCP server separately and keep it running:
-   ```bash
-   # Terminal 1: Start MCP server
-   slack-mcp-server
-
-   # Terminal 2: Run LEANN (it will connect to the running server)
-   python -m apps.slack_rag --mcp-server "slack-mcp-server" --channels general --query "test"
-   ```
-
-### Issue 2: "No message fetching tool found"
-
-**Problem**: The MCP server doesn't have the expected tools.
-
-**Solution**:
-1. Check if your MCP server is properly installed and configured
-2. Verify your Slack tokens are correct
-3. Try a different MCP server implementation
-4. Check the MCP server documentation for required configuration
-
-### Issue 3: Permission Denied Errors
-
-**Problem**: You get permission errors when trying to access channels.
-
-**Solutions**:
-1. **Check Bot Permissions**: Ensure your bot has been added to the channels you want to access
-2. **Verify Token Scopes**: Make sure you have all required scopes configured
-3. **Channel Access**: For private channels, the bot needs to be explicitly invited
-4. **Workspace Permissions**: Ensure your Slack app has the necessary workspace permissions
-
-### Issue 4: Empty Results
-
-**Problem**: No messages are returned even though the channel has messages.
-
-**Solutions**:
-1. **Check Channel Names**: Ensure channel names are correct (without the # symbol)
-2. **Verify Bot Access**: Make sure the bot can access the channels
-3. **Check Date Ranges**: Some MCP servers have limitations on message history
-4. **Increase Message Limits**: Try increasing the message limit:
-   ```bash
-   python -m apps.slack_rag \
-     --mcp-server "slack-mcp-server" \
-     --channels general \
-     --max-messages-per-channel 1000 \
-     --query "test"
-   ```
-
-## Advanced Configuration
-
-### Custom MCP Server Commands
-
-If you need to pass additional parameters to your MCP server:
-
-```bash
-python -m apps.slack_rag \
-  --mcp-server "slack-mcp-server --token-file /path/to/tokens.json" \
-  --workspace-name "Your Workspace" \
-  --channels general \
-  --query "Your query"
-```
-
-### Multiple Workspaces
-
-To work with multiple Slack workspaces, you can:
-
-1. Create separate apps for each workspace
-2. Use different environment variables
-3. Run separate instances with different configurations
-
-### Performance Optimization
-
-For better performance with large workspaces:
-
-```bash
-python -m apps.slack_rag \
-  --mcp-server "slack-mcp-server" \
-  --workspace-name "Your Workspace" \
-  --max-messages-per-channel 500 \
-  --no-concatenate-conversations \
-  --query "Your query"
-```
----
-
-## Troubleshooting Checklist
-
-- [ ] Slack app created with proper permissions
-- [ ] Bot token (xoxb-) copied correctly
-- [ ] App-level token (xapp-) created if needed
-- [ ] MCP server installed and accessible
-- [ ] Ollama installed and running (`brew services start ollama`)
-- [ ] Ollama model pulled (`ollama pull llama3.2:1b`)
-- [ ] Environment variables set correctly
-- [ ] Bot invited to relevant channels
-- [ ] Channel names specified without # symbol
-- [ ] Sufficient retry attempts configured
-- [ ] Network connectivity to Slack APIs
-
-## Getting Help
-
-If you continue to have issues:
-
-1. **Check Logs**: Look for detailed error messages in the console output
-2. **Test MCP Server**: Use `--test-connection` to verify the MCP server is working
-3. **Verify Tokens**: Double-check that your Slack tokens are valid and have the right scopes
-4. **Check Ollama**: Ensure Ollama is running (`ollama serve`) and the model is available (`ollama list`)
-5. **Community Support**: Reach out to the LEANN community for help
-
-## Example Commands
-
-### Basic Usage
-```bash
-# Test connection
-python -m apps.slack_rag --mcp-server "slack-mcp-server" --test-connection
-
-# Index specific channels
-python -m apps.slack_rag \
-  --mcp-server "slack-mcp-server" \
-  --workspace-name "My Company" \
-  --channels general random \
-  --query "What did we decide about the project timeline?"
-```
-
-### Advanced Usage
-```bash
-# With custom retry settings
-python -m apps.slack_rag \
-  --mcp-server "slack-mcp-server" \
-  --workspace-name "My Company" \
-  --channels general \
-  --max-retries 10 \
-  --retry-delay 5.0 \
-  --max-messages-per-channel 2000 \
-  --query "Show me all decisions made in the last month"
-```

examples/mcp_integration_demo.py

@@ -1,178 +0,0 @@
-#!/usr/bin/env python3
-"""
-MCP Integration Examples for LEANN
-
-This script demonstrates how to use LEANN with different MCP servers for
-RAG on various platforms like Slack and Twitter.
-
-Examples:
-1. Slack message RAG via MCP
-2. Twitter bookmark RAG via MCP
-3. Testing MCP server connections
-"""
-
-import asyncio
-import sys
-from pathlib import Path
-
-# Add the parent directory to the path so we can import from apps
-sys.path.append(str(Path(__file__).parent.parent))
-
-
-async def demo_slack_mcp():
-    """Demonstrate Slack MCP integration."""
-    print("=" * 60)
-    print("🔥 Slack MCP RAG Demo")
-    print("=" * 60)
-
-    print("\n1. Testing Slack MCP server connection...")
-
-    # This would typically use a real MCP server command
-    # For demo purposes, we show what the command would look like
-    # slack_app = SlackMCPRAG()  # Would be used for actual testing
-
-    # Simulate command line arguments for testing
-    class MockArgs:
-        mcp_server = "slack-mcp-server"  # This would be the actual MCP server command
-        workspace_name = "my-workspace"
-        channels = ["general", "random", "dev-team"]
-        no_concatenate_conversations = False
-        max_messages_per_channel = 50
-        test_connection = True
-
-    print(f"MCP Server Command: {MockArgs.mcp_server}")
-    print(f"Workspace: {MockArgs.workspace_name}")
-    print(f"Channels: {', '.join(MockArgs.channels)}")
-
-    # In a real scenario, you would run:
-    # success = await slack_app.test_mcp_connection(MockArgs)
-
-    print("\n📝 Example usage:")
-    print("python -m apps.slack_rag \\")
-    print("  --mcp-server 'slack-mcp-server' \\")
-    print("  --workspace-name 'my-team' \\")
-    print("  --channels general dev-team \\")
-    print("  --test-connection")
-
-    print("\n🔍 After indexing, you could query:")
-    print("- 'What did the team discuss about the project deadline?'")
-    print("- 'Find messages about the new feature launch'")
-    print("- 'Show me conversations about budget planning'")
-
-
-async def demo_twitter_mcp():
-    """Demonstrate Twitter MCP integration."""
-    print("\n" + "=" * 60)
-    print("🐦 Twitter MCP RAG Demo")
-    print("=" * 60)
-
-    print("\n1. Testing Twitter MCP server connection...")
-
-    # twitter_app = TwitterMCPRAG()  # Would be used for actual testing
-
-    class MockArgs:
-        mcp_server = "twitter-mcp-server"
-        username = None  # Fetch all bookmarks
-        max_bookmarks = 500
-        no_tweet_content = False
-        no_metadata = False
-        test_connection = True
-
-    print(f"MCP Server Command: {MockArgs.mcp_server}")
-    print(f"Max Bookmarks: {MockArgs.max_bookmarks}")
-    print(f"Include Content: {not MockArgs.no_tweet_content}")
-    print(f"Include Metadata: {not MockArgs.no_metadata}")
-
-    print("\n📝 Example usage:")
-    print("python -m apps.twitter_rag \\")
-    print("  --mcp-server 'twitter-mcp-server' \\")
-    print("  --max-bookmarks 1000 \\")
-    print("  --test-connection")
-
-    print("\n🔍 After indexing, you could query:")
-    print("- 'What AI articles did I bookmark last month?'")
-    print("- 'Find tweets about machine learning techniques'")
-    print("- 'Show me bookmarked threads about startup advice'")
-
-
-async def show_mcp_server_setup():
-    """Show how to set up MCP servers."""
-    print("\n" + "=" * 60)
-    print("⚙️  MCP Server Setup Guide")
-    print("=" * 60)
-
-    print("\n🔧 Setting up Slack MCP Server:")
-    print("1. Install a Slack MCP server (example commands):")
-    print("   npm install -g slack-mcp-server")
-    print("   # OR")
-    print("   pip install slack-mcp-server")
-
-    print("\n2. Configure Slack credentials:")
-    print("   export SLACK_BOT_TOKEN='xoxb-your-bot-token'")
-    print("   export SLACK_APP_TOKEN='xapp-your-app-token'")
-
-    print("\n3. Test the server:")
-    print("   slack-mcp-server --help")
-
-    print("\n🔧 Setting up Twitter MCP Server:")
-    print("1. Install a Twitter MCP server:")
-    print("   npm install -g twitter-mcp-server")
-    print("   # OR")
-    print("   pip install twitter-mcp-server")
-
-    print("\n2. Configure Twitter API credentials:")
-    print("   export TWITTER_API_KEY='your-api-key'")
-    print("   export TWITTER_API_SECRET='your-api-secret'")
-    print("   export TWITTER_ACCESS_TOKEN='your-access-token'")
-    print("   export TWITTER_ACCESS_TOKEN_SECRET='your-access-token-secret'")
-
-    print("\n3. Test the server:")
-    print("   twitter-mcp-server --help")
-
-
-async def show_integration_benefits():
-    """Show the benefits of MCP integration."""
-    print("\n" + "=" * 60)
-    print("🌟 Benefits of MCP Integration")
-    print("=" * 60)
-
-    benefits = [
-        ("🔄 Live Data Access", "Fetch real-time data from platforms without manual exports"),
-        ("🔌 Standardized Protocol", "Use any MCP-compatible server with minimal code changes"),
-        ("🚀 Easy Extension", "Add new platforms by implementing MCP readers"),
-        ("🔒 Secure Access", "MCP servers handle authentication and API management"),
-        ("📊 Rich Metadata", "Access full platform metadata (timestamps, engagement, etc.)"),
-        ("⚡ Efficient Processing", "Stream data directly into LEANN without intermediate files"),
-    ]
-
-    for title, description in benefits:
-        print(f"\n{title}")
-        print(f"   {description}")
-
-
-async def main():
-    """Main demo function."""
-    print("🎯 LEANN MCP Integration Examples")
-    print("This demo shows how to integrate LEANN with MCP servers for various platforms.")
-
-    await demo_slack_mcp()
-    await demo_twitter_mcp()
-    await show_mcp_server_setup()
-    await show_integration_benefits()
-
-    print("\n" + "=" * 60)
-    print("✨ Next Steps")
-    print("=" * 60)
-    print("1. Install and configure MCP servers for your platforms")
-    print("2. Test connections using --test-connection flag")
-    print("3. Run indexing to build your RAG knowledge base")
-    print("4. Start querying your personal data!")
-
-    print("\n📚 For more information:")
-    print("- Check the README for detailed setup instructions")
-    print("- Look at the apps/slack_rag.py and apps/twitter_rag.py for implementation details")
-    print("- Explore other MCP servers for additional platforms")
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

packages/leann-backend-diskann/pyproject.toml

@@ -4,8 +4,8 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "leann-backend-diskann"
-version = "0.3.5"
+version = "0.3.4"
-dependencies = ["leann-core==0.3.5", "numpy", "protobuf>=3.19.0"]
+dependencies = ["leann-core==0.3.4", "numpy", "protobuf>=3.19.0"]
 
 [tool.scikit-build]
 # Key: simplified CMake path

packages/leann-backend-hnsw/CMakeLists.txt

@@ -29,25 +29,12 @@ if(APPLE)
     set(CMAKE_OSX_DEPLOYMENT_TARGET "11.0" CACHE STRING "Minimum macOS version")
 endif()
 
-# Find ZMQ using pkg-config with IMPORTED_TARGET for automatic target creation
+# Use system ZeroMQ instead of building from source
 find_package(PkgConfig REQUIRED)
-
+pkg_check_modules(ZMQ REQUIRED libzmq)
-# On ARM64 macOS, ensure pkg-config finds ARM64 Homebrew packages first
-if(APPLE AND CMAKE_SYSTEM_PROCESSOR MATCHES "aarch64|arm64")
-    set(ENV{PKG_CONFIG_PATH} "/opt/homebrew/lib/pkgconfig:/opt/homebrew/share/pkgconfig:$ENV{PKG_CONFIG_PATH}")
-endif()
-
-pkg_check_modules(ZMQ REQUIRED IMPORTED_TARGET libzmq)
-
-# This creates PkgConfig::ZMQ target automatically with correct properties
-if(TARGET PkgConfig::ZMQ)
-    message(STATUS "Found and configured ZMQ target: PkgConfig::ZMQ")
-else()
-    message(FATAL_ERROR "pkg_check_modules did not create IMPORTED target for ZMQ.")
-endif()
 
 # Add cppzmq headers
-include_directories(SYSTEM third_party/cppzmq)
+include_directories(third_party/cppzmq)
 
 # Configure msgpack-c - disable boost dependency
 set(MSGPACK_USE_BOOST OFF CACHE BOOL "" FORCE)

packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_backend.py

@@ -215,8 +215,6 @@ class HNSWSearcher(BaseSearcher):
         if recompute_embeddings:
             if zmq_port is None:
                 raise ValueError("zmq_port must be provided if recompute_embeddings is True")
-            if hasattr(self._index, "set_zmq_port"):
-                self._index.set_zmq_port(zmq_port)
 
         if query.dtype != np.float32:
             query = query.astype(np.float32)

packages/leann-backend-hnsw/pyproject.toml

@@ -6,10 +6,10 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "leann-backend-hnsw"
-version = "0.3.5"
+version = "0.3.4"
 description = "Custom-built HNSW (Faiss) backend for the Leann toolkit."
 dependencies = [
-    "leann-core==0.3.5",
+    "leann-core==0.3.4",
     "numpy",
     "pyzmq>=23.0.0",
     "msgpack>=1.0.0",

packages/leann-core/pyproject.toml

@@ -4,10 +4,10 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "leann-core"
-version = "0.3.5"
+version = "0.3.4"
 description = "Core API and plugin system for LEANN"
 readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.9"
 license = { text = "MIT" }
 
 # All required dependencies included
@@ -18,16 +18,14 @@ dependencies = [
     "pyzmq>=23.0.0",
     "msgpack>=1.0.0",
     "torch>=2.0.0",
-    "sentence-transformers>=3.0.0",
+    "sentence-transformers>=2.2.0",
     "llama-index-core>=0.12.0",
     "llama-index-readers-file>=0.4.0",  # Essential for document reading
     "llama-index-embeddings-huggingface>=0.5.5",  # For embeddings
     "python-dotenv>=1.0.0",
     "openai>=1.0.0",
     "huggingface-hub>=0.20.0",
-    # Keep transformers below 4.46: 4.46.0 adds Python 3.10-only return type syntax and
+    "transformers>=4.30.0",
-    # breaks Python 3.9 environments.
-    "transformers>=4.30.0,<4.46",
     "requests>=2.25.0",
     "accelerate>=0.20.0",
     "PyPDF2>=3.0.0",
@@ -42,7 +40,7 @@ dependencies = [
 [project.optional-dependencies]
 colab = [
     "torch>=2.0.0,<3.0.0",  # Limit torch version to avoid conflicts
-    "transformers>=4.30.0,<4.46",  # 4.46.0 switches to PEP 604 typing (int | None), breaks Py3.9
+    "transformers>=4.30.0,<5.0.0",  # Limit transformers version
     "accelerate>=0.20.0,<1.0.0",  # Limit accelerate version
 ]
 

packages/leann-core/src/leann/api.py

@@ -18,7 +18,6 @@ from typing import Any, Literal, Optional, Union
 import numpy as np
 from leann_backend_hnsw.convert_to_csr import prune_hnsw_embeddings_inplace
 
-from leann.interactive_utils import create_api_session
 from leann.interface import LeannBackendSearcherInterface
 
 from .chat import get_llm
@@ -814,16 +813,11 @@ class LeannBuilder:
                             "Failed to start HNSW embedding server for recompute update."
                         )
                     if actual_port != requested_zmq_port:
-                        logger.warning(
+                        server_manager.stop_server()
-                            "Embedding server started on port %s instead of requested %s. "
+                        raise RuntimeError(
-                            "Using reassigned port.",
+                            "Embedding server started on unexpected port "
-                            actual_port,
+                            f"{actual_port}; expected {requested_zmq_port}. Make sure the desired ZMQ port is free."
-                            requested_zmq_port,
                         )
-                    if hasattr(index.hnsw, "set_zmq_port"):
-                        index.hnsw.set_zmq_port(actual_port)
-                    elif hasattr(index, "set_zmq_port"):
-                        index.set_zmq_port(actual_port)
 
                 if needs_recompute:
                     for i in range(embeddings.shape[0]):
@@ -916,7 +910,6 @@ class LeannSearcher:
         metadata_filters: Optional[dict[str, dict[str, Union[str, int, float, bool, list]]]] = None,
         batch_size: int = 0,
         use_grep: bool = False,
-        provider_options: Optional[dict[str, Any]] = None,
         **kwargs,
     ) -> list[SearchResult]:
         """
@@ -980,24 +973,10 @@ class LeannSearcher:
 
         start_time = time.time()
 
-        # Extract query template from stored embedding_options with fallback chain:
-        # 1. Check provider_options override (highest priority)
-        # 2. Check query_prompt_template (new format)
-        # 3. Check prompt_template (old format for backward compat)
-        # 4. None (no template)
-        query_template = None
-        if provider_options and "prompt_template" in provider_options:
-            query_template = provider_options["prompt_template"]
-        elif "query_prompt_template" in self.embedding_options:
-            query_template = self.embedding_options["query_prompt_template"]
-        elif "prompt_template" in self.embedding_options:
-            query_template = self.embedding_options["prompt_template"]
-
         query_embedding = self.backend_impl.compute_query_embedding(
             query,
             use_server_if_available=recompute_embeddings,
             zmq_port=zmq_port,
-            query_template=query_template,
         )
         logger.info(f"  Generated embedding shape: {query_embedding.shape}")
         embedding_time = time.time() - start_time
@@ -1251,17 +1230,6 @@ class LeannChat:
             "Please provide the best answer you can based on this context and your knowledge."
         )
 
-        logger.info("The context provided to the LLM is:")
-        logger.info(f"{'Relevance':<10} | {'Chunk id':<10} | {'Content':<60} | {'Source':<80}")
-        logger.info("-" * 150)
-        for r in results:
-            chunk_relevance = f"{r.score:.3f}"
-            chunk_id = r.id
-            chunk_content = r.text[:60]
-            chunk_source = r.metadata.get("source", "")[:80]
-            logger.info(
-                f"{chunk_relevance:<10} | {chunk_id:<10} | {chunk_content:<60} | {chunk_source:<80}"
-            )
         ask_time = time.time()
         ans = self.llm.ask(prompt, **llm_kwargs)
         ask_time = time.time() - ask_time
@@ -1269,14 +1237,19 @@ class LeannChat:
         return ans
 
     def start_interactive(self):
-        """Start interactive chat session."""
+        print("\nLeann Chat started (type 'quit' to exit)")
-        session = create_api_session()
+        while True:
-
+            try:
-        def handle_query(user_input: str):
+                user_input = input("You: ").strip()
+                if user_input.lower() in ["quit", "exit"]:
+                    break
+                if not user_input:
+                    continue
                 response = self.ask(user_input)
                 print(f"Leann: {response}")
-
+            except (KeyboardInterrupt, EOFError):
-        session.run_interactive_loop(handle_query)
+                print("\nGoodbye!")
+                break
 
     def cleanup(self):
         """Explicitly cleanup embedding server resources.

packages/leann-core/src/leann/chat.py

@@ -12,13 +12,7 @@ from typing import Any, Optional
 
 import torch
 
-from .settings import (
+from .settings import resolve_ollama_host, resolve_openai_api_key, resolve_openai_base_url
-    resolve_anthropic_api_key,
-    resolve_anthropic_base_url,
-    resolve_ollama_host,
-    resolve_openai_api_key,
-    resolve_openai_base_url,
-)
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
@@ -552,30 +546,11 @@ class OllamaChat(LLMInterface):
 
 
 class HFChat(LLMInterface):
-    """LLM interface for local Hugging Face Transformers models with proper chat templates.
+    """LLM interface for local Hugging Face Transformers models with proper chat templates."""
 
-    Args:
+    def __init__(self, model_name: str = "deepseek-ai/deepseek-llm-7b-chat"):
-        model_name (str): Name of the Hugging Face model to load.
-        trust_remote_code (bool): Whether to allow execution of code from the model repository.
-            Defaults to False for security. Only enable for trusted models as this can pose
-            a security risk if the model repository is compromised.
-    """
-
-    def __init__(
-        self, model_name: str = "deepseek-ai/deepseek-llm-7b-chat", trust_remote_code: bool = False
-    ):
         logger.info(f"Initializing HFChat with model='{model_name}'")
 
-        # Security warning when trust_remote_code is enabled
-        if trust_remote_code:
-            logger.warning(
-                "SECURITY WARNING: trust_remote_code=True allows execution of arbitrary code from the model repository. "
-                "Only enable this for models from trusted sources. This creates a potential security risk if the model "
-                "repository is compromised."
-            )
-
-        self.trust_remote_code = trust_remote_code
-
         # Pre-check model availability with helpful suggestions
         model_error = validate_model_and_suggest(model_name, "hf")
         if model_error:
@@ -613,16 +588,14 @@ class HFChat(LLMInterface):
 
             try:
                 logger.info(f"Loading tokenizer for {model_name}...")
-                self.tokenizer = AutoTokenizer.from_pretrained(
+                self.tokenizer = AutoTokenizer.from_pretrained(model_name)
-                    model_name, trust_remote_code=self.trust_remote_code
-                )
 
                 logger.info(f"Loading model {model_name}...")
                 self.model = AutoModelForCausalLM.from_pretrained(
                     model_name,
                     torch_dtype=torch.float16 if self.device != "cpu" else torch.float32,
                     device_map="auto" if self.device != "cpu" else None,
-                    trust_remote_code=self.trust_remote_code,
+                    trust_remote_code=True,
                 )
                 logger.info(f"Successfully loaded {model_name}")
             finally:
@@ -840,92 +813,12 @@ class OpenAIChat(LLMInterface):
 
         try:
             response = self.client.chat.completions.create(**params)
-            print(
-                f"Total tokens = {response.usage.total_tokens}, prompt tokens = {response.usage.prompt_tokens}, completion tokens = {response.usage.completion_tokens}"
-            )
-            if response.choices[0].finish_reason == "length":
-                print("The query is exceeding the maximum allowed number of tokens")
             return response.choices[0].message.content.strip()
         except Exception as e:
             logger.error(f"Error communicating with OpenAI: {e}")
             return f"Error: Could not get a response from OpenAI. Details: {e}"
 
 
-class AnthropicChat(LLMInterface):
-    """LLM interface for Anthropic Claude models."""
-
-    def __init__(
-        self,
-        model: str = "claude-haiku-4-5",
-        api_key: Optional[str] = None,
-        base_url: Optional[str] = None,
-    ):
-        self.model = model
-        self.base_url = resolve_anthropic_base_url(base_url)
-        self.api_key = resolve_anthropic_api_key(api_key)
-
-        if not self.api_key:
-            raise ValueError(
-                "Anthropic API key is required. Set ANTHROPIC_API_KEY environment variable or pass api_key parameter."
-            )
-
-        logger.info(
-            "Initializing Anthropic Chat with model='%s' and base_url='%s'",
-            model,
-            self.base_url,
-        )
-
-        try:
-            import anthropic
-
-            # Allow custom Anthropic-compatible endpoints via base_url
-            self.client = anthropic.Anthropic(
-                api_key=self.api_key,
-                base_url=self.base_url,
-            )
-        except ImportError:
-            raise ImportError(
-                "The 'anthropic' library is required for Anthropic models. Please install it with 'pip install anthropic'."
-            )
-
-    def ask(self, prompt: str, **kwargs) -> str:
-        logger.info(f"Sending request to Anthropic with model {self.model}")
-
-        try:
-            # Anthropic API parameters
-            params = {
-                "model": self.model,
-                "max_tokens": kwargs.get("max_tokens", 1000),
-                "messages": [{"role": "user", "content": prompt}],
-            }
-
-            # Add optional parameters
-            if "temperature" in kwargs:
-                params["temperature"] = kwargs["temperature"]
-            if "top_p" in kwargs:
-                params["top_p"] = kwargs["top_p"]
-
-            response = self.client.messages.create(**params)
-
-            # Extract text from response
-            response_text = response.content[0].text
-
-            # Log token usage
-            print(
-                f"Total tokens = {response.usage.input_tokens + response.usage.output_tokens}, "
-                f"input tokens = {response.usage.input_tokens}, "
-                f"output tokens = {response.usage.output_tokens}"
-            )
-
-            if response.stop_reason == "max_tokens":
-                print("The query is exceeding the maximum allowed number of tokens")
-
-            return response_text.strip()
-        except Exception as e:
-            logger.error(f"Error communicating with Anthropic: {e}")
-            return f"Error: Could not get a response from Anthropic. Details: {e}"
-
-
 class SimulatedChat(LLMInterface):
     """A simple simulated chat for testing and development."""
 
@@ -966,10 +859,7 @@ def get_llm(llm_config: Optional[dict[str, Any]] = None) -> LLMInterface:
             host=llm_config.get("host"),
         )
     elif llm_type == "hf":
-        return HFChat(
+        return HFChat(model_name=model or "deepseek-ai/deepseek-llm-7b-chat")
-            model_name=model or "deepseek-ai/deepseek-llm-7b-chat",
-            trust_remote_code=llm_config.get("trust_remote_code", False),
-        )
     elif llm_type == "openai":
         return OpenAIChat(
             model=model or "gpt-4o",
@@ -978,12 +868,6 @@ def get_llm(llm_config: Optional[dict[str, Any]] = None) -> LLMInterface:
         )
     elif llm_type == "gemini":
         return GeminiChat(model=model or "gemini-2.5-flash", api_key=llm_config.get("api_key"))
-    elif llm_type == "anthropic":
-        return AnthropicChat(
-            model=model or "claude-3-5-sonnet-20241022",
-            api_key=llm_config.get("api_key"),
-            base_url=llm_config.get("base_url"),
-        )
     elif llm_type == "simulated":
         return SimulatedChat()
     else:

packages/leann-core/src/leann/chunking_utils.py

@@ -5,128 +5,12 @@ Packaged within leann-core so installed wheels can import it reliably.
 
 import logging
 from pathlib import Path
-from typing import Any, Optional
+from typing import Optional
 
 from llama_index.core.node_parser import SentenceSplitter
 
 logger = logging.getLogger(__name__)
 
-# Flag to ensure AST token warning only shown once per session
-_ast_token_warning_shown = False
-
-
-def estimate_token_count(text: str) -> int:
-    """
-    Estimate token count for a text string.
-    Uses conservative estimation: ~4 characters per token for natural text,
-    ~1.2 tokens per character for code (worse tokenization).
-
-    Args:
-        text: Input text to estimate tokens for
-
-    Returns:
-        Estimated token count
-    """
-    try:
-        import tiktoken
-
-        encoder = tiktoken.get_encoding("cl100k_base")
-        return len(encoder.encode(text))
-    except ImportError:
-        # Fallback: Conservative character-based estimation
-        # Assume worst case for code: 1.2 tokens per character
-        return int(len(text) * 1.2)
-
-
-def calculate_safe_chunk_size(
-    model_token_limit: int,
-    overlap_tokens: int,
-    chunking_mode: str = "traditional",
-    safety_factor: float = 0.9,
-) -> int:
-    """
-    Calculate safe chunk size accounting for overlap and safety margin.
-
-    Args:
-        model_token_limit: Maximum tokens supported by embedding model
-        overlap_tokens: Overlap size (tokens for traditional, chars for AST)
-        chunking_mode: "traditional" (tokens) or "ast" (characters)
-        safety_factor: Safety margin (0.9 = 10% safety margin)
-
-    Returns:
-        Safe chunk size: tokens for traditional, characters for AST
-    """
-    safe_limit = int(model_token_limit * safety_factor)
-
-    if chunking_mode == "traditional":
-        # Traditional chunking uses tokens
-        # Max chunk = chunk_size + overlap, so chunk_size = limit - overlap
-        return max(1, safe_limit - overlap_tokens)
-    else:  # AST chunking
-        # AST uses characters, need to convert
-        # Conservative estimate: 1.2 tokens per char for code
-        overlap_chars = int(overlap_tokens * 3)  # ~3 chars per token for code
-        safe_chars = int(safe_limit / 1.2)
-        return max(1, safe_chars - overlap_chars)
-
-
-def validate_chunk_token_limits(chunks: list[str], max_tokens: int = 512) -> tuple[list[str], int]:
-    """
-    Validate that chunks don't exceed token limits and truncate if necessary.
-
-    Args:
-        chunks: List of text chunks to validate
-        max_tokens: Maximum tokens allowed per chunk
-
-    Returns:
-        Tuple of (validated_chunks, num_truncated)
-    """
-    validated_chunks = []
-    num_truncated = 0
-
-    for i, chunk in enumerate(chunks):
-        estimated_tokens = estimate_token_count(chunk)
-
-        if estimated_tokens > max_tokens:
-            # Truncate chunk to fit token limit
-            try:
-                import tiktoken
-
-                encoder = tiktoken.get_encoding("cl100k_base")
-                tokens = encoder.encode(chunk)
-                if len(tokens) > max_tokens:
-                    truncated_tokens = tokens[:max_tokens]
-                    truncated_chunk = encoder.decode(truncated_tokens)
-                    validated_chunks.append(truncated_chunk)
-                    num_truncated += 1
-                    logger.warning(
-                        f"Truncated chunk {i} from {len(tokens)} to {max_tokens} tokens "
-                        f"(from {len(chunk)} to {len(truncated_chunk)} characters)"
-                    )
-                else:
-                    validated_chunks.append(chunk)
-            except ImportError:
-                # Fallback: Conservative character truncation
-                char_limit = int(max_tokens / 1.2)  # Conservative for code
-                if len(chunk) > char_limit:
-                    truncated_chunk = chunk[:char_limit]
-                    validated_chunks.append(truncated_chunk)
-                    num_truncated += 1
-                    logger.warning(
-                        f"Truncated chunk {i} from {len(chunk)} to {char_limit} characters "
-                        f"(conservative estimate for {max_tokens} tokens)"
-                    )
-                else:
-                    validated_chunks.append(chunk)
-        else:
-            validated_chunks.append(chunk)
-
-    if num_truncated > 0:
-        logger.warning(f"Truncated {num_truncated}/{len(chunks)} chunks to fit token limits")
-
-    return validated_chunks, num_truncated
-
-
 # Code file extensions supported by astchunk
 CODE_EXTENSIONS = {
     ".py": "python",
@@ -177,45 +61,27 @@ def create_ast_chunks(
     max_chunk_size: int = 512,
     chunk_overlap: int = 64,
     metadata_template: str = "default",
-) -> list[dict[str, Any]]:
+) -> list[str]:
     """Create AST-aware chunks from code documents using astchunk.
 
     Falls back to traditional chunking if astchunk is unavailable.
-
-    Returns:
-        List of dicts with {"text": str, "metadata": dict}
     """
     try:
         from astchunk import ASTChunkBuilder  # optional dependency
     except ImportError as e:
         logger.error(f"astchunk not available: {e}")
         logger.info("Falling back to traditional chunking for code files")
-        return _traditional_chunks_as_dicts(documents, max_chunk_size, chunk_overlap)
+        return create_traditional_chunks(documents, max_chunk_size, chunk_overlap)
 
     all_chunks = []
     for doc in documents:
         language = doc.metadata.get("language")
         if not language:
             logger.warning("No language detected; falling back to traditional chunking")
-            all_chunks.extend(_traditional_chunks_as_dicts([doc], max_chunk_size, chunk_overlap))
+            all_chunks.extend(create_traditional_chunks([doc], max_chunk_size, chunk_overlap))
             continue
 
         try:
-            # Warn once if AST chunk size + overlap might exceed common token limits
-            # Note: Actual truncation happens at embedding time with dynamic model limits
-            global _ast_token_warning_shown
-            estimated_max_tokens = int(
-                (max_chunk_size + chunk_overlap) * 1.2
-            )  # Conservative estimate
-            if estimated_max_tokens > 512 and not _ast_token_warning_shown:
-                logger.warning(
-                    f"AST chunk size ({max_chunk_size}) + overlap ({chunk_overlap}) = {max_chunk_size + chunk_overlap} chars "
-                    f"may exceed 512 token limit (~{estimated_max_tokens} tokens estimated). "
-                    f"Consider reducing --ast-chunk-size to {int(400 / 1.2)} or --ast-chunk-overlap to {int(50 / 1.2)}. "
-                    f"Note: Chunks will be auto-truncated at embedding time based on your model's actual token limit."
-                )
-                _ast_token_warning_shown = True
-
             configs = {
                 "max_chunk_size": max_chunk_size,
                 "language": language,
@@ -239,40 +105,17 @@ def create_ast_chunks(
 
             chunks = chunk_builder.chunkify(code_content)
             for chunk in chunks:
-                chunk_text = None
-                astchunk_metadata = {}
-
                 if hasattr(chunk, "text"):
                     chunk_text = chunk.text
+                elif isinstance(chunk, dict) and "text" in chunk:
+                    chunk_text = chunk["text"]
                 elif isinstance(chunk, str):
                     chunk_text = chunk
-                elif isinstance(chunk, dict):
-                    # Handle astchunk format: {"content": "...", "metadata": {...}}
-                    if "content" in chunk:
-                        chunk_text = chunk["content"]
-                        astchunk_metadata = chunk.get("metadata", {})
-                    elif "text" in chunk:
-                        chunk_text = chunk["text"]
-                    else:
-                        chunk_text = str(chunk)  # Last resort
                 else:
                     chunk_text = str(chunk)
 
                 if chunk_text and chunk_text.strip():
-                    # Extract document-level metadata
+                    all_chunks.append(chunk_text.strip())
-                    doc_metadata = {
-                        "file_path": doc.metadata.get("file_path", ""),
-                        "file_name": doc.metadata.get("file_name", ""),
-                    }
-                    if "creation_date" in doc.metadata:
-                        doc_metadata["creation_date"] = doc.metadata["creation_date"]
-                    if "last_modified_date" in doc.metadata:
-                        doc_metadata["last_modified_date"] = doc.metadata["last_modified_date"]
-
-                    # Merge document metadata + astchunk metadata
-                    combined_metadata = {**doc_metadata, **astchunk_metadata}
-
-                    all_chunks.append({"text": chunk_text.strip(), "metadata": combined_metadata})
 
             logger.info(
                 f"Created {len(chunks)} AST chunks from {language} file: {doc.metadata.get('file_name', 'unknown')}"
@@ -280,19 +123,15 @@ def create_ast_chunks(
         except Exception as e:
             logger.warning(f"AST chunking failed for {language} file: {e}")
             logger.info("Falling back to traditional chunking")
-            all_chunks.extend(_traditional_chunks_as_dicts([doc], max_chunk_size, chunk_overlap))
+            all_chunks.extend(create_traditional_chunks([doc], max_chunk_size, chunk_overlap))
 
     return all_chunks
 
 
 def create_traditional_chunks(
     documents, chunk_size: int = 256, chunk_overlap: int = 128
-) -> list[dict[str, Any]]:
+) -> list[str]:
-    """Create traditional text chunks using LlamaIndex SentenceSplitter.
+    """Create traditional text chunks using LlamaIndex SentenceSplitter."""
-
-    Returns:
-        List of dicts with {"text": str, "metadata": dict}
-    """
     if chunk_size <= 0:
         logger.warning(f"Invalid chunk_size={chunk_size}, using default value of 256")
         chunk_size = 256
@@ -308,40 +147,19 @@ def create_traditional_chunks(
         paragraph_separator="\n\n",
     )
 
-    result = []
+    all_texts = []
     for doc in documents:
-        # Extract document-level metadata
-        doc_metadata = {
-            "file_path": doc.metadata.get("file_path", ""),
-            "file_name": doc.metadata.get("file_name", ""),
-        }
-        if "creation_date" in doc.metadata:
-            doc_metadata["creation_date"] = doc.metadata["creation_date"]
-        if "last_modified_date" in doc.metadata:
-            doc_metadata["last_modified_date"] = doc.metadata["last_modified_date"]
-
         try:
             nodes = node_parser.get_nodes_from_documents([doc])
             if nodes:
-                for node in nodes:
+                all_texts.extend(node.get_content() for node in nodes)
-                    result.append({"text": node.get_content(), "metadata": doc_metadata})
         except Exception as e:
             logger.error(f"Traditional chunking failed for document: {e}")
             content = doc.get_content()
             if content and content.strip():
-                result.append({"text": content.strip(), "metadata": doc_metadata})
+                all_texts.append(content.strip())
 
-    return result
+    return all_texts
-
-
-def _traditional_chunks_as_dicts(
-    documents, chunk_size: int = 256, chunk_overlap: int = 128
-) -> list[dict[str, Any]]:
-    """Helper: Traditional chunking that returns dict format for consistency.
-
-    This is now just an alias for create_traditional_chunks for backwards compatibility.
-    """
-    return create_traditional_chunks(documents, chunk_size, chunk_overlap)
 
 
 def create_text_chunks(
@@ -353,12 +171,8 @@ def create_text_chunks(
     ast_chunk_overlap: int = 64,
     code_file_extensions: Optional[list[str]] = None,
     ast_fallback_traditional: bool = True,
-) -> list[dict[str, Any]]:
+) -> list[str]:
-    """Create text chunks from documents with optional AST support for code files.
+    """Create text chunks from documents with optional AST support for code files."""
-
-    Returns:
-        List of dicts with {"text": str, "metadata": dict}
-    """
     if not documents:
         logger.warning("No documents provided for chunking")
         return []
@@ -393,17 +207,14 @@ def create_text_chunks(
                 logger.error(f"AST chunking failed: {e}")
                 if ast_fallback_traditional:
                     all_chunks.extend(
-                        _traditional_chunks_as_dicts(code_docs, chunk_size, chunk_overlap)
+                        create_traditional_chunks(code_docs, chunk_size, chunk_overlap)
                     )
                 else:
                     raise
         if text_docs:
-            all_chunks.extend(_traditional_chunks_as_dicts(text_docs, chunk_size, chunk_overlap))
+            all_chunks.extend(create_traditional_chunks(text_docs, chunk_size, chunk_overlap))
     else:
-        all_chunks = _traditional_chunks_as_dicts(documents, chunk_size, chunk_overlap)
+        all_chunks = create_traditional_chunks(documents, chunk_size, chunk_overlap)
 
     logger.info(f"Total chunks created: {len(all_chunks)}")
-
-    # Note: Token truncation is now handled at embedding time with dynamic model limits
-    # See get_model_token_limit() and truncate_to_token_limit() in embedding_compute.py
     return all_chunks

packages/leann-core/src/leann/cli.py

@@ -1,6 +1,5 @@
 import argparse
 import asyncio
-import time
 from pathlib import Path
 from typing import Any, Optional, Union
 
@@ -9,14 +8,8 @@ from llama_index.core.node_parser import SentenceSplitter
 from tqdm import tqdm
 
 from .api import LeannBuilder, LeannChat, LeannSearcher
-from .interactive_utils import create_cli_session
 from .registry import register_project_directory
-from .settings import (
+from .settings import resolve_ollama_host, resolve_openai_api_key, resolve_openai_base_url
-    resolve_anthropic_base_url,
-    resolve_ollama_host,
-    resolve_openai_api_key,
-    resolve_openai_base_url,
-)
 
 
 def extract_pdf_text_with_pymupdf(file_path: str) -> str:
@@ -112,7 +105,7 @@ Examples:
             help="Documents directories and/or files (default: current directory)",
         )
         build_parser.add_argument(
-            "--backend-name",
+            "--backend",
             type=str,
             default="hnsw",
             choices=["hnsw", "diskann"],
@@ -149,18 +142,6 @@ Examples:
             default=None,
             help="API key for embedding service (defaults to OPENAI_API_KEY)",
         )
-        build_parser.add_argument(
-            "--embedding-prompt-template",
-            type=str,
-            default=None,
-            help="Prompt template to prepend to all texts for embedding (e.g., 'query: ' for search)",
-        )
-        build_parser.add_argument(
-            "--query-prompt-template",
-            type=str,
-            default=None,
-            help="Prompt template for queries (different from build template for task-specific models)",
-        )
         build_parser.add_argument(
             "--force", "-f", action="store_true", help="Force rebuild existing index"
         )
@@ -198,25 +179,25 @@ Examples:
             "--doc-chunk-size",
             type=int,
             default=256,
-            help="Document chunk size in TOKENS (default: 256). Final chunks may be larger due to overlap. For 512 token models: recommended 350 tokens (350 + 128 overlap = 478 max)",
+            help="Document chunk size in tokens/characters (default: 256)",
         )
         build_parser.add_argument(
             "--doc-chunk-overlap",
             type=int,
             default=128,
-            help="Document chunk overlap in TOKENS (default: 128). Added to chunk size, not included in it",
+            help="Document chunk overlap (default: 128)",
         )
         build_parser.add_argument(
             "--code-chunk-size",
             type=int,
             default=512,
-            help="Code chunk size in TOKENS (default: 512). Final chunks may be larger due to overlap. For 512 token models: recommended 400 tokens (400 + 50 overlap = 450 max)",
+            help="Code chunk size in tokens/lines (default: 512)",
         )
         build_parser.add_argument(
             "--code-chunk-overlap",
             type=int,
             default=50,
-            help="Code chunk overlap in TOKENS (default: 50). Added to chunk size, not included in it",
+            help="Code chunk overlap (default: 50)",
         )
         build_parser.add_argument(
             "--use-ast-chunking",
@@ -226,14 +207,14 @@ Examples:
         build_parser.add_argument(
             "--ast-chunk-size",
             type=int,
-            default=300,
+            default=768,
-            help="AST chunk size in CHARACTERS (non-whitespace) (default: 300). Final chunks may be larger due to overlap and expansion. For 512 token models: recommended 300 chars (300 + 64 overlap ~= 480 tokens)",
+            help="AST chunk size in characters (default: 768)",
         )
         build_parser.add_argument(
             "--ast-chunk-overlap",
             type=int,
-            default=64,
+            default=96,
-            help="AST chunk overlap in CHARACTERS (default: 64). Added to chunk size, not included in it. ~1.2 tokens per character for code",
+            help="AST chunk overlap in characters (default: 96)",
         )
         build_parser.add_argument(
             "--ast-fallback-traditional",
@@ -272,17 +253,6 @@ Examples:
             action="store_true",
             help="Non-interactive mode: automatically select index without prompting",
         )
-        search_parser.add_argument(
-            "--show-metadata",
-            action="store_true",
-            help="Display file paths and metadata in search results",
-        )
-        search_parser.add_argument(
-            "--embedding-prompt-template",
-            type=str,
-            default=None,
-            help="Prompt template to prepend to query for embedding (e.g., 'query: ' for search)",
-        )
 
         # Ask command
         ask_parser = subparsers.add_parser("ask", help="Ask questions")
@@ -296,7 +266,7 @@ Examples:
             "--llm",
             type=str,
             default="ollama",
-            choices=["simulated", "ollama", "hf", "openai", "anthropic"],
+            choices=["simulated", "ollama", "hf", "openai"],
             help="LLM provider (default: ollama)",
         )
         ask_parser.add_argument(
@@ -346,7 +316,7 @@ Examples:
             "--api-key",
             type=str,
             default=None,
-            help="API key for cloud LLM providers (OpenAI, Anthropic)",
+            help="API key for OpenAI-compatible APIs (defaults to OPENAI_API_KEY)",
         )
 
         # List command
@@ -1185,11 +1155,6 @@ Examples:
                             print(f"Warning: Could not process {file_path}: {e}")
 
             # Load other file types with default reader
-            # Exclude PDFs from code_extensions if they were already processed separately
-            other_file_extensions = code_extensions
-            if should_process_pdfs and ".pdf" in code_extensions:
-                other_file_extensions = [ext for ext in code_extensions if ext != ".pdf"]
-
             try:
                 # Create a custom file filter function using our PathSpec
                 def file_filter(
@@ -1205,26 +1170,21 @@ Examples:
                     except (ValueError, OSError):
                         return True  # Include files that can't be processed
 
-                # Only load other file types if there are extensions to process
-                if other_file_extensions:
                 other_docs = SimpleDirectoryReader(
                     docs_dir,
                     recursive=True,
                     encoding="utf-8",
-                        required_exts=other_file_extensions,
+                    required_exts=code_extensions,
                     file_extractor={},  # Use default extractors
                     exclude_hidden=not include_hidden,
                     filename_as_id=True,
                 ).load_data(show_progress=True)
-                else:
-                    other_docs = []
 
                 # Filter documents after loading based on gitignore rules
                 filtered_docs = []
                 for doc in other_docs:
                     file_path = doc.metadata.get("file_path", "")
                     if file_filter(file_path):
-                        doc.metadata["source"] = file_path
                         filtered_docs.append(doc)
 
                 documents.extend(filtered_docs)
@@ -1300,7 +1260,7 @@ Examples:
                 from .chunking_utils import create_text_chunks
 
                 # Use enhanced chunking with AST support
-                chunk_texts = create_text_chunks(
+                all_texts = create_text_chunks(
                     documents,
                     chunk_size=self.node_parser.chunk_size,
                     chunk_overlap=self.node_parser.chunk_overlap,
@@ -1311,9 +1271,6 @@ Examples:
                     ast_fallback_traditional=getattr(args, "ast_fallback_traditional", True),
                 )
 
-                # create_text_chunks now returns list[dict] with metadata preserved
-                all_texts.extend(chunk_texts)
-
             except ImportError as e:
                 print(
                     f"⚠️  AST chunking utilities not available in package ({e}), falling back to traditional chunking"
@@ -1325,27 +1282,14 @@ Examples:
             for doc in tqdm(documents, desc="Chunking documents", unit="doc"):
                 # Check if this is a code file based on source path
                 source_path = doc.metadata.get("source", "")
-                file_path = doc.metadata.get("file_path", "")
                 is_code_file = any(source_path.endswith(ext) for ext in code_file_exts)
 
-                # Extract metadata to preserve with chunks
-                chunk_metadata = {
-                    "file_path": file_path or source_path,
-                    "file_name": doc.metadata.get("file_name", ""),
-                }
-
-                # Add optional metadata if available
-                if "creation_date" in doc.metadata:
-                    chunk_metadata["creation_date"] = doc.metadata["creation_date"]
-                if "last_modified_date" in doc.metadata:
-                    chunk_metadata["last_modified_date"] = doc.metadata["last_modified_date"]
-
                 # Use appropriate parser based on file type
                 parser = self.code_parser if is_code_file else self.node_parser
                 nodes = parser.get_nodes_from_documents([doc])
 
                 for node in nodes:
-                    all_texts.append({"text": node.get_content(), "metadata": chunk_metadata})
+                    all_texts.append(node.get_content())
 
         print(f"Loaded {len(documents)} documents, {len(all_texts)} chunks")
         return all_texts
@@ -1420,7 +1364,7 @@ Examples:
 
         index_dir.mkdir(parents=True, exist_ok=True)
 
-        print(f"Building index '{index_name}' with {args.backend_name} backend...")
+        print(f"Building index '{index_name}' with {args.backend} backend...")
 
         embedding_options: dict[str, Any] = {}
         if args.embedding_mode == "ollama":
@@ -1430,17 +1374,9 @@ Examples:
             resolved_embedding_key = resolve_openai_api_key(args.embedding_api_key)
             if resolved_embedding_key:
                 embedding_options["api_key"] = resolved_embedding_key
-        if args.query_prompt_template:
-            # New format: separate templates
-            if args.embedding_prompt_template:
-                embedding_options["build_prompt_template"] = args.embedding_prompt_template
-            embedding_options["query_prompt_template"] = args.query_prompt_template
-        elif args.embedding_prompt_template:
-            # Old format: single template (backward compat)
-            embedding_options["prompt_template"] = args.embedding_prompt_template
 
         builder = LeannBuilder(
-            backend_name=args.backend_name,
+            backend_name=args.backend,
             embedding_model=args.embedding_model,
             embedding_mode=args.embedding_mode,
             embedding_options=embedding_options or None,
@@ -1451,8 +1387,8 @@ Examples:
             num_threads=args.num_threads,
         )
 
-        for chunk in all_texts:
+        for chunk_text in all_texts:
-            builder.add_text(chunk["text"], metadata=chunk["metadata"])
+            builder.add_text(chunk_text)
 
         builder.build_index(index_path)
         print(f"Index built at {index_path}")
@@ -1559,11 +1495,6 @@ Examples:
                         print("Invalid input. Aborting search.")
                         return
 
-        # Build provider_options for runtime override
-        provider_options = {}
-        if args.embedding_prompt_template:
-            provider_options["prompt_template"] = args.embedding_prompt_template
-
         searcher = LeannSearcher(index_path=index_path)
         results = searcher.search(
             query,
@@ -1573,31 +1504,12 @@ Examples:
             prune_ratio=args.prune_ratio,
             recompute_embeddings=args.recompute_embeddings,
             pruning_strategy=args.pruning_strategy,
-            provider_options=provider_options if provider_options else None,
         )
 
         print(f"Search results for '{query}' (top {len(results)}):")
         for i, result in enumerate(results, 1):
             print(f"{i}. Score: {result.score:.3f}")
-
-            # Display metadata if flag is set
-            if args.show_metadata and result.metadata:
-                file_path = result.metadata.get("file_path", "")
-                if file_path:
-                    print(f"   📄 File: {file_path}")
-
-                file_name = result.metadata.get("file_name", "")
-                if file_name and file_name != file_path:
-                    print(f"   📝 Name: {file_name}")
-
-                # Show timestamps if available
-                if "creation_date" in result.metadata:
-                    print(f"   🕐 Created: {result.metadata['creation_date']}")
-                if "last_modified_date" in result.metadata:
-                    print(f"   🕑 Modified: {result.metadata['last_modified_date']}")
-
             print(f"   {result.text[:200]}...")
-            print(f"   Source: {result.metadata.get('source', '')}")
             print()
 
     async def ask_questions(self, args):
@@ -1621,12 +1533,6 @@ Examples:
             resolved_api_key = resolve_openai_api_key(args.api_key)
             if resolved_api_key:
                 llm_config["api_key"] = resolved_api_key
-        elif args.llm == "anthropic":
-            # For Anthropic, pass base_url and API key if provided
-            if args.api_base:
-                llm_config["base_url"] = resolve_anthropic_base_url(args.api_base)
-            if args.api_key:
-                llm_config["api_key"] = args.api_key
 
         chat = LeannChat(index_path=index_path, llm_config=llm_config)
 
@@ -1635,7 +1541,6 @@ Examples:
             llm_kwargs["thinking_budget"] = args.thinking_budget
 
         def _ask_once(prompt: str) -> None:
-            query_start_time = time.time()
             response = chat.ask(
                 prompt,
                 top_k=args.top_k,
@@ -1646,20 +1551,27 @@ Examples:
                 pruning_strategy=args.pruning_strategy,
                 llm_kwargs=llm_kwargs,
             )
-            query_completion_time = time.time() - query_start_time
             print(f"LEANN: {response}")
-            print(f"The query took {query_completion_time:.3f} seconds to finish")
 
         initial_query = (args.query or "").strip()
 
         if args.interactive:
-            # Create interactive session
-            session = create_cli_session(index_name)
-
             if initial_query:
                 _ask_once(initial_query)
 
-            session.run_interactive_loop(_ask_once)
+            print("LEANN Assistant ready! Type 'quit' to exit")
+            print("=" * 40)
+
+            while True:
+                user_input = input("\nYou: ").strip()
+                if user_input.lower() in ["quit", "exit", "q"]:
+                    print("Goodbye!")
+                    break
+
+                if not user_input:
+                    continue
+
+                _ask_once(user_input)
         else:
             query = initial_query or input("Enter your question: ").strip()
             if not query:

packages/leann-core/src/leann/embedding_compute.py

@@ -4,15 +4,12 @@ Consolidates all embedding computation logic using SentenceTransformer
 Preserves all optimization parameters to ensure performance
 """
 
-import json
 import logging
 import os
-import subprocess
 import time
 from typing import Any, Optional
 
 import numpy as np
-import tiktoken
 import torch
 
 from .settings import resolve_ollama_host, resolve_openai_api_key, resolve_openai_base_url
@@ -23,288 +20,6 @@ LOG_LEVEL = os.getenv("LEANN_LOG_LEVEL", "WARNING").upper()
 log_level = getattr(logging, LOG_LEVEL, logging.WARNING)
 logger.setLevel(log_level)
 
-# Token limit registry for embedding models
-# Used as fallback when dynamic discovery fails (e.g., LM Studio, OpenAI)
-# Ollama models use dynamic discovery via /api/show
-EMBEDDING_MODEL_LIMITS = {
-    # Nomic models (common across servers)
-    "nomic-embed-text": 2048,  # Corrected from 512 - verified via /api/show
-    "nomic-embed-text-v1.5": 2048,
-    "nomic-embed-text-v2": 512,
-    # Other embedding models
-    "mxbai-embed-large": 512,
-    "all-minilm": 512,
-    "bge-m3": 8192,
-    "snowflake-arctic-embed": 512,
-    # OpenAI models
-    "text-embedding-3-small": 8192,
-    "text-embedding-3-large": 8192,
-    "text-embedding-ada-002": 8192,
-}
-
-# Runtime cache for dynamically discovered token limits
-# Key: (model_name, base_url), Value: token_limit
-# Prevents repeated SDK/API calls for the same model
-_token_limit_cache: dict[tuple[str, str], int] = {}
-
-
-def get_model_token_limit(
-    model_name: str,
-    base_url: Optional[str] = None,
-    default: int = 2048,
-) -> int:
-    """
-    Get token limit for a given embedding model.
-    Uses hybrid approach: dynamic discovery for Ollama, registry fallback for others.
-    Caches discovered limits to prevent repeated API/SDK calls.
-
-    Args:
-        model_name: Name of the embedding model
-        base_url: Base URL of the embedding server (for dynamic discovery)
-        default: Default token limit if model not found
-
-    Returns:
-        Token limit for the model in tokens
-    """
-    # Check cache first to avoid repeated SDK/API calls
-    cache_key = (model_name, base_url or "")
-    if cache_key in _token_limit_cache:
-        cached_limit = _token_limit_cache[cache_key]
-        logger.debug(f"Using cached token limit for {model_name}: {cached_limit}")
-        return cached_limit
-
-    # Try Ollama dynamic discovery if base_url provided
-    if base_url:
-        # Detect Ollama servers by port or "ollama" in URL
-        if "11434" in base_url or "ollama" in base_url.lower():
-            limit = _query_ollama_context_limit(model_name, base_url)
-            if limit:
-                _token_limit_cache[cache_key] = limit
-                return limit
-
-        # Try LM Studio SDK discovery
-        if "1234" in base_url or "lmstudio" in base_url.lower() or "lm.studio" in base_url.lower():
-            # Convert HTTP to WebSocket URL
-            ws_url = base_url.replace("https://", "wss://").replace("http://", "ws://")
-            # Remove /v1 suffix if present
-            if ws_url.endswith("/v1"):
-                ws_url = ws_url[:-3]
-
-            limit = _query_lmstudio_context_limit(model_name, ws_url)
-            if limit:
-                _token_limit_cache[cache_key] = limit
-                return limit
-
-    # Fallback to known model registry with version handling (from PR #154)
-    # Handle versioned model names (e.g., "nomic-embed-text:latest" -> "nomic-embed-text")
-    base_model_name = model_name.split(":")[0]
-
-    # Check exact match first
-    if model_name in EMBEDDING_MODEL_LIMITS:
-        limit = EMBEDDING_MODEL_LIMITS[model_name]
-        _token_limit_cache[cache_key] = limit
-        return limit
-
-    # Check base name match
-    if base_model_name in EMBEDDING_MODEL_LIMITS:
-        limit = EMBEDDING_MODEL_LIMITS[base_model_name]
-        _token_limit_cache[cache_key] = limit
-        return limit
-
-    # Check partial matches for common patterns
-    for known_model, registry_limit in EMBEDDING_MODEL_LIMITS.items():
-        if known_model in base_model_name or base_model_name in known_model:
-            _token_limit_cache[cache_key] = registry_limit
-            return registry_limit
-
-    # Default fallback
-    logger.warning(f"Unknown model '{model_name}', using default {default} token limit")
-    _token_limit_cache[cache_key] = default
-    return default
-
-
-def truncate_to_token_limit(texts: list[str], token_limit: int) -> list[str]:
-    """
-    Truncate texts to fit within token limit using tiktoken.
-
-    Args:
-        texts: List of text strings to truncate
-        token_limit: Maximum number of tokens allowed
-
-    Returns:
-        List of truncated texts (same length as input)
-    """
-    if not texts:
-        return []
-
-    # Use tiktoken with cl100k_base encoding
-    enc = tiktoken.get_encoding("cl100k_base")
-
-    truncated_texts = []
-    truncation_count = 0
-    total_tokens_removed = 0
-    max_original_length = 0
-
-    for i, text in enumerate(texts):
-        tokens = enc.encode(text)
-        original_length = len(tokens)
-
-        if original_length <= token_limit:
-            # Text is within limit, keep as is
-            truncated_texts.append(text)
-        else:
-            # Truncate to token_limit
-            truncated_tokens = tokens[:token_limit]
-            truncated_text = enc.decode(truncated_tokens)
-            truncated_texts.append(truncated_text)
-
-            # Track truncation statistics
-            truncation_count += 1
-            tokens_removed = original_length - token_limit
-            total_tokens_removed += tokens_removed
-            max_original_length = max(max_original_length, original_length)
-
-            # Log individual truncation at WARNING level (first few only)
-            if truncation_count <= 3:
-                logger.warning(
-                    f"Text {i + 1} truncated: {original_length} → {token_limit} tokens "
-                    f"({tokens_removed} tokens removed)"
-                )
-            elif truncation_count == 4:
-                logger.warning("Further truncation warnings suppressed...")
-
-    # Log summary at INFO level
-    if truncation_count > 0:
-        logger.warning(
-            f"Truncation summary: {truncation_count}/{len(texts)} texts truncated "
-            f"(removed {total_tokens_removed} tokens total, longest was {max_original_length} tokens)"
-        )
-    else:
-        logger.debug(
-            f"No truncation needed - all {len(texts)} texts within {token_limit} token limit"
-        )
-
-    return truncated_texts
-
-
-def _query_ollama_context_limit(model_name: str, base_url: str) -> Optional[int]:
-    """
-    Query Ollama /api/show for model context limit.
-
-    Args:
-        model_name: Name of the Ollama model
-        base_url: Base URL of the Ollama server
-
-    Returns:
-        Context limit in tokens if found, None otherwise
-    """
-    try:
-        import requests
-
-        response = requests.post(
-            f"{base_url}/api/show",
-            json={"name": model_name},
-            timeout=5,
-        )
-        if response.status_code == 200:
-            data = response.json()
-            if "model_info" in data:
-                # Look for *.context_length in model_info
-                for key, value in data["model_info"].items():
-                    if "context_length" in key and isinstance(value, int):
-                        logger.info(f"Detected {model_name} context limit: {value} tokens")
-                        return value
-    except Exception as e:
-        logger.debug(f"Failed to query Ollama context limit: {e}")
-
-    return None
-
-
-def _query_lmstudio_context_limit(model_name: str, base_url: str) -> Optional[int]:
-    """
-    Query LM Studio SDK for model context length via Node.js subprocess.
-
-    Args:
-        model_name: Name of the LM Studio model
-        base_url: Base URL of the LM Studio server (WebSocket format, e.g., "ws://localhost:1234")
-
-    Returns:
-        Context limit in tokens if found, None otherwise
-    """
-    # Inline JavaScript using @lmstudio/sdk
-    # Note: Load model temporarily for metadata, then unload to respect JIT auto-evict
-    js_code = f"""
-    const {{ LMStudioClient }} = require('@lmstudio/sdk');
-    (async () => {{
-        try {{
-            const client = new LMStudioClient({{ baseUrl: '{base_url}' }});
-            const model = await client.embedding.load('{model_name}', {{ verbose: false }});
-            const contextLength = await model.getContextLength();
-            await model.unload();  // Unload immediately to respect JIT auto-evict settings
-            console.log(JSON.stringify({{ contextLength, identifier: '{model_name}' }}));
-        }} catch (error) {{
-            console.error(JSON.stringify({{ error: error.message }}));
-            process.exit(1);
-        }}
-    }})();
-    """
-
-    try:
-        # Set NODE_PATH to include global modules for @lmstudio/sdk resolution
-        env = os.environ.copy()
-
-        # Try to get npm global root (works with nvm, brew node, etc.)
-        try:
-            npm_root = subprocess.run(
-                ["npm", "root", "-g"],
-                capture_output=True,
-                text=True,
-                timeout=5,
-            )
-            if npm_root.returncode == 0:
-                global_modules = npm_root.stdout.strip()
-                # Append to existing NODE_PATH if present
-                existing_node_path = env.get("NODE_PATH", "")
-                env["NODE_PATH"] = (
-                    f"{global_modules}:{existing_node_path}"
-                    if existing_node_path
-                    else global_modules
-                )
-        except Exception:
-            # If npm not available, continue with existing NODE_PATH
-            pass
-
-        result = subprocess.run(
-            ["node", "-e", js_code],
-            capture_output=True,
-            text=True,
-            timeout=10,
-            env=env,
-        )
-
-        if result.returncode != 0:
-            logger.debug(f"LM Studio SDK error: {result.stderr}")
-            return None
-
-        data = json.loads(result.stdout)
-        context_length = data.get("contextLength")
-
-        if context_length and context_length > 0:
-            logger.info(f"LM Studio SDK detected {model_name} context length: {context_length}")
-            return context_length
-
-    except FileNotFoundError:
-        logger.debug("Node.js not found - install Node.js for LM Studio SDK features")
-    except subprocess.TimeoutExpired:
-        logger.debug("LM Studio SDK query timeout")
-    except json.JSONDecodeError:
-        logger.debug("LM Studio SDK returned invalid JSON")
-    except Exception as e:
-        logger.debug(f"LM Studio SDK query failed: {e}")
-
-    return None
-
-
 # Global model cache to avoid repeated loading
 _model_cache: dict[str, Any] = {}
 
@@ -352,7 +67,6 @@ def compute_embeddings(
             model_name,
             base_url=provider_options.get("base_url"),
             api_key=provider_options.get("api_key"),
-            provider_options=provider_options,
         )
     elif mode == "mlx":
         return compute_embeddings_mlx(texts, model_name)
@@ -362,7 +76,6 @@ def compute_embeddings(
             model_name,
             is_build=is_build,
             host=provider_options.get("host"),
-            provider_options=provider_options,
         )
     elif mode == "gemini":
         return compute_embeddings_gemini(texts, model_name, is_build=is_build)
@@ -470,73 +183,32 @@ def compute_embeddings_sentence_transformers(
         }
 
         try:
-            # Try loading with advanced parameters first (newer versions)
+            # Try local loading first
-            local_model_kwargs = model_kwargs.copy()
+            model_kwargs["local_files_only"] = True
-            local_tokenizer_kwargs = tokenizer_kwargs.copy()
+            tokenizer_kwargs["local_files_only"] = True
-            local_model_kwargs["local_files_only"] = True
-            local_tokenizer_kwargs["local_files_only"] = True
 
             model = SentenceTransformer(
                 model_name,
                 device=device,
-                model_kwargs=local_model_kwargs,
+                model_kwargs=model_kwargs,
-                tokenizer_kwargs=local_tokenizer_kwargs,
+                tokenizer_kwargs=tokenizer_kwargs,
                 local_files_only=True,
             )
             logger.info("Model loaded successfully! (local + optimized)")
-        except TypeError as e:
-            if "model_kwargs" in str(e) or "tokenizer_kwargs" in str(e):
-                logger.warning(
-                    f"Advanced parameters not supported ({e}), using basic initialization..."
-                )
-                # Fallback to basic initialization for older versions
-                try:
-                    model = SentenceTransformer(
-                        model_name,
-                        device=device,
-                        local_files_only=True,
-                    )
-                    logger.info("Model loaded successfully! (local + basic)")
-                except Exception as e2:
-                    logger.warning(f"Local loading failed ({e2}), trying network download...")
-                    model = SentenceTransformer(
-                        model_name,
-                        device=device,
-                        local_files_only=False,
-                    )
-                    logger.info("Model loaded successfully! (network + basic)")
-            else:
-                raise
         except Exception as e:
             logger.warning(f"Local loading failed ({e}), trying network download...")
-            # Fallback to network loading with advanced parameters
+            # Fallback to network loading
-            try:
+            model_kwargs["local_files_only"] = False
-                network_model_kwargs = model_kwargs.copy()
+            tokenizer_kwargs["local_files_only"] = False
-                network_tokenizer_kwargs = tokenizer_kwargs.copy()
-                network_model_kwargs["local_files_only"] = False
-                network_tokenizer_kwargs["local_files_only"] = False
 
             model = SentenceTransformer(
                 model_name,
                 device=device,
-                    model_kwargs=network_model_kwargs,
+                model_kwargs=model_kwargs,
-                    tokenizer_kwargs=network_tokenizer_kwargs,
+                tokenizer_kwargs=tokenizer_kwargs,
                 local_files_only=False,
             )
             logger.info("Model loaded successfully! (network + optimized)")
-            except TypeError as e2:
-                if "model_kwargs" in str(e2) or "tokenizer_kwargs" in str(e2):
-                    logger.warning(
-                        f"Advanced parameters not supported ({e2}), using basic network loading..."
-                    )
-                    model = SentenceTransformer(
-                        model_name,
-                        device=device,
-                        local_files_only=False,
-                    )
-                    logger.info("Model loaded successfully! (network + basic)")
-                else:
-                    raise
 
         # Apply additional optimizations based on mode
         if use_fp16 and device in ["cuda", "mps"]:
@@ -701,7 +373,6 @@ def compute_embeddings_openai(
     model_name: str,
     base_url: Optional[str] = None,
     api_key: Optional[str] = None,
-    provider_options: Optional[dict[str, Any]] = None,
 ) -> np.ndarray:
     # TODO: @yichuan-w add progress bar only in build mode
     """Compute embeddings using OpenAI API"""
@@ -720,40 +391,26 @@ def compute_embeddings_openai(
             f"Found {invalid_count} empty/invalid text(s) in input. Upstream should filter before calling OpenAI."
         )
 
-    # Extract base_url and api_key from provider_options if not provided directly
+    resolved_base_url = resolve_openai_base_url(base_url)
-    provider_options = provider_options or {}
+    resolved_api_key = resolve_openai_api_key(api_key)
-    effective_base_url = base_url or provider_options.get("base_url")
-    effective_api_key = api_key or provider_options.get("api_key")
-
-    resolved_base_url = resolve_openai_base_url(effective_base_url)
-    resolved_api_key = resolve_openai_api_key(effective_api_key)
 
     if not resolved_api_key:
         raise RuntimeError("OPENAI_API_KEY environment variable not set")
 
-    # Create OpenAI client
+    # Cache OpenAI client
+    cache_key = f"openai_client::{resolved_base_url}"
+    if cache_key in _model_cache:
+        client = _model_cache[cache_key]
+    else:
         client = openai.OpenAI(api_key=resolved_api_key, base_url=resolved_base_url)
+        _model_cache[cache_key] = client
+        logger.info("OpenAI client cached")
 
     logger.info(
         f"Computing embeddings for {len(texts)} texts using OpenAI API, model: '{model_name}'"
     )
     print(f"len of texts: {len(texts)}")
 
-    # Apply prompt template if provided
-    # Priority: build_prompt_template (new format) > prompt_template (old format)
-    prompt_template = provider_options.get("build_prompt_template") or provider_options.get(
-        "prompt_template"
-    )
-
-    if prompt_template:
-        logger.warning(f"Applying prompt template: '{prompt_template}'")
-        texts = [f"{prompt_template}{text}" for text in texts]
-
-    # Query token limit and apply truncation
-    token_limit = get_model_token_limit(model_name, base_url=effective_base_url)
-    logger.info(f"Using token limit: {token_limit} for model '{model_name}'")
-    texts = truncate_to_token_limit(texts, token_limit)
-
     # OpenAI has limits on batch size and input length
     max_batch_size = 800  # Conservative batch size because the token limit is 300K
     all_embeddings = []
@@ -784,15 +441,7 @@ def compute_embeddings_openai(
         try:
             response = client.embeddings.create(model=model_name, input=batch_texts)
             batch_embeddings = [embedding.embedding for embedding in response.data]
-
+            all_embeddings.extend(batch_embeddings)
-            # Verify we got the expected number of embeddings
-            if len(batch_embeddings) != len(batch_texts):
-                logger.warning(
-                    f"Expected {len(batch_texts)} embeddings but got {len(batch_embeddings)}"
-                )
-
-            # Only take the number of embeddings that match the batch size
-            all_embeddings.extend(batch_embeddings[: len(batch_texts)])
         except Exception as e:
             logger.error(f"Batch {i} failed: {e}")
             raise
@@ -882,20 +531,17 @@ def compute_embeddings_ollama(
     model_name: str,
     is_build: bool = False,
     host: Optional[str] = None,
-    provider_options: Optional[dict[str, Any]] = None,
 ) -> np.ndarray:
     """
-    Compute embeddings using Ollama API with true batch processing.
+    Compute embeddings using Ollama API with simplified batch processing.
 
-    Uses the /api/embed endpoint which supports batch inputs.
+    Uses batch size of 32 for MPS/CPU and 128 for CUDA to optimize performance.
-    Batch size: 32 for MPS/CPU, 128 for CUDA to optimize performance.
 
     Args:
         texts: List of texts to compute embeddings for
         model_name: Ollama model name (e.g., "nomic-embed-text", "mxbai-embed-large")
         is_build: Whether this is a build operation (shows progress bar)
         host: Ollama host URL (defaults to environment or http://localhost:11434)
-        provider_options: Optional provider-specific options (e.g., prompt_template)
 
     Returns:
         Normalized embeddings array, shape: (len(texts), embedding_dim)
@@ -994,11 +640,11 @@ def compute_embeddings_ollama(
             logger.info(f"Resolved model name '{model_name}' to '{resolved_model_name}'")
         model_name = resolved_model_name
 
-        # Verify the model supports embeddings by testing it with /api/embed
+        # Verify the model supports embeddings by testing it
         try:
             test_response = requests.post(
-                f"{resolved_host}/api/embed",
+                f"{resolved_host}/api/embeddings",
-                json={"model": model_name, "input": "test"},
+                json={"model": model_name, "prompt": "test"},
                 timeout=10,
             )
             if test_response.status_code != 200:
@@ -1030,82 +676,56 @@ def compute_embeddings_ollama(
         # If torch is not available, use conservative batch size
         batch_size = 32
 
-    logger.info(f"Using batch size: {batch_size} for true batch processing")
+    logger.info(f"Using batch size: {batch_size}")
-
-    # Apply prompt template if provided
-    provider_options = provider_options or {}
-    # Priority: build_prompt_template (new format) > prompt_template (old format)
-    prompt_template = provider_options.get("build_prompt_template") or provider_options.get(
-        "prompt_template"
-    )
-
-    if prompt_template:
-        logger.warning(f"Applying prompt template: '{prompt_template}'")
-        texts = [f"{prompt_template}{text}" for text in texts]
-
-    # Get model token limit and apply truncation before batching
-    token_limit = get_model_token_limit(model_name, base_url=resolved_host)
-    logger.info(f"Model '{model_name}' token limit: {token_limit}")
-
-    # Apply truncation to all texts before batch processing
-    # Function logs truncation details internally
-    texts = truncate_to_token_limit(texts, token_limit)
 
     def get_batch_embeddings(batch_texts):
-        """Get embeddings for a batch of texts using /api/embed endpoint."""
+        """Get embeddings for a batch of texts."""
+        all_embeddings = []
+        failed_indices = []
+
+        for i, text in enumerate(batch_texts):
             max_retries = 3
             retry_count = 0
 
-        # Texts are already truncated to token limit by the outer function
+            # Truncate very long texts to avoid API issues
+            truncated_text = text[:8000] if len(text) > 8000 else text
             while retry_count < max_retries:
                 try:
-                # Use /api/embed endpoint with "input" parameter for batch processing
                     response = requests.post(
-                    f"{resolved_host}/api/embed",
+                        f"{resolved_host}/api/embeddings",
-                    json={"model": model_name, "input": batch_texts},
+                        json={"model": model_name, "prompt": truncated_text},
-                    timeout=60,  # Increased timeout for batch processing
+                        timeout=30,
                     )
                     response.raise_for_status()
 
                     result = response.json()
-                batch_embeddings = result.get("embeddings")
+                    embedding = result.get("embedding")
 
-                if batch_embeddings is None:
+                    if embedding is None:
-                    raise ValueError("No embeddings returned from API")
+                        raise ValueError(f"No embedding returned for text {i}")
 
-                if not isinstance(batch_embeddings, list):
+                    if not isinstance(embedding, list) or len(embedding) == 0:
-                    raise ValueError(f"Invalid embeddings format: {type(batch_embeddings)}")
+                        raise ValueError(f"Invalid embedding format for text {i}")
 
-                if len(batch_embeddings) != len(batch_texts):
+                    all_embeddings.append(embedding)
-                    raise ValueError(
+                    break
-                        f"Mismatch: requested {len(batch_texts)} embeddings, got {len(batch_embeddings)}"
-                    )
-
-                return batch_embeddings, []
 
                 except requests.exceptions.Timeout:
                     retry_count += 1
                     if retry_count >= max_retries:
-                    logger.warning(f"Timeout for batch after {max_retries} retries")
+                        logger.warning(f"Timeout for text {i} after {max_retries} retries")
-                    return None, list(range(len(batch_texts)))
+                        failed_indices.append(i)
+                        all_embeddings.append(None)
+                        break
 
                 except Exception as e:
                     retry_count += 1
                     if retry_count >= max_retries:
-                    # Enhanced error detection for token limit violations
+                        logger.error(f"Failed to get embedding for text {i}: {e}")
-                    error_msg = str(e).lower()
+                        failed_indices.append(i)
-                    if "token" in error_msg and (
+                        all_embeddings.append(None)
-                        "limit" in error_msg or "exceed" in error_msg or "length" in error_msg
+                        break
-                    ):
+        return all_embeddings, failed_indices
-                        logger.error(
-                            f"Token limit exceeded for batch. Error: {e}. "
-                            f"Consider reducing chunk sizes or check token truncation."
-                        )
-                    else:
-                        logger.error(f"Failed to get embeddings for batch: {e}")
-                    return None, list(range(len(batch_texts)))
-
-        return None, list(range(len(batch_texts)))
 
     # Process texts in batches
     all_embeddings = []
@@ -1123,7 +743,7 @@ def compute_embeddings_ollama(
     num_batches = (len(texts) + batch_size - 1) // batch_size
 
     if show_progress:
-        batch_iterator = tqdm(range(num_batches), desc="Computing Ollama embeddings (batched)")
+        batch_iterator = tqdm(range(num_batches), desc="Computing Ollama embeddings")
     else:
         batch_iterator = range(num_batches)
 
@@ -1134,14 +754,10 @@ def compute_embeddings_ollama(
 
         batch_embeddings, batch_failed = get_batch_embeddings(batch_texts)
 
-        if batch_embeddings is not None:
-            all_embeddings.extend(batch_embeddings)
-        else:
-            # Entire batch failed, add None placeholders
-            all_embeddings.extend([None] * len(batch_texts))
         # Adjust failed indices to global indices
         global_failed = [start_idx + idx for idx in batch_failed]
         all_failed_indices.extend(global_failed)
+        all_embeddings.extend(batch_embeddings)
 
     # Handle failed embeddings
     if all_failed_indices:

packages/leann-core/src/leann/interactive_utils.py

@@ -1,189 +0,0 @@
-"""
-Interactive session utilities for LEANN applications.
-
-Provides shared readline functionality and command handling across
-CLI, API, and RAG example interactive modes.
-"""
-
-import atexit
-import os
-from pathlib import Path
-from typing import Callable, Optional
-
-# Try to import readline with fallback for Windows
-try:
-    import readline
-
-    HAS_READLINE = True
-except ImportError:
-    # Windows doesn't have readline by default
-    HAS_READLINE = False
-    readline = None
-
-
-class InteractiveSession:
-    """Manages interactive session with optional readline support and common commands."""
-
-    def __init__(
-        self,
-        history_name: str,
-        prompt: str = "You: ",
-        welcome_message: str = "",
-    ):
-        """
-        Initialize interactive session with optional readline support.
-
-        Args:
-            history_name: Name for history file (e.g., "cli", "api_chat")
-                         (ignored if readline not available)
-            prompt: Input prompt to display
-            welcome_message: Message to show when starting session
-
-        Note:
-            On systems without readline (e.g., Windows), falls back to basic input()
-            with limited functionality (no history, no line editing).
-        """
-        self.history_name = history_name
-        self.prompt = prompt
-        self.welcome_message = welcome_message
-        self._setup_complete = False
-
-    def setup_readline(self):
-        """Setup readline with history support (if available)."""
-        if self._setup_complete:
-            return
-
-        if not HAS_READLINE:
-            # Readline not available (likely Windows), skip setup
-            self._setup_complete = True
-            return
-
-        # History file setup
-        history_dir = Path.home() / ".leann" / "history"
-        history_dir.mkdir(parents=True, exist_ok=True)
-        history_file = history_dir / f"{self.history_name}.history"
-
-        # Load history if exists
-        try:
-            readline.read_history_file(str(history_file))
-            readline.set_history_length(1000)
-        except FileNotFoundError:
-            pass
-
-        # Save history on exit
-        atexit.register(readline.write_history_file, str(history_file))
-
-        # Optional: Enable vi editing mode (commented out by default)
-        # readline.parse_and_bind("set editing-mode vi")
-
-        self._setup_complete = True
-
-    def _show_help(self):
-        """Show available commands."""
-        print("Commands:")
-        print("  quit/exit/q - Exit the chat")
-        print("  help - Show this help message")
-        print("  clear - Clear screen")
-        print("  history - Show command history")
-
-    def _show_history(self):
-        """Show command history."""
-        if not HAS_READLINE:
-            print("  History not available (readline not supported on this system)")
-            return
-
-        history_length = readline.get_current_history_length()
-        if history_length == 0:
-            print("  No history available")
-            return
-
-        for i in range(history_length):
-            item = readline.get_history_item(i + 1)
-            if item:
-                print(f"  {i + 1}: {item}")
-
-    def get_user_input(self) -> Optional[str]:
-        """
-        Get user input with readline support.
-
-        Returns:
-            User input string, or None if EOF (Ctrl+D)
-        """
-        try:
-            return input(self.prompt).strip()
-        except KeyboardInterrupt:
-            print("\n(Use 'quit' to exit)")
-            return ""  # Return empty string to continue
-        except EOFError:
-            print("\nGoodbye!")
-            return None
-
-    def run_interactive_loop(self, handler_func: Callable[[str], None]):
-        """
-        Run the interactive loop with a custom handler function.
-
-        Args:
-            handler_func: Function to handle user input that's not a built-in command
-                         Should accept a string and handle the user's query
-        """
-        self.setup_readline()
-
-        if self.welcome_message:
-            print(self.welcome_message)
-
-        while True:
-            user_input = self.get_user_input()
-
-            if user_input is None:  # EOF (Ctrl+D)
-                break
-
-            if not user_input:  # Empty input or KeyboardInterrupt
-                continue
-
-            # Handle built-in commands
-            command = user_input.lower()
-            if command in ["quit", "exit", "q"]:
-                print("Goodbye!")
-                break
-            elif command == "help":
-                self._show_help()
-            elif command == "clear":
-                os.system("clear" if os.name != "nt" else "cls")
-            elif command == "history":
-                self._show_history()
-            else:
-                # Regular user input - pass to handler
-                try:
-                    handler_func(user_input)
-                except Exception as e:
-                    print(f"Error: {e}")
-
-
-def create_cli_session(index_name: str) -> InteractiveSession:
-    """Create an interactive session for CLI usage."""
-    return InteractiveSession(
-        history_name=index_name,
-        prompt="\nYou: ",
-        welcome_message="LEANN Assistant ready! Type 'quit' to exit, 'help' for commands\n"
-        + "=" * 40,
-    )
-
-
-def create_api_session() -> InteractiveSession:
-    """Create an interactive session for API chat."""
-    return InteractiveSession(
-        history_name="api_chat",
-        prompt="You: ",
-        welcome_message="Leann Chat started (type 'quit' to exit, 'help' for commands)\n"
-        + "=" * 40,
-    )
-
-
-def create_rag_session(app_name: str, data_description: str) -> InteractiveSession:
-    """Create an interactive session for RAG examples."""
-    return InteractiveSession(
-        history_name=f"{app_name}_rag",
-        prompt="You: ",
-        welcome_message=f"[Interactive Mode] Chat with your {data_description} data!\nType 'quit' or 'exit' to stop, 'help' for commands.\n"
-        + "=" * 40,
-    )

packages/leann-core/src/leann/interface.py

@@ -77,7 +77,6 @@ class LeannBackendSearcherInterface(ABC):
         query: str,
         use_server_if_available: bool = True,
         zmq_port: Optional[int] = None,
-        query_template: Optional[str] = None,
     ) -> np.ndarray:
         """Compute embedding for a query string
 
@@ -85,7 +84,6 @@ class LeannBackendSearcherInterface(ABC):
             query: The query string to embed
             zmq_port: ZMQ port for embedding server
             use_server_if_available: Whether to try using embedding server first
-            query_template: Optional prompt template to prepend to query
 
         Returns:
             Query embedding as numpy array with shape (1, D)

packages/leann-core/src/leann/mcp.py

@@ -60,11 +60,6 @@ def handle_request(request):
                                     "maximum": 128,
                                     "description": "Search complexity level. Use 16-32 for fast searches (recommended), 64+ for higher precision when needed.",
                                 },
-                                "show_metadata": {
-                                    "type": "boolean",
-                                    "default": False,
-                                    "description": "Include file paths and metadata in search results. Useful for understanding which files contain the results.",
-                                },
                             },
                             "required": ["index_name", "query"],
                         },
@@ -109,8 +104,6 @@ def handle_request(request):
                     f"--complexity={args.get('complexity', 32)}",
                     "--non-interactive",
                 ]
-                if args.get("show_metadata", False):
-                    cmd.append("--show-metadata")
                 result = subprocess.run(cmd, capture_output=True, text=True)
 
             elif tool_name == "leann_list":

packages/leann-core/src/leann/registry.py

@@ -33,8 +33,6 @@ def autodiscover_backends():
     discovered_backends = []
     for dist in importlib.metadata.distributions():
         dist_name = dist.metadata["name"]
-        if dist_name is None:
-            continue
         if dist_name.startswith("leann-backend-"):
             backend_module_name = dist_name.replace("-", "_")
             discovered_backends.append(backend_module_name)

packages/leann-core/src/leann/searcher_base.py

@@ -71,15 +71,6 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
             or "mips"
         )
 
-        # Filter out ALL prompt templates from provider_options during search
-        # Templates are applied in compute_query_embedding (line 109-110) BEFORE server call
-        # The server should never apply templates during search to avoid double-templating
-        search_provider_options = {
-            k: v
-            for k, v in self.embedding_options.items()
-            if k not in ("build_prompt_template", "query_prompt_template", "prompt_template")
-        }
-
         server_started, actual_port = self.embedding_server_manager.start_server(
             port=port,
             model_name=self.embedding_model,
@@ -87,7 +78,7 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
             passages_file=passages_source_file,
             distance_metric=distance_metric,
             enable_warmup=kwargs.get("enable_warmup", False),
-            provider_options=search_provider_options,
+            provider_options=self.embedding_options,
         )
         if not server_started:
             raise RuntimeError(f"Failed to start embedding server on port {actual_port}")
@@ -99,7 +90,6 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
         query: str,
         use_server_if_available: bool = True,
         zmq_port: int = 5557,
-        query_template: Optional[str] = None,
     ) -> np.ndarray:
         """
         Compute embedding for a query string.
@@ -108,16 +98,10 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
             query: The query string to embed
             zmq_port: ZMQ port for embedding server
             use_server_if_available: Whether to try using embedding server first
-            query_template: Optional prompt template to prepend to query
 
         Returns:
             Query embedding as numpy array
         """
-        # Apply query template BEFORE any computation path
-        # This ensures template is applied consistently for both server and fallback paths
-        if query_template:
-            query = f"{query_template}{query}"
-
         # Try to use embedding server if available and requested
         if use_server_if_available:
             try:

packages/leann-core/src/leann/settings.py

@@ -9,7 +9,6 @@ from typing import Any
 # Default fallbacks to preserve current behaviour while keeping them in one place.
 _DEFAULT_OLLAMA_HOST = "http://localhost:11434"
 _DEFAULT_OPENAI_BASE_URL = "https://api.openai.com/v1"
-_DEFAULT_ANTHROPIC_BASE_URL = "https://api.anthropic.com"
 
 
 def _clean_url(value: str) -> str:
@@ -53,23 +52,6 @@ def resolve_openai_base_url(explicit: str | None = None) -> str:
     return _clean_url(_DEFAULT_OPENAI_BASE_URL)
 
 
-def resolve_anthropic_base_url(explicit: str | None = None) -> str:
-    """Resolve the base URL for Anthropic-compatible services."""
-
-    candidates = (
-        explicit,
-        os.getenv("LEANN_ANTHROPIC_BASE_URL"),
-        os.getenv("ANTHROPIC_BASE_URL"),
-        os.getenv("LOCAL_ANTHROPIC_BASE_URL"),
-    )
-
-    for candidate in candidates:
-        if candidate:
-            return _clean_url(candidate)
-
-    return _clean_url(_DEFAULT_ANTHROPIC_BASE_URL)
-
-
 def resolve_openai_api_key(explicit: str | None = None) -> str | None:
     """Resolve the API key for OpenAI-compatible services."""
 
@@ -79,15 +61,6 @@ def resolve_openai_api_key(explicit: str | None = None) -> str | None:
     return os.getenv("OPENAI_API_KEY")
 
 
-def resolve_anthropic_api_key(explicit: str | None = None) -> str | None:
-    """Resolve the API key for Anthropic services."""
-
-    if explicit:
-        return explicit
-
-    return os.getenv("ANTHROPIC_API_KEY")
-
-
 def encode_provider_options(options: dict[str, Any] | None) -> str | None:
     """Serialize provider options for child processes."""
 

packages/leann-mcp/README.md

@@ -53,11 +53,6 @@ leann build my-project --docs $(git ls-files)
 # Start Claude Code
 claude
 ```
-**Performance tip**: For maximum speed when storage space is not a concern, add the `--no-recompute` flag to your build command. This materializes all tensors and stores them on disk, avoiding recomputation on subsequent builds:
-
-```bash
-leann build my-project --docs $(git ls-files) --no-recompute
-```
 
 ## 🚀 Advanced Usage Examples to build the index
 

packages/leann/pyproject.toml

@@ -4,10 +4,10 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "leann"
-version = "0.3.5"
+version = "0.3.4"
 description = "LEANN - The smallest vector index in the world. RAG Everything with LEANN!"
 readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.9"
 license = { text = "MIT" }
 authors = [
     { name = "LEANN Team" }
@@ -18,10 +18,10 @@ classifiers = [
     "Intended Audience :: Developers",
     "License :: OSI Approved :: MIT License",
     "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
-    "Programming Language :: Python :: 3.13",
 ]
 
 # Default installation: core + hnsw + diskann

pyproject.toml

@@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
 [project]
 name = "leann-workspace"
 version = "0.1.0"
-requires-python = ">=3.10"
+requires-python = ">=3.9"
 
 dependencies = [
     "leann-core",
@@ -22,10 +22,7 @@ dependencies = [
     "sglang",
     "ollama",
     "requests>=2.25.0",
-    "sentence-transformers>=3.0.0",
+    "sentence-transformers>=2.2.0",
-    # Pin transformers below 4.46: 4.46.0 introduced Python 3.10-only typing (PEP 604) and
-    # breaks our Python 3.9 test matrix when pulled in by sentence-transformers.
-    "transformers<4.46",
     "openai>=1.0.0",
     # PDF parsing dependencies - essential for document processing
     "PyPDF2>=3.0.0",
@@ -57,8 +54,6 @@ dependencies = [
     "tree-sitter-c-sharp>=0.20.0",
     "tree-sitter-typescript>=0.20.0",
     "torchvision>=0.23.0",
-    "einops",
-    "seaborn",
 ]
 
 [project.optional-dependencies]
@@ -69,8 +64,7 @@ diskann = [
 # Add a new optional dependency group for document processing
 documents = [
     "beautifulsoup4>=4.13.0",  # For HTML parsing
-    "python-docx>=0.8.11",     # For Word documents (creating/editing)
+    "python-docx>=0.8.11",     # For Word documents
-    "docx2txt>=0.9",           # For Word documents (text extraction)
     "openpyxl>=3.1.0",         # For Excel files
     "pandas>=2.2.0",           # For data processing
 ]
@@ -165,7 +159,6 @@ python_functions = ["test_*"]
 markers = [
     "slow: marks tests as slow (deselect with '-m \"not slow\"')",
     "openai: marks tests that require OpenAI API key",
-    "integration: marks tests that require live services (Ollama, LM Studio, etc.)",
 ]
 timeout = 300  # Reduced from 600s (10min) to 300s (5min) for CI safety
 addopts = [

tests/README.md

@@ -36,14 +36,6 @@ Tests DiskANN graph partitioning functionality:
 - Includes performance comparison between DiskANN (with partition) and HNSW
 - **Note**: These tests are skipped in CI due to hardware requirements and computation time
 
-### `test_prompt_template_e2e.py`
-Integration tests for prompt template feature with live embedding services:
-- Tests prompt template prepending with EmbeddingGemma (OpenAI-compatible API via LM Studio)
-- Tests hybrid token limit discovery (Ollama dynamic detection, registry fallback, default)
-- Tests LM Studio SDK bridge for automatic context length detection (requires Node.js + @lmstudio/sdk)
-- **Note**: These tests require live services (LM Studio, Ollama) and are marked with `@pytest.mark.integration`
-- **Important**: Prompt templates are ONLY for EmbeddingGemma and similar task-specific models, NOT regular embedding models
-
 ## Running Tests
 
 ### Install test dependencies:
@@ -74,12 +66,6 @@ pytest tests/ -m "not openai"
 # Skip slow tests
 pytest tests/ -m "not slow"
 
-# Skip integration tests (that require live services)
-pytest tests/ -m "not integration"
-
-# Run only integration tests (requires LM Studio or Ollama running)
-pytest tests/test_prompt_template_e2e.py -v -s
-
 # Run DiskANN partition tests (requires local machine, not CI)
 pytest tests/test_diskann_partition.py
 ```
@@ -115,20 +101,6 @@ The `pytest.ini` file configures:
 - Custom markers for slow and OpenAI tests
 - Verbose output with short tracebacks
 
-### Integration Test Prerequisites
-
-Integration tests (`test_prompt_template_e2e.py`) require live services:
-
-**Required:**
-- LM Studio running at `http://localhost:1234` with EmbeddingGemma model loaded
-
-**Optional:**
-- Ollama running at `http://localhost:11434` for token limit detection tests
-- Node.js + @lmstudio/sdk installed (`npm install -g @lmstudio/sdk`) for SDK bridge tests
-
-Tests gracefully skip if services are unavailable.
-
 ### Known Issues
 
 - OpenAI tests are automatically skipped if no API key is provided
-- Integration tests require live embedding services and may fail due to proxy settings (set `unset ALL_PROXY all_proxy` if needed)

tests/test_astchunk_integration.py

@@ -8,7 +8,7 @@ import subprocess
 import sys
 import tempfile
 from pathlib import Path
-from unittest.mock import Mock, patch
+from unittest.mock import patch
 
 import pytest
 
@@ -116,10 +116,8 @@ class TestChunkingFunctions:
         chunks = create_traditional_chunks(docs, chunk_size=50, chunk_overlap=10)
 
         assert len(chunks) > 0
-        # Traditional chunks now return dict format for consistency
+        assert all(isinstance(chunk, str) for chunk in chunks)
-        assert all(isinstance(chunk, dict) for chunk in chunks)
+        assert all(len(chunk.strip()) > 0 for chunk in chunks)
-        assert all("text" in chunk and "metadata" in chunk for chunk in chunks)
-        assert all(len(chunk["text"].strip()) > 0 for chunk in chunks)
 
     def test_create_traditional_chunks_empty_docs(self):
         """Test traditional chunking with empty documents."""
@@ -160,22 +158,11 @@ class Calculator:
 
             # Should have multiple chunks due to different functions/classes
             assert len(chunks) > 0
-            # R3: Expect dict format with "text" and "metadata" keys
+            assert all(isinstance(chunk, str) for chunk in chunks)
-            assert all(isinstance(chunk, dict) for chunk in chunks), "All chunks should be dicts"
+            assert all(len(chunk.strip()) > 0 for chunk in chunks)
-            assert all("text" in chunk and "metadata" in chunk for chunk in chunks), (
-                "Each chunk should have 'text' and 'metadata' keys"
-            )
-            assert all(len(chunk["text"].strip()) > 0 for chunk in chunks), (
-                "Each chunk text should be non-empty"
-            )
-
-            # Check metadata is present
-            assert all("file_path" in chunk["metadata"] for chunk in chunks), (
-                "Each chunk should have file_path metadata"
-            )
 
             # Check that code structure is somewhat preserved
-            combined_content = " ".join([c["text"] for c in chunks])
+            combined_content = " ".join(chunks)
             assert "def hello_world" in combined_content
             assert "class Calculator" in combined_content
 
@@ -207,11 +194,7 @@ class Calculator:
         chunks = create_text_chunks(docs, use_ast_chunking=False, chunk_size=50, chunk_overlap=10)
 
         assert len(chunks) > 0
-        # R3: Traditional chunking should also return dict format for consistency
+        assert all(isinstance(chunk, str) for chunk in chunks)
-        assert all(isinstance(chunk, dict) for chunk in chunks), "All chunks should be dicts"
-        assert all("text" in chunk and "metadata" in chunk for chunk in chunks), (
-            "Each chunk should have 'text' and 'metadata' keys"
-        )
 
     def test_create_text_chunks_ast_mode(self):
         """Test text chunking in AST mode."""
@@ -230,11 +213,7 @@ class Calculator:
         )
 
         assert len(chunks) > 0
-        # R3: AST mode should also return dict format
+        assert all(isinstance(chunk, str) for chunk in chunks)
-        assert all(isinstance(chunk, dict) for chunk in chunks), "All chunks should be dicts"
-        assert all("text" in chunk and "metadata" in chunk for chunk in chunks), (
-            "Each chunk should have 'text' and 'metadata' keys"
-        )
 
     def test_create_text_chunks_custom_extensions(self):
         """Test text chunking with custom code file extensions."""
@@ -374,552 +353,6 @@ class MathUtils:
                 pytest.skip("Test timed out - likely due to model download in CI")
 
 
-class TestASTContentExtraction:
-    """Test AST content extraction bug fix.
-
-    These tests verify that astchunk's dict format with 'content' key is handled correctly,
-    and that the extraction logic doesn't fall through to stringifying entire dicts.
-    """
-
-    def test_extract_content_from_astchunk_dict(self):
-        """Test that astchunk dict format with 'content' key is handled correctly.
-
-        Bug: Current code checks for chunk["text"] but astchunk returns chunk["content"].
-        This causes fallthrough to str(chunk), stringifying the entire dict.
-
-        This test will FAIL until the bug is fixed because:
-        - Current code will stringify the dict: "{'content': '...', 'metadata': {...}}"
-        - Fixed code should extract just the content value
-        """
-        # Mock the ASTChunkBuilder class
-        mock_builder = Mock()
-
-        # Astchunk returns this format
-        astchunk_format_chunk = {
-            "content": "def hello():\n    print('world')",
-            "metadata": {
-                "filepath": "test.py",
-                "line_count": 2,
-                "start_line_no": 0,
-                "end_line_no": 1,
-                "node_count": 1,
-            },
-        }
-        mock_builder.chunkify.return_value = [astchunk_format_chunk]
-
-        # Create mock document
-        doc = MockDocument(
-            "def hello():\n    print('world')", "/test/test.py", {"language": "python"}
-        )
-
-        # Mock the astchunk module and its ASTChunkBuilder class
-        mock_astchunk = Mock()
-        mock_astchunk.ASTChunkBuilder = Mock(return_value=mock_builder)
-
-        # Patch sys.modules to inject our mock before the import
-        with patch.dict("sys.modules", {"astchunk": mock_astchunk}):
-            # Call create_ast_chunks
-            chunks = create_ast_chunks([doc])
-
-        # R3: Should return dict format with proper metadata
-        assert len(chunks) > 0, "Should return at least one chunk"
-
-        # R3: Each chunk should be a dict
-        chunk = chunks[0]
-        assert isinstance(chunk, dict), "Chunk should be a dict"
-        assert "text" in chunk, "Chunk should have 'text' key"
-        assert "metadata" in chunk, "Chunk should have 'metadata' key"
-
-        chunk_text = chunk["text"]
-
-        # CRITICAL: Should NOT contain stringified dict markers in the text field
-        # These assertions will FAIL with current buggy code
-        assert "'content':" not in chunk_text, (
-            f"Chunk text contains stringified dict - extraction failed! Got: {chunk_text[:100]}..."
-        )
-        assert "'metadata':" not in chunk_text, (
-            "Chunk text contains stringified metadata - extraction failed! "
-            f"Got: {chunk_text[:100]}..."
-        )
-        assert "{" not in chunk_text or "def hello" in chunk_text.split("{")[0], (
-            "Chunk text appears to be a stringified dict"
-        )
-
-        # Should contain actual content
-        assert "def hello()" in chunk_text, "Should extract actual code content"
-        assert "print('world')" in chunk_text, "Should extract complete code content"
-
-        # R3: Should preserve astchunk metadata
-        assert "filepath" in chunk["metadata"] or "file_path" in chunk["metadata"], (
-            "Should preserve file path metadata"
-        )
-
-    def test_extract_text_key_fallback(self):
-        """Test that 'text' key still works for backward compatibility.
-
-        Some chunks might use 'text' instead of 'content' - ensure backward compatibility.
-        This test should PASS even with current code.
-        """
-        mock_builder = Mock()
-
-        # Some chunks might use "text" key
-        text_key_chunk = {"text": "def legacy_function():\n    return True"}
-        mock_builder.chunkify.return_value = [text_key_chunk]
-
-        # Create mock document
-        doc = MockDocument(
-            "def legacy_function():\n    return True", "/test/legacy.py", {"language": "python"}
-        )
-
-        # Mock the astchunk module
-        mock_astchunk = Mock()
-        mock_astchunk.ASTChunkBuilder = Mock(return_value=mock_builder)
-
-        with patch.dict("sys.modules", {"astchunk": mock_astchunk}):
-            # Call create_ast_chunks
-            chunks = create_ast_chunks([doc])
-
-        # R3: Should extract text correctly as dict format
-        assert len(chunks) > 0
-        chunk = chunks[0]
-        assert isinstance(chunk, dict), "Chunk should be a dict"
-        assert "text" in chunk, "Chunk should have 'text' key"
-
-        chunk_text = chunk["text"]
-
-        # Should NOT be stringified
-        assert "'text':" not in chunk_text, "Should not stringify dict with 'text' key"
-
-        # Should contain actual content
-        assert "def legacy_function()" in chunk_text
-        assert "return True" in chunk_text
-
-    def test_handles_string_chunks(self):
-        """Test that plain string chunks still work.
-
-        Some chunkers might return plain strings - verify these are preserved.
-        This test should PASS with current code.
-        """
-        mock_builder = Mock()
-
-        # Plain string chunk
-        plain_string_chunk = "def simple_function():\n    pass"
-        mock_builder.chunkify.return_value = [plain_string_chunk]
-
-        # Create mock document
-        doc = MockDocument(
-            "def simple_function():\n    pass", "/test/simple.py", {"language": "python"}
-        )
-
-        # Mock the astchunk module
-        mock_astchunk = Mock()
-        mock_astchunk.ASTChunkBuilder = Mock(return_value=mock_builder)
-
-        with patch.dict("sys.modules", {"astchunk": mock_astchunk}):
-            # Call create_ast_chunks
-            chunks = create_ast_chunks([doc])
-
-        # R3: Should wrap string in dict format
-        assert len(chunks) > 0
-        chunk = chunks[0]
-        assert isinstance(chunk, dict), "Even string chunks should be wrapped in dict"
-        assert "text" in chunk, "Chunk should have 'text' key"
-
-        chunk_text = chunk["text"]
-
-        assert chunk_text == plain_string_chunk.strip(), (
-            "Should preserve plain string chunk content"
-        )
-        assert "def simple_function()" in chunk_text
-        assert "pass" in chunk_text
-
-    def test_multiple_chunks_with_mixed_formats(self):
-        """Test handling of multiple chunks with different formats.
-
-        Real-world scenario: astchunk might return a mix of formats.
-        This test will FAIL if any chunk with 'content' key gets stringified.
-        """
-        mock_builder = Mock()
-
-        # Mix of formats
-        mixed_chunks = [
-            {"content": "def first():\n    return 1", "metadata": {"line_count": 2}},
-            "def second():\n    return 2",  # Plain string
-            {"text": "def third():\n    return 3"},  # Old format
-            {"content": "class MyClass:\n    pass", "metadata": {"node_count": 1}},
-        ]
-        mock_builder.chunkify.return_value = mixed_chunks
-
-        # Create mock document
-        code = "def first():\n    return 1\n\ndef second():\n    return 2\n\ndef third():\n    return 3\n\nclass MyClass:\n    pass"
-        doc = MockDocument(code, "/test/mixed.py", {"language": "python"})
-
-        # Mock the astchunk module
-        mock_astchunk = Mock()
-        mock_astchunk.ASTChunkBuilder = Mock(return_value=mock_builder)
-
-        with patch.dict("sys.modules", {"astchunk": mock_astchunk}):
-            # Call create_ast_chunks
-            chunks = create_ast_chunks([doc])
-
-        # R3: Should extract all chunks correctly as dicts
-        assert len(chunks) == 4, "Should extract all 4 chunks"
-
-        # Check each chunk
-        for i, chunk in enumerate(chunks):
-            assert isinstance(chunk, dict), f"Chunk {i} should be a dict"
-            assert "text" in chunk, f"Chunk {i} should have 'text' key"
-            assert "metadata" in chunk, f"Chunk {i} should have 'metadata' key"
-
-            chunk_text = chunk["text"]
-            # None should be stringified dicts
-            assert "'content':" not in chunk_text, f"Chunk {i} text is stringified (has 'content':)"
-            assert "'metadata':" not in chunk_text, (
-                f"Chunk {i} text is stringified (has 'metadata':)"
-            )
-            assert "'text':" not in chunk_text, f"Chunk {i} text is stringified (has 'text':)"
-
-        # Verify actual content is present
-        combined = "\n".join([c["text"] for c in chunks])
-        assert "def first()" in combined
-        assert "def second()" in combined
-        assert "def third()" in combined
-        assert "class MyClass:" in combined
-
-    def test_empty_content_value_handling(self):
-        """Test handling of chunks with empty content values.
-
-        Edge case: chunk has 'content' key but value is empty.
-        Should skip these chunks, not stringify them.
-        """
-        mock_builder = Mock()
-
-        chunks_with_empty = [
-            {"content": "", "metadata": {"line_count": 0}},  # Empty content
-            {"content": "   ", "metadata": {"line_count": 1}},  # Whitespace only
-            {"content": "def valid():\n    return True", "metadata": {"line_count": 2}},  # Valid
-        ]
-        mock_builder.chunkify.return_value = chunks_with_empty
-
-        doc = MockDocument(
-            "def valid():\n    return True", "/test/empty.py", {"language": "python"}
-        )
-
-        # Mock the astchunk module
-        mock_astchunk = Mock()
-        mock_astchunk.ASTChunkBuilder = Mock(return_value=mock_builder)
-
-        with patch.dict("sys.modules", {"astchunk": mock_astchunk}):
-            chunks = create_ast_chunks([doc])
-
-        # R3: Should only have the valid chunk (empty ones filtered out)
-        assert len(chunks) == 1, "Should filter out empty content chunks"
-
-        chunk = chunks[0]
-        assert isinstance(chunk, dict), "Chunk should be a dict"
-        assert "text" in chunk, "Chunk should have 'text' key"
-        assert "def valid()" in chunk["text"]
-
-        # Should not have stringified the empty dict
-        assert "'content': ''" not in chunk["text"]
-
-
-class TestASTMetadataPreservation:
-    """Test metadata preservation in AST chunk dictionaries.
-
-    R3: These tests define the contract for metadata preservation when returning
-    chunk dictionaries instead of plain strings. Each chunk dict should have:
-    - "text": str - the actual chunk content
-    - "metadata": dict - all metadata from document AND astchunk
-
-    These tests will FAIL until G3 implementation changes return type to list[dict].
-    """
-
-    def test_ast_chunks_preserve_file_metadata(self):
-        """Test that document metadata is preserved in chunk metadata.
-
-        This test verifies that all document-level metadata (file_path, file_name,
-        creation_date, last_modified_date) is included in each chunk's metadata dict.
-
-        This will FAIL because current code returns list[str], not list[dict].
-        """
-        # Create mock document with rich metadata
-        python_code = '''
-def calculate_sum(numbers):
-    """Calculate sum of numbers."""
-    return sum(numbers)
-
-class DataProcessor:
-    """Process data records."""
-
-    def process(self, data):
-        return [x * 2 for x in data]
-'''
-        doc = MockDocument(
-            python_code,
-            file_path="/project/src/utils.py",
-            metadata={
-                "language": "python",
-                "file_path": "/project/src/utils.py",
-                "file_name": "utils.py",
-                "creation_date": "2024-01-15T10:30:00",
-                "last_modified_date": "2024-10-31T15:45:00",
-            },
-        )
-
-        # Mock astchunk to return chunks with metadata
-        mock_builder = Mock()
-        astchunk_chunks = [
-            {
-                "content": "def calculate_sum(numbers):\n    return sum(numbers)",
-                "metadata": {
-                    "filepath": "/project/src/utils.py",
-                    "line_count": 2,
-                    "start_line_no": 1,
-                    "end_line_no": 2,
-                    "node_count": 1,
-                },
-            },
-            {
-                "content": "class DataProcessor:\n    def process(self, data):\n        return [x * 2 for x in data]",
-                "metadata": {
-                    "filepath": "/project/src/utils.py",
-                    "line_count": 3,
-                    "start_line_no": 5,
-                    "end_line_no": 7,
-                    "node_count": 2,
-                },
-            },
-        ]
-        mock_builder.chunkify.return_value = astchunk_chunks
-
-        mock_astchunk = Mock()
-        mock_astchunk.ASTChunkBuilder = Mock(return_value=mock_builder)
-
-        with patch.dict("sys.modules", {"astchunk": mock_astchunk}):
-            chunks = create_ast_chunks([doc])
-
-        # CRITICAL: These assertions will FAIL with current list[str] return type
-        assert len(chunks) == 2, "Should return 2 chunks"
-
-        for i, chunk in enumerate(chunks):
-            # Structure assertions - WILL FAIL: current code returns strings
-            assert isinstance(chunk, dict), f"Chunk {i} should be dict, got {type(chunk)}"
-            assert "text" in chunk, f"Chunk {i} must have 'text' key"
-            assert "metadata" in chunk, f"Chunk {i} must have 'metadata' key"
-            assert isinstance(chunk["metadata"], dict), f"Chunk {i} metadata should be dict"
-
-            # Document metadata preservation - WILL FAIL
-            metadata = chunk["metadata"]
-            assert "file_path" in metadata, f"Chunk {i} should preserve file_path"
-            assert metadata["file_path"] == "/project/src/utils.py", (
-                f"Chunk {i} file_path incorrect"
-            )
-
-            assert "file_name" in metadata, f"Chunk {i} should preserve file_name"
-            assert metadata["file_name"] == "utils.py", f"Chunk {i} file_name incorrect"
-
-            assert "creation_date" in metadata, f"Chunk {i} should preserve creation_date"
-            assert metadata["creation_date"] == "2024-01-15T10:30:00", (
-                f"Chunk {i} creation_date incorrect"
-            )
-
-            assert "last_modified_date" in metadata, f"Chunk {i} should preserve last_modified_date"
-            assert metadata["last_modified_date"] == "2024-10-31T15:45:00", (
-                f"Chunk {i} last_modified_date incorrect"
-            )
-
-        # Verify metadata is consistent across chunks from same document
-        assert chunks[0]["metadata"]["file_path"] == chunks[1]["metadata"]["file_path"], (
-            "All chunks from same document should have same file_path"
-        )
-
-        # Verify text content is present and not stringified
-        assert "def calculate_sum" in chunks[0]["text"]
-        assert "class DataProcessor" in chunks[1]["text"]
-
-    def test_ast_chunks_include_astchunk_metadata(self):
-        """Test that astchunk-specific metadata is merged into chunk metadata.
-
-        This test verifies that astchunk's metadata (line_count, start_line_no,
-        end_line_no, node_count) is merged with document metadata.
-
-        This will FAIL because current code returns list[str], not list[dict].
-        """
-        python_code = '''
-def function_one():
-    """First function."""
-    x = 1
-    y = 2
-    return x + y
-
-def function_two():
-    """Second function."""
-    return 42
-'''
-        doc = MockDocument(
-            python_code,
-            file_path="/test/code.py",
-            metadata={
-                "language": "python",
-                "file_path": "/test/code.py",
-                "file_name": "code.py",
-            },
-        )
-
-        # Mock astchunk with detailed metadata
-        mock_builder = Mock()
-        astchunk_chunks = [
-            {
-                "content": "def function_one():\n    x = 1\n    y = 2\n    return x + y",
-                "metadata": {
-                    "filepath": "/test/code.py",
-                    "line_count": 4,
-                    "start_line_no": 1,
-                    "end_line_no": 4,
-                    "node_count": 5,  # function, assignments, return
-                },
-            },
-            {
-                "content": "def function_two():\n    return 42",
-                "metadata": {
-                    "filepath": "/test/code.py",
-                    "line_count": 2,
-                    "start_line_no": 7,
-                    "end_line_no": 8,
-                    "node_count": 2,  # function, return
-                },
-            },
-        ]
-        mock_builder.chunkify.return_value = astchunk_chunks
-
-        mock_astchunk = Mock()
-        mock_astchunk.ASTChunkBuilder = Mock(return_value=mock_builder)
-
-        with patch.dict("sys.modules", {"astchunk": mock_astchunk}):
-            chunks = create_ast_chunks([doc])
-
-        # CRITICAL: These will FAIL with current list[str] return
-        assert len(chunks) == 2
-
-        # First chunk - function_one
-        chunk1 = chunks[0]
-        assert isinstance(chunk1, dict), "Chunk should be dict"
-        assert "metadata" in chunk1
-
-        metadata1 = chunk1["metadata"]
-
-        # Check astchunk metadata is present
-        assert "line_count" in metadata1, "Should include astchunk line_count"
-        assert metadata1["line_count"] == 4, "line_count should be 4"
-
-        assert "start_line_no" in metadata1, "Should include astchunk start_line_no"
-        assert metadata1["start_line_no"] == 1, "start_line_no should be 1"
-
-        assert "end_line_no" in metadata1, "Should include astchunk end_line_no"
-        assert metadata1["end_line_no"] == 4, "end_line_no should be 4"
-
-        assert "node_count" in metadata1, "Should include astchunk node_count"
-        assert metadata1["node_count"] == 5, "node_count should be 5"
-
-        # Second chunk - function_two
-        chunk2 = chunks[1]
-        metadata2 = chunk2["metadata"]
-
-        assert metadata2["line_count"] == 2, "line_count should be 2"
-        assert metadata2["start_line_no"] == 7, "start_line_no should be 7"
-        assert metadata2["end_line_no"] == 8, "end_line_no should be 8"
-        assert metadata2["node_count"] == 2, "node_count should be 2"
-
-        # Verify document metadata is ALSO present (merged, not replaced)
-        assert metadata1["file_path"] == "/test/code.py"
-        assert metadata1["file_name"] == "code.py"
-        assert metadata2["file_path"] == "/test/code.py"
-        assert metadata2["file_name"] == "code.py"
-
-        # Verify text content is correct
-        assert "def function_one" in chunk1["text"]
-        assert "def function_two" in chunk2["text"]
-
-    def test_traditional_chunks_as_dicts_helper(self):
-        """Test the helper function that wraps traditional chunks as dicts.
-
-        This test verifies that when create_traditional_chunks is called,
-        its plain string chunks are wrapped into dict format with metadata.
-
-        This will FAIL because the helper function _traditional_chunks_as_dicts()
-        doesn't exist yet, and create_traditional_chunks returns list[str].
-        """
-        # Create documents with various metadata
-        docs = [
-            MockDocument(
-                "This is the first paragraph of text. It contains multiple sentences. "
-                "This should be split into chunks based on size.",
-                file_path="/docs/readme.txt",
-                metadata={
-                    "file_path": "/docs/readme.txt",
-                    "file_name": "readme.txt",
-                    "creation_date": "2024-01-01",
-                },
-            ),
-            MockDocument(
-                "Second document with different metadata. It also has content that needs chunking.",
-                file_path="/docs/guide.md",
-                metadata={
-                    "file_path": "/docs/guide.md",
-                    "file_name": "guide.md",
-                    "last_modified_date": "2024-10-31",
-                },
-            ),
-        ]
-
-        # Call create_traditional_chunks (which should now return list[dict])
-        chunks = create_traditional_chunks(docs, chunk_size=50, chunk_overlap=10)
-
-        # CRITICAL: Will FAIL - current code returns list[str]
-        assert len(chunks) > 0, "Should return chunks"
-
-        for i, chunk in enumerate(chunks):
-            # Structure assertions - WILL FAIL
-            assert isinstance(chunk, dict), f"Chunk {i} should be dict, got {type(chunk)}"
-            assert "text" in chunk, f"Chunk {i} must have 'text' key"
-            assert "metadata" in chunk, f"Chunk {i} must have 'metadata' key"
-
-            # Text should be non-empty
-            assert len(chunk["text"].strip()) > 0, f"Chunk {i} text should be non-empty"
-
-            # Metadata should include document info
-            metadata = chunk["metadata"]
-            assert "file_path" in metadata, f"Chunk {i} should have file_path in metadata"
-            assert "file_name" in metadata, f"Chunk {i} should have file_name in metadata"
-
-        # Verify metadata tracking works correctly
-        # At least one chunk should be from readme.txt
-        readme_chunks = [c for c in chunks if "readme.txt" in c["metadata"]["file_name"]]
-        assert len(readme_chunks) > 0, "Should have chunks from readme.txt"
-
-        # At least one chunk should be from guide.md
-        guide_chunks = [c for c in chunks if "guide.md" in c["metadata"]["file_name"]]
-        assert len(guide_chunks) > 0, "Should have chunks from guide.md"
-
-        # Verify creation_date is preserved for readme chunks
-        for chunk in readme_chunks:
-            assert chunk["metadata"].get("creation_date") == "2024-01-01", (
-                "readme.txt chunks should preserve creation_date"
-            )
-
-        # Verify last_modified_date is preserved for guide chunks
-        for chunk in guide_chunks:
-            assert chunk["metadata"].get("last_modified_date") == "2024-10-31", (
-                "guide.md chunks should preserve last_modified_date"
-            )
-
-        # Verify text content is present
-        all_text = " ".join([c["text"] for c in chunks])
-        assert "first paragraph" in all_text
-        assert "Second document" in all_text
-
-
 class TestErrorHandling:
     """Test error handling and edge cases."""
 

tests/test_cli_prompt_template.py

@@ -1,533 +0,0 @@
-"""
-Tests for CLI argument integration of --embedding-prompt-template.
-
-These tests verify that:
-1. The --embedding-prompt-template flag is properly registered on build and search commands
-2. The template value flows from CLI args to embedding_options dict
-3. The template is passed through to compute_embeddings() function
-4. Default behavior (no flag) is handled correctly
-"""
-
-from unittest.mock import Mock, patch
-
-from leann.cli import LeannCLI
-
-
-class TestCLIPromptTemplateArgument:
-    """Tests for --embedding-prompt-template on build and search commands."""
-
-    def test_commands_accept_prompt_template_argument(self):
-        """Verify that build and search parsers accept --embedding-prompt-template flag."""
-        cli = LeannCLI()
-        parser = cli.create_parser()
-        template_value = "search_query: "
-
-        # Test build command
-        build_args = parser.parse_args(
-            [
-                "build",
-                "test-index",
-                "--docs",
-                "/tmp/test-docs",
-                "--embedding-prompt-template",
-                template_value,
-            ]
-        )
-        assert build_args.command == "build"
-        assert hasattr(build_args, "embedding_prompt_template"), (
-            "build command should have embedding_prompt_template attribute"
-        )
-        assert build_args.embedding_prompt_template == template_value
-
-        # Test search command
-        search_args = parser.parse_args(
-            ["search", "test-index", "my query", "--embedding-prompt-template", template_value]
-        )
-        assert search_args.command == "search"
-        assert hasattr(search_args, "embedding_prompt_template"), (
-            "search command should have embedding_prompt_template attribute"
-        )
-        assert search_args.embedding_prompt_template == template_value
-
-    def test_commands_default_to_none(self):
-        """Verify default value is None when flag not provided (backward compatibility)."""
-        cli = LeannCLI()
-        parser = cli.create_parser()
-
-        # Test build command default
-        build_args = parser.parse_args(["build", "test-index", "--docs", "/tmp/test-docs"])
-        assert hasattr(build_args, "embedding_prompt_template"), (
-            "build command should have embedding_prompt_template attribute"
-        )
-        assert build_args.embedding_prompt_template is None, (
-            "Build default value should be None when flag not provided"
-        )
-
-        # Test search command default
-        search_args = parser.parse_args(["search", "test-index", "my query"])
-        assert hasattr(search_args, "embedding_prompt_template"), (
-            "search command should have embedding_prompt_template attribute"
-        )
-        assert search_args.embedding_prompt_template is None, (
-            "Search default value should be None when flag not provided"
-        )
-
-
-class TestBuildCommandPromptTemplateArgumentExtras:
-    """Additional build-specific tests for prompt template argument."""
-
-    def test_build_command_prompt_template_with_multiword_value(self):
-        """
-        Verify that template values with spaces are handled correctly.
-
-        Templates like "search_document: " or "Represent this sentence for searching: "
-        should be accepted as a single string argument.
-        """
-        cli = LeannCLI()
-        parser = cli.create_parser()
-
-        template = "Represent this sentence for searching: "
-        args = parser.parse_args(
-            [
-                "build",
-                "test-index",
-                "--docs",
-                "/tmp/test-docs",
-                "--embedding-prompt-template",
-                template,
-            ]
-        )
-
-        assert args.embedding_prompt_template == template
-
-
-class TestPromptTemplateStoredInEmbeddingOptions:
-    """Tests for template storage in embedding_options dict."""
-
-    @patch("leann.cli.LeannBuilder")
-    def test_prompt_template_stored_in_embedding_options_on_build(
-        self, mock_builder_class, tmp_path
-    ):
-        """
-        Verify that when --embedding-prompt-template is provided to build command,
-        the value is stored in embedding_options dict passed to LeannBuilder.
-
-        This test will fail because the CLI doesn't currently process this argument
-        and add it to embedding_options.
-        """
-        # Setup mocks
-        mock_builder = Mock()
-        mock_builder_class.return_value = mock_builder
-
-        # Create CLI and run build command
-        cli = LeannCLI()
-
-        # Mock load_documents to return a document so builder is created
-        cli.load_documents = Mock(return_value=[{"text": "test content", "metadata": {}}])
-
-        parser = cli.create_parser()
-
-        template = "search_query: "
-        args = parser.parse_args(
-            [
-                "build",
-                "test-index",
-                "--docs",
-                str(tmp_path),
-                "--embedding-prompt-template",
-                template,
-                "--force",  # Force rebuild to ensure LeannBuilder is called
-            ]
-        )
-
-        # Run the build command
-        import asyncio
-
-        asyncio.run(cli.build_index(args))
-
-        # Check that LeannBuilder was called with embedding_options containing prompt_template
-        call_kwargs = mock_builder_class.call_args.kwargs
-        assert "embedding_options" in call_kwargs, "LeannBuilder should receive embedding_options"
-
-        embedding_options = call_kwargs["embedding_options"]
-        assert embedding_options is not None, (
-            "embedding_options should not be None when template provided"
-        )
-        assert "prompt_template" in embedding_options, (
-            "embedding_options should contain 'prompt_template' key"
-        )
-        assert embedding_options["prompt_template"] == template, (
-            f"Template should be '{template}', got {embedding_options.get('prompt_template')}"
-        )
-
-    @patch("leann.cli.LeannBuilder")
-    def test_prompt_template_not_in_options_when_not_provided(self, mock_builder_class, tmp_path):
-        """
-        Verify that when --embedding-prompt-template is NOT provided,
-        embedding_options either doesn't have the key or it's None.
-
-        This ensures we don't pass empty/None values unnecessarily.
-        """
-        # Setup mocks
-        mock_builder = Mock()
-        mock_builder_class.return_value = mock_builder
-
-        cli = LeannCLI()
-
-        # Mock load_documents to return a document so builder is created
-        cli.load_documents = Mock(return_value=[{"text": "test content", "metadata": {}}])
-
-        parser = cli.create_parser()
-
-        args = parser.parse_args(
-            [
-                "build",
-                "test-index",
-                "--docs",
-                str(tmp_path),
-                "--force",  # Force rebuild to ensure LeannBuilder is called
-            ]
-        )
-
-        import asyncio
-
-        asyncio.run(cli.build_index(args))
-
-        # Check that if embedding_options is passed, it doesn't have prompt_template
-        call_kwargs = mock_builder_class.call_args.kwargs
-        if call_kwargs.get("embedding_options"):
-            embedding_options = call_kwargs["embedding_options"]
-            # Either the key shouldn't exist, or it should be None
-            assert (
-                "prompt_template" not in embedding_options
-                or embedding_options["prompt_template"] is None
-            ), "prompt_template should not be set when flag not provided"
-
-    # R1 Tests: Build-time separate template storage
-    @patch("leann.cli.LeannBuilder")
-    def test_build_stores_separate_templates(self, mock_builder_class, tmp_path):
-        """
-        R1 Test 1: Verify that when both --embedding-prompt-template and
-        --query-prompt-template are provided to build command, both values
-        are stored separately in embedding_options dict as build_prompt_template
-        and query_prompt_template.
-
-        This test will fail because:
-        1. CLI doesn't accept --query-prompt-template flag yet
-        2. CLI doesn't store templates as separate build_prompt_template and
-           query_prompt_template keys
-
-        Expected behavior after implementation:
-        - .meta.json contains: {"embedding_options": {
-            "build_prompt_template": "doc: ",
-            "query_prompt_template": "query: "
-          }}
-        """
-        # Setup mocks
-        mock_builder = Mock()
-        mock_builder_class.return_value = mock_builder
-
-        cli = LeannCLI()
-
-        # Mock load_documents to return a document so builder is created
-        cli.load_documents = Mock(return_value=[{"text": "test content", "metadata": {}}])
-
-        parser = cli.create_parser()
-
-        build_template = "doc: "
-        query_template = "query: "
-        args = parser.parse_args(
-            [
-                "build",
-                "test-index",
-                "--docs",
-                str(tmp_path),
-                "--embedding-prompt-template",
-                build_template,
-                "--query-prompt-template",
-                query_template,
-                "--force",
-            ]
-        )
-
-        # Run the build command
-        import asyncio
-
-        asyncio.run(cli.build_index(args))
-
-        # Check that LeannBuilder was called with separate template keys
-        call_kwargs = mock_builder_class.call_args.kwargs
-        assert "embedding_options" in call_kwargs, "LeannBuilder should receive embedding_options"
-
-        embedding_options = call_kwargs["embedding_options"]
-        assert embedding_options is not None, (
-            "embedding_options should not be None when templates provided"
-        )
-
-        assert "build_prompt_template" in embedding_options, (
-            "embedding_options should contain 'build_prompt_template' key"
-        )
-        assert embedding_options["build_prompt_template"] == build_template, (
-            f"build_prompt_template should be '{build_template}'"
-        )
-
-        assert "query_prompt_template" in embedding_options, (
-            "embedding_options should contain 'query_prompt_template' key"
-        )
-        assert embedding_options["query_prompt_template"] == query_template, (
-            f"query_prompt_template should be '{query_template}'"
-        )
-
-        # Old key should NOT be present when using new separate template format
-        assert "prompt_template" not in embedding_options, (
-            "Old 'prompt_template' key should not be present with separate templates"
-        )
-
-    @patch("leann.cli.LeannBuilder")
-    def test_build_backward_compat_single_template(self, mock_builder_class, tmp_path):
-        """
-        R1 Test 2: Verify backward compatibility - when only
-        --embedding-prompt-template is provided (old behavior), it should
-        still be stored as 'prompt_template' in embedding_options.
-
-        This ensures existing workflows continue to work unchanged.
-
-        This test currently passes because it matches existing behavior, but it
-        documents the requirement that this behavior must be preserved after
-        implementing the separate template feature.
-
-        Expected behavior:
-        - .meta.json contains: {"embedding_options": {"prompt_template": "prompt: "}}
-        - No build_prompt_template or query_prompt_template keys
-        """
-        # Setup mocks
-        mock_builder = Mock()
-        mock_builder_class.return_value = mock_builder
-
-        cli = LeannCLI()
-
-        # Mock load_documents to return a document so builder is created
-        cli.load_documents = Mock(return_value=[{"text": "test content", "metadata": {}}])
-
-        parser = cli.create_parser()
-
-        template = "prompt: "
-        args = parser.parse_args(
-            [
-                "build",
-                "test-index",
-                "--docs",
-                str(tmp_path),
-                "--embedding-prompt-template",
-                template,
-                "--force",
-            ]
-        )
-
-        # Run the build command
-        import asyncio
-
-        asyncio.run(cli.build_index(args))
-
-        # Check that LeannBuilder was called with old format
-        call_kwargs = mock_builder_class.call_args.kwargs
-        assert "embedding_options" in call_kwargs, "LeannBuilder should receive embedding_options"
-
-        embedding_options = call_kwargs["embedding_options"]
-        assert embedding_options is not None, (
-            "embedding_options should not be None when template provided"
-        )
-
-        assert "prompt_template" in embedding_options, (
-            "embedding_options should contain old 'prompt_template' key for backward compat"
-        )
-        assert embedding_options["prompt_template"] == template, (
-            f"prompt_template should be '{template}'"
-        )
-
-        # New keys should NOT be present in backward compat mode
-        assert "build_prompt_template" not in embedding_options, (
-            "build_prompt_template should not be present with single template flag"
-        )
-        assert "query_prompt_template" not in embedding_options, (
-            "query_prompt_template should not be present with single template flag"
-        )
-
-    @patch("leann.cli.LeannBuilder")
-    def test_build_no_templates(self, mock_builder_class, tmp_path):
-        """
-        R1 Test 3: Verify that when no template flags are provided,
-        embedding_options has no prompt template keys.
-
-        This ensures clean defaults and no unnecessary keys in .meta.json.
-
-        This test currently passes because it matches existing behavior, but it
-        documents the requirement that this behavior must be preserved after
-        implementing the separate template feature.
-
-        Expected behavior:
-        - .meta.json has no prompt_template, build_prompt_template, or
-          query_prompt_template keys (or embedding_options is empty/None)
-        """
-        # Setup mocks
-        mock_builder = Mock()
-        mock_builder_class.return_value = mock_builder
-
-        cli = LeannCLI()
-
-        # Mock load_documents to return a document so builder is created
-        cli.load_documents = Mock(return_value=[{"text": "test content", "metadata": {}}])
-
-        parser = cli.create_parser()
-
-        args = parser.parse_args(["build", "test-index", "--docs", str(tmp_path), "--force"])
-
-        # Run the build command
-        import asyncio
-
-        asyncio.run(cli.build_index(args))
-
-        # Check that no template keys are present
-        call_kwargs = mock_builder_class.call_args.kwargs
-        if call_kwargs.get("embedding_options"):
-            embedding_options = call_kwargs["embedding_options"]
-
-            # None of the template keys should be present
-            assert "prompt_template" not in embedding_options, (
-                "prompt_template should not be present when no flags provided"
-            )
-            assert "build_prompt_template" not in embedding_options, (
-                "build_prompt_template should not be present when no flags provided"
-            )
-            assert "query_prompt_template" not in embedding_options, (
-                "query_prompt_template should not be present when no flags provided"
-            )
-
-
-class TestPromptTemplateFlowsToComputeEmbeddings:
-    """Tests for template flowing through to compute_embeddings function."""
-
-    @patch("leann.api.compute_embeddings")
-    def test_prompt_template_flows_to_compute_embeddings_via_provider_options(
-        self, mock_compute_embeddings, tmp_path
-    ):
-        """
-        Verify that the prompt template flows from CLI args through LeannBuilder
-        to compute_embeddings() function via provider_options parameter.
-
-        This is an integration test that verifies the complete flow:
-        CLI → embedding_options → LeannBuilder → compute_embeddings(provider_options)
-
-        This test will fail because:
-        1. CLI doesn't capture the argument yet
-        2. embedding_options doesn't include prompt_template
-        3. LeannBuilder doesn't pass it through to compute_embeddings
-        """
-        # Mock compute_embeddings to return dummy embeddings as numpy array
-        import numpy as np
-
-        mock_compute_embeddings.return_value = np.array([[0.1, 0.2, 0.3]], dtype=np.float32)
-
-        # Use real LeannBuilder (not mocked) to test the actual flow
-        cli = LeannCLI()
-
-        # Mock load_documents to return a simple document
-        cli.load_documents = Mock(return_value=[{"text": "test content", "metadata": {}}])
-
-        parser = cli.create_parser()
-
-        template = "search_document: "
-        args = parser.parse_args(
-            [
-                "build",
-                "test-index",
-                "--docs",
-                str(tmp_path),
-                "--embedding-prompt-template",
-                template,
-                "--backend-name",
-                "hnsw",  # Use hnsw backend
-                "--force",  # Force rebuild to ensure index is created
-            ]
-        )
-
-        # This should fail because the flow isn't implemented yet
-        import asyncio
-
-        asyncio.run(cli.build_index(args))
-
-        # Verify compute_embeddings was called with provider_options containing prompt_template
-        assert mock_compute_embeddings.called, "compute_embeddings should have been called"
-
-        # Check the call arguments
-        call_kwargs = mock_compute_embeddings.call_args.kwargs
-        assert "provider_options" in call_kwargs, (
-            "compute_embeddings should receive provider_options parameter"
-        )
-
-        provider_options = call_kwargs["provider_options"]
-        assert provider_options is not None, "provider_options should not be None"
-        assert "prompt_template" in provider_options, (
-            "provider_options should contain prompt_template key"
-        )
-        assert provider_options["prompt_template"] == template, (
-            f"Template should be '{template}', got {provider_options.get('prompt_template')}"
-        )
-
-
-class TestPromptTemplateArgumentHelp:
-    """Tests for argument help text and documentation."""
-
-    def test_build_command_prompt_template_has_help_text(self):
-        """
-        Verify that --embedding-prompt-template has descriptive help text.
-
-        Good help text is crucial for CLI usability.
-        """
-        cli = LeannCLI()
-        parser = cli.create_parser()
-
-        # Get the build subparser
-        # This is a bit tricky - we need to parse to get the help
-        # We'll check that the help includes relevant keywords
-        import io
-        from contextlib import redirect_stdout
-
-        f = io.StringIO()
-        try:
-            with redirect_stdout(f):
-                parser.parse_args(["build", "--help"])
-        except SystemExit:
-            pass  # --help causes sys.exit(0)
-
-        help_text = f.getvalue()
-        assert "--embedding-prompt-template" in help_text, (
-            "Help text should mention --embedding-prompt-template"
-        )
-        # Check for keywords that should be in the help
-        help_lower = help_text.lower()
-        assert any(keyword in help_lower for keyword in ["template", "prompt", "prepend"]), (
-            "Help text should explain what the prompt template does"
-        )
-
-    def test_search_command_prompt_template_has_help_text(self):
-        """
-        Verify that search command also has help text for --embedding-prompt-template.
-        """
-        cli = LeannCLI()
-        parser = cli.create_parser()
-
-        import io
-        from contextlib import redirect_stdout
-
-        f = io.StringIO()
-        try:
-            with redirect_stdout(f):
-                parser.parse_args(["search", "--help"])
-        except SystemExit:
-            pass  # --help causes sys.exit(0)
-
-        help_text = f.getvalue()
-        assert "--embedding-prompt-template" in help_text, (
-            "Search help text should mention --embedding-prompt-template"
-        )

tests/test_embedding_prompt_template.py

@@ -1,281 +0,0 @@
-"""Unit tests for prompt template prepending in OpenAI embeddings.
-
-This test suite defines the contract for prompt template functionality that allows
-users to prepend a consistent prompt to all embedding inputs. These tests verify:
-
-1. Template prepending to all input texts before embedding computation
-2. Graceful handling of None/missing provider_options
-3. Empty string template behavior (no-op)
-4. Logging of template application for observability
-5. Template application before token truncation
-
-All tests are written in Red Phase - they should FAIL initially because the
-implementation does not exist yet.
-"""
-
-from unittest.mock import MagicMock, Mock, patch
-
-import numpy as np
-import pytest
-from leann.embedding_compute import compute_embeddings_openai
-
-
-class TestPromptTemplatePrepending:
-    """Tests for prompt template prepending in compute_embeddings_openai."""
-
-    @pytest.fixture
-    def mock_openai_client(self):
-        """Create mock OpenAI client that captures input texts."""
-        mock_client = MagicMock()
-
-        # Mock the embeddings.create response
-        mock_response = Mock()
-        mock_response.data = [
-            Mock(embedding=[0.1, 0.2, 0.3]),
-            Mock(embedding=[0.4, 0.5, 0.6]),
-        ]
-        mock_client.embeddings.create.return_value = mock_response
-
-        return mock_client
-
-    @pytest.fixture
-    def mock_openai_module(self, mock_openai_client, monkeypatch):
-        """Mock the openai module to return our mock client."""
-        # Mock the API key environment variable
-        monkeypatch.setenv("OPENAI_API_KEY", "fake-test-key-for-mocking")
-
-        # openai is imported inside the function, so we need to patch it there
-        with patch("openai.OpenAI", return_value=mock_openai_client) as mock_openai:
-            yield mock_openai
-
-    def test_prompt_template_prepended_to_all_texts(self, mock_openai_module, mock_openai_client):
-        """Verify template is prepended to all input texts.
-
-        When provider_options contains "prompt_template", that template should
-        be prepended to every text in the input list before sending to OpenAI API.
-
-        This is the core functionality: the template acts as a consistent prefix
-        that provides context or instruction for the embedding model.
-        """
-        texts = ["First document", "Second document"]
-        template = "search_document: "
-        provider_options = {"prompt_template": template}
-
-        # Call compute_embeddings_openai with provider_options
-        result = compute_embeddings_openai(
-            texts=texts,
-            model_name="text-embedding-3-small",
-            provider_options=provider_options,
-        )
-
-        # Verify embeddings.create was called with templated texts
-        mock_openai_client.embeddings.create.assert_called_once()
-        call_args = mock_openai_client.embeddings.create.call_args
-
-        # Extract the input texts sent to API
-        sent_texts = call_args.kwargs["input"]
-
-        # Verify template was prepended to all texts
-        assert len(sent_texts) == 2, "Should send same number of texts"
-        assert sent_texts[0] == "search_document: First document", (
-            "Template should be prepended to first text"
-        )
-        assert sent_texts[1] == "search_document: Second document", (
-            "Template should be prepended to second text"
-        )
-
-        # Verify result is valid embeddings array
-        assert isinstance(result, np.ndarray)
-        assert result.shape == (2, 3), "Should return correct shape"
-
-    def test_template_not_applied_when_missing_or_empty(
-        self, mock_openai_module, mock_openai_client
-    ):
-        """Verify template not applied when provider_options is None, missing key, or empty string.
-
-        This consolidated test covers three scenarios where templates should NOT be applied:
-        1. provider_options is None (default behavior)
-        2. provider_options exists but missing 'prompt_template' key
-        3. prompt_template is explicitly set to empty string ""
-
-        In all cases, texts should be sent to the API unchanged.
-        """
-        # Scenario 1: None provider_options
-        texts = ["Original text one", "Original text two"]
-        result = compute_embeddings_openai(
-            texts=texts,
-            model_name="text-embedding-3-small",
-            provider_options=None,
-        )
-        call_args = mock_openai_client.embeddings.create.call_args
-        sent_texts = call_args.kwargs["input"]
-        assert sent_texts[0] == "Original text one", (
-            "Text should be unchanged with None provider_options"
-        )
-        assert sent_texts[1] == "Original text two"
-        assert isinstance(result, np.ndarray)
-        assert result.shape == (2, 3)
-
-        # Reset mock for next scenario
-        mock_openai_client.reset_mock()
-        mock_response = Mock()
-        mock_response.data = [
-            Mock(embedding=[0.1, 0.2, 0.3]),
-            Mock(embedding=[0.4, 0.5, 0.6]),
-        ]
-        mock_openai_client.embeddings.create.return_value = mock_response
-
-        # Scenario 2: Missing 'prompt_template' key
-        texts = ["Text without template", "Another text"]
-        provider_options = {"base_url": "https://api.openai.com/v1"}
-        result = compute_embeddings_openai(
-            texts=texts,
-            model_name="text-embedding-3-small",
-            provider_options=provider_options,
-        )
-        call_args = mock_openai_client.embeddings.create.call_args
-        sent_texts = call_args.kwargs["input"]
-        assert sent_texts[0] == "Text without template", "Text should be unchanged with missing key"
-        assert sent_texts[1] == "Another text"
-        assert isinstance(result, np.ndarray)
-
-        # Reset mock for next scenario
-        mock_openai_client.reset_mock()
-        mock_openai_client.embeddings.create.return_value = mock_response
-
-        # Scenario 3: Empty string template
-        texts = ["Text one", "Text two"]
-        provider_options = {"prompt_template": ""}
-        result = compute_embeddings_openai(
-            texts=texts,
-            model_name="text-embedding-3-small",
-            provider_options=provider_options,
-        )
-        call_args = mock_openai_client.embeddings.create.call_args
-        sent_texts = call_args.kwargs["input"]
-        assert sent_texts[0] == "Text one", "Empty template should not modify text"
-        assert sent_texts[1] == "Text two"
-        assert isinstance(result, np.ndarray)
-
-    def test_prompt_template_with_multiple_batches(self, mock_openai_module, mock_openai_client):
-        """Verify template is prepended in all batches when texts exceed batch size.
-
-        OpenAI API has batch size limits. When input texts are split into
-        multiple batches, the template should be prepended to texts in every batch.
-
-        This ensures consistency across all API calls.
-        """
-        # Create many texts that will be split into multiple batches
-        texts = [f"Document {i}" for i in range(1000)]
-        template = "passage: "
-        provider_options = {"prompt_template": template}
-
-        # Mock multiple batch responses
-        mock_response = Mock()
-        mock_response.data = [Mock(embedding=[0.1, 0.2, 0.3]) for _ in range(1000)]
-        mock_openai_client.embeddings.create.return_value = mock_response
-
-        result = compute_embeddings_openai(
-            texts=texts,
-            model_name="text-embedding-3-small",
-            provider_options=provider_options,
-        )
-
-        # Verify embeddings.create was called multiple times (batching)
-        assert mock_openai_client.embeddings.create.call_count >= 2, (
-            "Should make multiple API calls for large text list"
-        )
-
-        # Verify template was prepended in ALL batches
-        for call in mock_openai_client.embeddings.create.call_args_list:
-            sent_texts = call.kwargs["input"]
-            for text in sent_texts:
-                assert text.startswith(template), (
-                    f"All texts in all batches should start with template. Got: {text}"
-                )
-
-        # Verify result shape
-        assert result.shape[0] == 1000, "Should return embeddings for all texts"
-
-    def test_prompt_template_with_special_characters(self, mock_openai_module, mock_openai_client):
-        """Verify template with special characters is handled correctly.
-
-        Templates may contain special characters, Unicode, newlines, etc.
-        These should all be prepended correctly without encoding issues.
-        """
-        texts = ["Document content"]
-        # Template with various special characters
-        template = "🔍 Search query [EN]: "
-        provider_options = {"prompt_template": template}
-
-        result = compute_embeddings_openai(
-            texts=texts,
-            model_name="text-embedding-3-small",
-            provider_options=provider_options,
-        )
-
-        # Verify special characters in template were preserved
-        call_args = mock_openai_client.embeddings.create.call_args
-        sent_texts = call_args.kwargs["input"]
-
-        assert sent_texts[0] == "🔍 Search query [EN]: Document content", (
-            "Special characters in template should be preserved"
-        )
-
-        assert isinstance(result, np.ndarray)
-
-    def test_prompt_template_integration_with_existing_validation(
-        self, mock_openai_module, mock_openai_client
-    ):
-        """Verify template works with existing input validation.
-
-        compute_embeddings_openai has validation for empty texts and whitespace.
-        Template prepending should happen AFTER validation, so validation errors
-        are thrown based on original texts, not templated texts.
-
-        This ensures users get clear error messages about their input.
-        """
-        # Empty text should still raise ValueError even with template
-        texts = [""]
-        provider_options = {"prompt_template": "prefix: "}
-
-        with pytest.raises(ValueError, match="empty/invalid"):
-            compute_embeddings_openai(
-                texts=texts,
-                model_name="text-embedding-3-small",
-                provider_options=provider_options,
-            )
-
-    def test_prompt_template_with_api_key_and_base_url(
-        self, mock_openai_module, mock_openai_client
-    ):
-        """Verify template works alongside other provider_options.
-
-        provider_options may contain multiple settings: prompt_template,
-        base_url, api_key. All should work together correctly.
-        """
-        texts = ["Test document"]
-        provider_options = {
-            "prompt_template": "embed: ",
-            "base_url": "https://custom.api.com/v1",
-            "api_key": "test-key-123",
-        }
-
-        result = compute_embeddings_openai(
-            texts=texts,
-            model_name="text-embedding-3-small",
-            provider_options=provider_options,
-        )
-
-        # Verify template was applied
-        call_args = mock_openai_client.embeddings.create.call_args
-        sent_texts = call_args.kwargs["input"]
-        assert sent_texts[0] == "embed: Test document"
-
-        # Verify OpenAI client was created with correct base_url
-        mock_openai_module.assert_called()
-        client_init_kwargs = mock_openai_module.call_args.kwargs
-        assert client_init_kwargs["base_url"] == "https://custom.api.com/v1"
-        assert client_init_kwargs["api_key"] == "test-key-123"
-
-        assert isinstance(result, np.ndarray)

tests/test_lmstudio_bridge.py

@@ -1,315 +0,0 @@
-"""Unit tests for LM Studio TypeScript SDK bridge functionality.
-
-This test suite defines the contract for the LM Studio SDK bridge that queries
-model context length via Node.js subprocess. These tests verify:
-
-1. Successful SDK query returns context length
-2. Graceful fallback when Node.js not installed (FileNotFoundError)
-3. Graceful fallback when SDK not installed (npm error)
-4. Timeout handling (subprocess.TimeoutExpired)
-5. Invalid JSON response handling
-
-All tests are written in Red Phase - they should FAIL initially because the
-`_query_lmstudio_context_limit` function does not exist yet.
-
-The function contract:
-- Inputs: model_name (str), base_url (str, WebSocket format "ws://localhost:1234")
-- Outputs: context_length (int) or None on error
-- Requirements:
-  1. Call Node.js with inline JavaScript using @lmstudio/sdk
-  2. 10-second timeout (accounts for Node.js startup)
-  3. Graceful fallback on any error (returns None, doesn't raise)
-  4. Parse JSON response with contextLength field
-  5. Log errors at debug level (not warning/error)
-"""
-
-import subprocess
-from unittest.mock import Mock
-
-import pytest
-
-# Try to import the function - if it doesn't exist, tests will fail as expected
-try:
-    from leann.embedding_compute import _query_lmstudio_context_limit
-except ImportError:
-    # Function doesn't exist yet (Red Phase) - create a placeholder that will fail
-    def _query_lmstudio_context_limit(*args, **kwargs):
-        raise NotImplementedError(
-            "_query_lmstudio_context_limit not implemented yet - this is the Red Phase"
-        )
-
-
-class TestLMStudioBridge:
-    """Tests for LM Studio TypeScript SDK bridge integration."""
-
-    def test_query_lmstudio_success(self, monkeypatch):
-        """Verify successful SDK query returns context length.
-
-        When the Node.js subprocess successfully queries the LM Studio SDK,
-        it should return a JSON response with contextLength field. The function
-        should parse this and return the integer context length.
-        """
-
-        def mock_run(*args, **kwargs):
-            # Verify timeout is set to 10 seconds
-            assert kwargs.get("timeout") == 10, "Should use 10-second timeout for Node.js startup"
-
-            # Verify capture_output and text=True are set
-            assert kwargs.get("capture_output") is True, "Should capture stdout/stderr"
-            assert kwargs.get("text") is True, "Should decode output as text"
-
-            # Return successful JSON response
-            mock_result = Mock()
-            mock_result.returncode = 0
-            mock_result.stdout = '{"contextLength": 8192, "identifier": "custom-model"}'
-            mock_result.stderr = ""
-            return mock_result
-
-        monkeypatch.setattr("subprocess.run", mock_run)
-
-        # Test with typical LM Studio model
-        limit = _query_lmstudio_context_limit(
-            model_name="custom-model", base_url="ws://localhost:1234"
-        )
-
-        assert limit == 8192, "Should return context length from SDK response"
-
-    def test_query_lmstudio_nodejs_not_found(self, monkeypatch):
-        """Verify graceful fallback when Node.js not installed.
-
-        When Node.js is not installed, subprocess.run will raise FileNotFoundError.
-        The function should catch this and return None (graceful fallback to registry).
-        """
-
-        def mock_run(*args, **kwargs):
-            raise FileNotFoundError("node: command not found")
-
-        monkeypatch.setattr("subprocess.run", mock_run)
-
-        limit = _query_lmstudio_context_limit(
-            model_name="custom-model", base_url="ws://localhost:1234"
-        )
-
-        assert limit is None, "Should return None when Node.js not installed"
-
-    def test_query_lmstudio_sdk_not_installed(self, monkeypatch):
-        """Verify graceful fallback when @lmstudio/sdk not installed.
-
-        When the SDK npm package is not installed, Node.js will return non-zero
-        exit code with error message in stderr. The function should detect this
-        and return None.
-        """
-
-        def mock_run(*args, **kwargs):
-            mock_result = Mock()
-            mock_result.returncode = 1
-            mock_result.stdout = ""
-            mock_result.stderr = (
-                "Error: Cannot find module '@lmstudio/sdk'\nRequire stack:\n- /path/to/script.js"
-            )
-            return mock_result
-
-        monkeypatch.setattr("subprocess.run", mock_run)
-
-        limit = _query_lmstudio_context_limit(
-            model_name="custom-model", base_url="ws://localhost:1234"
-        )
-
-        assert limit is None, "Should return None when SDK not installed"
-
-    def test_query_lmstudio_timeout(self, monkeypatch):
-        """Verify graceful fallback when subprocess times out.
-
-        When the Node.js process takes longer than 10 seconds (e.g., LM Studio
-        not responding), subprocess.TimeoutExpired should be raised. The function
-        should catch this and return None.
-        """
-
-        def mock_run(*args, **kwargs):
-            raise subprocess.TimeoutExpired(cmd=["node", "lmstudio_bridge.js"], timeout=10)
-
-        monkeypatch.setattr("subprocess.run", mock_run)
-
-        limit = _query_lmstudio_context_limit(
-            model_name="custom-model", base_url="ws://localhost:1234"
-        )
-
-        assert limit is None, "Should return None on timeout"
-
-    def test_query_lmstudio_invalid_json(self, monkeypatch):
-        """Verify graceful fallback when response is invalid JSON.
-
-        When the subprocess returns malformed JSON (e.g., due to SDK error),
-        json.loads will raise ValueError/JSONDecodeError. The function should
-        catch this and return None.
-        """
-
-        def mock_run(*args, **kwargs):
-            mock_result = Mock()
-            mock_result.returncode = 0
-            mock_result.stdout = "This is not valid JSON"
-            mock_result.stderr = ""
-            return mock_result
-
-        monkeypatch.setattr("subprocess.run", mock_run)
-
-        limit = _query_lmstudio_context_limit(
-            model_name="custom-model", base_url="ws://localhost:1234"
-        )
-
-        assert limit is None, "Should return None when JSON parsing fails"
-
-    def test_query_lmstudio_missing_context_length_field(self, monkeypatch):
-        """Verify graceful fallback when JSON lacks contextLength field.
-
-        When the SDK returns valid JSON but without the expected contextLength
-        field (e.g., error response), the function should return None.
-        """
-
-        def mock_run(*args, **kwargs):
-            mock_result = Mock()
-            mock_result.returncode = 0
-            mock_result.stdout = '{"identifier": "test-model", "error": "Model not found"}'
-            mock_result.stderr = ""
-            return mock_result
-
-        monkeypatch.setattr("subprocess.run", mock_run)
-
-        limit = _query_lmstudio_context_limit(
-            model_name="nonexistent-model", base_url="ws://localhost:1234"
-        )
-
-        assert limit is None, "Should return None when contextLength field missing"
-
-    def test_query_lmstudio_null_context_length(self, monkeypatch):
-        """Verify graceful fallback when contextLength is null.
-
-        When the SDK returns contextLength: null (model couldn't be loaded),
-        the function should return None for registry fallback.
-        """
-
-        def mock_run(*args, **kwargs):
-            mock_result = Mock()
-            mock_result.returncode = 0
-            mock_result.stdout = '{"contextLength": null, "identifier": "test-model"}'
-            mock_result.stderr = ""
-            return mock_result
-
-        monkeypatch.setattr("subprocess.run", mock_run)
-
-        limit = _query_lmstudio_context_limit(
-            model_name="test-model", base_url="ws://localhost:1234"
-        )
-
-        assert limit is None, "Should return None when contextLength is null"
-
-    def test_query_lmstudio_zero_context_length(self, monkeypatch):
-        """Verify graceful fallback when contextLength is zero.
-
-        When the SDK returns contextLength: 0 (invalid value), the function
-        should return None to trigger registry fallback.
-        """
-
-        def mock_run(*args, **kwargs):
-            mock_result = Mock()
-            mock_result.returncode = 0
-            mock_result.stdout = '{"contextLength": 0, "identifier": "test-model"}'
-            mock_result.stderr = ""
-            return mock_result
-
-        monkeypatch.setattr("subprocess.run", mock_run)
-
-        limit = _query_lmstudio_context_limit(
-            model_name="test-model", base_url="ws://localhost:1234"
-        )
-
-        assert limit is None, "Should return None when contextLength is zero"
-
-    def test_query_lmstudio_with_custom_port(self, monkeypatch):
-        """Verify SDK query works with non-default WebSocket port.
-
-        LM Studio can run on custom ports. The function should pass the
-        provided base_url to the Node.js subprocess.
-        """
-
-        def mock_run(*args, **kwargs):
-            # Verify the base_url argument is passed correctly
-            command = args[0] if args else kwargs.get("args", [])
-            assert "ws://localhost:8080" in " ".join(command), (
-                "Should pass custom port to subprocess"
-            )
-
-            mock_result = Mock()
-            mock_result.returncode = 0
-            mock_result.stdout = '{"contextLength": 4096, "identifier": "custom-model"}'
-            mock_result.stderr = ""
-            return mock_result
-
-        monkeypatch.setattr("subprocess.run", mock_run)
-
-        limit = _query_lmstudio_context_limit(
-            model_name="custom-model", base_url="ws://localhost:8080"
-        )
-
-        assert limit == 4096, "Should work with custom WebSocket port"
-
-    @pytest.mark.parametrize(
-        "context_length,expected",
-        [
-            (512, 512),  # Small context
-            (2048, 2048),  # Common context
-            (8192, 8192),  # Large context
-            (32768, 32768),  # Very large context
-        ],
-    )
-    def test_query_lmstudio_various_context_lengths(self, monkeypatch, context_length, expected):
-        """Verify SDK query handles various context length values.
-
-        Different models have different context lengths. The function should
-        correctly parse and return any positive integer value.
-        """
-
-        def mock_run(*args, **kwargs):
-            mock_result = Mock()
-            mock_result.returncode = 0
-            mock_result.stdout = f'{{"contextLength": {context_length}, "identifier": "test"}}'
-            mock_result.stderr = ""
-            return mock_result
-
-        monkeypatch.setattr("subprocess.run", mock_run)
-
-        limit = _query_lmstudio_context_limit(
-            model_name="test-model", base_url="ws://localhost:1234"
-        )
-
-        assert limit == expected, f"Should return {expected} for context length {context_length}"
-
-    def test_query_lmstudio_logs_at_debug_level(self, monkeypatch, caplog):
-        """Verify errors are logged at DEBUG level, not WARNING/ERROR.
-
-        Following the graceful fallback pattern from Ollama implementation,
-        errors should be logged at debug level to avoid alarming users when
-        fallback to registry works fine.
-        """
-        import logging
-
-        caplog.set_level(logging.DEBUG, logger="leann.embedding_compute")
-
-        def mock_run(*args, **kwargs):
-            raise FileNotFoundError("node: command not found")
-
-        monkeypatch.setattr("subprocess.run", mock_run)
-
-        _query_lmstudio_context_limit(model_name="test-model", base_url="ws://localhost:1234")
-
-        # Check that debug logging occurred (not warning/error)
-        debug_logs = [record for record in caplog.records if record.levelname == "DEBUG"]
-        assert len(debug_logs) > 0, "Should log error at DEBUG level"
-
-        # Verify no WARNING or ERROR logs
-        warning_or_error_logs = [
-            record for record in caplog.records if record.levelname in ["WARNING", "ERROR"]
-        ]
-        assert len(warning_or_error_logs) == 0, (
-            "Should not log at WARNING/ERROR level for expected failures"
-        )

tests/test_mcp_integration.py

@@ -1,208 +0,0 @@
-#!/usr/bin/env python3
-"""
-Test script for MCP integration implementations.
-
-This script tests the basic functionality of the MCP readers and RAG applications
-without requiring actual MCP servers to be running.
-"""
-
-import sys
-from pathlib import Path
-
-# Add the parent directory to the path so we can import from apps
-sys.path.append(str(Path(__file__).parent.parent))
-
-from apps.slack_data.slack_mcp_reader import SlackMCPReader
-from apps.slack_rag import SlackMCPRAG
-from apps.twitter_data.twitter_mcp_reader import TwitterMCPReader
-from apps.twitter_rag import TwitterMCPRAG
-
-
-def test_slack_reader_initialization():
-    """Test that SlackMCPReader can be initialized with various parameters."""
-    print("Testing SlackMCPReader initialization...")
-
-    # Test basic initialization
-    reader = SlackMCPReader("slack-mcp-server")
-    assert reader.mcp_server_command == "slack-mcp-server"
-    assert reader.concatenate_conversations
-    assert reader.max_messages_per_conversation == 100
-
-    # Test with custom parameters
-    reader = SlackMCPReader(
-        "custom-slack-server",
-        workspace_name="test-workspace",
-        concatenate_conversations=False,
-        max_messages_per_conversation=50,
-    )
-    assert reader.workspace_name == "test-workspace"
-    assert not reader.concatenate_conversations
-    assert reader.max_messages_per_conversation == 50
-
-    print("✅ SlackMCPReader initialization tests passed")
-
-
-def test_twitter_reader_initialization():
-    """Test that TwitterMCPReader can be initialized with various parameters."""
-    print("Testing TwitterMCPReader initialization...")
-
-    # Test basic initialization
-    reader = TwitterMCPReader("twitter-mcp-server")
-    assert reader.mcp_server_command == "twitter-mcp-server"
-    assert reader.include_tweet_content
-    assert reader.include_metadata
-    assert reader.max_bookmarks == 1000
-
-    # Test with custom parameters
-    reader = TwitterMCPReader(
-        "custom-twitter-server",
-        username="testuser",
-        include_tweet_content=False,
-        include_metadata=False,
-        max_bookmarks=500,
-    )
-    assert reader.username == "testuser"
-    assert not reader.include_tweet_content
-    assert not reader.include_metadata
-    assert reader.max_bookmarks == 500
-
-    print("✅ TwitterMCPReader initialization tests passed")
-
-
-def test_slack_message_formatting():
-    """Test Slack message formatting functionality."""
-    print("Testing Slack message formatting...")
-
-    reader = SlackMCPReader("slack-mcp-server")
-
-    # Test basic message formatting
-    message = {
-        "text": "Hello, world!",
-        "user": "john_doe",
-        "channel": "general",
-        "ts": "1234567890.123456",
-    }
-
-    formatted = reader._format_message(message)
-    assert "Channel: #general" in formatted
-    assert "User: john_doe" in formatted
-    assert "Message: Hello, world!" in formatted
-    assert "Time:" in formatted
-
-    # Test with missing fields
-    message = {"text": "Simple message"}
-    formatted = reader._format_message(message)
-    assert "Message: Simple message" in formatted
-
-    print("✅ Slack message formatting tests passed")
-
-
-def test_twitter_bookmark_formatting():
-    """Test Twitter bookmark formatting functionality."""
-    print("Testing Twitter bookmark formatting...")
-
-    reader = TwitterMCPReader("twitter-mcp-server")
-
-    # Test basic bookmark formatting
-    bookmark = {
-        "text": "This is a great article about AI!",
-        "author": "ai_researcher",
-        "created_at": "2024-01-01T12:00:00Z",
-        "url": "https://twitter.com/ai_researcher/status/123456789",
-        "likes": 42,
-        "retweets": 15,
-    }
-
-    formatted = reader._format_bookmark(bookmark)
-    assert "=== Twitter Bookmark ===" in formatted
-    assert "Author: @ai_researcher" in formatted
-    assert "Content:" in formatted
-    assert "This is a great article about AI!" in formatted
-    assert "URL: https://twitter.com" in formatted
-    assert "Likes: 42" in formatted
-    assert "Retweets: 15" in formatted
-
-    # Test with minimal data
-    bookmark = {"text": "Simple tweet"}
-    formatted = reader._format_bookmark(bookmark)
-    assert "=== Twitter Bookmark ===" in formatted
-    assert "Simple tweet" in formatted
-
-    print("✅ Twitter bookmark formatting tests passed")
-
-
-def test_slack_rag_initialization():
-    """Test that SlackMCPRAG can be initialized."""
-    print("Testing SlackMCPRAG initialization...")
-
-    app = SlackMCPRAG()
-    assert app.default_index_name == "slack_messages"
-    assert hasattr(app, "parser")
-
-    print("✅ SlackMCPRAG initialization tests passed")
-
-
-def test_twitter_rag_initialization():
-    """Test that TwitterMCPRAG can be initialized."""
-    print("Testing TwitterMCPRAG initialization...")
-
-    app = TwitterMCPRAG()
-    assert app.default_index_name == "twitter_bookmarks"
-    assert hasattr(app, "parser")
-
-    print("✅ TwitterMCPRAG initialization tests passed")
-
-
-def test_concatenated_content_creation():
-    """Test creation of concatenated content from multiple messages."""
-    print("Testing concatenated content creation...")
-
-    reader = SlackMCPReader("slack-mcp-server", workspace_name="test-workspace")
-
-    messages = [
-        {"text": "First message", "user": "alice", "ts": "1000"},
-        {"text": "Second message", "user": "bob", "ts": "2000"},
-        {"text": "Third message", "user": "charlie", "ts": "3000"},
-    ]
-
-    content = reader._create_concatenated_content(messages, "general")
-
-    assert "Slack Channel: #general" in content
-    assert "Message Count: 3" in content
-    assert "Workspace: test-workspace" in content
-    assert "First message" in content
-    assert "Second message" in content
-    assert "Third message" in content
-
-    print("✅ Concatenated content creation tests passed")
-
-
-def main():
-    """Run all tests."""
-    print("🧪 Running MCP Integration Tests")
-    print("=" * 50)
-
-    try:
-        test_slack_reader_initialization()
-        test_twitter_reader_initialization()
-        test_slack_message_formatting()
-        test_twitter_bookmark_formatting()
-        test_slack_rag_initialization()
-        test_twitter_rag_initialization()
-        test_concatenated_content_creation()
-
-        print("\n" + "=" * 50)
-        print("🎉 All tests passed! MCP integration is working correctly.")
-        print("\nNext steps:")
-        print("1. Install actual MCP servers for Slack and Twitter")
-        print("2. Configure API credentials")
-        print("3. Test with --test-connection flag")
-        print("4. Start indexing your live data!")
-
-    except Exception as e:
-        print(f"\n❌ Test failed: {e}")
-        sys.exit(1)
-
-
-if __name__ == "__main__":
-    main()

tests/test_mcp_standalone.py

@@ -1,221 +0,0 @@
-#!/usr/bin/env python3
-"""
-Standalone test script for MCP integration implementations.
-
-This script tests the basic functionality of the MCP readers
-without requiring LEANN core dependencies.
-"""
-
-import json
-import sys
-from pathlib import Path
-
-# Add the parent directory to the path so we can import from apps
-sys.path.append(str(Path(__file__).parent.parent))
-
-
-def test_slack_reader_basic():
-    """Test basic SlackMCPReader functionality without async operations."""
-    print("Testing SlackMCPReader basic functionality...")
-
-    # Import and test initialization
-    from apps.slack_data.slack_mcp_reader import SlackMCPReader
-
-    reader = SlackMCPReader("slack-mcp-server")
-    assert reader.mcp_server_command == "slack-mcp-server"
-    assert reader.concatenate_conversations
-
-    # Test message formatting
-    message = {
-        "text": "Hello team! How's the project going?",
-        "user": "john_doe",
-        "channel": "general",
-        "ts": "1234567890.123456",
-    }
-
-    formatted = reader._format_message(message)
-    assert "Channel: #general" in formatted
-    assert "User: john_doe" in formatted
-    assert "Message: Hello team!" in formatted
-
-    # Test concatenated content creation
-    messages = [
-        {"text": "First message", "user": "alice", "ts": "1000"},
-        {"text": "Second message", "user": "bob", "ts": "2000"},
-    ]
-
-    content = reader._create_concatenated_content(messages, "dev-team")
-    assert "Slack Channel: #dev-team" in content
-    assert "Message Count: 2" in content
-    assert "First message" in content
-    assert "Second message" in content
-
-    print("✅ SlackMCPReader basic tests passed")
-
-
-def test_twitter_reader_basic():
-    """Test basic TwitterMCPReader functionality."""
-    print("Testing TwitterMCPReader basic functionality...")
-
-    from apps.twitter_data.twitter_mcp_reader import TwitterMCPReader
-
-    reader = TwitterMCPReader("twitter-mcp-server")
-    assert reader.mcp_server_command == "twitter-mcp-server"
-    assert reader.include_tweet_content
-    assert reader.max_bookmarks == 1000
-
-    # Test bookmark formatting
-    bookmark = {
-        "text": "Amazing article about the future of AI! Must read for everyone interested in tech.",
-        "author": "tech_guru",
-        "created_at": "2024-01-15T14:30:00Z",
-        "url": "https://twitter.com/tech_guru/status/123456789",
-        "likes": 156,
-        "retweets": 42,
-        "replies": 23,
-        "hashtags": ["AI", "tech", "future"],
-        "mentions": ["@openai", "@anthropic"],
-    }
-
-    formatted = reader._format_bookmark(bookmark)
-    assert "=== Twitter Bookmark ===" in formatted
-    assert "Author: @tech_guru" in formatted
-    assert "Amazing article about the future of AI!" in formatted
-    assert "Likes: 156" in formatted
-    assert "Retweets: 42" in formatted
-    assert "Hashtags: AI, tech, future" in formatted
-    assert "Mentions: @openai, @anthropic" in formatted
-
-    # Test with minimal data
-    simple_bookmark = {"text": "Short tweet", "author": "user123"}
-    formatted_simple = reader._format_bookmark(simple_bookmark)
-    assert "=== Twitter Bookmark ===" in formatted_simple
-    assert "Short tweet" in formatted_simple
-    assert "Author: @user123" in formatted_simple
-
-    print("✅ TwitterMCPReader basic tests passed")
-
-
-def test_mcp_request_format():
-    """Test MCP request formatting."""
-    print("Testing MCP request formatting...")
-
-    # Test initialization request format
-    init_request = {
-        "jsonrpc": "2.0",
-        "id": 1,
-        "method": "initialize",
-        "params": {
-            "protocolVersion": "2024-11-05",
-            "capabilities": {},
-            "clientInfo": {"name": "leann-slack-reader", "version": "1.0.0"},
-        },
-    }
-
-    # Verify it's valid JSON
-    json_str = json.dumps(init_request)
-    parsed = json.loads(json_str)
-    assert parsed["jsonrpc"] == "2.0"
-    assert parsed["method"] == "initialize"
-    assert parsed["params"]["protocolVersion"] == "2024-11-05"
-
-    # Test tools/list request
-    list_request = {"jsonrpc": "2.0", "id": 2, "method": "tools/list", "params": {}}
-
-    json_str = json.dumps(list_request)
-    parsed = json.loads(json_str)
-    assert parsed["method"] == "tools/list"
-
-    print("✅ MCP request formatting tests passed")
-
-
-def test_data_processing():
-    """Test data processing capabilities."""
-    print("Testing data processing capabilities...")
-
-    from apps.slack_data.slack_mcp_reader import SlackMCPReader
-    from apps.twitter_data.twitter_mcp_reader import TwitterMCPReader
-
-    # Test Slack message processing with various formats
-    slack_reader = SlackMCPReader("test-server")
-
-    messages_with_timestamps = [
-        {"text": "Meeting in 5 minutes", "user": "alice", "ts": "1000.123"},
-        {"text": "On my way!", "user": "bob", "ts": "1001.456"},
-        {"text": "Starting now", "user": "charlie", "ts": "1002.789"},
-    ]
-
-    content = slack_reader._create_concatenated_content(messages_with_timestamps, "meetings")
-    assert "Meeting in 5 minutes" in content
-    assert "On my way!" in content
-    assert "Starting now" in content
-
-    # Test Twitter bookmark processing with engagement data
-    twitter_reader = TwitterMCPReader("test-server", include_metadata=True)
-
-    high_engagement_bookmark = {
-        "text": "Thread about startup lessons learned 🧵",
-        "author": "startup_founder",
-        "likes": 1250,
-        "retweets": 340,
-        "replies": 89,
-    }
-
-    formatted = twitter_reader._format_bookmark(high_engagement_bookmark)
-    assert "Thread about startup lessons learned" in formatted
-    assert "Likes: 1250" in formatted
-    assert "Retweets: 340" in formatted
-    assert "Replies: 89" in formatted
-
-    # Test with metadata disabled
-    twitter_reader_no_meta = TwitterMCPReader("test-server", include_metadata=False)
-    formatted_no_meta = twitter_reader_no_meta._format_bookmark(high_engagement_bookmark)
-    assert "Thread about startup lessons learned" in formatted_no_meta
-    assert "Likes:" not in formatted_no_meta
-    assert "Retweets:" not in formatted_no_meta
-
-    print("✅ Data processing tests passed")
-
-
-def main():
-    """Run all standalone tests."""
-    print("🧪 Running MCP Integration Standalone Tests")
-    print("=" * 60)
-    print("Testing core functionality without LEANN dependencies...")
-    print()
-
-    try:
-        test_slack_reader_basic()
-        test_twitter_reader_basic()
-        test_mcp_request_format()
-        test_data_processing()
-
-        print("\n" + "=" * 60)
-        print("🎉 All standalone tests passed!")
-        print("\n✨ MCP Integration Summary:")
-        print("- SlackMCPReader: Ready for Slack message processing")
-        print("- TwitterMCPReader: Ready for Twitter bookmark processing")
-        print("- MCP Protocol: Properly formatted JSON-RPC requests")
-        print("- Data Processing: Handles various message/bookmark formats")
-
-        print("\n🚀 Next Steps:")
-        print("1. Install MCP servers: npm install -g slack-mcp-server twitter-mcp-server")
-        print("2. Configure API credentials for Slack and Twitter")
-        print("3. Test connections: python -m apps.slack_rag --test-connection")
-        print("4. Start indexing live data from your platforms!")
-
-        print("\n📖 Documentation:")
-        print("- Check README.md for detailed setup instructions")
-        print("- Run examples/mcp_integration_demo.py for usage examples")
-        print("- Explore apps/slack_rag.py and apps/twitter_rag.py for implementation details")
-
-    except Exception as e:
-        print(f"\n❌ Test failed: {e}")
-        import traceback
-
-        traceback.print_exc()
-        sys.exit(1)
-
-
-if __name__ == "__main__":
-    main()

tests/test_prompt_template_e2e.py

@@ -1,400 +0,0 @@
-"""End-to-end integration tests for prompt template and token limit features.
-
-These tests verify real-world functionality with live services:
-- OpenAI-compatible APIs (OpenAI, LM Studio) with prompt template support
-- Ollama with dynamic token limit detection
-- Hybrid token limit discovery mechanism
-
-Run with: pytest tests/test_prompt_template_e2e.py -v -s
-Skip if services unavailable: pytest tests/test_prompt_template_e2e.py -m "not integration"
-
-Prerequisites:
-1. LM Studio running with embedding model: http://localhost:1234
-2. [Optional] Ollama running: ollama serve
-3. [Optional] Ollama model: ollama pull nomic-embed-text
-4. [Optional] Node.js + @lmstudio/sdk for context length detection
-"""
-
-import logging
-import socket
-
-import numpy as np
-import pytest
-import requests
-from leann.embedding_compute import (
-    compute_embeddings_ollama,
-    compute_embeddings_openai,
-    get_model_token_limit,
-)
-
-# Test markers for conditional execution
-pytestmark = pytest.mark.integration
-
-logger = logging.getLogger(__name__)
-
-
-def check_service_available(host: str, port: int, timeout: float = 2.0) -> bool:
-    """Check if a service is available on the given host:port."""
-    try:
-        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-        sock.settimeout(timeout)
-        result = sock.connect_ex((host, port))
-        sock.close()
-        return result == 0
-    except Exception:
-        return False
-
-
-def check_ollama_available() -> bool:
-    """Check if Ollama service is available."""
-    if not check_service_available("localhost", 11434):
-        return False
-    try:
-        response = requests.get("http://localhost:11434/api/tags", timeout=2.0)
-        return response.status_code == 200
-    except Exception:
-        return False
-
-
-def check_lmstudio_available() -> bool:
-    """Check if LM Studio service is available."""
-    if not check_service_available("localhost", 1234):
-        return False
-    try:
-        response = requests.get("http://localhost:1234/v1/models", timeout=2.0)
-        return response.status_code == 200
-    except Exception:
-        return False
-
-
-def get_lmstudio_first_model() -> str:
-    """Get the first available model from LM Studio."""
-    try:
-        response = requests.get("http://localhost:1234/v1/models", timeout=5.0)
-        data = response.json()
-        models = data.get("data", [])
-        if models:
-            return models[0]["id"]
-    except Exception:
-        pass
-    return None
-
-
-class TestPromptTemplateOpenAI:
-    """End-to-end tests for prompt template with OpenAI-compatible APIs (LM Studio)."""
-
-    @pytest.mark.skipif(
-        not check_lmstudio_available(), reason="LM Studio service not available on localhost:1234"
-    )
-    def test_lmstudio_embedding_with_prompt_template(self):
-        """Test prompt templates with LM Studio using OpenAI-compatible API."""
-        model_name = get_lmstudio_first_model()
-        if not model_name:
-            pytest.skip("No models loaded in LM Studio")
-
-        texts = ["artificial intelligence", "machine learning"]
-        prompt_template = "search_query: "
-
-        # Get embeddings with prompt template via provider_options
-        provider_options = {"prompt_template": prompt_template}
-        embeddings = compute_embeddings_openai(
-            texts=texts,
-            model_name=model_name,
-            base_url="http://localhost:1234/v1",
-            api_key="lm-studio",  # LM Studio doesn't require real key
-            provider_options=provider_options,
-        )
-
-        assert embeddings is not None
-        assert len(embeddings) == 2
-        assert all(isinstance(emb, np.ndarray) for emb in embeddings)
-        assert all(len(emb) > 0 for emb in embeddings)
-
-        logger.info(
-            f"✓ LM Studio embeddings with prompt template: {len(embeddings)} vectors, {len(embeddings[0])} dimensions"
-        )
-
-    @pytest.mark.skipif(not check_lmstudio_available(), reason="LM Studio service not available")
-    def test_lmstudio_prompt_template_affects_embeddings(self):
-        """Verify that prompt templates actually change embedding values."""
-        model_name = get_lmstudio_first_model()
-        if not model_name:
-            pytest.skip("No models loaded in LM Studio")
-
-        text = "machine learning"
-        base_url = "http://localhost:1234/v1"
-        api_key = "lm-studio"
-
-        # Get embeddings without template
-        embeddings_no_template = compute_embeddings_openai(
-            texts=[text],
-            model_name=model_name,
-            base_url=base_url,
-            api_key=api_key,
-            provider_options={},
-        )
-
-        # Get embeddings with template
-        embeddings_with_template = compute_embeddings_openai(
-            texts=[text],
-            model_name=model_name,
-            base_url=base_url,
-            api_key=api_key,
-            provider_options={"prompt_template": "search_query: "},
-        )
-
-        # Embeddings should be different when template is applied
-        assert not np.allclose(embeddings_no_template[0], embeddings_with_template[0])
-
-        logger.info("✓ Prompt template changes embedding values as expected")
-
-
-class TestPromptTemplateOllama:
-    """End-to-end tests for prompt template with Ollama."""
-
-    @pytest.mark.skipif(
-        not check_ollama_available(), reason="Ollama service not available on localhost:11434"
-    )
-    def test_ollama_embedding_with_prompt_template(self):
-        """Test prompt templates with Ollama using any available embedding model."""
-        # Get any available embedding model
-        try:
-            response = requests.get("http://localhost:11434/api/tags", timeout=2.0)
-            models = response.json().get("models", [])
-
-            embedding_models = []
-            for model in models:
-                name = model["name"]
-                base_name = name.split(":")[0]
-                if any(emb in base_name for emb in ["embed", "bge", "minilm", "e5", "nomic"]):
-                    embedding_models.append(name)
-
-            if not embedding_models:
-                pytest.skip("No embedding models available in Ollama")
-
-            model_name = embedding_models[0]
-
-            texts = ["artificial intelligence", "machine learning"]
-            prompt_template = "search_query: "
-
-            # Get embeddings with prompt template via provider_options
-            provider_options = {"prompt_template": prompt_template}
-            embeddings = compute_embeddings_ollama(
-                texts=texts,
-                model_name=model_name,
-                is_build=False,
-                host="http://localhost:11434",
-                provider_options=provider_options,
-            )
-
-            assert embeddings is not None
-            assert len(embeddings) == 2
-            assert all(isinstance(emb, np.ndarray) for emb in embeddings)
-            assert all(len(emb) > 0 for emb in embeddings)
-
-            logger.info(
-                f"✓ Ollama embeddings with prompt template: {len(embeddings)} vectors, {len(embeddings[0])} dimensions"
-            )
-
-        except Exception as e:
-            pytest.skip(f"Could not test Ollama prompt template: {e}")
-
-    @pytest.mark.skipif(not check_ollama_available(), reason="Ollama service not available")
-    def test_ollama_prompt_template_affects_embeddings(self):
-        """Verify that prompt templates actually change embedding values with Ollama."""
-        # Get any available embedding model
-        try:
-            response = requests.get("http://localhost:11434/api/tags", timeout=2.0)
-            models = response.json().get("models", [])
-
-            embedding_models = []
-            for model in models:
-                name = model["name"]
-                base_name = name.split(":")[0]
-                if any(emb in base_name for emb in ["embed", "bge", "minilm", "e5", "nomic"]):
-                    embedding_models.append(name)
-
-            if not embedding_models:
-                pytest.skip("No embedding models available in Ollama")
-
-            model_name = embedding_models[0]
-            text = "machine learning"
-            host = "http://localhost:11434"
-
-            # Get embeddings without template
-            embeddings_no_template = compute_embeddings_ollama(
-                texts=[text], model_name=model_name, is_build=False, host=host, provider_options={}
-            )
-
-            # Get embeddings with template
-            embeddings_with_template = compute_embeddings_ollama(
-                texts=[text],
-                model_name=model_name,
-                is_build=False,
-                host=host,
-                provider_options={"prompt_template": "search_query: "},
-            )
-
-            # Embeddings should be different when template is applied
-            assert not np.allclose(embeddings_no_template[0], embeddings_with_template[0])
-
-            logger.info("✓ Ollama prompt template changes embedding values as expected")
-
-        except Exception as e:
-            pytest.skip(f"Could not test Ollama prompt template: {e}")
-
-
-class TestLMStudioSDK:
-    """End-to-end tests for LM Studio SDK integration."""
-
-    @pytest.mark.skipif(not check_lmstudio_available(), reason="LM Studio service not available")
-    def test_lmstudio_model_listing(self):
-        """Test that we can list models from LM Studio."""
-        try:
-            response = requests.get("http://localhost:1234/v1/models", timeout=5.0)
-            assert response.status_code == 200
-
-            data = response.json()
-            assert "data" in data
-
-            models = data["data"]
-            logger.info(f"✓ LM Studio models available: {len(models)}")
-
-            if models:
-                logger.info(f"  First model: {models[0].get('id', 'unknown')}")
-        except Exception as e:
-            pytest.skip(f"LM Studio API error: {e}")
-
-    @pytest.mark.skipif(not check_lmstudio_available(), reason="LM Studio service not available")
-    def test_lmstudio_sdk_context_length_detection(self):
-        """Test context length detection via LM Studio SDK bridge (requires Node.js + SDK)."""
-        model_name = get_lmstudio_first_model()
-        if not model_name:
-            pytest.skip("No models loaded in LM Studio")
-
-        try:
-            from leann.embedding_compute import _query_lmstudio_context_limit
-
-            # SDK requires WebSocket URL (ws://)
-            context_length = _query_lmstudio_context_limit(
-                model_name=model_name, base_url="ws://localhost:1234"
-            )
-
-            if context_length is None:
-                logger.warning(
-                    "⚠ LM Studio SDK bridge returned None (Node.js or SDK may not be available)"
-                )
-                pytest.skip("Node.js or @lmstudio/sdk not available - SDK bridge unavailable")
-            else:
-                assert context_length > 0
-                logger.info(
-                    f"✓ LM Studio context length detected via SDK: {context_length} for {model_name}"
-                )
-
-        except ImportError:
-            pytest.skip("_query_lmstudio_context_limit not implemented yet")
-        except Exception as e:
-            logger.error(f"LM Studio SDK test error: {e}")
-            raise
-
-
-class TestOllamaTokenLimit:
-    """End-to-end tests for Ollama token limit discovery."""
-
-    @pytest.mark.skipif(not check_ollama_available(), reason="Ollama service not available")
-    def test_ollama_token_limit_detection(self):
-        """Test dynamic token limit detection from Ollama /api/show endpoint."""
-        # Get any available embedding model
-        try:
-            response = requests.get("http://localhost:11434/api/tags", timeout=2.0)
-            models = response.json().get("models", [])
-
-            embedding_models = []
-            for model in models:
-                name = model["name"]
-                base_name = name.split(":")[0]
-                if any(emb in base_name for emb in ["embed", "bge", "minilm", "e5", "nomic"]):
-                    embedding_models.append(name)
-
-            if not embedding_models:
-                pytest.skip("No embedding models available in Ollama")
-
-            test_model = embedding_models[0]
-
-            # Test token limit detection
-            limit = get_model_token_limit(model_name=test_model, base_url="http://localhost:11434")
-
-            assert limit > 0
-            logger.info(f"✓ Ollama token limit detected: {limit} for {test_model}")
-
-        except Exception as e:
-            pytest.skip(f"Could not test Ollama token detection: {e}")
-
-
-class TestHybridTokenLimit:
-    """End-to-end tests for hybrid token limit discovery mechanism."""
-
-    def test_hybrid_discovery_registry_fallback(self):
-        """Test fallback to static registry for known OpenAI models."""
-        # Use a known OpenAI model (should be in registry)
-        limit = get_model_token_limit(
-            model_name="text-embedding-3-small",
-            base_url="http://fake-server:9999",  # Fake URL to force registry lookup
-        )
-
-        # text-embedding-3-small should have 8192 in registry
-        assert limit == 8192
-        logger.info(f"✓ Hybrid discovery (registry fallback): {limit} tokens")
-
-    def test_hybrid_discovery_default_fallback(self):
-        """Test fallback to safe default for completely unknown models."""
-        limit = get_model_token_limit(
-            model_name="completely-unknown-model-xyz-12345",
-            base_url="http://fake-server:9999",
-            default=512,
-        )
-
-        # Should get the specified default
-        assert limit == 512
-        logger.info(f"✓ Hybrid discovery (default fallback): {limit} tokens")
-
-    @pytest.mark.skipif(not check_ollama_available(), reason="Ollama service not available")
-    def test_hybrid_discovery_ollama_dynamic_first(self):
-        """Test that Ollama models use dynamic discovery first."""
-        # Get any available embedding model
-        try:
-            response = requests.get("http://localhost:11434/api/tags", timeout=2.0)
-            models = response.json().get("models", [])
-
-            embedding_models = []
-            for model in models:
-                name = model["name"]
-                base_name = name.split(":")[0]
-                if any(emb in base_name for emb in ["embed", "bge", "minilm", "e5", "nomic"]):
-                    embedding_models.append(name)
-
-            if not embedding_models:
-                pytest.skip("No embedding models available in Ollama")
-
-            test_model = embedding_models[0]
-
-            # Should query Ollama /api/show dynamically
-            limit = get_model_token_limit(model_name=test_model, base_url="http://localhost:11434")
-
-            assert limit > 0
-            logger.info(f"✓ Hybrid discovery (Ollama dynamic): {limit} tokens for {test_model}")
-
-        except Exception as e:
-            pytest.skip(f"Could not test hybrid Ollama discovery: {e}")
-
-
-if __name__ == "__main__":
-    print("\n" + "=" * 70)
-    print("INTEGRATION TEST SUITE - Real Service Testing")
-    print("=" * 70)
-    print("\nThese tests require live services:")
-    print("  • LM Studio: http://localhost:1234 (with embedding model loaded)")
-    print("  • [Optional] Ollama: http://localhost:11434")
-    print("  • [Optional] Node.js + @lmstudio/sdk for SDK bridge tests")
-    print("\nRun with: pytest tests/test_prompt_template_e2e.py -v -s")
-    print("=" * 70 + "\n")

tests/test_prompt_template_persistence.py

@@ -1,808 +0,0 @@
-"""
-Integration tests for prompt template metadata persistence and reuse.
-
-These tests verify the complete lifecycle of prompt template persistence:
-1. Template is saved to .meta.json during index build
-2. Template is automatically loaded during search operations
-3. Template can be overridden with explicit flag during search
-4. Template is reused during chat/ask operations
-
-These are integration tests that:
-- Use real file system with temporary directories
-- Run actual build and search operations
-- Inspect .meta.json file contents directly
-- Mock embedding servers to avoid external dependencies
-- Use small test codebases for fast execution
-
-Expected to FAIL in Red Phase because metadata persistence verification is not yet implemented.
-"""
-
-import json
-import tempfile
-from pathlib import Path
-from unittest.mock import Mock, patch
-
-import numpy as np
-import pytest
-from leann.api import LeannBuilder, LeannSearcher
-
-
-class TestPromptTemplateMetadataPersistence:
-    """Tests for prompt template storage in .meta.json during build."""
-
-    @pytest.fixture
-    def temp_index_dir(self):
-        """Create temporary directory for test indexes."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            yield Path(tmpdir)
-
-    @pytest.fixture
-    def mock_embeddings(self):
-        """Mock compute_embeddings to return dummy embeddings."""
-        with patch("leann.api.compute_embeddings") as mock_compute:
-            # Return dummy embeddings as numpy array
-            mock_compute.return_value = np.array([[0.1, 0.2, 0.3]], dtype=np.float32)
-            yield mock_compute
-
-    def test_prompt_template_saved_to_metadata(self, temp_index_dir, mock_embeddings):
-        """
-        Verify that when build is run with embedding_options containing prompt_template,
-        the template value is saved to .meta.json file.
-
-        This is the core persistence requirement - templates must be saved to allow
-        reuse in subsequent search operations without re-specifying the flag.
-
-        Expected failure: .meta.json exists but doesn't contain embedding_options
-        with prompt_template, or the value is not persisted correctly.
-        """
-        # Setup test data
-        index_path = temp_index_dir / "test_index.leann"
-        template = "search_document: "
-
-        # Build index with prompt template in embedding_options
-        builder = LeannBuilder(
-            backend_name="hnsw",
-            embedding_model="text-embedding-3-small",
-            embedding_mode="openai",
-            embedding_options={"prompt_template": template},
-        )
-
-        # Add a simple document
-        builder.add_text("This is a test document for indexing")
-
-        # Build the index
-        builder.build_index(str(index_path))
-
-        # Verify .meta.json was created and contains the template
-        meta_path = temp_index_dir / "test_index.leann.meta.json"
-        assert meta_path.exists(), ".meta.json file should be created during build"
-
-        # Read and parse metadata
-        with open(meta_path, encoding="utf-8") as f:
-            meta_data = json.load(f)
-
-        # Verify embedding_options exists in metadata
-        assert "embedding_options" in meta_data, (
-            "embedding_options should be saved to .meta.json when provided"
-        )
-
-        # Verify prompt_template is in embedding_options
-        embedding_options = meta_data["embedding_options"]
-        assert "prompt_template" in embedding_options, (
-            "prompt_template should be saved within embedding_options"
-        )
-
-        # Verify the template value matches what we provided
-        assert embedding_options["prompt_template"] == template, (
-            f"Template should be '{template}', got '{embedding_options.get('prompt_template')}'"
-        )
-
-    def test_prompt_template_absent_when_not_provided(self, temp_index_dir, mock_embeddings):
-        """
-        Verify that when no prompt template is provided during build,
-        .meta.json either doesn't have embedding_options or prompt_template key.
-
-        This ensures clean metadata without unnecessary keys when features aren't used.
-
-        Expected behavior: Build succeeds, .meta.json doesn't contain prompt_template.
-        """
-        index_path = temp_index_dir / "test_no_template.leann"
-
-        # Build index WITHOUT prompt template
-        builder = LeannBuilder(
-            backend_name="hnsw",
-            embedding_model="text-embedding-3-small",
-            embedding_mode="openai",
-            # No embedding_options provided
-        )
-
-        builder.add_text("Document without template")
-        builder.build_index(str(index_path))
-
-        # Verify metadata
-        meta_path = temp_index_dir / "test_no_template.leann.meta.json"
-        assert meta_path.exists()
-
-        with open(meta_path, encoding="utf-8") as f:
-            meta_data = json.load(f)
-
-        # If embedding_options exists, it should not contain prompt_template
-        if "embedding_options" in meta_data:
-            embedding_options = meta_data["embedding_options"]
-            assert "prompt_template" not in embedding_options, (
-                "prompt_template should not be in metadata when not provided"
-            )
-
-
-class TestPromptTemplateAutoLoadOnSearch:
-    """Tests for automatic loading of prompt template during search operations.
-
-    NOTE: Over-mocked test removed (test_prompt_template_auto_loaded_on_search).
-    This functionality is now comprehensively tested by TestQueryPromptTemplateAutoLoad
-    which uses simpler mocking and doesn't hang.
-    """
-
-    @pytest.fixture
-    def temp_index_dir(self):
-        """Create temporary directory for test indexes."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            yield Path(tmpdir)
-
-    @pytest.fixture
-    def mock_embeddings(self):
-        """Mock compute_embeddings to capture calls and return dummy embeddings."""
-        with patch("leann.api.compute_embeddings") as mock_compute:
-            mock_compute.return_value = np.array([[0.1, 0.2, 0.3]], dtype=np.float32)
-            yield mock_compute
-
-    def test_search_without_template_in_metadata(self, temp_index_dir, mock_embeddings):
-        """
-        Verify that searching an index built WITHOUT a prompt template
-        works correctly (backward compatibility).
-
-        The searcher should handle missing prompt_template gracefully.
-
-        Expected behavior: Search succeeds, no template is used.
-        """
-        # Build index without template
-        index_path = temp_index_dir / "no_template.leann"
-
-        builder = LeannBuilder(
-            backend_name="hnsw",
-            embedding_model="text-embedding-3-small",
-            embedding_mode="openai",
-        )
-        builder.add_text("Document without template")
-        builder.build_index(str(index_path))
-
-        # Reset mocks
-        mock_embeddings.reset_mock()
-
-        # Create searcher and search
-        searcher = LeannSearcher(index_path=str(index_path))
-
-        # Verify no template in embedding_options
-        assert "prompt_template" not in searcher.embedding_options, (
-            "Searcher should not have prompt_template when not in metadata"
-        )
-
-
-class TestQueryPromptTemplateAutoLoad:
-    """Tests for automatic loading of separate query_prompt_template during search (R2).
-
-    These tests verify the new two-template system where:
-    - build_prompt_template: Applied during index building
-    - query_prompt_template: Applied during search operations
-
-    Expected to FAIL in Red Phase (R2) because query template extraction
-    and application is not yet implemented in LeannSearcher.search().
-    """
-
-    @pytest.fixture
-    def temp_index_dir(self):
-        """Create temporary directory for test indexes."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            yield Path(tmpdir)
-
-    @pytest.fixture
-    def mock_compute_embeddings(self):
-        """Mock compute_embeddings to capture calls and return dummy embeddings."""
-        with patch("leann.embedding_compute.compute_embeddings") as mock_compute:
-            mock_compute.return_value = np.array([[0.1, 0.2, 0.3]], dtype=np.float32)
-            yield mock_compute
-
-    def test_search_auto_loads_query_template(self, temp_index_dir, mock_compute_embeddings):
-        """
-        Verify that search() automatically loads and applies query_prompt_template from .meta.json.
-
-        Given: Index built with separate build_prompt_template and query_prompt_template
-        When: LeannSearcher.search("my query") is called
-        Then: Query embedding is computed with "query: my query" (query template applied)
-
-        This is the core R2 requirement - query templates must be auto-loaded and applied
-        during search without user intervention.
-
-        Expected failure: compute_embeddings called with raw "my query" instead of
-        "query: my query" because query template extraction is not implemented.
-        """
-        # Setup: Build index with separate templates in new format
-        index_path = temp_index_dir / "query_template.leann"
-
-        builder = LeannBuilder(
-            backend_name="hnsw",
-            embedding_model="text-embedding-3-small",
-            embedding_mode="openai",
-            embedding_options={
-                "build_prompt_template": "doc: ",
-                "query_prompt_template": "query: ",
-            },
-        )
-        builder.add_text("Test document")
-        builder.build_index(str(index_path))
-
-        # Reset mock to ignore build calls
-        mock_compute_embeddings.reset_mock()
-
-        # Act: Search with query
-        searcher = LeannSearcher(index_path=str(index_path))
-
-        # Mock the backend search to avoid actual search
-        with patch.object(searcher.backend_impl, "search") as mock_backend_search:
-            mock_backend_search.return_value = {
-                "labels": [["test_id_0"]],  # IDs (nested list for batch support)
-                "distances": [[0.9]],  # Distances (nested list for batch support)
-            }
-
-            searcher.search("my query", top_k=1, recompute_embeddings=False)
-
-        # Assert: compute_embeddings was called with query template applied
-        assert mock_compute_embeddings.called, "compute_embeddings should be called during search"
-
-        # Get the actual text passed to compute_embeddings
-        call_args = mock_compute_embeddings.call_args
-        texts_arg = call_args[0][0]  # First positional arg (list of texts)
-
-        assert len(texts_arg) == 1, "Should compute embedding for one query"
-        assert texts_arg[0] == "query: my query", (
-            f"Query template should be applied: expected 'query: my query', got '{texts_arg[0]}'"
-        )
-
-    def test_search_backward_compat_single_template(self, temp_index_dir, mock_compute_embeddings):
-        """
-        Verify backward compatibility with old single prompt_template format.
-
-        Given: Index with old format (single prompt_template, no query_prompt_template)
-        When: LeannSearcher.search("my query") is called
-        Then: Query embedding computed with "doc: my query" (old template applied)
-
-        This ensures indexes built with the old single-template system continue
-        to work correctly with the new search implementation.
-
-        Expected failure: Old template not recognized/applied because backward
-        compatibility logic is not implemented.
-        """
-        # Setup: Build index with old single-template format
-        index_path = temp_index_dir / "old_template.leann"
-
-        builder = LeannBuilder(
-            backend_name="hnsw",
-            embedding_model="text-embedding-3-small",
-            embedding_mode="openai",
-            embedding_options={"prompt_template": "doc: "},  # Old format
-        )
-        builder.add_text("Test document")
-        builder.build_index(str(index_path))
-
-        # Reset mock
-        mock_compute_embeddings.reset_mock()
-
-        # Act: Search
-        searcher = LeannSearcher(index_path=str(index_path))
-
-        with patch.object(searcher.backend_impl, "search") as mock_backend_search:
-            mock_backend_search.return_value = {"labels": [["test_id_0"]], "distances": [[0.9]]}
-
-            searcher.search("my query", top_k=1, recompute_embeddings=False)
-
-        # Assert: Old template was applied
-        call_args = mock_compute_embeddings.call_args
-        texts_arg = call_args[0][0]
-
-        assert texts_arg[0] == "doc: my query", (
-            f"Old prompt_template should be applied for backward compatibility: "
-            f"expected 'doc: my query', got '{texts_arg[0]}'"
-        )
-
-    def test_search_backward_compat_no_template(self, temp_index_dir, mock_compute_embeddings):
-        """
-        Verify backward compatibility when no template is present in .meta.json.
-
-        Given: Index with no template in .meta.json (very old indexes)
-        When: LeannSearcher.search("my query") is called
-        Then: Query embedding computed with "my query" (no template, raw query)
-
-        This ensures the most basic backward compatibility - indexes without
-        any template support continue to work as before.
-
-        Expected failure: May fail if default template is incorrectly applied,
-        or if missing template causes error.
-        """
-        # Setup: Build index without any template
-        index_path = temp_index_dir / "no_template.leann"
-
-        builder = LeannBuilder(
-            backend_name="hnsw",
-            embedding_model="text-embedding-3-small",
-            embedding_mode="openai",
-            # No embedding_options at all
-        )
-        builder.add_text("Test document")
-        builder.build_index(str(index_path))
-
-        # Reset mock
-        mock_compute_embeddings.reset_mock()
-
-        # Act: Search
-        searcher = LeannSearcher(index_path=str(index_path))
-
-        with patch.object(searcher.backend_impl, "search") as mock_backend_search:
-            mock_backend_search.return_value = {"labels": [["test_id_0"]], "distances": [[0.9]]}
-
-            searcher.search("my query", top_k=1, recompute_embeddings=False)
-
-        # Assert: No template applied (raw query)
-        call_args = mock_compute_embeddings.call_args
-        texts_arg = call_args[0][0]
-
-        assert texts_arg[0] == "my query", (
-            f"No template should be applied when missing from metadata: "
-            f"expected 'my query', got '{texts_arg[0]}'"
-        )
-
-    def test_search_override_via_provider_options(self, temp_index_dir, mock_compute_embeddings):
-        """
-        Verify that explicit provider_options can override stored query template.
-
-        Given: Index with query_prompt_template: "query: "
-        When: search() called with provider_options={"prompt_template": "override: "}
-        Then: Query embedding computed with "override: test" (override takes precedence)
-
-        This enables users to experiment with different query templates without
-        rebuilding the index, or to handle special query types differently.
-
-        Expected failure: provider_options parameter is accepted via **kwargs but
-        not used. Query embedding computed with raw "test" instead of "override: test"
-        because override logic is not implemented.
-        """
-        # Setup: Build index with query template
-        index_path = temp_index_dir / "override_template.leann"
-
-        builder = LeannBuilder(
-            backend_name="hnsw",
-            embedding_model="text-embedding-3-small",
-            embedding_mode="openai",
-            embedding_options={
-                "build_prompt_template": "doc: ",
-                "query_prompt_template": "query: ",
-            },
-        )
-        builder.add_text("Test document")
-        builder.build_index(str(index_path))
-
-        # Reset mock
-        mock_compute_embeddings.reset_mock()
-
-        # Act: Search with override
-        searcher = LeannSearcher(index_path=str(index_path))
-
-        with patch.object(searcher.backend_impl, "search") as mock_backend_search:
-            mock_backend_search.return_value = {"labels": [["test_id_0"]], "distances": [[0.9]]}
-
-            # This should accept provider_options parameter
-            searcher.search(
-                "test",
-                top_k=1,
-                recompute_embeddings=False,
-                provider_options={"prompt_template": "override: "},
-            )
-
-        # Assert: Override template was applied
-        call_args = mock_compute_embeddings.call_args
-        texts_arg = call_args[0][0]
-
-        assert texts_arg[0] == "override: test", (
-            f"Override template should take precedence: "
-            f"expected 'override: test', got '{texts_arg[0]}'"
-        )
-
-
-class TestPromptTemplateReuseInChat:
-    """Tests for prompt template reuse in chat/ask operations."""
-
-    @pytest.fixture
-    def temp_index_dir(self):
-        """Create temporary directory for test indexes."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            yield Path(tmpdir)
-
-    @pytest.fixture
-    def mock_embeddings(self):
-        """Mock compute_embeddings to return dummy embeddings."""
-        with patch("leann.api.compute_embeddings") as mock_compute:
-            mock_compute.return_value = np.array([[0.1, 0.2, 0.3]], dtype=np.float32)
-            yield mock_compute
-
-    @pytest.fixture
-    def mock_embedding_server_manager(self):
-        """Mock EmbeddingServerManager for chat tests."""
-        with patch("leann.searcher_base.EmbeddingServerManager") as mock_manager_class:
-            mock_manager = Mock()
-            mock_manager.start_server.return_value = (True, 5557)
-            mock_manager_class.return_value = mock_manager
-            yield mock_manager
-
-    @pytest.fixture
-    def index_with_template(self, temp_index_dir, mock_embeddings):
-        """Build an index with a prompt template."""
-        index_path = temp_index_dir / "chat_template_index.leann"
-        template = "document_query: "
-
-        builder = LeannBuilder(
-            backend_name="hnsw",
-            embedding_model="text-embedding-3-small",
-            embedding_mode="openai",
-            embedding_options={"prompt_template": template},
-        )
-
-        builder.add_text("Test document for chat")
-        builder.build_index(str(index_path))
-
-        return str(index_path), template
-
-
-class TestPromptTemplateIntegrationWithEmbeddingModes:
-    """Tests for prompt template compatibility with different embedding modes."""
-
-    @pytest.fixture
-    def temp_index_dir(self):
-        """Create temporary directory for test indexes."""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            yield Path(tmpdir)
-
-    @pytest.mark.parametrize(
-        "mode,model,template,filename_prefix",
-        [
-            (
-                "openai",
-                "text-embedding-3-small",
-                "Represent this for searching: ",
-                "openai_template",
-            ),
-            ("ollama", "nomic-embed-text", "search_query: ", "ollama_template"),
-            ("sentence-transformers", "facebook/contriever", "query: ", "st_template"),
-        ],
-    )
-    def test_prompt_template_metadata_with_embedding_modes(
-        self, temp_index_dir, mode, model, template, filename_prefix
-    ):
-        """Verify prompt template is saved correctly across different embedding modes.
-
-        Tests that prompt templates are persisted to .meta.json for:
-        - OpenAI mode (primary use case)
-        - Ollama mode (also supports templates)
-        - Sentence-transformers mode (saved for forward compatibility)
-
-        Expected behavior: Template is saved to .meta.json regardless of mode.
-        """
-        with patch("leann.api.compute_embeddings") as mock_compute:
-            mock_compute.return_value = np.array([[0.1, 0.2, 0.3]], dtype=np.float32)
-
-            index_path = temp_index_dir / f"{filename_prefix}.leann"
-
-            builder = LeannBuilder(
-                backend_name="hnsw",
-                embedding_model=model,
-                embedding_mode=mode,
-                embedding_options={"prompt_template": template},
-            )
-
-            builder.add_text(f"{mode.capitalize()} test document")
-            builder.build_index(str(index_path))
-
-            # Verify metadata
-            meta_path = temp_index_dir / f"{filename_prefix}.leann.meta.json"
-            with open(meta_path, encoding="utf-8") as f:
-                meta_data = json.load(f)
-
-            assert meta_data["embedding_mode"] == mode
-            # Template should be saved for all modes (even if not used by some)
-            if "embedding_options" in meta_data:
-                assert meta_data["embedding_options"]["prompt_template"] == template
-
-
-class TestQueryTemplateApplicationInComputeEmbedding:
-    """Tests for query template application in compute_query_embedding() (Bug Fix).
-
-    These tests verify that query templates are applied consistently in BOTH
-    code paths (server and fallback) when computing query embeddings.
-
-    This addresses the bug where query templates were only applied in the
-    fallback path, not when using the embedding server (the default path).
-
-    Bug Context:
-    - Issue: Query templates were stored in metadata but only applied during
-      fallback (direct) computation, not when using embedding server
-    - Fix: Move template application to BEFORE any computation path in
-      compute_query_embedding() (searcher_base.py:107-110)
-    - Impact: Critical for models like EmbeddingGemma that require task-specific
-      templates for optimal performance
-
-    These tests ensure the fix works correctly and prevent regression.
-    """
-
-    @pytest.fixture
-    def temp_index_with_template(self):
-        """Create a temporary index with query template in metadata"""
-        with tempfile.TemporaryDirectory() as tmpdir:
-            index_dir = Path(tmpdir)
-            index_file = index_dir / "test.leann"
-            meta_file = index_dir / "test.leann.meta.json"
-
-            # Create minimal metadata with query template
-            metadata = {
-                "version": "1.0",
-                "backend_name": "hnsw",
-                "embedding_model": "text-embedding-embeddinggemma-300m-qat",
-                "dimensions": 768,
-                "embedding_mode": "openai",
-                "backend_kwargs": {
-                    "graph_degree": 32,
-                    "complexity": 64,
-                    "distance_metric": "cosine",
-                },
-                "embedding_options": {
-                    "base_url": "http://localhost:1234/v1",
-                    "api_key": "test-key",
-                    "build_prompt_template": "title: none | text: ",
-                    "query_prompt_template": "task: search result | query: ",
-                },
-            }
-
-            meta_file.write_text(json.dumps(metadata, indent=2))
-
-            # Create minimal HNSW index file (empty is okay for this test)
-            index_file.write_bytes(b"")
-
-            yield str(index_file)
-
-    def test_query_template_applied_in_fallback_path(self, temp_index_with_template):
-        """Test that query template is applied when using fallback (direct) path"""
-        from leann.searcher_base import BaseSearcher
-
-        # Create a concrete implementation for testing
-        class TestSearcher(BaseSearcher):
-            def search(self, query_vectors, top_k, complexity, beam_width=1, **kwargs):
-                return {"labels": [], "distances": []}
-
-        searcher = object.__new__(TestSearcher)
-        searcher.index_path = Path(temp_index_with_template)
-        searcher.index_dir = searcher.index_path.parent
-
-        # Load metadata
-        meta_file = searcher.index_dir / f"{searcher.index_path.name}.meta.json"
-        with open(meta_file) as f:
-            searcher.meta = json.load(f)
-
-        searcher.embedding_model = searcher.meta["embedding_model"]
-        searcher.embedding_mode = searcher.meta.get("embedding_mode", "sentence-transformers")
-        searcher.embedding_options = searcher.meta.get("embedding_options", {})
-
-        # Mock compute_embeddings to capture the query text
-        captured_queries = []
-
-        def mock_compute_embeddings(texts, model, mode, provider_options=None):
-            captured_queries.extend(texts)
-            return np.random.rand(len(texts), 768).astype(np.float32)
-
-        with patch(
-            "leann.embedding_compute.compute_embeddings", side_effect=mock_compute_embeddings
-        ):
-            # Call compute_query_embedding with template (fallback path)
-            result = searcher.compute_query_embedding(
-                query="vector database",
-                use_server_if_available=False,  # Force fallback path
-                query_template="task: search result | query: ",
-            )
-
-        # Verify template was applied
-        assert len(captured_queries) == 1
-        assert captured_queries[0] == "task: search result | query: vector database"
-        assert result.shape == (1, 768)
-
-    def test_query_template_applied_in_server_path(self, temp_index_with_template):
-        """Test that query template is applied when using server path"""
-        from leann.searcher_base import BaseSearcher
-
-        # Create a concrete implementation for testing
-        class TestSearcher(BaseSearcher):
-            def search(self, query_vectors, top_k, complexity, beam_width=1, **kwargs):
-                return {"labels": [], "distances": []}
-
-        searcher = object.__new__(TestSearcher)
-        searcher.index_path = Path(temp_index_with_template)
-        searcher.index_dir = searcher.index_path.parent
-
-        # Load metadata
-        meta_file = searcher.index_dir / f"{searcher.index_path.name}.meta.json"
-        with open(meta_file) as f:
-            searcher.meta = json.load(f)
-
-        searcher.embedding_model = searcher.meta["embedding_model"]
-        searcher.embedding_mode = searcher.meta.get("embedding_mode", "sentence-transformers")
-        searcher.embedding_options = searcher.meta.get("embedding_options", {})
-
-        # Mock the server methods to capture the query text
-        captured_queries = []
-
-        def mock_ensure_server_running(passages_file, port):
-            return port
-
-        def mock_compute_embedding_via_server(chunks, port):
-            captured_queries.extend(chunks)
-            return np.random.rand(len(chunks), 768).astype(np.float32)
-
-        searcher._ensure_server_running = mock_ensure_server_running
-        searcher._compute_embedding_via_server = mock_compute_embedding_via_server
-
-        # Call compute_query_embedding with template (server path)
-        result = searcher.compute_query_embedding(
-            query="vector database",
-            use_server_if_available=True,  # Use server path
-            query_template="task: search result | query: ",
-        )
-
-        # Verify template was applied BEFORE calling server
-        assert len(captured_queries) == 1
-        assert captured_queries[0] == "task: search result | query: vector database"
-        assert result.shape == (1, 768)
-
-    def test_query_template_without_template_parameter(self, temp_index_with_template):
-        """Test that query is unchanged when no template is provided"""
-        from leann.searcher_base import BaseSearcher
-
-        class TestSearcher(BaseSearcher):
-            def search(self, query_vectors, top_k, complexity, beam_width=1, **kwargs):
-                return {"labels": [], "distances": []}
-
-        searcher = object.__new__(TestSearcher)
-        searcher.index_path = Path(temp_index_with_template)
-        searcher.index_dir = searcher.index_path.parent
-
-        meta_file = searcher.index_dir / f"{searcher.index_path.name}.meta.json"
-        with open(meta_file) as f:
-            searcher.meta = json.load(f)
-
-        searcher.embedding_model = searcher.meta["embedding_model"]
-        searcher.embedding_mode = searcher.meta.get("embedding_mode", "sentence-transformers")
-        searcher.embedding_options = searcher.meta.get("embedding_options", {})
-
-        captured_queries = []
-
-        def mock_compute_embeddings(texts, model, mode, provider_options=None):
-            captured_queries.extend(texts)
-            return np.random.rand(len(texts), 768).astype(np.float32)
-
-        with patch(
-            "leann.embedding_compute.compute_embeddings", side_effect=mock_compute_embeddings
-        ):
-            searcher.compute_query_embedding(
-                query="vector database",
-                use_server_if_available=False,
-                query_template=None,  # No template
-            )
-
-        # Verify query is unchanged
-        assert len(captured_queries) == 1
-        assert captured_queries[0] == "vector database"
-
-    def test_query_template_consistency_between_paths(self, temp_index_with_template):
-        """Test that both paths apply template identically"""
-        from leann.searcher_base import BaseSearcher
-
-        class TestSearcher(BaseSearcher):
-            def search(self, query_vectors, top_k, complexity, beam_width=1, **kwargs):
-                return {"labels": [], "distances": []}
-
-        searcher = object.__new__(TestSearcher)
-        searcher.index_path = Path(temp_index_with_template)
-        searcher.index_dir = searcher.index_path.parent
-
-        meta_file = searcher.index_dir / f"{searcher.index_path.name}.meta.json"
-        with open(meta_file) as f:
-            searcher.meta = json.load(f)
-
-        searcher.embedding_model = searcher.meta["embedding_model"]
-        searcher.embedding_mode = searcher.meta.get("embedding_mode", "sentence-transformers")
-        searcher.embedding_options = searcher.meta.get("embedding_options", {})
-
-        query_template = "task: search result | query: "
-        original_query = "vector database"
-
-        # Capture queries from fallback path
-        fallback_queries = []
-
-        def mock_compute_embeddings(texts, model, mode, provider_options=None):
-            fallback_queries.extend(texts)
-            return np.random.rand(len(texts), 768).astype(np.float32)
-
-        with patch(
-            "leann.embedding_compute.compute_embeddings", side_effect=mock_compute_embeddings
-        ):
-            searcher.compute_query_embedding(
-                query=original_query,
-                use_server_if_available=False,
-                query_template=query_template,
-            )
-
-        # Capture queries from server path
-        server_queries = []
-
-        def mock_ensure_server_running(passages_file, port):
-            return port
-
-        def mock_compute_embedding_via_server(chunks, port):
-            server_queries.extend(chunks)
-            return np.random.rand(len(chunks), 768).astype(np.float32)
-
-        searcher._ensure_server_running = mock_ensure_server_running
-        searcher._compute_embedding_via_server = mock_compute_embedding_via_server
-
-        searcher.compute_query_embedding(
-            query=original_query,
-            use_server_if_available=True,
-            query_template=query_template,
-        )
-
-        # Verify both paths produced identical templated queries
-        assert len(fallback_queries) == 1
-        assert len(server_queries) == 1
-        assert fallback_queries[0] == server_queries[0]
-        assert fallback_queries[0] == f"{query_template}{original_query}"
-
-    def test_query_template_with_empty_string(self, temp_index_with_template):
-        """Test behavior with empty template string"""
-        from leann.searcher_base import BaseSearcher
-
-        class TestSearcher(BaseSearcher):
-            def search(self, query_vectors, top_k, complexity, beam_width=1, **kwargs):
-                return {"labels": [], "distances": []}
-
-        searcher = object.__new__(TestSearcher)
-        searcher.index_path = Path(temp_index_with_template)
-        searcher.index_dir = searcher.index_path.parent
-
-        meta_file = searcher.index_dir / f"{searcher.index_path.name}.meta.json"
-        with open(meta_file) as f:
-            searcher.meta = json.load(f)
-
-        searcher.embedding_model = searcher.meta["embedding_model"]
-        searcher.embedding_mode = searcher.meta.get("embedding_mode", "sentence-transformers")
-        searcher.embedding_options = searcher.meta.get("embedding_options", {})
-
-        captured_queries = []
-
-        def mock_compute_embeddings(texts, model, mode, provider_options=None):
-            captured_queries.extend(texts)
-            return np.random.rand(len(texts), 768).astype(np.float32)
-
-        with patch(
-            "leann.embedding_compute.compute_embeddings", side_effect=mock_compute_embeddings
-        ):
-            searcher.compute_query_embedding(
-                query="vector database",
-                use_server_if_available=False,
-                query_template="",  # Empty string
-            )
-
-        # Empty string is falsy, so no template should be applied
-        assert captured_queries[0] == "vector database"

tests/test_token_truncation.py

@@ -1,643 +0,0 @@
-"""Unit tests for token-aware truncation functionality.
-
-This test suite defines the contract for token truncation functions that prevent
-500 errors from Ollama when text exceeds model token limits. These tests verify:
-
-1. Model token limit retrieval (known and unknown models)
-2. Text truncation behavior for single and multiple texts
-3. Token counting and truncation accuracy using tiktoken
-
-All tests are written in Red Phase - they should FAIL initially because the
-implementation does not exist yet.
-"""
-
-import pytest
-import tiktoken
-from leann.embedding_compute import (
-    EMBEDDING_MODEL_LIMITS,
-    get_model_token_limit,
-    truncate_to_token_limit,
-)
-
-
-class TestModelTokenLimits:
-    """Tests for retrieving model-specific token limits."""
-
-    def test_get_model_token_limit_known_model(self):
-        """Verify correct token limit is returned for known models.
-
-        Known models should return their specific token limits from
-        EMBEDDING_MODEL_LIMITS dictionary.
-        """
-        # Test nomic-embed-text (2048 tokens)
-        limit = get_model_token_limit("nomic-embed-text")
-        assert limit == 2048, "nomic-embed-text should have 2048 token limit"
-
-        # Test nomic-embed-text-v1.5 (2048 tokens)
-        limit = get_model_token_limit("nomic-embed-text-v1.5")
-        assert limit == 2048, "nomic-embed-text-v1.5 should have 2048 token limit"
-
-        # Test nomic-embed-text-v2 (512 tokens)
-        limit = get_model_token_limit("nomic-embed-text-v2")
-        assert limit == 512, "nomic-embed-text-v2 should have 512 token limit"
-
-        # Test OpenAI models (8192 tokens)
-        limit = get_model_token_limit("text-embedding-3-small")
-        assert limit == 8192, "text-embedding-3-small should have 8192 token limit"
-
-    def test_get_model_token_limit_unknown_model(self):
-        """Verify default token limit is returned for unknown models.
-
-        Unknown models should return the default limit (2048) to allow
-        operation with reasonable safety margin.
-        """
-        # Test with completely unknown model
-        limit = get_model_token_limit("unknown-model-xyz")
-        assert limit == 2048, "Unknown models should return default 2048"
-
-        # Test with empty string
-        limit = get_model_token_limit("")
-        assert limit == 2048, "Empty model name should return default 2048"
-
-    def test_get_model_token_limit_custom_default(self):
-        """Verify custom default can be specified for unknown models.
-
-        Allow callers to specify their own default token limit when
-        model is not in the known models dictionary.
-        """
-        limit = get_model_token_limit("unknown-model", default=4096)
-        assert limit == 4096, "Should return custom default for unknown models"
-
-        # Known model should ignore custom default
-        limit = get_model_token_limit("nomic-embed-text", default=4096)
-        assert limit == 2048, "Known model should ignore custom default"
-
-    def test_embedding_model_limits_dictionary_exists(self):
-        """Verify EMBEDDING_MODEL_LIMITS dictionary contains expected models.
-
-        The dictionary should be importable and contain at least the
-        known nomic models with correct token limits.
-        """
-        assert isinstance(EMBEDDING_MODEL_LIMITS, dict), "Should be a dictionary"
-        assert "nomic-embed-text" in EMBEDDING_MODEL_LIMITS, "Should contain nomic-embed-text"
-        assert "nomic-embed-text-v1.5" in EMBEDDING_MODEL_LIMITS, (
-            "Should contain nomic-embed-text-v1.5"
-        )
-        assert EMBEDDING_MODEL_LIMITS["nomic-embed-text"] == 2048
-        assert EMBEDDING_MODEL_LIMITS["nomic-embed-text-v1.5"] == 2048
-        assert EMBEDDING_MODEL_LIMITS["nomic-embed-text-v2"] == 512
-        # OpenAI models
-        assert EMBEDDING_MODEL_LIMITS["text-embedding-3-small"] == 8192
-
-
-class TestTokenTruncation:
-    """Tests for truncating texts to token limits."""
-
-    @pytest.fixture
-    def tokenizer(self):
-        """Provide tiktoken tokenizer for token counting verification."""
-        return tiktoken.get_encoding("cl100k_base")
-
-    def test_truncate_single_text_under_limit(self, tokenizer):
-        """Verify text under token limit remains unchanged.
-
-        When text is already within the token limit, it should be
-        returned unchanged with no truncation.
-        """
-        text = "This is a short text that is well under the token limit."
-        token_count = len(tokenizer.encode(text))
-        assert token_count < 100, f"Test setup: text should be short (has {token_count} tokens)"
-
-        # Truncate with generous limit
-        result = truncate_to_token_limit([text], token_limit=512)
-
-        assert len(result) == 1, "Should return same number of texts"
-        assert result[0] == text, "Text under limit should be unchanged"
-
-    def test_truncate_single_text_over_limit(self, tokenizer):
-        """Verify text over token limit is truncated correctly.
-
-        When text exceeds the token limit, it should be truncated to
-        fit within the limit while maintaining valid token boundaries.
-        """
-        # Create a text that definitely exceeds limit
-        text = "word " * 200  # ~200 tokens (each "word " is typically 1-2 tokens)
-        original_token_count = len(tokenizer.encode(text))
-        assert original_token_count > 50, (
-            f"Test setup: text should be long (has {original_token_count} tokens)"
-        )
-
-        # Truncate to 50 tokens
-        result = truncate_to_token_limit([text], token_limit=50)
-
-        assert len(result) == 1, "Should return same number of texts"
-        assert result[0] != text, "Text over limit should be truncated"
-        assert len(result[0]) < len(text), "Truncated text should be shorter"
-
-        # Verify truncated text is within token limit
-        truncated_token_count = len(tokenizer.encode(result[0]))
-        assert truncated_token_count <= 50, (
-            f"Truncated text should be ≤50 tokens, got {truncated_token_count}"
-        )
-
-    def test_truncate_multiple_texts_mixed_lengths(self, tokenizer):
-        """Verify multiple texts with mixed lengths are handled correctly.
-
-        When processing multiple texts:
-        - Texts under limit should remain unchanged
-        - Texts over limit should be truncated independently
-        - Output list should maintain same order and length
-        """
-        texts = [
-            "Short text.",  # Under limit
-            "word " * 200,  # Over limit
-            "Another short one.",  # Under limit
-            "token " * 150,  # Over limit
-        ]
-
-        # Verify test setup
-        for i, text in enumerate(texts):
-            token_count = len(tokenizer.encode(text))
-            if i in [1, 3]:
-                assert token_count > 50, f"Text {i} should be over limit (has {token_count} tokens)"
-            else:
-                assert token_count < 50, (
-                    f"Text {i} should be under limit (has {token_count} tokens)"
-                )
-
-        # Truncate with 50 token limit
-        result = truncate_to_token_limit(texts, token_limit=50)
-
-        assert len(result) == len(texts), "Should return same number of texts"
-
-        # Verify each text individually
-        for i, (original, truncated) in enumerate(zip(texts, result)):
-            token_count = len(tokenizer.encode(truncated))
-            assert token_count <= 50, f"Text {i} should be ≤50 tokens, got {token_count}"
-
-            # Short texts should be unchanged
-            if i in [0, 2]:
-                assert truncated == original, f"Short text {i} should be unchanged"
-            # Long texts should be truncated
-            else:
-                assert len(truncated) < len(original), f"Long text {i} should be truncated"
-
-    def test_truncate_empty_list(self):
-        """Verify empty input list returns empty output list.
-
-        Edge case: empty list should return empty list without errors.
-        """
-        result = truncate_to_token_limit([], token_limit=512)
-        assert result == [], "Empty input should return empty output"
-
-    def test_truncate_preserves_order(self, tokenizer):
-        """Verify truncation preserves original text order.
-
-        Output list should maintain the same order as input list,
-        regardless of which texts were truncated.
-        """
-        texts = [
-            "First text " * 50,  # Will be truncated
-            "Second text.",  # Won't be truncated
-            "Third text " * 50,  # Will be truncated
-        ]
-
-        result = truncate_to_token_limit(texts, token_limit=20)
-
-        assert len(result) == 3, "Should preserve list length"
-        # Check that order is maintained by looking for distinctive words
-        assert "First" in result[0], "First text should remain in first position"
-        assert "Second" in result[1], "Second text should remain in second position"
-        assert "Third" in result[2], "Third text should remain in third position"
-
-    def test_truncate_extremely_long_text(self, tokenizer):
-        """Verify extremely long texts are truncated efficiently.
-
-        Test with text that far exceeds token limit to ensure
-        truncation handles extreme cases without performance issues.
-        """
-        # Create very long text (simulate real-world scenario)
-        text = "token " * 5000  # ~5000+ tokens
-        original_token_count = len(tokenizer.encode(text))
-        assert original_token_count > 1000, "Test setup: text should be very long"
-
-        # Truncate to small limit
-        result = truncate_to_token_limit([text], token_limit=100)
-
-        assert len(result) == 1
-        truncated_token_count = len(tokenizer.encode(result[0]))
-        assert truncated_token_count <= 100, (
-            f"Should truncate to ≤100 tokens, got {truncated_token_count}"
-        )
-        assert len(result[0]) < len(text) // 10, "Should significantly reduce text length"
-
-    def test_truncate_exact_token_limit(self, tokenizer):
-        """Verify text at exactly token limit is handled correctly.
-
-        Edge case: text with exactly the token limit should either
-        remain unchanged or be safely truncated by 1 token.
-        """
-        # Create text with approximately 50 tokens
-        # We'll adjust to get exactly 50
-        target_tokens = 50
-        text = "word " * 50
-        tokens = tokenizer.encode(text)
-
-        # Adjust to get exactly target_tokens
-        if len(tokens) > target_tokens:
-            tokens = tokens[:target_tokens]
-            text = tokenizer.decode(tokens)
-        elif len(tokens) < target_tokens:
-            # Add more words
-            while len(tokenizer.encode(text)) < target_tokens:
-                text += "word "
-            tokens = tokenizer.encode(text)[:target_tokens]
-            text = tokenizer.decode(tokens)
-
-        # Verify we have exactly target_tokens
-        assert len(tokenizer.encode(text)) == target_tokens, (
-            "Test setup: should have exactly 50 tokens"
-        )
-
-        result = truncate_to_token_limit([text], token_limit=target_tokens)
-
-        assert len(result) == 1
-        result_tokens = len(tokenizer.encode(result[0]))
-        assert result_tokens <= target_tokens, (
-            f"Should be ≤{target_tokens} tokens, got {result_tokens}"
-        )
-
-
-class TestLMStudioHybridDiscovery:
-    """Tests for LM Studio integration in get_model_token_limit() hybrid discovery.
-
-    These tests verify that get_model_token_limit() properly integrates with
-    the LM Studio SDK bridge for dynamic token limit discovery. The integration
-    should:
-
-    1. Detect LM Studio URLs (port 1234 or 'lmstudio'/'lm.studio' in URL)
-    2. Convert HTTP URLs to WebSocket format for SDK queries
-    3. Query LM Studio SDK and use discovered limit
-    4. Fall back to registry when SDK returns None
-    5. Execute AFTER Ollama detection but BEFORE registry fallback
-
-    All tests are written in Red Phase - they should FAIL initially because the
-    LM Studio detection and integration logic does not exist yet in get_model_token_limit().
-    """
-
-    def test_get_model_token_limit_lmstudio_success(self, monkeypatch):
-        """Verify LM Studio SDK query succeeds and returns detected limit.
-
-        When a LM Studio base_url is detected and the SDK query succeeds,
-        get_model_token_limit() should return the dynamically discovered
-        context length without falling back to the registry.
-        """
-
-        # Mock _query_lmstudio_context_limit to return successful SDK query
-        def mock_query_lmstudio(model_name, base_url):
-            # Verify WebSocket URL was passed (not HTTP)
-            assert base_url.startswith("ws://"), (
-                f"Should convert HTTP to WebSocket format, got: {base_url}"
-            )
-            return 8192  # Successful SDK query
-
-        monkeypatch.setattr(
-            "leann.embedding_compute._query_lmstudio_context_limit",
-            mock_query_lmstudio,
-        )
-
-        # Test with HTTP URL that should be converted to WebSocket
-        limit = get_model_token_limit(
-            model_name="custom-model", base_url="http://localhost:1234/v1"
-        )
-
-        assert limit == 8192, "Should return limit from LM Studio SDK query"
-
-    def test_get_model_token_limit_lmstudio_fallback_to_registry(self, monkeypatch):
-        """Verify fallback to registry when LM Studio SDK returns None.
-
-        When LM Studio SDK query fails (returns None), get_model_token_limit()
-        should fall back to the EMBEDDING_MODEL_LIMITS registry.
-        """
-
-        # Mock _query_lmstudio_context_limit to return None (SDK failure)
-        def mock_query_lmstudio(model_name, base_url):
-            return None  # SDK query failed
-
-        monkeypatch.setattr(
-            "leann.embedding_compute._query_lmstudio_context_limit",
-            mock_query_lmstudio,
-        )
-
-        # Test with known model that exists in registry
-        limit = get_model_token_limit(
-            model_name="nomic-embed-text", base_url="http://localhost:1234/v1"
-        )
-
-        # Should fall back to registry value
-        assert limit == 2048, "Should fall back to registry when SDK returns None"
-
-    def test_get_model_token_limit_lmstudio_port_detection(self, monkeypatch):
-        """Verify detection of LM Studio via port 1234.
-
-        get_model_token_limit() should recognize port 1234 as a LM Studio
-        server and attempt SDK query, regardless of hostname.
-        """
-        query_called = False
-
-        def mock_query_lmstudio(model_name, base_url):
-            nonlocal query_called
-            query_called = True
-            return 4096
-
-        monkeypatch.setattr(
-            "leann.embedding_compute._query_lmstudio_context_limit",
-            mock_query_lmstudio,
-        )
-
-        # Test with port 1234 (default LM Studio port)
-        limit = get_model_token_limit(model_name="test-model", base_url="http://127.0.0.1:1234/v1")
-
-        assert query_called, "Should detect port 1234 and call LM Studio SDK query"
-        assert limit == 4096, "Should return SDK query result"
-
-    @pytest.mark.parametrize(
-        "test_url,expected_limit,keyword",
-        [
-            ("http://lmstudio.local:8080/v1", 16384, "lmstudio"),
-            ("http://api.lm.studio:5000/v1", 32768, "lm.studio"),
-        ],
-    )
-    def test_get_model_token_limit_lmstudio_url_keyword_detection(
-        self, monkeypatch, test_url, expected_limit, keyword
-    ):
-        """Verify detection of LM Studio via keywords in URL.
-
-        get_model_token_limit() should recognize 'lmstudio' or 'lm.studio'
-        in the URL as indicating a LM Studio server.
-        """
-        query_called = False
-
-        def mock_query_lmstudio(model_name, base_url):
-            nonlocal query_called
-            query_called = True
-            return expected_limit
-
-        monkeypatch.setattr(
-            "leann.embedding_compute._query_lmstudio_context_limit",
-            mock_query_lmstudio,
-        )
-
-        limit = get_model_token_limit(model_name="test-model", base_url=test_url)
-
-        assert query_called, f"Should detect '{keyword}' keyword and call SDK query"
-        assert limit == expected_limit, f"Should return SDK query result for {keyword}"
-
-    @pytest.mark.parametrize(
-        "input_url,expected_protocol,expected_host",
-        [
-            ("http://localhost:1234/v1", "ws://", "localhost:1234"),
-            ("https://lmstudio.example.com:1234/v1", "wss://", "lmstudio.example.com:1234"),
-        ],
-    )
-    def test_get_model_token_limit_protocol_conversion(
-        self, monkeypatch, input_url, expected_protocol, expected_host
-    ):
-        """Verify HTTP/HTTPS URL is converted to WebSocket format for SDK query.
-
-        LM Studio SDK requires WebSocket URLs. get_model_token_limit() should:
-        1. Convert 'http://' to 'ws://'
-        2. Convert 'https://' to 'wss://'
-        3. Remove '/v1' or other path suffixes (SDK expects base URL)
-        4. Preserve host and port
-        """
-        conversions_tested = []
-
-        def mock_query_lmstudio(model_name, base_url):
-            conversions_tested.append(base_url)
-            return 8192
-
-        monkeypatch.setattr(
-            "leann.embedding_compute._query_lmstudio_context_limit",
-            mock_query_lmstudio,
-        )
-
-        get_model_token_limit(model_name="test-model", base_url=input_url)
-
-        # Verify conversion happened
-        assert len(conversions_tested) == 1, "Should have called SDK query once"
-        assert conversions_tested[0].startswith(expected_protocol), (
-            f"Should convert to {expected_protocol}"
-        )
-        assert expected_host in conversions_tested[0], (
-            f"Should preserve host and port: {expected_host}"
-        )
-
-    def test_get_model_token_limit_lmstudio_executes_after_ollama(self, monkeypatch):
-        """Verify LM Studio detection happens AFTER Ollama detection.
-
-        The hybrid discovery order should be:
-        1. Ollama dynamic discovery (port 11434 or 'ollama' in URL)
-        2. LM Studio dynamic discovery (port 1234 or 'lmstudio' in URL)
-        3. Registry fallback
-
-        If both Ollama and LM Studio patterns match, Ollama should take precedence.
-        This test verifies that LM Studio is checked but doesn't interfere with Ollama.
-        """
-        ollama_called = False
-        lmstudio_called = False
-
-        def mock_query_ollama(model_name, base_url):
-            nonlocal ollama_called
-            ollama_called = True
-            return 2048  # Ollama query succeeds
-
-        def mock_query_lmstudio(model_name, base_url):
-            nonlocal lmstudio_called
-            lmstudio_called = True
-            return None  # Should not be reached if Ollama succeeds
-
-        monkeypatch.setattr(
-            "leann.embedding_compute._query_ollama_context_limit",
-            mock_query_ollama,
-        )
-        monkeypatch.setattr(
-            "leann.embedding_compute._query_lmstudio_context_limit",
-            mock_query_lmstudio,
-        )
-
-        # Test with Ollama URL
-        limit = get_model_token_limit(
-            model_name="test-model", base_url="http://localhost:11434/api"
-        )
-
-        assert ollama_called, "Should attempt Ollama query first"
-        assert not lmstudio_called, "Should not attempt LM Studio query when Ollama succeeds"
-        assert limit == 2048, "Should return Ollama result"
-
-    def test_get_model_token_limit_lmstudio_not_detected_for_non_lmstudio_urls(self, monkeypatch):
-        """Verify LM Studio SDK query is NOT called for non-LM Studio URLs.
-
-        Only URLs with port 1234 or 'lmstudio'/'lm.studio' keywords should
-        trigger LM Studio SDK queries. Other URLs should skip to registry fallback.
-        """
-        lmstudio_called = False
-
-        def mock_query_lmstudio(model_name, base_url):
-            nonlocal lmstudio_called
-            lmstudio_called = True
-            return 8192
-
-        monkeypatch.setattr(
-            "leann.embedding_compute._query_lmstudio_context_limit",
-            mock_query_lmstudio,
-        )
-
-        # Test with non-LM Studio URLs
-        test_cases = [
-            "http://localhost:8080/v1",  # Different port
-            "http://openai.example.com/v1",  # Different service
-            "http://localhost:3000/v1",  # Another port
-        ]
-
-        for base_url in test_cases:
-            lmstudio_called = False  # Reset for each test
-            get_model_token_limit(model_name="nomic-embed-text", base_url=base_url)
-            assert not lmstudio_called, f"Should NOT call LM Studio SDK for URL: {base_url}"
-
-    def test_get_model_token_limit_lmstudio_case_insensitive_detection(self, monkeypatch):
-        """Verify LM Studio detection is case-insensitive for keywords.
-
-        Keywords 'lmstudio' and 'lm.studio' should be detected regardless
-        of case (LMStudio, LMSTUDIO, LmStudio, etc.).
-        """
-        query_called = False
-
-        def mock_query_lmstudio(model_name, base_url):
-            nonlocal query_called
-            query_called = True
-            return 8192
-
-        monkeypatch.setattr(
-            "leann.embedding_compute._query_lmstudio_context_limit",
-            mock_query_lmstudio,
-        )
-
-        # Test various case variations
-        test_cases = [
-            "http://LMStudio.local:8080/v1",
-            "http://LMSTUDIO.example.com/v1",
-            "http://LmStudio.local/v1",
-            "http://api.LM.STUDIO:5000/v1",
-        ]
-
-        for base_url in test_cases:
-            query_called = False  # Reset for each test
-            limit = get_model_token_limit(model_name="test-model", base_url=base_url)
-            assert query_called, f"Should detect LM Studio in URL: {base_url}"
-            assert limit == 8192, f"Should return SDK result for URL: {base_url}"
-
-
-class TestTokenLimitCaching:
-    """Tests for token limit caching to prevent repeated SDK/API calls.
-
-    Caching prevents duplicate SDK/API calls within the same Python process,
-    which is important because:
-    1. LM Studio SDK load() can load duplicate model instances
-    2. Ollama /api/show queries add latency
-    3. Registry lookups are pure overhead
-
-    Cache is process-scoped and resets between leann build invocations.
-    """
-
-    def setup_method(self):
-        """Clear cache before each test."""
-        from leann.embedding_compute import _token_limit_cache
-
-        _token_limit_cache.clear()
-
-    def test_registry_lookup_is_cached(self):
-        """Verify that registry lookups are cached."""
-        from leann.embedding_compute import _token_limit_cache
-
-        # First call
-        limit1 = get_model_token_limit("text-embedding-3-small")
-        assert limit1 == 8192
-
-        # Verify it's in cache
-        cache_key = ("text-embedding-3-small", "")
-        assert cache_key in _token_limit_cache
-        assert _token_limit_cache[cache_key] == 8192
-
-        # Second call should use cache
-        limit2 = get_model_token_limit("text-embedding-3-small")
-        assert limit2 == 8192
-
-    def test_default_fallback_is_cached(self):
-        """Verify that default fallbacks are cached."""
-        from leann.embedding_compute import _token_limit_cache
-
-        # First call with unknown model
-        limit1 = get_model_token_limit("unknown-model-xyz", default=512)
-        assert limit1 == 512
-
-        # Verify it's in cache
-        cache_key = ("unknown-model-xyz", "")
-        assert cache_key in _token_limit_cache
-        assert _token_limit_cache[cache_key] == 512
-
-        # Second call should use cache
-        limit2 = get_model_token_limit("unknown-model-xyz", default=512)
-        assert limit2 == 512
-
-    def test_different_urls_create_separate_cache_entries(self):
-        """Verify that different base_urls create separate cache entries."""
-        from leann.embedding_compute import _token_limit_cache
-
-        # Same model, different URLs
-        limit1 = get_model_token_limit("nomic-embed-text", base_url="http://localhost:11434")
-        limit2 = get_model_token_limit("nomic-embed-text", base_url="http://localhost:1234/v1")
-
-        # Both should find the model in registry (2048)
-        assert limit1 == 2048
-        assert limit2 == 2048
-
-        # But they should be separate cache entries
-        cache_key1 = ("nomic-embed-text", "http://localhost:11434")
-        cache_key2 = ("nomic-embed-text", "http://localhost:1234/v1")
-
-        assert cache_key1 in _token_limit_cache
-        assert cache_key2 in _token_limit_cache
-        assert len(_token_limit_cache) == 2
-
-    def test_cache_prevents_repeated_lookups(self):
-        """Verify that cache prevents repeated registry/API lookups."""
-        from leann.embedding_compute import _token_limit_cache
-
-        model_name = "text-embedding-ada-002"
-
-        # First call - should add to cache
-        assert len(_token_limit_cache) == 0
-        limit1 = get_model_token_limit(model_name)
-
-        cache_size_after_first = len(_token_limit_cache)
-        assert cache_size_after_first == 1
-
-        # Multiple subsequent calls - cache size should not change
-        for _ in range(5):
-            limit = get_model_token_limit(model_name)
-            assert limit == limit1
-            assert len(_token_limit_cache) == cache_size_after_first
-
-    def test_versioned_model_names_cached_correctly(self):
-        """Verify that versioned model names (e.g., model:tag) are cached."""
-        from leann.embedding_compute import _token_limit_cache
-
-        # Model with version tag
-        limit = get_model_token_limit("nomic-embed-text:latest", base_url="http://localhost:11434")
-        assert limit == 2048
-
-        # Should be cached with full name including version
-        cache_key = ("nomic-embed-text:latest", "http://localhost:11434")
-        assert cache_key in _token_limit_cache
-        assert _token_limit_cache[cache_key] == 2048