CI: add build venv scripts path for wheel repair

- Remove unused variables in benchmark scripts
- Rename unused loop variables to follow convention

.gitignore

@@ -99,12 +99,3 @@ benchmarks/data/
 
 ## multi vector
 apps/multimodal/vision-based-pdf-multi-vector/multi-vector-colpali-native-weaviate.py
-
-# Ignore all PDFs (keep data exceptions above) and do not track demo PDFs
-# If you need to commit a specific demo PDF, remove this negation locally.
-# 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/

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!**

README.md

@@ -8,12 +8,8 @@
   <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">
-    <img src="https://img.shields.io/badge/Slack-Join-4A154B?logo=slack&logoColor=white" alt="Join Slack">
-  </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>
+  <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">
+  <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>
 
 <h2 align="center" tabindex="-1" class="heading-element" dir="auto">
@@ -24,7 +20,7 @@ LEANN is an innovative vector database that democratizes personal AI. Transform
 
 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)
@@ -76,9 +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>
@@ -181,7 +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.
 
 
 
@@ -547,386 +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.
 
@@ -954,7 +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.
 
@@ -1196,7 +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.
@@ -1213,7 +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

@@ -10,39 +10,8 @@ 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
-
-# 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")
-
+from leann.settings import resolve_ollama_host, resolve_openai_api_key, resolve_openai_base_url
 
 dotenv.load_dotenv()
 
@@ -180,14 +149,14 @@ class BaseRAGExample(ABC):
         ast_group.add_argument(
             "--ast-chunk-size",
             type=int,
-            default=300,
-            help="Maximum CHARACTERS per AST chunk (default: 300). Final chunks may be larger due to overlap. For 512 token models: recommended 300 chars",
+            default=512,
+            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",
@@ -338,26 +307,37 @@ class BaseRAGExample(ABC):
             complexity=args.search_complexity,
         )
 
-        # Create interactive session
-        session = create_rag_session(
-            app_name=self.name.lower().replace(" ", "_"), data_description=self.name
-        )
+        print(f"\n[Interactive Mode] Chat with your {self.name} data!")
+        print("Type 'quit' or 'exit' to stop.\n")
 
-        def handle_query(query: str):
-            # Prepare LLM kwargs with thinking budget if specified
-            llm_kwargs = {}
-            if hasattr(args, "thinking_budget") and args.thinking_budget:
-                llm_kwargs["thinking_budget"] = args.thinking_budget
+        while True:
+            try:
+                query = input("You: ").strip()
+                if query.lower() in ["quit", "exit", "q"]:
+                    print("Goodbye!")
+                    break
 
-            response = chat.ask(
-                query,
-                top_k=args.top_k,
-                complexity=args.search_complexity,
-                llm_kwargs=llm_kwargs,
-            )
-            print(f"\nAssistant: {response}\n")
+                if not query:
+                    continue
 
-        session.run_interactive_loop(handle_query)
+                # Prepare LLM kwargs with thinking budget if specified
+                llm_kwargs = {}
+                if hasattr(args, "thinking_budget") and args.thinking_budget:
+                    llm_kwargs["thinking_budget"] = args.thinking_budget
+
+                response = chat.ask(
+                    query,
+                    top_k=args.top_k,
+                    complexity=args.search_complexity,
+                    llm_kwargs=llm_kwargs,
+                )
+                print(f"\nAssistant: {response}\n")
+
+            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/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/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/README.md

@@ -1,113 +0,0 @@
-## Vision-based PDF Multi-Vector Demos (macOS/MPS)
-
-This folder contains two demos to index PDF pages as images and run multi-vector retrieval with ColPali/ColQwen2, plus optional similarity map visualization and answer generation.
-
-### What you’ll run
-- `multi-vector-leann-paper-example.py`: local PDF → pages → embed → build HNSW index → search.
-- `multi-vector-leann-similarity-map.py`: HF dataset (default) or local pages → embed → index → retrieve → similarity maps → optional Qwen-VL answer.
-
-## Prerequisites (macOS)
-
-### 1) Homebrew poppler (for pdf2image)
-```bash
-brew install poppler
-which pdfinfo && pdfinfo -v
-```
-
-### 2) Python environment
-Use uv (recommended) or pip. Python 3.9+.
-
-Using uv:
-```bash
-uv pip install \
-  colpali_engine \
-  pdf2image \
-  pillow \
-  matplotlib qwen_vl_utils \
-  einops \
-  seaborn
-```
-
-Notes:
-- On first run, models download from Hugging Face. Login/config if needed.
-- The scripts auto-select device: CUDA > MPS > CPU. Verify MPS:
-```bash
-python -c "import torch; print('MPS available:', bool(getattr(torch.backends, 'mps', None) and torch.backends.mps.is_available()))"
-```
-
-## Run the demos
-
-### A) Local PDF example
-Converts a local PDF into page images, embeds them, builds an index, and searches.
-
-```bash
-cd apps/multimodal/vision-based-pdf-multi-vector
-# If you don't have the sample PDF locally, download it (ignored by Git)
-mkdir -p pdfs
-curl -L -o pdfs/2004.12832v2.pdf https://arxiv.org/pdf/2004.12832.pdf
-ls pdfs/2004.12832v2.pdf
-# Ensure output dir exists
-mkdir -p pages
-python multi-vector-leann-paper-example.py
-```
-Expected:
-- Page images in `pages/`.
-- Console prints like `Using device=mps, dtype=...` and retrieved file paths for queries.
-
-To use your own PDF: edit `pdf_path` near the top of the script.
-
-### B) Similarity map + answer demo
-Uses HF dataset `weaviate/arXiv-AI-papers-multi-vector` by default; can switch to local pages.
-
-```bash
-cd apps/multimodal/vision-based-pdf-multi-vector
-python multi-vector-leann-similarity-map.py
-```
-Artifacts (when enabled):
-- Retrieved pages: `./figures/retrieved_page_rank{K}.png`
-- Similarity maps: `./figures/similarity_map_rank{K}.png`
-
-Key knobs in the script (top of file):
-- `QUERY`: your question
-- `MODEL`: `"colqwen2"` or `"colpali"`
-- `USE_HF_DATASET`: set `False` to use local pages
-- `PDF`, `PAGES_DIR`: for local mode
-- `INDEX_PATH`, `TOPK`, `FIRST_STAGE_K`, `REBUILD_INDEX`
-- `SIMILARITY_MAP`, `SIM_TOKEN_IDX`, `SIM_OUTPUT`
-- `ANSWER`, `MAX_NEW_TOKENS` (Qwen-VL)
-
-## Troubleshooting
-- pdf2image errors on macOS: ensure `brew install poppler` and `pdfinfo` works in terminal.
-- Slow or OOM on MPS: reduce dataset size (e.g., set `MAX_DOCS`) or switch to CPU.
-- NaNs on MPS: keep fp32 on MPS (default in similarity-map script); avoid fp16 there.
-- First-run model downloads can be large; ensure network access (HF mirrors if needed).
-
-## Notes
-- Index files are under `./indexes/`. Delete or set `REBUILD_INDEX=True` to rebuild.
-- For local PDFs, page images go to `./pages/`.
-
-
-### Retrieval and Visualization Example
-
-Example settings in `multi-vector-leann-similarity-map.py`:
-- `QUERY = "How does DeepSeek-V2 compare against the LLaMA family of LLMs?"`
-- `SIMILARITY_MAP = True` (to generate heatmaps)
-- `TOPK = 1` (save the top retrieved page and its similarity map)
-
-Run:
-```bash
-cd apps/multimodal/vision-based-pdf-multi-vector
-python multi-vector-leann-similarity-map.py
-```
-
-Outputs (by default):
-- Retrieved page: `./figures/retrieved_page_rank1.png`
-- Similarity map: `./figures/similarity_map_rank1.png`
-
-Sample visualization (example result, and the query is "QUERY = "How does Vim model performance and efficiency compared to other models?"
-"):
-![Similarity map example](fig/image.png)
-
-Notes:
-- Set `SIM_TOKEN_IDX` to visualize a specific token index; set `-1` to auto-select the most salient token.
-- If you change `SIM_OUTPUT` to a file path (e.g., `./figures/my_map.png`), multiple ranks are saved as `my_map_rank{K}.png`.

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

@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 import sys
-import concurrent.futures
 from pathlib import Path
 
 import numpy as np
@@ -46,7 +45,6 @@ class LeannMultiVector:
             "is_recompute": is_recompute,
         }
         self._labels_meta: list[dict] = []
-        self._docid_to_indices: dict[int, list[int]] | None = None
 
     def _meta_dict(self) -> dict:
         return {
@@ -82,10 +80,6 @@ class LeannMultiVector:
         index_path_obj = Path(self.index_path)
         return index_path_obj.parent / f"{index_path_obj.name}.meta.json"
 
-    def _embeddings_path(self) -> Path:
-        index_path_obj = Path(self.index_path)
-        return index_path_obj.parent / f"{index_path_obj.name}.emb.npy"
-
     def create_index(self) -> None:
         if not self._pending_items:
             return
@@ -127,9 +121,6 @@ class LeannMultiVector:
         with open(self._labels_path(), "w", encoding="utf-8") as f:
             _json.dump(labels_meta, f)
 
-        # Persist embeddings for exact reranking
-        np.save(self._embeddings_path(), embeddings_np)
-
         self._labels_meta = labels_meta
 
     def _load_labels_meta_if_needed(self) -> None:
@@ -142,19 +133,6 @@ class LeannMultiVector:
             with open(labels_path, encoding="utf-8") as f:
                 self._labels_meta = _json.load(f)
 
-    def _build_docid_to_indices_if_needed(self) -> None:
-        if self._docid_to_indices is not None:
-            return
-        self._load_labels_meta_if_needed()
-        mapping: dict[int, list[int]] = {}
-        for idx, meta in enumerate(self._labels_meta):
-            try:
-                doc_id = int(meta["doc_id"])  # type: ignore[index]
-            except Exception:
-                continue
-            mapping.setdefault(doc_id, []).append(idx)
-        self._docid_to_indices = mapping
-
     def search(
         self, data: np.ndarray, topk: int, first_stage_k: int = 50
     ) -> list[tuple[float, int]]:
@@ -202,139 +180,3 @@ class LeannMultiVector:
 
         scores = sorted(((v, k) for k, v in doc_scores.items()), key=lambda x: x[0], reverse=True)
         return scores[:topk] if len(scores) >= topk else scores
-
-    def search_exact(
-        self,
-        data: np.ndarray,
-        topk: int,
-        *,
-        first_stage_k: int = 200,
-        max_workers: int = 32,
-    ) -> list[tuple[float, int]]:
-        """
-        High-precision MaxSim reranking over candidate documents.
-
-        Steps:
-        1) Run a first-stage ANN to collect candidate doc_ids (using seq-level neighbors).
-        2) For each candidate doc, load all its token embeddings and compute
-           MaxSim(query_tokens, doc_tokens) exactly: sum(max(dot(q_i, d_j))).
-
-        Returns top-k list of (score, doc_id).
-        """
-        # Normalize inputs
-        if data.ndim == 1:
-            data = data.reshape(1, -1)
-        if data.dtype != np.float32:
-            data = data.astype(np.float32)
-
-        self._load_labels_meta_if_needed()
-        self._build_docid_to_indices_if_needed()
-
-        emb_path = self._embeddings_path()
-        if not emb_path.exists():
-            # Fallback to approximate if we don't have persisted embeddings
-            return self.search(data, topk, first_stage_k=first_stage_k)
-
-        # Memory-map embeddings to avoid loading all into RAM
-        all_embeddings = np.load(emb_path, mmap_mode="r")
-        if all_embeddings.dtype != np.float32:
-            all_embeddings = all_embeddings.astype(np.float32)
-
-        # First-stage ANN to collect candidate doc_ids
-        searcher = HNSWSearcher(self.index_path, meta=self._meta_dict())
-        raw = searcher.search(
-            data,
-            first_stage_k,
-            recompute_embeddings=False,
-            complexity=128,
-            beam_width=1,
-            prune_ratio=0.0,
-            batch_size=0,
-        )
-        labels = raw.get("labels")
-        if labels is None:
-            return []
-        candidate_doc_ids: set[int] = set()
-        for batch in labels:
-            for sid in batch:
-                try:
-                    idx = int(sid)
-                except Exception:
-                    continue
-                if 0 <= idx < len(self._labels_meta):
-                    candidate_doc_ids.add(int(self._labels_meta[idx]["doc_id"]))  # type: ignore[index]
-
-        # Exact scoring per doc (parallelized)
-        assert self._docid_to_indices is not None
-
-        def _score_one(doc_id: int) -> tuple[float, int]:
-            token_indices = self._docid_to_indices.get(doc_id, [])
-            if not token_indices:
-                return (0.0, doc_id)
-            doc_vecs = np.asarray(all_embeddings[token_indices], dtype=np.float32)
-            # (Q, D) x (P, D)^T -> (Q, P) then MaxSim over P, sum over Q
-            sim = np.dot(data, doc_vecs.T)
-            # nan-safe
-            sim = np.nan_to_num(sim, nan=-1e30, posinf=1e30, neginf=-1e30)
-            score = sim.max(axis=2).sum(axis=1) if sim.ndim == 3 else sim.max(axis=1).sum()
-            return (float(score), doc_id)
-
-        scores: list[tuple[float, int]] = []
-        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as ex:
-            futures = [ex.submit(_score_one, doc_id) for doc_id in candidate_doc_ids]
-            for fut in concurrent.futures.as_completed(futures):
-                scores.append(fut.result())
-
-        scores.sort(key=lambda x: x[0], reverse=True)
-        return scores[:topk] if len(scores) >= topk else scores
-
-    def search_exact_all(
-        self,
-        data: np.ndarray,
-        topk: int,
-        *,
-        max_workers: int = 32,
-    ) -> list[tuple[float, int]]:
-        """
-        Exact MaxSim over ALL documents (no ANN pre-filtering).
-
-        This computes, for each document, sum_i max_j dot(q_i, d_j).
-        It memory-maps the persisted token-embedding matrix for scalability.
-        """
-        if data.ndim == 1:
-            data = data.reshape(1, -1)
-        if data.dtype != np.float32:
-            data = data.astype(np.float32)
-
-        self._load_labels_meta_if_needed()
-        self._build_docid_to_indices_if_needed()
-
-        emb_path = self._embeddings_path()
-        if not emb_path.exists():
-            return self.search(data, topk)
-
-        all_embeddings = np.load(emb_path, mmap_mode="r")
-        if all_embeddings.dtype != np.float32:
-            all_embeddings = all_embeddings.astype(np.float32)
-
-        assert self._docid_to_indices is not None
-        candidate_doc_ids = list(self._docid_to_indices.keys())
-
-        def _score_one(doc_id: int) -> tuple[float, int]:
-            token_indices = self._docid_to_indices.get(doc_id, [])
-            if not token_indices:
-                return (0.0, doc_id)
-            doc_vecs = np.asarray(all_embeddings[token_indices], dtype=np.float32)
-            sim = np.dot(data, doc_vecs.T)
-            sim = np.nan_to_num(sim, nan=-1e30, posinf=1e30, neginf=-1e30)
-            score = sim.max(axis=2).sum(axis=1) if sim.ndim == 3 else sim.max(axis=1).sum()
-            return (float(score), doc_id)
-
-        scores: list[tuple[float, int]] = []
-        with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as ex:
-            futures = [ex.submit(_score_one, d) for d in candidate_doc_ids]
-            for fut in concurrent.futures.as_completed(futures):
-                scores.append(fut.result())
-
-        scores.sort(key=lambda x: x[0], reverse=True)
-        return scores[:topk] if len(scores) >= topk else scores

apps/multimodal/vision-based-pdf-multi-vector/multi-vector-leann-similarity-map.py

@@ -2,7 +2,6 @@
 # %%
 # uv pip install matplotlib qwen_vl_utils
 import os
-import json
 import re
 import sys
 from pathlib import Path
@@ -170,7 +169,7 @@ def _embed_images(model, processor, images: list[Image.Image]) -> list[Any]:
     )
 
     doc_vecs: list[Any] = []
-    for batch_doc in tqdm(dataloader, desc="Embedding images"):
+    for batch_doc in dataloader:
         with torch.no_grad():
             batch_doc = {k: v.to(model.device) for k, v in batch_doc.items()}
             # autocast on CUDA for bf16/fp16; on CPU/MPS stay in fp32
@@ -201,7 +200,7 @@ def _embed_queries(model, processor, queries: list[str]) -> list[Any]:
     )
 
     q_vecs: list[Any] = []
-    for batch_query in tqdm(dataloader, desc="Embedding queries"):
+    for batch_query in dataloader:
         with torch.no_grad():
             batch_query = {k: v.to(model.device) for k, v in batch_query.items()}
             if model.device.type == "cuda":
@@ -231,18 +230,12 @@ def _build_index(index_path: str, doc_vecs: list[Any], filepaths: list[str]) ->
     return retriever
 
 
-def _load_retriever_if_index_exists(index_path: str) -> Optional[LeannMultiVector]:
+def _load_retriever_if_index_exists(index_path: str, dim: int) -> Optional[LeannMultiVector]:
     index_base = Path(index_path)
     # Rough heuristic: index dir exists AND meta+labels files exist
     meta = index_base.parent / f"{index_base.name}.meta.json"
     labels = index_base.parent / f"{index_base.name}.labels.json"
     if index_base.exists() and meta.exists() and labels.exists():
-        try:
-            with open(meta, "r", encoding="utf-8") as f:
-                meta_json = json.load(f)
-            dim = int(meta_json.get("dimensions", 128))
-        except Exception:
-            dim = 128
         return LeannMultiVector(index_path=index_path, dim=dim)
     return None
 
@@ -369,7 +362,7 @@ if USE_HF_DATASET:
     N = len(dataset) if MAX_DOCS is None else min(MAX_DOCS, len(dataset))
     filepaths: list[str] = []
     images: list[Image.Image] = []
-    for i in tqdm(range(N), desc="Loading dataset", total=N ):
+    for i in tqdm(range(N), desc="Loading dataset"):
         p = dataset[i]
         # Compose a descriptive identifier for printing later
         identifier = f"arXiv:{p['paper_arxiv_id']}|title:{p['paper_title']}|page:{int(p['page_number'])}|id:{p['page_id']}"
@@ -397,7 +390,11 @@ print(f"Using model={model_name}, device={device_str}, dtype={dtype}")
 # Step 3: Build or load index
 retriever: Optional[LeannMultiVector] = None
 if not REBUILD_INDEX:
-    retriever = _load_retriever_if_index_exists(INDEX_PATH)
+    try:
+        one_vec = _embed_images(model, processor, [images[0]])[0]
+        retriever = _load_retriever_if_index_exists(INDEX_PATH, dim=int(one_vec.shape[-1]))
+    except Exception:
+        retriever = None
 
 if retriever is None:
     doc_vecs = _embed_images(model, processor, images)

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

@@ -4,24 +4,39 @@
 # pip install tqdm
 # pip install pillow
 
+# %%
+from pdf2image import convert_from_path
+
+pdf_path = "pdfs/2004.12832v2.pdf"
+images = convert_from_path(pdf_path)
+
+for i, image in enumerate(images):
+    image.save(f"pages/page_{i + 1}.png", "PNG")
+
+# %%
 import os
-import re
-import sys
 from pathlib import Path
-from typing import cast
 
-from PIL import Image
-from tqdm import tqdm
-
-# Ensure local leann packages are importable before importing them
+# Make local leann packages importable without installing
 _repo_root = Path(__file__).resolve().parents[3]
 _leann_core_src = _repo_root / "packages" / "leann-core" / "src"
 _leann_hnsw_pkg = _repo_root / "packages" / "leann-backend-hnsw"
+import sys
+
 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))
 
+from leann_multi_vector import LeannMultiVector
+
+
+class LeannRetriever(LeannMultiVector):
+    pass
+
+
+# %%
+from typing import cast
 
 import torch
 from colpali_engine.models import ColPali
@@ -73,6 +88,13 @@ for batch_query in dataloader:
     qs.extend(list(torch.unbind(embeddings_query.to("cpu"))))
 print(qs[0].shape)
 # %%
+
+
+import re
+
+from PIL import Image
+from tqdm import tqdm
+
 page_filenames = sorted(os.listdir("./pages"), key=lambda n: int(re.search(r"\d+", n).group()))
 images = [Image.open(os.path.join("./pages", name)) for name in page_filenames]
 

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

@@ -1,510 +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 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 = eval(match.group(1))
-                        except (ValueError, SyntaxError, NameError):
-                            pass
-                    else:
-                        # Try alternative format
-                        match = re.search(r"Failed to fetch messages:\s*(\{[^}]+\})", str(e))
-                        if match:
-                            try:
-                                error_dict = eval(match.group(1))
-                            except (ValueError, SyntaxError, NameError):
-                                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):
-    """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.
-    """
+def load_hf_model(model_name="Qwen/Qwen3-8B"):
+    """Load HuggingFace model"""
     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):
-    """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.
-    """
+def load_vllm_model(model_name="Qwen/Qwen3-8B"):
+    """Load vLLM model"""
     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):
-    """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.
-    """
+def load_qwen_vl_model(model_name="Qwen/Qwen2.5-VL-7B-Instruct"):
+    """Load Qwen2.5-VL multimodal model"""
     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,
-            torch_dtype=torch.bfloat16,
-            device_map="auto",
-            trust_remote_code=trust_remote_code,
+            model_name, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
         )
 
         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(
-                model_name, trust_remote_code=trust_remote_code
-            )
+            processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
             model = Qwen2VLForConditionalGeneration.from_pretrained(
-                model_name,
-                torch_dtype=torch.bfloat16,
-                device_map="auto",
-                trust_remote_code=trust_remote_code,
+                model_name, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
             )
 
             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/configuration-guide.md

@@ -455,5 +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/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/dynamic_update_no_recompute.py

@@ -43,11 +43,7 @@ from apps.chunking import create_text_chunks
 REPO_ROOT = Path(__file__).resolve().parents[1]
 
 DEFAULT_QUERY = "What's LEANN?"
-DEFAULT_INITIAL_FILES = [
-    REPO_ROOT / "data" / "2501.14312v1 (1).pdf",
-    REPO_ROOT / "data" / "huawei_pangu.md",
-    REPO_ROOT / "data" / "PrideandPrejudice.txt",
-]
+DEFAULT_INITIAL_FILES = [REPO_ROOT / "data" / "2501.14312v1 (1).pdf"]
 DEFAULT_UPDATE_FILES = [REPO_ROOT / "data" / "2506.08276v1.pdf"]
 
 
@@ -186,7 +182,6 @@ def run_workflow(
     is_recompute: bool,
     query: str,
     top_k: int,
-    skip_search: bool,
 ) -> dict[str, Any]:
     prefix = f"[{label}] " if label else ""
 
@@ -203,15 +198,12 @@ def run_workflow(
     )
 
     initial_size = index_file_size(index_path)
-    if not skip_search:
-        before_results = run_search(
-            index_path,
-            query,
-            top_k,
-            recompute_embeddings=is_recompute,
-        )
-    else:
-        before_results = None
+    before_results = run_search(
+        index_path,
+        query,
+        top_k,
+        recompute_embeddings=is_recompute,
+    )
 
     print(f"\n{prefix}Updating index with additional passages...")
     update_index(
@@ -223,23 +215,20 @@ def run_workflow(
         is_recompute=is_recompute,
     )
 
-    if not skip_search:
-        after_results = run_search(
-            index_path,
-            query,
-            top_k,
-            recompute_embeddings=is_recompute,
-        )
-    else:
-        after_results = None
+    after_results = run_search(
+        index_path,
+        query,
+        top_k,
+        recompute_embeddings=is_recompute,
+    )
     updated_size = index_file_size(index_path)
 
     return {
         "initial_size": initial_size,
         "updated_size": updated_size,
         "delta": updated_size - initial_size,
-        "before_results": before_results if not skip_search else None,
-        "after_results": after_results if not skip_search else None,
+        "before_results": before_results,
+        "after_results": after_results,
         "metadata": load_metadata_snapshot(index_path),
     }
 
@@ -325,12 +314,6 @@ def main() -> None:
         action="store_false",
         help="Skip building the no-recompute baseline.",
     )
-    parser.add_argument(
-        "--skip-search",
-        dest="skip_search",
-        action="store_true",
-        help="Skip the search step.",
-    )
     parser.set_defaults(compare_no_recompute=True)
     args = parser.parse_args()
 
@@ -367,13 +350,10 @@ def main() -> None:
         is_recompute=True,
         query=args.query,
         top_k=args.top_k,
-        skip_search=args.skip_search,
     )
 
-    if not args.skip_search:
-        print_results("initial search", recompute_stats["before_results"])
-    if not args.skip_search:
-        print_results("after update", recompute_stats["after_results"])
+    print_results("initial search", recompute_stats["before_results"])
+    print_results("after update", recompute_stats["after_results"])
     print(
         f"\n[recompute] Index file size change: {recompute_stats['initial_size']} -> {recompute_stats['updated_size']} bytes"
         f" (Δ {recompute_stats['delta']})"
@@ -398,7 +378,6 @@ def main() -> None:
             is_recompute=False,
             query=args.query,
             top_k=args.top_k,
-            skip_search=args.skip_search,
         )
 
         print(
@@ -406,12 +385,8 @@ def main() -> None:
             f" (Δ {baseline_stats['delta']})"
         )
 
-        after_texts = (
-            [res.text for res in recompute_stats["after_results"]] if not args.skip_search else None
-        )
-        baseline_after_texts = (
-            [res.text for res in baseline_stats["after_results"]] if not args.skip_search else None
-        )
+        after_texts = [res.text for res in recompute_stats["after_results"]]
+        baseline_after_texts = [res.text for res in baseline_stats["after_results"]]
         if after_texts == baseline_after_texts:
             print(
                 "[no-recompute] Search results match recompute baseline; see above for the shared output."

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-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)
-
-# 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()
+pkg_check_modules(ZMQ REQUIRED libzmq)
 
 # 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-core/pyproject.toml

@@ -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
-    # breaks Python 3.9 environments.
-    "transformers>=4.30.0,<4.46",
+    "transformers>=4.30.0",
     "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

@@ -5,7 +5,6 @@ with the correct, original embedding logic from the user's reference code.
 
 import json
 import logging
-import os
 import pickle
 import re
 import subprocess
@@ -18,11 +17,9 @@ 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
-from .embedding_server_manager import EmbeddingServerManager
 from .interface import LeannBackendFactoryInterface
 from .metadata_filter import MetadataFilterEngine
 from .registry import BACKEND_REGISTRY
@@ -731,7 +728,6 @@ class LeannBuilder:
         index = faiss.read_index(str(index_file))
         if hasattr(index, "is_recompute"):
             index.is_recompute = needs_recompute
-            print(f"index.is_recompute: {index.is_recompute}")
         if getattr(index, "storage", None) is None:
             if index.metric_type == faiss.METRIC_INNER_PRODUCT:
                 storage_index = faiss.IndexFlatIP(index.d)
@@ -739,112 +735,37 @@ class LeannBuilder:
                 storage_index = faiss.IndexFlatL2(index.d)
             index.storage = storage_index
             index.own_fields = True
-            # Faiss expects storage.ntotal to reflect the existing graph's
-            # population (even if the vectors themselves were pruned from disk
-            # for recompute mode).  When we attach a fresh IndexFlat here its
-            # ntotal starts at zero, which later causes IndexHNSW::add to
-            # believe new "preset" levels were provided and trips the
-            # `n0 + n == levels.size()` assertion.  Seed the temporary storage
-            # with the current ntotal so Faiss maintains the proper offset for
-            # incoming vectors.
-            try:
-                storage_index.ntotal = index.ntotal
-            except AttributeError:
-                # Older Faiss builds may not expose ntotal as a writable
-                # attribute; in that case we fall back to the default behaviour.
-                pass
         if index.d != embedding_dim:
             raise ValueError(
                 f"Existing index dimension ({index.d}) does not match new embeddings ({embedding_dim})."
             )
 
-        passage_meta_mode = meta.get("embedding_mode", self.embedding_mode)
-        passage_provider_options = meta.get("embedding_options", self.embedding_options)
-
         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
 
-        # Append passages/offsets before we attempt index.add so the ZMQ server
-        # can resolve newly assigned IDs during recompute. Keep rollback hooks
-        # so we can restore files if the update fails mid-way.
-        rollback_passages_size = passages_file.stat().st_size if passages_file.exists() else 0
-        offset_map_backup = offset_map.copy()
+        index.add(embeddings.shape[0], faiss.swig_ptr(embeddings))
+        faiss.write_index(index, str(index_file))
 
-        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(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: Optional[EmbeddingServerManager] = None
-            server_started = False
-            requested_zmq_port = int(os.getenv("LEANN_UPDATE_ZMQ_PORT", "5557"))
-
-            try:
-                if needs_recompute:
-                    server_manager = EmbeddingServerManager(
-                        backend_module_name="leann_backend_hnsw.hnsw_embedding_server"
-                    )
-                    server_started, actual_port = server_manager.start_server(
-                        port=requested_zmq_port,
-                        model_name=self.embedding_model,
-                        embedding_mode=passage_meta_mode,
-                        passages_file=str(meta_path),
-                        distance_metric=distance_metric,
-                        provider_options=passage_provider_options,
-                    )
-                    if not server_started:
-                        raise RuntimeError(
-                            "Failed to start HNSW embedding server for recompute update."
-                        )
-                    if actual_port != requested_zmq_port:
-                        logger.warning(
-                            "Embedding server started on port %s instead of requested %s. "
-                            "Using reassigned port.",
-                            actual_port,
-                            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]):
-                        print(f"add {i} embeddings")
-                        index.add(1, faiss.swig_ptr(embeddings[i : i + 1]))
-                else:
-                    index.add(embeddings.shape[0], faiss.swig_ptr(embeddings))
-                faiss.write_index(index, str(index_file))
-            finally:
-                if server_started and server_manager is not None:
-                    server_manager.stop_server()
-
-        except Exception:
-            # Roll back appended passages/offset map to keep files consistent.
-            if passages_file.exists():
-                with open(passages_file, "rb+") as f:
-                    f.truncate(rollback_passages_size)
-            offset_map = offset_map_backup
-            with open(offset_file, "wb") as f:
-                pickle.dump(offset_map, f)
-            raise
+        with open(offset_file, "wb") as f:
+            pickle.dump(offset_map, f)
 
         meta["total_passages"] = len(offset_map)
         with open(meta_path, "w", encoding="utf-8") as f:
@@ -1236,17 +1157,6 @@ class LeannChat:
             "Please provide the best answer you can based on this context and your knowledge."
         )
 
-        print("The context provided to the LLM is:")
-        print(f"{'Relevance':<10} | {'Chunk id':<10} | {'Content':<60} | {'Source':<80}")
-        print("-" * 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]
-            print(
-                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
@@ -1254,14 +1164,19 @@ class LeannChat:
         return ans
 
     def start_interactive(self):
-        """Start interactive chat session."""
-        session = create_api_session()
-
-        def handle_query(user_input: str):
-            response = self.ask(user_input)
-            print(f"Leann: {response}")
-
-        session.run_interactive_loop(handle_query)
+        print("\nLeann Chat started (type 'quit' to exit)")
+        while True:
+            try:
+                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):
+                print("\nGoodbye!")
+                break
 
     def cleanup(self):
         """Explicitly cleanup embedding server resources.

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

@@ -546,30 +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:
-        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
-    ):
+    def __init__(self, model_name: str = "deepseek-ai/deepseek-llm-7b-chat"):
         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:
@@ -607,16 +588,14 @@ class HFChat(LLMInterface):
 
             try:
                 logger.info(f"Loading tokenizer for {model_name}...")
-                self.tokenizer = AutoTokenizer.from_pretrained(
-                    model_name, trust_remote_code=self.trust_remote_code
-                )
+                self.tokenizer = AutoTokenizer.from_pretrained(model_name)
 
                 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:
@@ -834,11 +813,6 @@ 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}")
@@ -885,10 +859,7 @@ def get_llm(llm_config: Optional[dict[str, Any]] = None) -> LLMInterface:
             host=llm_config.get("host"),
         )
     elif llm_type == "hf":
-        return HFChat(
-            model_name=model or "deepseek-ai/deepseek-llm-7b-chat",
-            trust_remote_code=llm_config.get("trust_remote_code", False),
-        )
+        return HFChat(model_name=model or "deepseek-ai/deepseek-llm-7b-chat")
     elif llm_type == "openai":
         return OpenAIChat(
             model=model or "gpt-4o",

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

@@ -11,119 +11,6 @@ from llama_index.core.node_parser import SentenceSplitter
 
 logger = logging.getLogger(__name__)
 
-
-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",
@@ -195,17 +82,6 @@ def create_ast_chunks(
             continue
 
         try:
-            # Warn if AST chunk size + overlap might exceed common token limits
-            estimated_max_tokens = int(
-                (max_chunk_size + chunk_overlap) * 1.2
-            )  # Conservative estimate
-            if estimated_max_tokens > 512:
-                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)}"
-                )
-
             configs = {
                 "max_chunk_size": max_chunk_size,
                 "language": language,
@@ -341,14 +217,4 @@ def create_text_chunks(
         all_chunks = create_traditional_chunks(documents, chunk_size, chunk_overlap)
 
     logger.info(f"Total chunks created: {len(all_chunks)}")
-
-    # Validate chunk token limits (default to 512 for safety)
-    # This provides a safety net for embedding models with token limits
-    validated_chunks, num_truncated = validate_chunk_token_limits(all_chunks, max_tokens=512)
-
-    if num_truncated > 0:
-        logger.info(
-            f"Post-chunking validation: {num_truncated} chunks were truncated to fit 512 token limit"
-        )
-
-    return validated_chunks
+    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
 
@@ -9,7 +8,6 @@ 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 resolve_ollama_host, resolve_openai_api_key, resolve_openai_base_url
 
@@ -107,7 +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"],
@@ -181,25 +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",
@@ -209,14 +207,14 @@ Examples:
         build_parser.add_argument(
             "--ast-chunk-size",
             type=int,
-            default=300,
-            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)",
+            default=768,
+            help="AST chunk size in characters (default: 768)",
         )
         build_parser.add_argument(
             "--ast-chunk-overlap",
             type=int,
-            default=64,
-            help="AST chunk overlap in CHARACTERS (default: 64). Added to chunk size, not included in it. ~1.2 tokens per character for code",
+            default=96,
+            help="AST chunk overlap in characters (default: 96)",
         )
         build_parser.add_argument(
             "--ast-fallback-traditional",
@@ -255,11 +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",
-        )
 
         # Ask command
         ask_parser = subparsers.add_parser("ask", help="Ask questions")
@@ -1162,11 +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(
@@ -1182,26 +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,
-                        file_extractor={},  # Use default extractors
-                        exclude_hidden=not include_hidden,
-                        filename_as_id=True,
-                    ).load_data(show_progress=True)
-                else:
-                    other_docs = []
+                other_docs = SimpleDirectoryReader(
+                    docs_dir,
+                    recursive=True,
+                    encoding="utf-8",
+                    required_exts=code_extensions,
+                    file_extractor={},  # Use default extractors
+                    exclude_hidden=not include_hidden,
+                    filename_as_id=True,
+                ).load_data(show_progress=True)
 
                 # 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)
@@ -1277,7 +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,
@@ -1288,14 +1271,6 @@ Examples:
                     ast_fallback_traditional=getattr(args, "ast_fallback_traditional", True),
                 )
 
-                # Note: AST chunking currently returns plain text chunks without metadata
-                # We preserve basic file info by associating chunks with their source documents
-                # For better metadata preservation, documents list order should be maintained
-                for chunk_text in chunk_texts:
-                    # TODO: Enhance create_text_chunks to return metadata alongside text
-                    # For now, we store chunks with empty metadata
-                    all_texts.append({"text": chunk_text, "metadata": {}})
-
             except ImportError as e:
                 print(
                     f"⚠️  AST chunking utilities not available in package ({e}), falling back to traditional chunking"
@@ -1307,27 +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
@@ -1402,7 +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":
@@ -1414,7 +1376,7 @@ Examples:
                 embedding_options["api_key"] = resolved_embedding_key
 
         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,
@@ -1425,8 +1387,8 @@ Examples:
             num_threads=args.num_threads,
         )
 
-        for chunk in all_texts:
-            builder.add_text(chunk["text"], metadata=chunk["metadata"])
+        for chunk_text in all_texts:
+            builder.add_text(chunk_text)
 
         builder.build_index(index_path)
         print(f"Index built at {index_path}")
@@ -1547,25 +1509,7 @@ Examples:
         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):
@@ -1597,7 +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,
@@ -1608,20 +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

@@ -14,89 +14,6 @@ import torch
 
 from .settings import resolve_ollama_host, resolve_openai_api_key, resolve_openai_base_url
 
-
-def truncate_to_token_limit(texts: list[str], max_tokens: int = 512) -> list[str]:
-    """
-    Truncate texts to token limit using tiktoken or conservative character truncation.
-
-    Args:
-        texts: List of texts to truncate
-        max_tokens: Maximum tokens allowed per text
-
-    Returns:
-        List of truncated texts that should fit within token limit
-    """
-    try:
-        import tiktoken
-
-        encoder = tiktoken.get_encoding("cl100k_base")
-        truncated = []
-
-        for text in texts:
-            tokens = encoder.encode(text)
-            if len(tokens) > max_tokens:
-                # Truncate to max_tokens and decode back to text
-                truncated_tokens = tokens[:max_tokens]
-                truncated_text = encoder.decode(truncated_tokens)
-                truncated.append(truncated_text)
-                logger.warning(
-                    f"Truncated text from {len(tokens)} to {max_tokens} tokens "
-                    f"(from {len(text)} to {len(truncated_text)} characters)"
-                )
-            else:
-                truncated.append(text)
-        return truncated
-
-    except ImportError:
-        # Fallback: Conservative character truncation
-        # Assume worst case: 1.5 tokens per character for code content
-        char_limit = int(max_tokens / 1.5)
-        truncated = []
-
-        for text in texts:
-            if len(text) > char_limit:
-                truncated_text = text[:char_limit]
-                truncated.append(truncated_text)
-                logger.warning(
-                    f"Truncated text from {len(text)} to {char_limit} characters "
-                    f"(conservative estimate for {max_tokens} tokens)"
-                )
-            else:
-                truncated.append(text)
-        return truncated
-
-
-def get_model_token_limit(model_name: str) -> int:
-    """
-    Get token limit for a given embedding model.
-
-    Args:
-        model_name: Name of the embedding model
-
-    Returns:
-        Token limit for the model, defaults to 512 if unknown
-    """
-    # 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:
-        return EMBEDDING_MODEL_LIMITS[model_name]
-
-    # Check base name match
-    if base_model_name in EMBEDDING_MODEL_LIMITS:
-        return EMBEDDING_MODEL_LIMITS[base_model_name]
-
-    # Check partial matches for common patterns
-    for known_model, limit in EMBEDDING_MODEL_LIMITS.items():
-        if known_model in base_model_name or base_model_name in known_model:
-            return limit
-
-    # Default to conservative 512 token limit
-    logger.warning(f"Unknown model '{model_name}', using default 512 token limit")
-    return 512
-
-
 # Set up logger with proper level
 logger = logging.getLogger(__name__)
 LOG_LEVEL = os.getenv("LEANN_LOG_LEVEL", "WARNING").upper()
@@ -106,17 +23,6 @@ logger.setLevel(log_level)
 # Global model cache to avoid repeated loading
 _model_cache: dict[str, Any] = {}
 
-# Known embedding model token limits
-EMBEDDING_MODEL_LIMITS = {
-    "nomic-embed-text": 512,
-    "nomic-embed-text-v2": 512,
-    "mxbai-embed-large": 512,
-    "all-minilm": 512,
-    "bge-m3": 8192,
-    "snowflake-arctic-embed": 512,
-    # Add more models as needed
-}
-
 
 def compute_embeddings(
     texts: list[str],
@@ -277,73 +183,32 @@ def compute_embeddings_sentence_transformers(
         }
 
         try:
-            # Try loading with advanced parameters first (newer versions)
-            local_model_kwargs = model_kwargs.copy()
-            local_tokenizer_kwargs = tokenizer_kwargs.copy()
-            local_model_kwargs["local_files_only"] = True
-            local_tokenizer_kwargs["local_files_only"] = True
+            # Try local loading first
+            model_kwargs["local_files_only"] = True
+            tokenizer_kwargs["local_files_only"] = True
 
             model = SentenceTransformer(
                 model_name,
                 device=device,
-                model_kwargs=local_model_kwargs,
-                tokenizer_kwargs=local_tokenizer_kwargs,
+                model_kwargs=model_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
-            try:
-                network_model_kwargs = model_kwargs.copy()
-                network_tokenizer_kwargs = tokenizer_kwargs.copy()
-                network_model_kwargs["local_files_only"] = False
-                network_tokenizer_kwargs["local_files_only"] = False
+            # Fallback to network loading
+            model_kwargs["local_files_only"] = False
+            tokenizer_kwargs["local_files_only"] = False
 
-                model = SentenceTransformer(
-                    model_name,
-                    device=device,
-                    model_kwargs=network_model_kwargs,
-                    tokenizer_kwargs=network_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
+            model = SentenceTransformer(
+                model_name,
+                device=device,
+                model_kwargs=model_kwargs,
+                tokenizer_kwargs=tokenizer_kwargs,
+                local_files_only=False,
+            )
+            logger.info("Model loaded successfully! (network + optimized)")
 
         # Apply additional optimizations based on mode
         if use_fp16 and device in ["cuda", "mps"]:
@@ -668,10 +533,9 @@ def compute_embeddings_ollama(
     host: Optional[str] = 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.
-    Batch size: 32 for MPS/CPU, 128 for CUDA to optimize performance.
+    Uses batch size of 32 for MPS/CPU and 128 for CUDA to optimize performance.
 
     Args:
         texts: List of texts to compute embeddings for
@@ -776,11 +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",
-                json={"model": model_name, "input": "test"},
+                f"{resolved_host}/api/embeddings",
+                json={"model": model_name, "prompt": "test"},
                 timeout=10,
             )
             if test_response.status_code != 200:
@@ -812,80 +676,63 @@ 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")
-
-    # Get model token limit and apply truncation
-    token_limit = get_model_token_limit(model_name)
-    logger.info(f"Model '{model_name}' token limit: {token_limit}")
-
-    # Apply token-aware truncation to all texts
-    truncated_texts = truncate_to_token_limit(texts, token_limit)
-    if len(truncated_texts) != len(texts):
-        logger.error("Truncation failed - text count mismatch")
-        truncated_texts = texts  # Fallback to original texts
+    logger.info(f"Using batch size: {batch_size}")
 
     def get_batch_embeddings(batch_texts):
-        """Get embeddings for a batch of texts using /api/embed endpoint."""
-        max_retries = 3
-        retry_count = 0
+        """Get embeddings for a batch of texts."""
+        all_embeddings = []
+        failed_indices = []
 
-        # Texts are already truncated to token limit by the outer function
-        while retry_count < max_retries:
-            try:
-                # Use /api/embed endpoint with "input" parameter for batch processing
-                response = requests.post(
-                    f"{resolved_host}/api/embed",
-                    json={"model": model_name, "input": batch_texts},
-                    timeout=60,  # Increased timeout for batch processing
-                )
-                response.raise_for_status()
+        for i, text in enumerate(batch_texts):
+            max_retries = 3
+            retry_count = 0
 
-                result = response.json()
-                batch_embeddings = result.get("embeddings")
-
-                if batch_embeddings is None:
-                    raise ValueError("No embeddings returned from API")
-
-                if not isinstance(batch_embeddings, list):
-                    raise ValueError(f"Invalid embeddings format: {type(batch_embeddings)}")
-
-                if len(batch_embeddings) != len(batch_texts):
-                    raise ValueError(
-                        f"Mismatch: requested {len(batch_texts)} embeddings, got {len(batch_embeddings)}"
+            # Truncate very long texts to avoid API issues
+            truncated_text = text[:8000] if len(text) > 8000 else text
+            while retry_count < max_retries:
+                try:
+                    response = requests.post(
+                        f"{resolved_host}/api/embeddings",
+                        json={"model": model_name, "prompt": truncated_text},
+                        timeout=30,
                     )
+                    response.raise_for_status()
 
-                return batch_embeddings, []
+                    result = response.json()
+                    embedding = result.get("embedding")
 
-            except requests.exceptions.Timeout:
-                retry_count += 1
-                if retry_count >= max_retries:
-                    logger.warning(f"Timeout for batch after {max_retries} retries")
-                    return None, list(range(len(batch_texts)))
+                    if embedding is None:
+                        raise ValueError(f"No embedding returned for text {i}")
 
-            except Exception as e:
-                retry_count += 1
-                if retry_count >= max_retries:
-                    # Enhanced error detection for token limit violations
-                    error_msg = str(e).lower()
-                    if "token" in error_msg and (
-                        "limit" in error_msg or "exceed" in error_msg or "length" in error_msg
-                    ):
-                        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)))
+                    if not isinstance(embedding, list) or len(embedding) == 0:
+                        raise ValueError(f"Invalid embedding format for text {i}")
 
-        return None, list(range(len(batch_texts)))
+                    all_embeddings.append(embedding)
+                    break
 
-    # Process truncated texts in batches
+                except requests.exceptions.Timeout:
+                    retry_count += 1
+                    if retry_count >= max_retries:
+                        logger.warning(f"Timeout for text {i} after {max_retries} retries")
+                        failed_indices.append(i)
+                        all_embeddings.append(None)
+                        break
+
+                except Exception as e:
+                    retry_count += 1
+                    if retry_count >= max_retries:
+                        logger.error(f"Failed to get embedding for text {i}: {e}")
+                        failed_indices.append(i)
+                        all_embeddings.append(None)
+                        break
+        return all_embeddings, failed_indices
+
+    # Process texts in batches
     all_embeddings = []
     all_failed_indices = []
 
     # Setup progress bar if needed
-    show_progress = is_build or len(truncated_texts) > 10
+    show_progress = is_build or len(texts) > 10
     try:
         if show_progress:
             from tqdm import tqdm
@@ -893,36 +740,32 @@ def compute_embeddings_ollama(
         show_progress = False
 
     # Process batches
-    num_batches = (len(truncated_texts) + batch_size - 1) // batch_size
+    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)
 
     for batch_idx in batch_iterator:
         start_idx = batch_idx * batch_size
-        end_idx = min(start_idx + batch_size, len(truncated_texts))
-        batch_texts = truncated_texts[start_idx:end_idx]
+        end_idx = min(start_idx + batch_size, len(texts))
+        batch_texts = texts[start_idx:end_idx]
 
         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)
+        # 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:
-        if len(all_failed_indices) == len(truncated_texts):
+        if len(all_failed_indices) == len(texts):
             raise RuntimeError("Failed to compute any embeddings")
 
         logger.warning(
-            f"Failed to compute embeddings for {len(all_failed_indices)}/{len(truncated_texts)} texts"
+            f"Failed to compute embeddings for {len(all_failed_indices)}/{len(texts)} texts"
         )
 
         # Use zero embeddings as fallback for failed ones

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

@@ -1,5 +1,4 @@
 import atexit
-import json
 import logging
 import os
 import socket
@@ -49,85 +48,6 @@ def _check_port(port: int) -> bool:
 # Note: All cross-process scanning helpers removed for simplicity
 
 
-def _safe_resolve(path: Path) -> str:
-    """Resolve paths safely even if the target does not yet exist."""
-    try:
-        return str(path.resolve(strict=False))
-    except Exception:
-        return str(path)
-
-
-def _safe_stat_signature(path: Path) -> dict:
-    """Return a lightweight signature describing the current state of a path."""
-    signature: dict[str, object] = {"path": _safe_resolve(path)}
-    try:
-        stat = path.stat()
-    except FileNotFoundError:
-        signature["missing"] = True
-    except Exception as exc:  # pragma: no cover - unexpected filesystem errors
-        signature["error"] = str(exc)
-    else:
-        signature["mtime_ns"] = stat.st_mtime_ns
-        signature["size"] = stat.st_size
-    return signature
-
-
-def _build_passages_signature(passages_file: Optional[str]) -> Optional[dict]:
-    """Collect modification signatures for metadata and referenced passage files."""
-    if not passages_file:
-        return None
-
-    meta_path = Path(passages_file)
-    signature: dict[str, object] = {"meta": _safe_stat_signature(meta_path)}
-
-    try:
-        with meta_path.open(encoding="utf-8") as fh:
-            meta = json.load(fh)
-    except FileNotFoundError:
-        signature["meta_missing"] = True
-        signature["sources"] = []
-        return signature
-    except json.JSONDecodeError as exc:
-        signature["meta_error"] = f"json_error:{exc}"
-        signature["sources"] = []
-        return signature
-    except Exception as exc:  # pragma: no cover - unexpected errors
-        signature["meta_error"] = str(exc)
-        signature["sources"] = []
-        return signature
-
-    base_dir = meta_path.parent
-    seen_paths: set[str] = set()
-    source_signatures: list[dict[str, object]] = []
-
-    for source in meta.get("passage_sources", []):
-        for key, kind in (
-            ("path", "passages"),
-            ("path_relative", "passages"),
-            ("index_path", "index"),
-            ("index_path_relative", "index"),
-        ):
-            raw_path = source.get(key)
-            if not raw_path:
-                continue
-            candidate = Path(raw_path)
-            if not candidate.is_absolute():
-                candidate = base_dir / candidate
-            resolved = _safe_resolve(candidate)
-            if resolved in seen_paths:
-                continue
-            seen_paths.add(resolved)
-            sig = _safe_stat_signature(candidate)
-            sig["kind"] = kind
-            source_signatures.append(sig)
-
-    signature["sources"] = source_signatures
-    return signature
-
-
-# Note: All cross-process scanning helpers removed for simplicity
-
-
 class EmbeddingServerManager:
     """
     A simplified manager for embedding server processes that avoids complex update mechanisms.
@@ -165,14 +85,13 @@ class EmbeddingServerManager:
         """Start the embedding server."""
         # passages_file may be present in kwargs for server CLI, but we don't need it here
         provider_options = kwargs.pop("provider_options", None)
-        passages_file = kwargs.get("passages_file", "")
 
-        config_signature = self._build_config_signature(
-            model_name=model_name,
-            embedding_mode=embedding_mode,
-            provider_options=provider_options,
-            passages_file=passages_file,
-        )
+        config_signature = {
+            "model_name": model_name,
+            "passages_file": kwargs.get("passages_file", ""),
+            "embedding_mode": embedding_mode,
+            "provider_options": provider_options or {},
+        }
 
         # If this manager already has a live server, just reuse it
         if (
@@ -196,7 +115,6 @@ class EmbeddingServerManager:
                 port,
                 model_name,
                 embedding_mode,
-                config_signature=config_signature,
                 provider_options=provider_options,
                 **kwargs,
             )
@@ -218,30 +136,11 @@ class EmbeddingServerManager:
             **kwargs,
         )
 
-    def _build_config_signature(
-        self,
-        *,
-        model_name: str,
-        embedding_mode: str,
-        provider_options: Optional[dict],
-        passages_file: Optional[str],
-    ) -> dict:
-        """Create a signature describing the current server configuration."""
-        return {
-            "model_name": model_name,
-            "passages_file": passages_file or "",
-            "embedding_mode": embedding_mode,
-            "provider_options": provider_options or {},
-            "passages_signature": _build_passages_signature(passages_file),
-        }
-
     def _start_server_colab(
         self,
         port: int,
         model_name: str,
         embedding_mode: str = "sentence-transformers",
-        *,
-        config_signature: Optional[dict] = None,
         provider_options: Optional[dict] = None,
         **kwargs,
     ) -> tuple[bool, int]:
@@ -264,11 +163,10 @@ class EmbeddingServerManager:
                 command,
                 actual_port,
                 provider_options=provider_options,
-                config_signature=config_signature,
             )
             started, ready_port = self._wait_for_server_ready_colab(actual_port)
             if started:
-                self._server_config = config_signature or {
+                self._server_config = {
                     "model_name": model_name,
                     "passages_file": kwargs.get("passages_file", ""),
                     "embedding_mode": embedding_mode,
@@ -300,7 +198,6 @@ class EmbeddingServerManager:
                 command,
                 port,
                 provider_options=provider_options,
-                config_signature=config_signature,
             )
             started, ready_port = self._wait_for_server_ready(port)
             if started:
@@ -344,9 +241,7 @@ class EmbeddingServerManager:
         self,
         command: list,
         port: int,
-        *,
         provider_options: Optional[dict] = None,
-        config_signature: Optional[dict] = None,
     ) -> None:
         """Launch the server process."""
         project_root = Path(__file__).parent.parent.parent.parent.parent
@@ -381,29 +276,26 @@ class EmbeddingServerManager:
         )
         self.server_port = port
         # Record config for in-process reuse (best effort; refined later when ready)
-        if config_signature is not None:
-            self._server_config = config_signature
-        else:  # Fallback for unexpected code paths
-            try:
-                self._server_config = {
-                    "model_name": command[command.index("--model-name") + 1]
-                    if "--model-name" in command
-                    else "",
-                    "passages_file": command[command.index("--passages-file") + 1]
-                    if "--passages-file" in command
-                    else "",
-                    "embedding_mode": command[command.index("--embedding-mode") + 1]
-                    if "--embedding-mode" in command
-                    else "sentence-transformers",
-                    "provider_options": provider_options or {},
-                }
-            except Exception:
-                self._server_config = {
-                    "model_name": "",
-                    "passages_file": "",
-                    "embedding_mode": "sentence-transformers",
-                    "provider_options": provider_options or {},
-                }
+        try:
+            self._server_config = {
+                "model_name": command[command.index("--model-name") + 1]
+                if "--model-name" in command
+                else "",
+                "passages_file": command[command.index("--passages-file") + 1]
+                if "--passages-file" in command
+                else "",
+                "embedding_mode": command[command.index("--embedding-mode") + 1]
+                if "--embedding-mode" in command
+                else "sentence-transformers",
+                "provider_options": provider_options or {},
+            }
+        except Exception:
+            self._server_config = {
+                "model_name": "",
+                "passages_file": "",
+                "embedding_mode": "sentence-transformers",
+                "provider_options": provider_options or {},
+            }
         logger.info(f"Server process started with PID: {self.server_process.pid}")
 
         # Register atexit callback only when we actually start a process
@@ -511,9 +403,7 @@ class EmbeddingServerManager:
         self,
         command: list,
         port: int,
-        *,
         provider_options: Optional[dict] = None,
-        config_signature: Optional[dict] = None,
     ) -> None:
         """Launch the server process with Colab-specific settings."""
         logger.info(f"Colab Command: {' '.join(command)}")
@@ -539,15 +429,12 @@ class EmbeddingServerManager:
             atexit.register(self._finalize_process)
             self._atexit_registered = True
         # Record config for in-process reuse is best-effort in Colab mode
-        if config_signature is not None:
-            self._server_config = config_signature
-        else:
-            self._server_config = {
-                "model_name": "",
-                "passages_file": "",
-                "embedding_mode": "sentence-transformers",
-                "provider_options": provider_options or {},
-            }
+        self._server_config = {
+            "model_name": "",
+            "passages_file": "",
+            "embedding_mode": "sentence-transformers",
+            "provider_options": provider_options or {},
+        }
 
     def _wait_for_server_ready_colab(self, port: int) -> tuple[bool, int]:
         """Wait for the server to be ready with Colab-specific timeout."""

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/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":

pyproject.toml

@@ -22,10 +22,7 @@ dependencies = [
     "sglang",
     "ollama",
     "requests>=2.25.0",
-    "sentence-transformers>=3.0.0",
-    # 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",
+    "sentence-transformers>=2.2.0",
     "openai>=1.0.0",
     # PDF parsing dependencies - essential for document processing
     "PyPDF2>=3.0.0",
@@ -114,7 +111,7 @@ target-version = "py39"
 line-length = 100
 extend-exclude = [
     "third_party",
-    "apps/multimodal/vision-based-pdf-multi-vector/multi-vector-leann-paper-example.py",
+    "apps/multimodal/vision-based-pdf-multi-vector/multi-vector-leann.py",
     "apps/multimodal/vision-based-pdf-multi-vector/multi-vector-leann-similarity-map.py"
 ]
 

test_colqwen_reproduction.py

@@ -1,162 +0,0 @@
-#!/usr/bin/env python3
-"""
-Test script to reproduce ColQwen results from issue #119
-https://github.com/yichuan-w/LEANN/issues/119
-
-This script demonstrates the ColQwen workflow:
-1. Download sample PDF
-2. Convert to images
-3. Build multimodal index
-4. Run test queries
-5. Generate similarity maps
-"""
-
-import importlib.util
-import os
-from pathlib import Path
-
-
-def main():
-    print("🧪 ColQwen Reproduction Test - Issue #119")
-    print("=" * 50)
-
-    # Check if we're in the right directory
-    repo_root = Path.cwd()
-    if not (repo_root / "apps" / "colqwen_rag.py").exists():
-        print("❌ Please run this script from the LEANN repository root")
-        print("   cd /path/to/LEANN && python test_colqwen_reproduction.py")
-        return
-
-    print("✅ Repository structure looks good")
-
-    # Step 1: Check dependencies
-    print("\n📦 Checking dependencies...")
-    try:
-        import torch
-
-        # Check if pdf2image is available
-        if importlib.util.find_spec("pdf2image") is None:
-            raise ImportError("pdf2image not found")
-        # Check if colpali_engine is available
-        if importlib.util.find_spec("colpali_engine") is None:
-            raise ImportError("colpali_engine not found")
-
-        print("✅ Core dependencies available")
-        print(f"   - PyTorch: {torch.__version__}")
-        print(f"   - CUDA available: {torch.cuda.is_available()}")
-        print(
-            f"   - MPS available: {hasattr(torch.backends, 'mps') and torch.backends.mps.is_available()}"
-        )
-    except ImportError as e:
-        print(f"❌ Missing dependency: {e}")
-        print("\n📥 Install missing dependencies:")
-        print(
-            "   uv pip install colpali_engine pdf2image pillow matplotlib qwen_vl_utils einops seaborn"
-        )
-        return
-
-    # Step 2: Download sample PDF
-    print("\n📄 Setting up sample PDF...")
-    pdf_dir = repo_root / "test_pdfs"
-    pdf_dir.mkdir(exist_ok=True)
-    sample_pdf = pdf_dir / "attention_paper.pdf"
-
-    if not sample_pdf.exists():
-        print("📥 Downloading sample paper (Attention Is All You Need)...")
-        import urllib.request
-
-        try:
-            urllib.request.urlretrieve("https://arxiv.org/pdf/1706.03762.pdf", sample_pdf)
-            print(f"✅ Downloaded: {sample_pdf}")
-        except Exception as e:
-            print(f"❌ Download failed: {e}")
-            print("   Please manually download a PDF to test_pdfs/attention_paper.pdf")
-            return
-    else:
-        print(f"✅ Using existing PDF: {sample_pdf}")
-
-    # Step 3: Test ColQwen RAG
-    print("\n🚀 Testing ColQwen RAG...")
-
-    # Build index
-    print("\n1️⃣ Building multimodal index...")
-    build_cmd = f"python -m apps.colqwen_rag build --pdfs {pdf_dir} --index test_attention --model colqwen2 --pages-dir test_pages"
-    print(f"   Command: {build_cmd}")
-
-    try:
-        result = os.system(build_cmd)
-        if result == 0:
-            print("✅ Index built successfully!")
-        else:
-            print("❌ Index building failed")
-            return
-    except Exception as e:
-        print(f"❌ Error building index: {e}")
-        return
-
-    # Test search
-    print("\n2️⃣ Testing search...")
-    test_queries = [
-        "How does attention mechanism work?",
-        "What is the transformer architecture?",
-        "How do you compute self-attention?",
-    ]
-
-    for query in test_queries:
-        print(f"\n🔍 Query: '{query}'")
-        search_cmd = f'python -m apps.colqwen_rag search test_attention "{query}" --top-k 3'
-        print(f"   Command: {search_cmd}")
-
-        try:
-            result = os.system(search_cmd)
-            if result == 0:
-                print("✅ Search completed")
-            else:
-                print("❌ Search failed")
-        except Exception as e:
-            print(f"❌ Search error: {e}")
-
-    # Test interactive mode (briefly)
-    print("\n3️⃣ Testing interactive mode...")
-    print("   You can test interactive mode with:")
-    print("   python -m apps.colqwen_rag ask test_attention --interactive")
-
-    # Step 4: Test similarity maps (using existing script)
-    print("\n4️⃣ Testing similarity maps...")
-    similarity_script = (
-        repo_root
-        / "apps"
-        / "multimodal"
-        / "vision-based-pdf-multi-vector"
-        / "multi-vector-leann-similarity-map.py"
-    )
-
-    if similarity_script.exists():
-        print("   You can generate similarity maps with:")
-        print(f"   cd {similarity_script.parent}")
-        print("   python multi-vector-leann-similarity-map.py")
-        print("   (Edit the script to use your local PDF)")
-
-    print("\n🎉 ColQwen reproduction test completed!")
-    print("\n📋 Summary:")
-    print("   ✅ Dependencies checked")
-    print("   ✅ Sample PDF prepared")
-    print("   ✅ Index building tested")
-    print("   ✅ Search functionality tested")
-    print("   ✅ Interactive mode available")
-    print("   ✅ Similarity maps available")
-
-    print("\n🔗 Related repositories to check:")
-    print("   - https://github.com/lightonai/fast-plaid")
-    print("   - https://github.com/lightonai/pylate")
-    print("   - https://github.com/stanford-futuredata/ColBERT")
-
-    print("\n📝 Next steps:")
-    print("   1. Test with your own PDFs")
-    print("   2. Experiment with different queries")
-    print("   3. Generate similarity maps for visual analysis")
-    print("   4. Compare ColQwen2 vs ColPali performance")
-
-
-if __name__ == "__main__":
-    main()

tests/test_embedding_server_manager.py

@@ -1,137 +0,0 @@
-import json
-import time
-
-import pytest
-from leann.embedding_server_manager import EmbeddingServerManager
-
-
-class DummyProcess:
-    def __init__(self):
-        self.pid = 12345
-        self._terminated = False
-
-    def poll(self):
-        return 0 if self._terminated else None
-
-    def terminate(self):
-        self._terminated = True
-
-    def kill(self):
-        self._terminated = True
-
-    def wait(self, timeout=None):
-        self._terminated = True
-        return 0
-
-
-@pytest.fixture
-def embedding_manager(monkeypatch):
-    manager = EmbeddingServerManager("leann_backend_hnsw.hnsw_embedding_server")
-
-    def fake_get_available_port(start_port):
-        return start_port
-
-    monkeypatch.setattr(
-        "leann.embedding_server_manager._get_available_port",
-        fake_get_available_port,
-    )
-
-    start_calls = []
-
-    def fake_start_new_server(self, port, model_name, embedding_mode, **kwargs):
-        config_signature = kwargs.get("config_signature")
-        start_calls.append(config_signature)
-        self.server_process = DummyProcess()
-        self.server_port = port
-        self._server_config = config_signature
-        return True, port
-
-    monkeypatch.setattr(
-        EmbeddingServerManager,
-        "_start_new_server",
-        fake_start_new_server,
-    )
-
-    # Ensure stop_server doesn't try to operate on real subprocesses
-    def fake_stop_server(self):
-        self.server_process = None
-        self.server_port = None
-        self._server_config = None
-
-    monkeypatch.setattr(EmbeddingServerManager, "stop_server", fake_stop_server)
-
-    return manager, start_calls
-
-
-def _write_meta(meta_path, passages_name, index_name, total):
-    meta_path.write_text(
-        json.dumps(
-            {
-                "backend_name": "hnsw",
-                "embedding_model": "test-model",
-                "embedding_mode": "sentence-transformers",
-                "dimensions": 3,
-                "backend_kwargs": {},
-                "passage_sources": [
-                    {
-                        "type": "jsonl",
-                        "path": passages_name,
-                        "index_path": index_name,
-                    }
-                ],
-                "total_passages": total,
-            }
-        ),
-        encoding="utf-8",
-    )
-
-
-def test_server_restarts_when_metadata_changes(tmp_path, embedding_manager):
-    manager, start_calls = embedding_manager
-
-    meta_path = tmp_path / "example.meta.json"
-    passages_path = tmp_path / "example.passages.jsonl"
-    index_path = tmp_path / "example.passages.idx"
-
-    passages_path.write_text("first\n", encoding="utf-8")
-    index_path.write_bytes(b"index")
-    _write_meta(meta_path, passages_path.name, index_path.name, total=1)
-
-    # Initial start populates signature
-    ok, port = manager.start_server(
-        port=6000,
-        model_name="test-model",
-        passages_file=str(meta_path),
-    )
-    assert ok
-    assert port == 6000
-    assert len(start_calls) == 1
-
-    initial_signature = start_calls[0]["passages_signature"]
-
-    # No metadata change => reuse existing server
-    ok, port_again = manager.start_server(
-        port=6000,
-        model_name="test-model",
-        passages_file=str(meta_path),
-    )
-    assert ok
-    assert port_again == 6000
-    assert len(start_calls) == 1
-
-    # Modify passage data and metadata to force signature change
-    time.sleep(0.01)  # Ensure filesystem timestamps move forward
-    passages_path.write_text("second\n", encoding="utf-8")
-    _write_meta(meta_path, passages_path.name, index_path.name, total=2)
-
-    ok, port_third = manager.start_server(
-        port=6000,
-        model_name="test-model",
-        passages_file=str(meta_path),
-    )
-    assert ok
-    assert port_third == 6000
-    assert len(start_calls) == 2
-
-    updated_signature = start_calls[1]["passages_signature"]
-    assert updated_signature != initial_signature

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()

uv.lock

@@ -14,7 +14,9 @@ version = "1.10.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
-    { name = "numpy" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "packaging" },
     { name = "psutil" },
     { name = "pyyaml" },
@@ -201,7 +203,9 @@ name = "astchunk"
 version = "0.1.0"
 source = { editable = "packages/astchunk-leann" }
 dependencies = [
-    { name = "numpy" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "pyrsistent" },
     { name = "tree-sitter", version = "0.23.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
     { name = "tree-sitter", version = "0.25.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
@@ -585,7 +589,7 @@ resolution-markers = [
     "python_full_version < '3.10'",
 ]
 dependencies = [
-    { name = "numpy", marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/f5/f6/31a8f28b4a2a4fa0e01085e542f3081ab0588eff8e589d39d775172c9792/contourpy-1.3.0.tar.gz", hash = "sha256:7ffa0db17717a8ffb127efd0c95a4362d996b892c2904db72428d5b52e1938a4", size = 13464370 }
 wheels = [
@@ -663,7 +667,7 @@ resolution-markers = [
     "python_full_version == '3.10.*'",
 ]
 dependencies = [
-    { name = "numpy", marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/66/54/eb9bfc647b19f2009dd5c7f5ec51c4e6ca831725f1aea7a993034f483147/contourpy-1.3.2.tar.gz", hash = "sha256:b6945942715a034c671b7fc54f9588126b0b8bf23db2696e3ca8328f3ff0ab54", size = 13466130 }
 wheels = [
@@ -734,7 +738,7 @@ resolution-markers = [
     "python_full_version == '3.11.*'",
 ]
 dependencies = [
-    { name = "numpy", marker = "python_full_version >= '3.11'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174 }
 wheels = [
@@ -1024,7 +1028,9 @@ dependencies = [
     { name = "fsspec", extra = ["http"] },
     { name = "huggingface-hub" },
     { name = "multiprocess" },
-    { name = "numpy" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "packaging" },
     { name = "pandas" },
     { name = "pyarrow" },
@@ -1165,7 +1171,9 @@ dependencies = [
     { name = "fsspec", extra = ["http"] },
     { name = "huggingface-hub" },
     { name = "multiprocess" },
-    { name = "numpy" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "packaging" },
     { name = "pandas" },
     { name = "requests" },
@@ -1602,7 +1610,7 @@ name = "importlib-metadata"
 version = "8.7.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "zipp", marker = "python_full_version < '3.10'" },
+    { name = "zipp" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/76/66/650a33bd90f786193e4de4b3ad86ea60b53c89b669a5c7be931fac31cdb0/importlib_metadata-8.7.0.tar.gz", hash = "sha256:d13b81ad223b890aa16c5471f2ac3056cf76c5f10f82d6f9292f0b415f389000", size = 56641 }
 wheels = [
@@ -2159,7 +2167,9 @@ version = "0.3.4"
 source = { editable = "packages/leann-backend-diskann" }
 dependencies = [
     { name = "leann-core" },
-    { name = "numpy" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "protobuf" },
 ]
 
@@ -2177,7 +2187,9 @@ source = { editable = "packages/leann-backend-hnsw" }
 dependencies = [
     { name = "leann-core" },
     { name = "msgpack" },
-    { name = "numpy" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "pyzmq" },
 ]
 
@@ -2204,7 +2216,9 @@ dependencies = [
     { name = "mlx-lm", marker = "platform_machine == 'arm64' and sys_platform == 'darwin'" },
     { name = "msgpack" },
     { name = "nbconvert" },
-    { name = "numpy" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "openai" },
     { name = "pdfplumber" },
     { name = "psutil" },
@@ -2241,12 +2255,12 @@ requires-dist = [
     { name = "python-dotenv", specifier = ">=1.0.0" },
     { name = "pyzmq", specifier = ">=23.0.0" },
     { name = "requests", specifier = ">=2.25.0" },
-    { name = "sentence-transformers", specifier = ">=3.0.0" },
+    { name = "sentence-transformers", specifier = ">=2.2.0" },
     { name = "torch", specifier = ">=2.0.0" },
     { name = "torch", marker = "extra == 'colab'", specifier = ">=2.0.0,<3.0.0" },
     { name = "tqdm", specifier = ">=4.60.0" },
-    { name = "transformers", specifier = ">=4.30.0,<4.46" },
-    { name = "transformers", marker = "extra == 'colab'", specifier = ">=4.30.0,<4.46" },
+    { name = "transformers", specifier = ">=4.30.0" },
+    { name = "transformers", marker = "extra == 'colab'", specifier = ">=4.30.0,<5.0.0" },
 ]
 provides-extras = ["colab"]
 
@@ -2272,7 +2286,9 @@ dependencies = [
     { name = "mlx-lm", marker = "platform_machine == 'arm64' and sys_platform == 'darwin'" },
     { name = "msgpack" },
     { name = "nbconvert" },
-    { name = "numpy" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "ollama" },
     { name = "openai" },
     { name = "pathspec" },
@@ -2290,7 +2306,6 @@ dependencies = [
     { name = "torch" },
     { name = "torchvision" },
     { name = "tqdm" },
-    { name = "transformers" },
     { name = "tree-sitter", version = "0.23.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
     { name = "tree-sitter", version = "0.25.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
     { name = "tree-sitter-c-sharp" },
@@ -2366,12 +2381,11 @@ requires-dist = [
     { name = "pypdfium2", specifier = ">=4.30.0" },
     { name = "python-docx", marker = "extra == 'documents'", specifier = ">=0.8.11" },
     { name = "requests", specifier = ">=2.25.0" },
-    { name = "sentence-transformers", specifier = ">=3.0.0" },
+    { name = "sentence-transformers", specifier = ">=2.2.0" },
     { name = "sglang" },
     { name = "torch" },
     { name = "torchvision", specifier = ">=0.23.0" },
     { name = "tqdm" },
-    { name = "transformers", specifier = "<4.46" },
     { name = "tree-sitter", specifier = ">=0.20.0" },
     { name = "tree-sitter-c-sharp", specifier = ">=0.20.0" },
     { name = "tree-sitter-java", specifier = ">=0.20.0" },
@@ -2486,7 +2500,9 @@ dependencies = [
     { name = "networkx", version = "3.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
     { name = "networkx", version = "3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "nltk" },
-    { name = "numpy" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "pillow" },
     { name = "platformdirs" },
     { name = "pydantic" },
@@ -2907,7 +2923,7 @@ dependencies = [
     { name = "fonttools", marker = "python_full_version < '3.10'" },
     { name = "importlib-resources", marker = "python_full_version < '3.10'" },
     { name = "kiwisolver", version = "1.4.7", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
-    { name = "numpy", marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
     { name = "packaging", marker = "python_full_version < '3.10'" },
     { name = "pillow", marker = "python_full_version < '3.10'" },
     { name = "pyparsing", marker = "python_full_version < '3.10'" },
@@ -2972,7 +2988,8 @@ dependencies = [
     { name = "cycler", marker = "python_full_version >= '3.10'" },
     { name = "fonttools", marker = "python_full_version >= '3.10'" },
     { name = "kiwisolver", version = "1.4.9", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
-    { name = "numpy", marker = "python_full_version >= '3.10'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "packaging", marker = "python_full_version >= '3.10'" },
     { name = "pillow", marker = "python_full_version >= '3.10'" },
     { name = "pyparsing", marker = "python_full_version >= '3.10'" },
@@ -3101,7 +3118,9 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "jinja2" },
     { name = "mlx" },
-    { name = "numpy" },
+    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "protobuf" },
     { name = "pyyaml" },
     { name = "transformers" },
@@ -3466,45 +3485,207 @@ wheels = [
 
 [[package]]
 name = "numpy"
-version = "1.26.4"
+version = "2.0.2"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/65/6e/09db70a523a96d25e115e71cc56a6f9031e7b8cd166c1ac8438307c14058/numpy-1.26.4.tar.gz", hash = "sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010", size = 15786129 }
+resolution-markers = [
+    "python_full_version < '3.10'",
+]
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