Fix: Prevent duplicate PDF processing when using --file-types .pdf

Fixes #175

Problem:
When --file-types .pdf is specified, PDFs were being processed twice:
1. Separately with PyMuPDF/pdfplumber extractors
2. Again in the 'other file types' section via SimpleDirectoryReader

This caused duplicate processing and potential conflicts.

Solution:
- Exclude .pdf from other_file_extensions when PDFs are already
  processed separately
- Only load other file types if there are extensions to process
- Prevents duplicate PDF processing

Changes:
- Added logic to filter out .pdf from code_extensions when loading
  other file types if PDFs were processed separately
- Updated SimpleDirectoryReader to use filtered extensions
- Added check to skip loading if no other extensions to process

.github/workflows/build-reusable.yml

@@ -16,10 +16,8 @@ jobs:
     steps:
       - uses: actions/checkout@v4
         with:
-          submodules: recursive
-          fetch-depth: 1
-          token: ${{ secrets.GH_PAT != '' && secrets.GH_PAT || secrets.GITHUB_TOKEN }}
           ref: ${{ inputs.ref }}
+          submodules: recursive
 
       - name: Install uv and Python
         uses: astral-sh/setup-uv@v6
@@ -93,10 +91,8 @@ jobs:
     steps:
       - uses: actions/checkout@v5
         with:
-          submodules: recursive
-          fetch-depth: 1
-          token: ${{ secrets.GH_PAT != '' && secrets.GH_PAT || secrets.GITHUB_TOKEN }}
           ref: ${{ inputs.ref }}
+          submodules: recursive
 
       - name: Install uv and Python
         uses: astral-sh/setup-uv@v6

.github/workflows/link-check.yml

@@ -12,11 +12,8 @@ jobs:
     runs-on: ubuntu-latest
     steps:
       - uses: actions/checkout@v4
-        with:
-          fetch-depth: 1
-          token: ${{ secrets.GH_PAT != '' && secrets.GH_PAT || secrets.GITHUB_TOKEN }}
       - uses: lycheeverse/lychee-action@v2
         with:
-          args: --no-progress --insecure --user-agent 'curl/7.68.0' --exclude '.*api\.star-history\.com.*' --accept 200,201,202,203,204,205,206,207,208,226,300,301,302,303,304,305,306,307,308,503 README.md docs/ apps/ examples/ benchmarks/
+          args: --no-progress --insecure --user-agent 'curl/7.68.0' README.md docs/ apps/ examples/ benchmarks/
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/release-manual.yml

@@ -19,9 +19,6 @@ jobs:
 
     steps:
       - uses: actions/checkout@v4
-        with:
-          fetch-depth: 1
-          token: ${{ secrets.GH_PAT != '' && secrets.GH_PAT || secrets.GITHUB_TOKEN }}
 
       - name: Validate version
         run: |
@@ -76,8 +73,6 @@ jobs:
     steps:
       - uses: actions/checkout@v4
         with:
-          fetch-depth: 1
-          token: ${{ secrets.GH_PAT != '' && secrets.GH_PAT || secrets.GITHUB_TOKEN }}
           ref: 'main'
 
       - name: Download all artifacts

.gitignore

@@ -91,8 +91,7 @@ packages/leann-backend-diskann/third_party/DiskANN/_deps/
 
 *.meta.json
 *.passages.json
-*.npy
-*.db
+
 batchtest.py
 tests/__pytest_cache__/
 tests/__pycache__/

README.md

@@ -16,24 +16,12 @@
   </a>
 </p>
 
-<div align="center">
-  <a href="https://forms.gle/rDbZf864gMNxhpTq8">
-    <img src="https://img.shields.io/badge/📣_Community_Survey-Help_Shape_v0.4-007ec6?style=for-the-badge&logo=google-forms&logoColor=white" alt="Take Survey">
-  </a>
-  <p>
-    We track <b>zero telemetry</b>. This survey is the ONLY way to tell us if you want <br>
-    <b>GPU Acceleration</b> or <b>More Integrations</b> next.<br>
-    👉 <a href="https://forms.gle/rDbZf864gMNxhpTq8"><b>Click here to cast your vote (2 mins)</b></a>
-  </p>
-</div>
-
 <h2 align="center" tabindex="-1" class="heading-element" dir="auto">
     The smallest vector index in the world. RAG Everything with LEANN!
 </h2>
 
 LEANN is an innovative vector database that democratizes personal AI. Transform your laptop into a powerful RAG system that can index and search through millions of documents while using **97% less storage** than traditional solutions **without accuracy loss**.
 
-
 LEANN achieves this through *graph-based selective recomputation* with *high-degree preserving pruning*, computing embeddings on-demand instead of storing them all. [Illustration Fig →](#️-architecture--how-it-works) | [Paper →](https://arxiv.org/abs/2506.08276)
 
 **Ready to RAG Everything?** Transform your laptop into a personal AI assistant that can semantic search your **[file system](#-personal-data-manager-process-any-documents-pdf-txt-md)**, **[emails](#-your-personal-email-secretary-rag-on-apple-mail)**, **[browser history](#-time-machine-for-the-web-rag-your-entire-browser-history)**, **[chat history](#-wechat-detective-unlock-your-golden-memories)** ([WeChat](#-wechat-detective-unlock-your-golden-memories), [iMessage](#-imessage-history-your-personal-conversation-archive)), **[agent memory](#-chatgpt-chat-history-your-personal-ai-conversation-archive)** ([ChatGPT](#-chatgpt-chat-history-your-personal-ai-conversation-archive), [Claude](#-claude-chat-history-your-personal-ai-conversation-archive)), **[live data](#mcp-integration-rag-on-live-data-from-any-platform)** ([Slack](#slack-messages-search-your-team-conversations), [Twitter](#-twitter-bookmarks-your-personal-tweet-library)), **[codebase](#-claude-code-integration-transform-your-development-workflow)**\* , or external knowledge bases (i.e., 60M documents) - all on your laptop, with zero cloud costs and complete privacy.
@@ -201,7 +189,7 @@ LEANN supports RAG on various data sources including documents (`.pdf`, `.txt`,
 
 #### LLM Backend
 
-LEANN supports many LLM providers for text generation (HuggingFace, Ollama, Anthropic, and Any OpenAI compatible API).
+LEANN supports many LLM providers for text generation (HuggingFace, Ollama, and Any OpenAI compatible API).
 
 
 <details>
@@ -269,7 +257,6 @@ Below is a list of base URLs for common providers to get you started.
 | **SiliconFlow** | `https://api.siliconflow.cn/v1`                            |
 | **Zhipu (BigModel)** | `https://open.bigmodel.cn/api/paas/v4/`                |
 | **Mistral AI** | `https://api.mistral.ai/v1`                                |
-| **Anthropic** | `https://api.anthropic.com/v1`                                |
 
 
 
@@ -329,7 +316,7 @@ All RAG examples share these common parameters. **Interactive mode** is availabl
 --embedding-mode MODE        # sentence-transformers, openai, mlx, or ollama
 
 # LLM Parameters (Text generation models)
---llm TYPE                   # LLM backend: openai, ollama, hf, or anthropic (default: openai)
+--llm TYPE                   # LLM backend: openai, ollama, or hf (default: openai)
 --llm-model MODEL            # Model name (default: gpt-4o) e.g., gpt-4o-mini, llama3.2:1b, Qwen/Qwen2.5-1.5B-Instruct
 --thinking-budget LEVEL      # Thinking budget for reasoning models: low/medium/high (supported by o3, o3-mini, GPT-Oss:20b, and other reasoning models)
 
@@ -392,54 +379,6 @@ python -m apps.code_rag --repo-dir "./my_codebase" --query "How does authenticat
 
 </details>
 
-### 🎨 ColQwen: Multimodal PDF Retrieval with Vision-Language Models
-
-Search through PDFs using both text and visual understanding with ColQwen2/ColPali models. Perfect for research papers, technical documents, and any PDFs with complex layouts, figures, or diagrams.
-
-> **🍎 Mac Users**: ColQwen is optimized for Apple Silicon with MPS acceleration for faster inference!
-
-```bash
-# Build index from PDFs
-python -m apps.colqwen_rag build --pdfs ./my_papers/ --index research_papers
-
-# Search with text queries
-python -m apps.colqwen_rag search research_papers "How does attention mechanism work?"
-
-# Interactive Q&A
-python -m apps.colqwen_rag ask research_papers --interactive
-```
-
-<details>
-<summary><strong>📋 Click to expand: ColQwen Setup & Usage</strong></summary>
-
-#### Prerequisites
-```bash
-# Install dependencies
-uv pip install colpali_engine pdf2image pillow matplotlib qwen_vl_utils einops seaborn
-brew install poppler  # macOS only, for PDF processing
-```
-
-#### Build Index
-```bash
-python -m apps.colqwen_rag build \
-  --pdfs ./pdf_directory/ \
-  --index my_index \
-  --model colqwen2  # or colpali
-```
-
-#### Search
-```bash
-python -m apps.colqwen_rag search my_index "your question here" --top-k 5
-```
-
-#### Models
-- **ColQwen2** (`colqwen2`): Latest vision-language model with improved performance
-- **ColPali** (`colpali`): Proven multimodal retriever
-
-For detailed usage, see the [ColQwen Guide](docs/COLQWEN_GUIDE.md).
-
-</details>
-
 ### 📧 Your Personal Email Secretary: RAG on Apple Mail!
 
 > **Note:** The examples below currently support macOS only. Windows support coming soon.
@@ -1106,10 +1045,10 @@ Options:
 leann ask INDEX_NAME [OPTIONS]
 
 Options:
-  --llm {ollama,openai,hf,anthropic}    LLM provider (default: ollama)
-  --model MODEL                         Model name (default: qwen3:8b)
-  --interactive                         Interactive chat mode
-  --top-k N                             Retrieval count (default: 20)
+  --llm {ollama,openai,hf}    LLM provider (default: ollama)
+  --model MODEL               Model name (default: qwen3:8b)
+  --interactive              Interactive chat mode
+  --top-k N                  Retrieval count (default: 20)
 ```
 
 **List Command:**

apps/base_rag_example.py

@@ -6,7 +6,7 @@ Provides common parameters and functionality for all RAG examples.
 import argparse
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Any, Union
+from typing import Any
 
 import dotenv
 from leann.api import LeannBuilder, LeannChat
@@ -257,8 +257,8 @@ class BaseRAGExample(ABC):
         pass
 
     @abstractmethod
-    async def load_data(self, args) -> list[Union[str, dict[str, Any]]]:
-        """Load data from the source. Returns list of text chunks (strings or dicts with 'text' key)."""
+    async def load_data(self, args) -> list[str]:
+        """Load data from the source. Returns list of text chunks."""
         pass
 
     def get_llm_config(self, args) -> dict[str, Any]:
@@ -282,8 +282,8 @@ class BaseRAGExample(ABC):
 
         return config
 
-    async def build_index(self, args, texts: list[Union[str, dict[str, Any]]]) -> str:
-        """Build LEANN index from texts (accepts strings or dicts with 'text' key)."""
+    async def build_index(self, args, texts: list[str]) -> str:
+        """Build LEANN index from texts."""
         index_path = str(Path(args.index_dir) / f"{self.default_index_name}.leann")
 
         print(f"\n[Building Index] Creating {self.name} index...")
@@ -314,14 +314,8 @@ class BaseRAGExample(ABC):
         batch_size = 1000
         for i in range(0, len(texts), batch_size):
             batch = texts[i : i + batch_size]
-            for item in batch:
-                # Handle both dict format (from create_text_chunks) and plain strings
-                if isinstance(item, dict):
-                    text = item.get("text", "")
-                    metadata = item.get("metadata")
-                    builder.add_text(text, metadata)
-                else:
-                    builder.add_text(item)
+            for text in batch:
+                builder.add_text(text)
             print(f"Added {min(i + batch_size, len(texts))}/{len(texts)} texts...")
 
         print("Building index structure...")

apps/chunking/__init__.py

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

apps/document_rag.py

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

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

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

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

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

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

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

apps/slack_data/slack_mcp_reader.py

@@ -7,7 +7,6 @@ for indexing in LEANN. It supports various Slack MCP server implementations and
 flexible message processing options.
 """
 
-import ast
 import asyncio
 import json
 import logging
@@ -147,16 +146,16 @@ class SlackMCPReader:
                     match = re.search(r"'error':\s*(\{[^}]+\})", str(e))
                     if match:
                         try:
-                            error_dict = ast.literal_eval(match.group(1))
-                        except (ValueError, SyntaxError):
+                            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 = ast.literal_eval(match.group(1))
-                            except (ValueError, SyntaxError):
+                                error_dict = eval(match.group(1))
+                            except (ValueError, SyntaxError, NameError):
                                 pass
 
                 if self._is_cache_sync_error(error_dict):

docs/configuration-guide.md

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

docs/faq.md

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

packages/leann-backend-diskann/pyproject.toml

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

packages/leann-backend-hnsw/pyproject.toml

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

packages/leann-core/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "leann-core"
-version = "0.3.5"
+version = "0.3.4"
 description = "Core API and plugin system for LEANN"
 readme = "README.md"
 requires-python = ">=3.9"

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

@@ -916,7 +916,6 @@ class LeannSearcher:
         metadata_filters: Optional[dict[str, dict[str, Union[str, int, float, bool, list]]]] = None,
         batch_size: int = 0,
         use_grep: bool = False,
-        provider_options: Optional[dict[str, Any]] = None,
         **kwargs,
     ) -> list[SearchResult]:
         """
@@ -980,24 +979,10 @@ class LeannSearcher:
 
         start_time = time.time()
 
-        # Extract query template from stored embedding_options with fallback chain:
-        # 1. Check provider_options override (highest priority)
-        # 2. Check query_prompt_template (new format)
-        # 3. Check prompt_template (old format for backward compat)
-        # 4. None (no template)
-        query_template = None
-        if provider_options and "prompt_template" in provider_options:
-            query_template = provider_options["prompt_template"]
-        elif "query_prompt_template" in self.embedding_options:
-            query_template = self.embedding_options["query_prompt_template"]
-        elif "prompt_template" in self.embedding_options:
-            query_template = self.embedding_options["prompt_template"]
-
         query_embedding = self.backend_impl.compute_query_embedding(
             query,
             use_server_if_available=recompute_embeddings,
             zmq_port=zmq_port,
-            query_template=query_template,
         )
         logger.info(f"  Generated embedding shape: {query_embedding.shape}")
         embedding_time = time.time() - start_time
@@ -1251,15 +1236,15 @@ class LeannChat:
             "Please provide the best answer you can based on this context and your knowledge."
         )
 
-        logger.info("The context provided to the LLM is:")
-        logger.info(f"{'Relevance':<10} | {'Chunk id':<10} | {'Content':<60} | {'Source':<80}")
-        logger.info("-" * 150)
+        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]
-            logger.info(
+            print(
                 f"{chunk_relevance:<10} | {chunk_id:<10} | {chunk_content:<60} | {chunk_source:<80}"
             )
         ask_time = time.time()

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

@@ -12,13 +12,7 @@ from typing import Any, Optional
 
 import torch
 
-from .settings import (
-    resolve_anthropic_api_key,
-    resolve_anthropic_base_url,
-    resolve_ollama_host,
-    resolve_openai_api_key,
-    resolve_openai_base_url,
-)
+from .settings import resolve_ollama_host, resolve_openai_api_key, resolve_openai_base_url
 
 # Configure logging
 logging.basicConfig(level=logging.INFO)
@@ -851,81 +845,6 @@ class OpenAIChat(LLMInterface):
             return f"Error: Could not get a response from OpenAI. Details: {e}"
 
 
-class AnthropicChat(LLMInterface):
-    """LLM interface for Anthropic Claude models."""
-
-    def __init__(
-        self,
-        model: str = "claude-haiku-4-5",
-        api_key: Optional[str] = None,
-        base_url: Optional[str] = None,
-    ):
-        self.model = model
-        self.base_url = resolve_anthropic_base_url(base_url)
-        self.api_key = resolve_anthropic_api_key(api_key)
-
-        if not self.api_key:
-            raise ValueError(
-                "Anthropic API key is required. Set ANTHROPIC_API_KEY environment variable or pass api_key parameter."
-            )
-
-        logger.info(
-            "Initializing Anthropic Chat with model='%s' and base_url='%s'",
-            model,
-            self.base_url,
-        )
-
-        try:
-            import anthropic
-
-            # Allow custom Anthropic-compatible endpoints via base_url
-            self.client = anthropic.Anthropic(
-                api_key=self.api_key,
-                base_url=self.base_url,
-            )
-        except ImportError:
-            raise ImportError(
-                "The 'anthropic' library is required for Anthropic models. Please install it with 'pip install anthropic'."
-            )
-
-    def ask(self, prompt: str, **kwargs) -> str:
-        logger.info(f"Sending request to Anthropic with model {self.model}")
-
-        try:
-            # Anthropic API parameters
-            params = {
-                "model": self.model,
-                "max_tokens": kwargs.get("max_tokens", 1000),
-                "messages": [{"role": "user", "content": prompt}],
-            }
-
-            # Add optional parameters
-            if "temperature" in kwargs:
-                params["temperature"] = kwargs["temperature"]
-            if "top_p" in kwargs:
-                params["top_p"] = kwargs["top_p"]
-
-            response = self.client.messages.create(**params)
-
-            # Extract text from response
-            response_text = response.content[0].text
-
-            # Log token usage
-            print(
-                f"Total tokens = {response.usage.input_tokens + response.usage.output_tokens}, "
-                f"input tokens = {response.usage.input_tokens}, "
-                f"output tokens = {response.usage.output_tokens}"
-            )
-
-            if response.stop_reason == "max_tokens":
-                print("The query is exceeding the maximum allowed number of tokens")
-
-            return response_text.strip()
-        except Exception as e:
-            logger.error(f"Error communicating with Anthropic: {e}")
-            return f"Error: Could not get a response from Anthropic. Details: {e}"
-
-
 class SimulatedChat(LLMInterface):
     """A simple simulated chat for testing and development."""
 
@@ -978,12 +897,6 @@ def get_llm(llm_config: Optional[dict[str, Any]] = None) -> LLMInterface:
         )
     elif llm_type == "gemini":
         return GeminiChat(model=model or "gemini-2.5-flash", api_key=llm_config.get("api_key"))
-    elif llm_type == "anthropic":
-        return AnthropicChat(
-            model=model or "claude-3-5-sonnet-20241022",
-            api_key=llm_config.get("api_key"),
-            base_url=llm_config.get("base_url"),
-        )
     elif llm_type == "simulated":
         return SimulatedChat()
     else:

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

@@ -5,15 +5,12 @@ Packaged within leann-core so installed wheels can import it reliably.
 
 import logging
 from pathlib import Path
-from typing import Any, Optional
+from typing import Optional
 
 from llama_index.core.node_parser import SentenceSplitter
 
 logger = logging.getLogger(__name__)
 
-# Flag to ensure AST token warning only shown once per session
-_ast_token_warning_shown = False
-
 
 def estimate_token_count(text: str) -> int:
     """
@@ -177,44 +174,37 @@ def create_ast_chunks(
     max_chunk_size: int = 512,
     chunk_overlap: int = 64,
     metadata_template: str = "default",
-) -> list[dict[str, Any]]:
+) -> list[str]:
     """Create AST-aware chunks from code documents using astchunk.
 
     Falls back to traditional chunking if astchunk is unavailable.
-
-    Returns:
-        List of dicts with {"text": str, "metadata": dict}
     """
     try:
         from astchunk import ASTChunkBuilder  # optional dependency
     except ImportError as e:
         logger.error(f"astchunk not available: {e}")
         logger.info("Falling back to traditional chunking for code files")
-        return _traditional_chunks_as_dicts(documents, max_chunk_size, chunk_overlap)
+        return create_traditional_chunks(documents, max_chunk_size, chunk_overlap)
 
     all_chunks = []
     for doc in documents:
         language = doc.metadata.get("language")
         if not language:
             logger.warning("No language detected; falling back to traditional chunking")
-            all_chunks.extend(_traditional_chunks_as_dicts([doc], max_chunk_size, chunk_overlap))
+            all_chunks.extend(create_traditional_chunks([doc], max_chunk_size, chunk_overlap))
             continue
 
         try:
-            # Warn once if AST chunk size + overlap might exceed common token limits
-            # Note: Actual truncation happens at embedding time with dynamic model limits
-            global _ast_token_warning_shown
+            # 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 and not _ast_token_warning_shown:
+            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)}. "
-                    f"Note: Chunks will be auto-truncated at embedding time based on your model's actual token limit."
+                    f"Consider reducing --ast-chunk-size to {int(400 / 1.2)} or --ast-chunk-overlap to {int(50 / 1.2)}"
                 )
-                _ast_token_warning_shown = True
 
             configs = {
                 "max_chunk_size": max_chunk_size,
@@ -239,40 +229,17 @@ def create_ast_chunks(
 
             chunks = chunk_builder.chunkify(code_content)
             for chunk in chunks:
-                chunk_text = None
-                astchunk_metadata = {}
-
                 if hasattr(chunk, "text"):
                     chunk_text = chunk.text
+                elif isinstance(chunk, dict) and "text" in chunk:
+                    chunk_text = chunk["text"]
                 elif isinstance(chunk, str):
                     chunk_text = chunk
-                elif isinstance(chunk, dict):
-                    # Handle astchunk format: {"content": "...", "metadata": {...}}
-                    if "content" in chunk:
-                        chunk_text = chunk["content"]
-                        astchunk_metadata = chunk.get("metadata", {})
-                    elif "text" in chunk:
-                        chunk_text = chunk["text"]
-                    else:
-                        chunk_text = str(chunk)  # Last resort
                 else:
                     chunk_text = str(chunk)
 
                 if chunk_text and chunk_text.strip():
-                    # Extract document-level metadata
-                    doc_metadata = {
-                        "file_path": doc.metadata.get("file_path", ""),
-                        "file_name": doc.metadata.get("file_name", ""),
-                    }
-                    if "creation_date" in doc.metadata:
-                        doc_metadata["creation_date"] = doc.metadata["creation_date"]
-                    if "last_modified_date" in doc.metadata:
-                        doc_metadata["last_modified_date"] = doc.metadata["last_modified_date"]
-
-                    # Merge document metadata + astchunk metadata
-                    combined_metadata = {**doc_metadata, **astchunk_metadata}
-
-                    all_chunks.append({"text": chunk_text.strip(), "metadata": combined_metadata})
+                    all_chunks.append(chunk_text.strip())
 
             logger.info(
                 f"Created {len(chunks)} AST chunks from {language} file: {doc.metadata.get('file_name', 'unknown')}"
@@ -280,19 +247,15 @@ def create_ast_chunks(
         except Exception as e:
             logger.warning(f"AST chunking failed for {language} file: {e}")
             logger.info("Falling back to traditional chunking")
-            all_chunks.extend(_traditional_chunks_as_dicts([doc], max_chunk_size, chunk_overlap))
+            all_chunks.extend(create_traditional_chunks([doc], max_chunk_size, chunk_overlap))
 
     return all_chunks
 
 
 def create_traditional_chunks(
     documents, chunk_size: int = 256, chunk_overlap: int = 128
-) -> list[dict[str, Any]]:
-    """Create traditional text chunks using LlamaIndex SentenceSplitter.
-
-    Returns:
-        List of dicts with {"text": str, "metadata": dict}
-    """
+) -> list[str]:
+    """Create traditional text chunks using LlamaIndex SentenceSplitter."""
     if chunk_size <= 0:
         logger.warning(f"Invalid chunk_size={chunk_size}, using default value of 256")
         chunk_size = 256
@@ -308,40 +271,19 @@ def create_traditional_chunks(
         paragraph_separator="\n\n",
     )
 
-    result = []
+    all_texts = []
     for doc in documents:
-        # Extract document-level metadata
-        doc_metadata = {
-            "file_path": doc.metadata.get("file_path", ""),
-            "file_name": doc.metadata.get("file_name", ""),
-        }
-        if "creation_date" in doc.metadata:
-            doc_metadata["creation_date"] = doc.metadata["creation_date"]
-        if "last_modified_date" in doc.metadata:
-            doc_metadata["last_modified_date"] = doc.metadata["last_modified_date"]
-
         try:
             nodes = node_parser.get_nodes_from_documents([doc])
             if nodes:
-                for node in nodes:
-                    result.append({"text": node.get_content(), "metadata": doc_metadata})
+                all_texts.extend(node.get_content() for node in nodes)
         except Exception as e:
             logger.error(f"Traditional chunking failed for document: {e}")
             content = doc.get_content()
             if content and content.strip():
-                result.append({"text": content.strip(), "metadata": doc_metadata})
+                all_texts.append(content.strip())
 
-    return result
-
-
-def _traditional_chunks_as_dicts(
-    documents, chunk_size: int = 256, chunk_overlap: int = 128
-) -> list[dict[str, Any]]:
-    """Helper: Traditional chunking that returns dict format for consistency.
-
-    This is now just an alias for create_traditional_chunks for backwards compatibility.
-    """
-    return create_traditional_chunks(documents, chunk_size, chunk_overlap)
+    return all_texts
 
 
 def create_text_chunks(
@@ -353,12 +295,8 @@ def create_text_chunks(
     ast_chunk_overlap: int = 64,
     code_file_extensions: Optional[list[str]] = None,
     ast_fallback_traditional: bool = True,
-) -> list[dict[str, Any]]:
-    """Create text chunks from documents with optional AST support for code files.
-
-    Returns:
-        List of dicts with {"text": str, "metadata": dict}
-    """
+) -> list[str]:
+    """Create text chunks from documents with optional AST support for code files."""
     if not documents:
         logger.warning("No documents provided for chunking")
         return []
@@ -393,17 +331,24 @@ def create_text_chunks(
                 logger.error(f"AST chunking failed: {e}")
                 if ast_fallback_traditional:
                     all_chunks.extend(
-                        _traditional_chunks_as_dicts(code_docs, chunk_size, chunk_overlap)
+                        create_traditional_chunks(code_docs, chunk_size, chunk_overlap)
                     )
                 else:
                     raise
         if text_docs:
-            all_chunks.extend(_traditional_chunks_as_dicts(text_docs, chunk_size, chunk_overlap))
+            all_chunks.extend(create_traditional_chunks(text_docs, chunk_size, chunk_overlap))
     else:
-        all_chunks = _traditional_chunks_as_dicts(documents, chunk_size, chunk_overlap)
+        all_chunks = create_traditional_chunks(documents, chunk_size, chunk_overlap)
 
     logger.info(f"Total chunks created: {len(all_chunks)}")
 
-    # Note: Token truncation is now handled at embedding time with dynamic model limits
-    # See get_model_token_limit() and truncate_to_token_limit() in embedding_compute.py
-    return all_chunks
+    # 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

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

@@ -11,12 +11,7 @@ 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_anthropic_base_url,
-    resolve_ollama_host,
-    resolve_openai_api_key,
-    resolve_openai_base_url,
-)
+from .settings import resolve_ollama_host, resolve_openai_api_key, resolve_openai_base_url
 
 
 def extract_pdf_text_with_pymupdf(file_path: str) -> str:
@@ -149,18 +144,6 @@ Examples:
             default=None,
             help="API key for embedding service (defaults to OPENAI_API_KEY)",
         )
-        build_parser.add_argument(
-            "--embedding-prompt-template",
-            type=str,
-            default=None,
-            help="Prompt template to prepend to all texts for embedding (e.g., 'query: ' for search)",
-        )
-        build_parser.add_argument(
-            "--query-prompt-template",
-            type=str,
-            default=None,
-            help="Prompt template for queries (different from build template for task-specific models)",
-        )
         build_parser.add_argument(
             "--force", "-f", action="store_true", help="Force rebuild existing index"
         )
@@ -277,12 +260,6 @@ Examples:
             action="store_true",
             help="Display file paths and metadata in search results",
         )
-        search_parser.add_argument(
-            "--embedding-prompt-template",
-            type=str,
-            default=None,
-            help="Prompt template to prepend to query for embedding (e.g., 'query: ' for search)",
-        )
 
         # Ask command
         ask_parser = subparsers.add_parser("ask", help="Ask questions")
@@ -296,7 +273,7 @@ Examples:
             "--llm",
             type=str,
             default="ollama",
-            choices=["simulated", "ollama", "hf", "openai", "anthropic"],
+            choices=["simulated", "ollama", "hf", "openai"],
             help="LLM provider (default: ollama)",
         )
         ask_parser.add_argument(
@@ -346,7 +323,7 @@ Examples:
             "--api-key",
             type=str,
             default=None,
-            help="API key for cloud LLM providers (OpenAI, Anthropic)",
+            help="API key for OpenAI-compatible APIs (defaults to OPENAI_API_KEY)",
         )
 
         # List command
@@ -1311,8 +1288,13 @@ Examples:
                     ast_fallback_traditional=getattr(args, "ast_fallback_traditional", True),
                 )
 
-                # create_text_chunks now returns list[dict] with metadata preserved
-                all_texts.extend(chunk_texts)
+                # 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(
@@ -1430,14 +1412,6 @@ Examples:
             resolved_embedding_key = resolve_openai_api_key(args.embedding_api_key)
             if resolved_embedding_key:
                 embedding_options["api_key"] = resolved_embedding_key
-        if args.query_prompt_template:
-            # New format: separate templates
-            if args.embedding_prompt_template:
-                embedding_options["build_prompt_template"] = args.embedding_prompt_template
-            embedding_options["query_prompt_template"] = args.query_prompt_template
-        elif args.embedding_prompt_template:
-            # Old format: single template (backward compat)
-            embedding_options["prompt_template"] = args.embedding_prompt_template
 
         builder = LeannBuilder(
             backend_name=args.backend_name,
@@ -1559,11 +1533,6 @@ Examples:
                         print("Invalid input. Aborting search.")
                         return
 
-        # Build provider_options for runtime override
-        provider_options = {}
-        if args.embedding_prompt_template:
-            provider_options["prompt_template"] = args.embedding_prompt_template
-
         searcher = LeannSearcher(index_path=index_path)
         results = searcher.search(
             query,
@@ -1573,7 +1542,6 @@ Examples:
             prune_ratio=args.prune_ratio,
             recompute_embeddings=args.recompute_embeddings,
             pruning_strategy=args.pruning_strategy,
-            provider_options=provider_options if provider_options else None,
         )
 
         print(f"Search results for '{query}' (top {len(results)}):")
@@ -1621,12 +1589,6 @@ Examples:
             resolved_api_key = resolve_openai_api_key(args.api_key)
             if resolved_api_key:
                 llm_config["api_key"] = resolved_api_key
-        elif args.llm == "anthropic":
-            # For Anthropic, pass base_url and API key if provided
-            if args.api_base:
-                llm_config["base_url"] = resolve_anthropic_base_url(args.api_base)
-            if args.api_key:
-                llm_config["api_key"] = args.api_key
 
         chat = LeannChat(index_path=index_path, llm_config=llm_config)
 

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

@@ -4,310 +4,119 @@ Consolidates all embedding computation logic using SentenceTransformer
 Preserves all optimization parameters to ensure performance
 """
 
-import json
 import logging
 import os
-import subprocess
 import time
 from typing import Any, Optional
 
 import numpy as np
-import tiktoken
 import torch
 
 from .settings import resolve_ollama_host, resolve_openai_api_key, resolve_openai_base_url
 
+
+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()
 log_level = getattr(logging, LOG_LEVEL, logging.WARNING)
 logger.setLevel(log_level)
 
-# Token limit registry for embedding models
-# Used as fallback when dynamic discovery fails (e.g., LM Studio, OpenAI)
-# Ollama models use dynamic discovery via /api/show
+# Global model cache to avoid repeated loading
+_model_cache: dict[str, Any] = {}
+
+# Known embedding model token limits
 EMBEDDING_MODEL_LIMITS = {
-    # Nomic models (common across servers)
-    "nomic-embed-text": 2048,  # Corrected from 512 - verified via /api/show
-    "nomic-embed-text-v1.5": 2048,
+    "nomic-embed-text": 512,
     "nomic-embed-text-v2": 512,
-    # Other embedding models
     "mxbai-embed-large": 512,
     "all-minilm": 512,
     "bge-m3": 8192,
     "snowflake-arctic-embed": 512,
-    # OpenAI models
-    "text-embedding-3-small": 8192,
-    "text-embedding-3-large": 8192,
-    "text-embedding-ada-002": 8192,
+    # Add more models as needed
 }
 
-# Runtime cache for dynamically discovered token limits
-# Key: (model_name, base_url), Value: token_limit
-# Prevents repeated SDK/API calls for the same model
-_token_limit_cache: dict[tuple[str, str], int] = {}
-
-
-def get_model_token_limit(
-    model_name: str,
-    base_url: Optional[str] = None,
-    default: int = 2048,
-) -> int:
-    """
-    Get token limit for a given embedding model.
-    Uses hybrid approach: dynamic discovery for Ollama, registry fallback for others.
-    Caches discovered limits to prevent repeated API/SDK calls.
-
-    Args:
-        model_name: Name of the embedding model
-        base_url: Base URL of the embedding server (for dynamic discovery)
-        default: Default token limit if model not found
-
-    Returns:
-        Token limit for the model in tokens
-    """
-    # Check cache first to avoid repeated SDK/API calls
-    cache_key = (model_name, base_url or "")
-    if cache_key in _token_limit_cache:
-        cached_limit = _token_limit_cache[cache_key]
-        logger.debug(f"Using cached token limit for {model_name}: {cached_limit}")
-        return cached_limit
-
-    # Try Ollama dynamic discovery if base_url provided
-    if base_url:
-        # Detect Ollama servers by port or "ollama" in URL
-        if "11434" in base_url or "ollama" in base_url.lower():
-            limit = _query_ollama_context_limit(model_name, base_url)
-            if limit:
-                _token_limit_cache[cache_key] = limit
-                return limit
-
-        # Try LM Studio SDK discovery
-        if "1234" in base_url or "lmstudio" in base_url.lower() or "lm.studio" in base_url.lower():
-            # Convert HTTP to WebSocket URL
-            ws_url = base_url.replace("https://", "wss://").replace("http://", "ws://")
-            # Remove /v1 suffix if present
-            if ws_url.endswith("/v1"):
-                ws_url = ws_url[:-3]
-
-            limit = _query_lmstudio_context_limit(model_name, ws_url)
-            if limit:
-                _token_limit_cache[cache_key] = limit
-                return limit
-
-    # Fallback to known model registry with version handling (from PR #154)
-    # Handle versioned model names (e.g., "nomic-embed-text:latest" -> "nomic-embed-text")
-    base_model_name = model_name.split(":")[0]
-
-    # Check exact match first
-    if model_name in EMBEDDING_MODEL_LIMITS:
-        limit = EMBEDDING_MODEL_LIMITS[model_name]
-        _token_limit_cache[cache_key] = limit
-        return limit
-
-    # Check base name match
-    if base_model_name in EMBEDDING_MODEL_LIMITS:
-        limit = EMBEDDING_MODEL_LIMITS[base_model_name]
-        _token_limit_cache[cache_key] = limit
-        return limit
-
-    # Check partial matches for common patterns
-    for known_model, registry_limit in EMBEDDING_MODEL_LIMITS.items():
-        if known_model in base_model_name or base_model_name in known_model:
-            _token_limit_cache[cache_key] = registry_limit
-            return registry_limit
-
-    # Default fallback
-    logger.warning(f"Unknown model '{model_name}', using default {default} token limit")
-    _token_limit_cache[cache_key] = default
-    return default
-
-
-def truncate_to_token_limit(texts: list[str], token_limit: int) -> list[str]:
-    """
-    Truncate texts to fit within token limit using tiktoken.
-
-    Args:
-        texts: List of text strings to truncate
-        token_limit: Maximum number of tokens allowed
-
-    Returns:
-        List of truncated texts (same length as input)
-    """
-    if not texts:
-        return []
-
-    # Use tiktoken with cl100k_base encoding
-    enc = tiktoken.get_encoding("cl100k_base")
-
-    truncated_texts = []
-    truncation_count = 0
-    total_tokens_removed = 0
-    max_original_length = 0
-
-    for i, text in enumerate(texts):
-        tokens = enc.encode(text)
-        original_length = len(tokens)
-
-        if original_length <= token_limit:
-            # Text is within limit, keep as is
-            truncated_texts.append(text)
-        else:
-            # Truncate to token_limit
-            truncated_tokens = tokens[:token_limit]
-            truncated_text = enc.decode(truncated_tokens)
-            truncated_texts.append(truncated_text)
-
-            # Track truncation statistics
-            truncation_count += 1
-            tokens_removed = original_length - token_limit
-            total_tokens_removed += tokens_removed
-            max_original_length = max(max_original_length, original_length)
-
-            # Log individual truncation at WARNING level (first few only)
-            if truncation_count <= 3:
-                logger.warning(
-                    f"Text {i + 1} truncated: {original_length} → {token_limit} tokens "
-                    f"({tokens_removed} tokens removed)"
-                )
-            elif truncation_count == 4:
-                logger.warning("Further truncation warnings suppressed...")
-
-    # Log summary at INFO level
-    if truncation_count > 0:
-        logger.warning(
-            f"Truncation summary: {truncation_count}/{len(texts)} texts truncated "
-            f"(removed {total_tokens_removed} tokens total, longest was {max_original_length} tokens)"
-        )
-    else:
-        logger.debug(
-            f"No truncation needed - all {len(texts)} texts within {token_limit} token limit"
-        )
-
-    return truncated_texts
-
-
-def _query_ollama_context_limit(model_name: str, base_url: str) -> Optional[int]:
-    """
-    Query Ollama /api/show for model context limit.
-
-    Args:
-        model_name: Name of the Ollama model
-        base_url: Base URL of the Ollama server
-
-    Returns:
-        Context limit in tokens if found, None otherwise
-    """
-    try:
-        import requests
-
-        response = requests.post(
-            f"{base_url}/api/show",
-            json={"name": model_name},
-            timeout=5,
-        )
-        if response.status_code == 200:
-            data = response.json()
-            if "model_info" in data:
-                # Look for *.context_length in model_info
-                for key, value in data["model_info"].items():
-                    if "context_length" in key and isinstance(value, int):
-                        logger.info(f"Detected {model_name} context limit: {value} tokens")
-                        return value
-    except Exception as e:
-        logger.debug(f"Failed to query Ollama context limit: {e}")
-
-    return None
-
-
-def _query_lmstudio_context_limit(model_name: str, base_url: str) -> Optional[int]:
-    """
-    Query LM Studio SDK for model context length via Node.js subprocess.
-
-    Args:
-        model_name: Name of the LM Studio model
-        base_url: Base URL of the LM Studio server (WebSocket format, e.g., "ws://localhost:1234")
-
-    Returns:
-        Context limit in tokens if found, None otherwise
-    """
-    # Inline JavaScript using @lmstudio/sdk
-    # Note: Load model temporarily for metadata, then unload to respect JIT auto-evict
-    js_code = f"""
-    const {{ LMStudioClient }} = require('@lmstudio/sdk');
-    (async () => {{
-        try {{
-            const client = new LMStudioClient({{ baseUrl: '{base_url}' }});
-            const model = await client.embedding.load('{model_name}', {{ verbose: false }});
-            const contextLength = await model.getContextLength();
-            await model.unload();  // Unload immediately to respect JIT auto-evict settings
-            console.log(JSON.stringify({{ contextLength, identifier: '{model_name}' }}));
-        }} catch (error) {{
-            console.error(JSON.stringify({{ error: error.message }}));
-            process.exit(1);
-        }}
-    }})();
-    """
-
-    try:
-        # Set NODE_PATH to include global modules for @lmstudio/sdk resolution
-        env = os.environ.copy()
-
-        # Try to get npm global root (works with nvm, brew node, etc.)
-        try:
-            npm_root = subprocess.run(
-                ["npm", "root", "-g"],
-                capture_output=True,
-                text=True,
-                timeout=5,
-            )
-            if npm_root.returncode == 0:
-                global_modules = npm_root.stdout.strip()
-                # Append to existing NODE_PATH if present
-                existing_node_path = env.get("NODE_PATH", "")
-                env["NODE_PATH"] = (
-                    f"{global_modules}:{existing_node_path}"
-                    if existing_node_path
-                    else global_modules
-                )
-        except Exception:
-            # If npm not available, continue with existing NODE_PATH
-            pass
-
-        result = subprocess.run(
-            ["node", "-e", js_code],
-            capture_output=True,
-            text=True,
-            timeout=10,
-            env=env,
-        )
-
-        if result.returncode != 0:
-            logger.debug(f"LM Studio SDK error: {result.stderr}")
-            return None
-
-        data = json.loads(result.stdout)
-        context_length = data.get("contextLength")
-
-        if context_length and context_length > 0:
-            logger.info(f"LM Studio SDK detected {model_name} context length: {context_length}")
-            return context_length
-
-    except FileNotFoundError:
-        logger.debug("Node.js not found - install Node.js for LM Studio SDK features")
-    except subprocess.TimeoutExpired:
-        logger.debug("LM Studio SDK query timeout")
-    except json.JSONDecodeError:
-        logger.debug("LM Studio SDK returned invalid JSON")
-    except Exception as e:
-        logger.debug(f"LM Studio SDK query failed: {e}")
-
-    return None
-
-
-# Global model cache to avoid repeated loading
-_model_cache: dict[str, Any] = {}
-
 
 def compute_embeddings(
     texts: list[str],
@@ -352,7 +161,6 @@ def compute_embeddings(
             model_name,
             base_url=provider_options.get("base_url"),
             api_key=provider_options.get("api_key"),
-            provider_options=provider_options,
         )
     elif mode == "mlx":
         return compute_embeddings_mlx(texts, model_name)
@@ -362,7 +170,6 @@ def compute_embeddings(
             model_name,
             is_build=is_build,
             host=provider_options.get("host"),
-            provider_options=provider_options,
         )
     elif mode == "gemini":
         return compute_embeddings_gemini(texts, model_name, is_build=is_build)
@@ -701,7 +508,6 @@ def compute_embeddings_openai(
     model_name: str,
     base_url: Optional[str] = None,
     api_key: Optional[str] = None,
-    provider_options: Optional[dict[str, Any]] = None,
 ) -> np.ndarray:
     # TODO: @yichuan-w add progress bar only in build mode
     """Compute embeddings using OpenAI API"""
@@ -720,40 +526,26 @@ def compute_embeddings_openai(
             f"Found {invalid_count} empty/invalid text(s) in input. Upstream should filter before calling OpenAI."
         )
 
-    # Extract base_url and api_key from provider_options if not provided directly
-    provider_options = provider_options or {}
-    effective_base_url = base_url or provider_options.get("base_url")
-    effective_api_key = api_key or provider_options.get("api_key")
-
-    resolved_base_url = resolve_openai_base_url(effective_base_url)
-    resolved_api_key = resolve_openai_api_key(effective_api_key)
+    resolved_base_url = resolve_openai_base_url(base_url)
+    resolved_api_key = resolve_openai_api_key(api_key)
 
     if not resolved_api_key:
         raise RuntimeError("OPENAI_API_KEY environment variable not set")
 
-    # Create OpenAI client
-    client = openai.OpenAI(api_key=resolved_api_key, base_url=resolved_base_url)
+    # Cache OpenAI client
+    cache_key = f"openai_client::{resolved_base_url}"
+    if cache_key in _model_cache:
+        client = _model_cache[cache_key]
+    else:
+        client = openai.OpenAI(api_key=resolved_api_key, base_url=resolved_base_url)
+        _model_cache[cache_key] = client
+        logger.info("OpenAI client cached")
 
     logger.info(
         f"Computing embeddings for {len(texts)} texts using OpenAI API, model: '{model_name}'"
     )
     print(f"len of texts: {len(texts)}")
 
-    # Apply prompt template if provided
-    # Priority: build_prompt_template (new format) > prompt_template (old format)
-    prompt_template = provider_options.get("build_prompt_template") or provider_options.get(
-        "prompt_template"
-    )
-
-    if prompt_template:
-        logger.warning(f"Applying prompt template: '{prompt_template}'")
-        texts = [f"{prompt_template}{text}" for text in texts]
-
-    # Query token limit and apply truncation
-    token_limit = get_model_token_limit(model_name, base_url=effective_base_url)
-    logger.info(f"Using token limit: {token_limit} for model '{model_name}'")
-    texts = truncate_to_token_limit(texts, token_limit)
-
     # OpenAI has limits on batch size and input length
     max_batch_size = 800  # Conservative batch size because the token limit is 300K
     all_embeddings = []
@@ -784,15 +576,7 @@ def compute_embeddings_openai(
         try:
             response = client.embeddings.create(model=model_name, input=batch_texts)
             batch_embeddings = [embedding.embedding for embedding in response.data]
-
-            # Verify we got the expected number of embeddings
-            if len(batch_embeddings) != len(batch_texts):
-                logger.warning(
-                    f"Expected {len(batch_texts)} embeddings but got {len(batch_embeddings)}"
-                )
-
-            # Only take the number of embeddings that match the batch size
-            all_embeddings.extend(batch_embeddings[: len(batch_texts)])
+            all_embeddings.extend(batch_embeddings)
         except Exception as e:
             logger.error(f"Batch {i} failed: {e}")
             raise
@@ -882,7 +666,6 @@ def compute_embeddings_ollama(
     model_name: str,
     is_build: bool = False,
     host: Optional[str] = None,
-    provider_options: Optional[dict[str, Any]] = None,
 ) -> np.ndarray:
     """
     Compute embeddings using Ollama API with true batch processing.
@@ -895,7 +678,6 @@ def compute_embeddings_ollama(
         model_name: Ollama model name (e.g., "nomic-embed-text", "mxbai-embed-large")
         is_build: Whether this is a build operation (shows progress bar)
         host: Ollama host URL (defaults to environment or http://localhost:11434)
-        provider_options: Optional provider-specific options (e.g., prompt_template)
 
     Returns:
         Normalized embeddings array, shape: (len(texts), embedding_dim)
@@ -1032,24 +814,15 @@ def compute_embeddings_ollama(
 
     logger.info(f"Using batch size: {batch_size} for true batch processing")
 
-    # Apply prompt template if provided
-    provider_options = provider_options or {}
-    # Priority: build_prompt_template (new format) > prompt_template (old format)
-    prompt_template = provider_options.get("build_prompt_template") or provider_options.get(
-        "prompt_template"
-    )
-
-    if prompt_template:
-        logger.warning(f"Applying prompt template: '{prompt_template}'")
-        texts = [f"{prompt_template}{text}" for text in texts]
-
-    # Get model token limit and apply truncation before batching
-    token_limit = get_model_token_limit(model_name, base_url=resolved_host)
+    # 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 truncation to all texts before batch processing
-    # Function logs truncation details internally
-    texts = truncate_to_token_limit(texts, 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
 
     def get_batch_embeddings(batch_texts):
         """Get embeddings for a batch of texts using /api/embed endpoint."""
@@ -1107,12 +880,12 @@ def compute_embeddings_ollama(
 
         return None, list(range(len(batch_texts)))
 
-    # Process texts in batches
+    # Process truncated texts in batches
     all_embeddings = []
     all_failed_indices = []
 
     # Setup progress bar if needed
-    show_progress = is_build or len(texts) > 10
+    show_progress = is_build or len(truncated_texts) > 10
     try:
         if show_progress:
             from tqdm import tqdm
@@ -1120,7 +893,7 @@ def compute_embeddings_ollama(
         show_progress = False
 
     # Process batches
-    num_batches = (len(texts) + batch_size - 1) // batch_size
+    num_batches = (len(truncated_texts) + batch_size - 1) // batch_size
 
     if show_progress:
         batch_iterator = tqdm(range(num_batches), desc="Computing Ollama embeddings (batched)")
@@ -1129,8 +902,8 @@ def compute_embeddings_ollama(
 
     for batch_idx in batch_iterator:
         start_idx = batch_idx * batch_size
-        end_idx = min(start_idx + batch_size, len(texts))
-        batch_texts = texts[start_idx:end_idx]
+        end_idx = min(start_idx + batch_size, len(truncated_texts))
+        batch_texts = truncated_texts[start_idx:end_idx]
 
         batch_embeddings, batch_failed = get_batch_embeddings(batch_texts)
 
@@ -1145,11 +918,11 @@ def compute_embeddings_ollama(
 
     # Handle failed embeddings
     if all_failed_indices:
-        if len(all_failed_indices) == len(texts):
+        if len(all_failed_indices) == len(truncated_texts):
             raise RuntimeError("Failed to compute any embeddings")
 
         logger.warning(
-            f"Failed to compute embeddings for {len(all_failed_indices)}/{len(texts)} texts"
+            f"Failed to compute embeddings for {len(all_failed_indices)}/{len(truncated_texts)} texts"
         )
 
         # Use zero embeddings as fallback for failed ones

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

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

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

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

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

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

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

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

packages/leann-mcp/README.md

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

packages/leann/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "leann"
-version = "0.3.5"
+version = "0.3.4"
 description = "LEANN - The smallest vector index in the world. RAG Everything with LEANN!"
 readme = "README.md"
 requires-python = ">=3.9"

pyproject.toml

@@ -57,8 +57,6 @@ dependencies = [
     "tree-sitter-c-sharp>=0.20.0",
     "tree-sitter-typescript>=0.20.0",
     "torchvision>=0.23.0",
-    "einops",
-    "seaborn",
 ]
 
 [project.optional-dependencies]
@@ -69,8 +67,7 @@ diskann = [
 # Add a new optional dependency group for document processing
 documents = [
     "beautifulsoup4>=4.13.0",  # For HTML parsing
-    "python-docx>=0.8.11",     # For Word documents (creating/editing)
-    "docx2txt>=0.9",           # For Word documents (text extraction)
+    "python-docx>=0.8.11",     # For Word documents
     "openpyxl>=3.1.0",         # For Excel files
     "pandas>=2.2.0",           # For data processing
 ]
@@ -165,7 +162,6 @@ python_functions = ["test_*"]
 markers = [
     "slow: marks tests as slow (deselect with '-m \"not slow\"')",
     "openai: marks tests that require OpenAI API key",
-    "integration: marks tests that require live services (Ollama, LM Studio, etc.)",
 ]
 timeout = 300  # Reduced from 600s (10min) to 300s (5min) for CI safety
 addopts = [

test_colqwen_reproduction.py

@@ -0,0 +1,162 @@
+#!/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/README.md

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

tests/test_astchunk_integration.py

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

tests/test_cli_prompt_template.py

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

tests/test_embedding_prompt_template.py

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

tests/test_lmstudio_bridge.py

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

tests/test_prompt_template_e2e.py

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

tests/test_prompt_template_persistence.py

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

tests/test_token_truncation.py

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