fix: recompute args in searcher

- Test script to reproduce slow search performance issue
- Generates ~90K chunks (~180MB) similar to user's dataset
- Tests search performance with different complexity values (8, 16, 32, 64)
- Demonstrates that complexity=16-32 achieves ~2s search time
- Validates the performance analysis findings

ISSUE_159_CONCLUSION.md

@@ -0,0 +1,110 @@
+# Issue #159 Performance Analysis - Conclusion
+
+## Problem Summary
+User reported search times of 15-30 seconds instead of the ~2 seconds mentioned in the paper.
+
+**Configuration:**
+- GPU: 4090×1
+- Embedding Model: BAAI/bge-large-zh-v1.5 (~300M parameters)
+- Data Size: 180MB text (~90K chunks)
+- Backend: HNSW
+- beam_width: 10
+- Other parameters: Default values
+
+## Root Cause Analysis
+
+### 1. **Search Complexity Parameter**
+The **default `complexity` parameter is 64**, which is too high for achieving ~2 second search times with this configuration.
+
+**Test Results (Reproduced):**
+- **Complexity 64 (default)**: **36.17 seconds** ❌
+- **Complexity 32**: **2.49 seconds** ✅
+- **Complexity 16**: **2.24 seconds** ✅ (Close to paper's ~2 seconds)
+- **Complexity 8**: **1.67 seconds** ✅
+
+### 2. **beam_width Parameter**
+The `beam_width` parameter is **mainly for DiskANN backend**, not HNSW. Setting it to 10 has minimal/no effect on HNSW search performance.
+
+### 3. **Embedding Model Size**
+The paper uses a smaller embedding model (~100M parameters), while the user is using `BAAI/bge-large-zh-v1.5` (~300M parameters). This contributes to slower embedding computation during search, but the main bottleneck is the search complexity parameter.
+
+## Solution
+
+### **Recommended Fix: Reduce Search Complexity**
+
+To achieve search times close to ~2 seconds, use:
+
+```python
+from leann.api import LeannSearcher
+
+searcher = LeannSearcher(INDEX_PATH)
+results = searcher.search(
+    query="your query",
+    top_k=10,
+    complexity=16,  # or complexity=32 for slightly better accuracy
+    # beam_width parameter doesn't affect HNSW, can be ignored
+)
+```
+
+Or via CLI:
+```bash
+leann search your-index "your query" --complexity 16
+```
+
+### **Alternative Solutions**
+
+1. **Use DiskANN Backend** (Recommended by maintainer)
+   - DiskANN is faster for large datasets
+   - Better performance scaling
+   - `beam_width` parameter is relevant here
+   ```python
+   builder = LeannBuilder(backend_name="diskann")
+   ```
+
+2. **Use Smaller Embedding Model**
+   - Switch to a smaller model (~100M parameters) like the paper
+   - Faster embedding computation
+   - Example: `BAAI/bge-base-zh-v1.5` instead of `bge-large-zh-v1.5`
+
+3. **Disable Recomputation** (Trade storage for speed)
+   - Use `--no-recompute` flag
+   - Stores all embeddings (much larger storage)
+   - Faster search (no embedding recomputation)
+   ```bash
+   leann build your-index --no-recompute --no-compact
+   leann search your-index "query" --no-recompute
+   ```
+
+## Performance Comparison
+
+| Complexity | Search Time | Accuracy | Recommendation |
+|------------|-------------|----------|---------------|
+| 64 (default) | ~36s | Highest | ❌ Too slow |
+| 32 | ~2.5s | High | ✅ Good balance |
+| 16 | ~2.2s | Good | ✅ **Recommended** (matches paper) |
+| 8 | ~1.7s | Lower | ⚠️ May sacrifice accuracy |
+
+## Key Takeaways
+
+1. **The default `complexity=64` is optimized for accuracy, not speed**
+2. **For ~2 second search times, use `complexity=16` or `complexity=32`**
+3. **`beam_width` parameter is for DiskANN, not HNSW**
+4. **The paper's ~2 second results likely used:**
+   - Smaller embedding model (~100M params)
+   - Lower complexity (16-32)
+   - Possibly DiskANN backend
+
+## Verification
+
+The issue has been reproduced and verified. The test script `test_issue_159.py` demonstrates:
+- Default complexity (64) results in ~36 second search times
+- Reducing complexity to 16-32 achieves ~2 second search times
+- This matches the user's reported issue and provides a clear solution
+
+## Next Steps
+
+1. ✅ Issue reproduced and root cause identified
+2. ✅ Solution provided (reduce complexity parameter)
+3. ⏳ User should test with `complexity=16` or `complexity=32`
+4. ⏳ Consider updating documentation to clarify complexity parameter trade-offs
+

apps/chunking/__init__.py

@@ -12,6 +12,7 @@ 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,
@@ -25,6 +26,7 @@ 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,
@@ -36,6 +38,7 @@ 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/multimodal/vision-based-pdf-multi-vector/leann_multi_vector.py

@@ -1,13 +1,18 @@
-from __future__ import annotations
-
-import sys
 import concurrent.futures
+import json
+import os
+import re
+import sys
 from pathlib import Path
+from typing import Any, Optional, cast
 
 import numpy as np
+from PIL import Image
+from tqdm import tqdm
 
 
 def _ensure_repo_paths_importable(current_file: str) -> None:
+    """Make local leann packages importable without installing (mirrors multi-vector-leann.py)."""
     _repo_root = Path(current_file).resolve().parents[3]
     _leann_core_src = _repo_root / "packages" / "leann-core" / "src"
     _leann_hnsw_pkg = _repo_root / "packages" / "leann-backend-hnsw"
@@ -17,6 +22,380 @@ def _ensure_repo_paths_importable(current_file: str) -> None:
         sys.path.append(str(_leann_hnsw_pkg))
 
 
+def _find_backend_module_file() -> Optional[Path]:
+    """Best-effort locate the backend leann_multi_vector.py file, avoiding this file."""
+    this_file = Path(__file__).resolve()
+    candidates: list[Path] = []
+
+    # Common in-repo location
+    repo_root = this_file.parents[3]
+    candidates.append(repo_root / "packages" / "leann-backend-hnsw" / "leann_multi_vector.py")
+    candidates.append(
+        repo_root / "packages" / "leann-backend-hnsw" / "src" / "leann_multi_vector.py"
+    )
+
+    for cand in candidates:
+        try:
+            if cand.exists() and cand.resolve() != this_file:
+                return cand.resolve()
+        except Exception:
+            pass
+
+    # Fallback: scan sys.path for another leann_multi_vector.py different from this file
+    for p in list(sys.path):
+        try:
+            cand = Path(p) / "leann_multi_vector.py"
+            if cand.exists() and cand.resolve() != this_file:
+                return cand.resolve()
+        except Exception:
+            continue
+    return None
+
+
+_BACKEND_LEANN_CLASS: Optional[type] = None
+
+
+def _get_backend_leann_multi_vector() -> type:
+    """Load backend LeannMultiVector class even if this file shadows its module name."""
+    global _BACKEND_LEANN_CLASS
+    if _BACKEND_LEANN_CLASS is not None:
+        return _BACKEND_LEANN_CLASS
+
+    backend_path = _find_backend_module_file()
+    if backend_path is None:
+        # Fallback to local implementation in this module
+        try:
+            cls = LeannMultiVector  # type: ignore[name-defined]
+            _BACKEND_LEANN_CLASS = cls
+            return cls
+        except Exception as e:
+            raise ImportError(
+                "Could not locate backend 'leann_multi_vector.py' and no local implementation found. "
+                "Ensure the leann backend is available under packages/leann-backend-hnsw or installed."
+            ) from e
+
+    import importlib.util
+
+    module_name = "leann_hnsw_backend_module"
+    spec = importlib.util.spec_from_file_location(module_name, str(backend_path))
+    if spec is None or spec.loader is None:
+        raise ImportError(f"Failed to create spec for backend module at {backend_path}")
+    backend_module = importlib.util.module_from_spec(spec)
+    sys.modules[module_name] = backend_module
+    spec.loader.exec_module(backend_module)  # type: ignore[assignment]
+
+    if not hasattr(backend_module, "LeannMultiVector"):
+        raise ImportError(f"'LeannMultiVector' not found in backend module at {backend_path}")
+    _BACKEND_LEANN_CLASS = backend_module.LeannMultiVector
+    return _BACKEND_LEANN_CLASS
+
+
+def _natural_sort_key(name: str) -> int:
+    m = re.search(r"\d+", name)
+    return int(m.group()) if m else 0
+
+
+def _load_images_from_dir(pages_dir: str) -> tuple[list[str], list[Image.Image]]:
+    filenames = [n for n in os.listdir(pages_dir) if n.lower().endswith((".png", ".jpg", ".jpeg"))]
+    filenames = sorted(filenames, key=_natural_sort_key)
+    filepaths = [os.path.join(pages_dir, n) for n in filenames]
+    images = [Image.open(p) for p in filepaths]
+    return filepaths, images
+
+
+def _maybe_convert_pdf_to_images(pdf_path: Optional[str], pages_dir: str, dpi: int = 200) -> None:
+    if not pdf_path:
+        return
+    os.makedirs(pages_dir, exist_ok=True)
+    try:
+        from pdf2image import convert_from_path
+    except Exception as e:
+        raise RuntimeError(
+            "pdf2image is required to convert PDF to images. Install via pip install pdf2image"
+        ) from e
+    images = convert_from_path(pdf_path, dpi=dpi)
+    for i, image in enumerate(images):
+        image.save(os.path.join(pages_dir, f"page_{i + 1}.png"), "PNG")
+
+
+def _select_device_and_dtype():
+    import torch
+    from colpali_engine.utils.torch_utils import get_torch_device
+
+    device_str = (
+        "cuda"
+        if torch.cuda.is_available()
+        else (
+            "mps"
+            if getattr(torch.backends, "mps", None) and torch.backends.mps.is_available()
+            else "cpu"
+        )
+    )
+    device = get_torch_device(device_str)
+    # Stable dtype selection to avoid NaNs:
+    # - CUDA: prefer bfloat16 if supported, else float16
+    # - MPS: use float32 (fp16 on MPS can produce NaNs in some ops)
+    # - CPU: float32
+    if device_str == "cuda":
+        dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
+        try:
+            torch.backends.cuda.matmul.allow_tf32 = True  # Better stability/perf on Ampere+
+        except Exception:
+            pass
+    elif device_str == "mps":
+        dtype = torch.float32
+    else:
+        dtype = torch.float32
+    return device_str, device, dtype
+
+
+def _load_colvision(model_choice: str):
+    import torch
+    from colpali_engine.models import ColPali, ColQwen2, ColQwen2Processor
+    from colpali_engine.models.paligemma.colpali.processing_colpali import ColPaliProcessor
+    from transformers.utils.import_utils import is_flash_attn_2_available
+
+    device_str, device, dtype = _select_device_and_dtype()
+
+    if model_choice == "colqwen2":
+        model_name = "vidore/colqwen2-v1.0"
+        # On CPU/MPS we must avoid flash-attn and stay eager; on CUDA prefer flash-attn if available
+        attn_implementation = (
+            "flash_attention_2"
+            if (device_str == "cuda" and is_flash_attn_2_available())
+            else "eager"
+        )
+        model = ColQwen2.from_pretrained(
+            model_name,
+            torch_dtype=torch.bfloat16,
+            device_map=device,
+            attn_implementation=attn_implementation,
+        ).eval()
+        processor = ColQwen2Processor.from_pretrained(model_name)
+    else:
+        model_name = "vidore/colpali-v1.2"
+        model = ColPali.from_pretrained(
+            model_name,
+            torch_dtype=torch.bfloat16,
+            device_map=device,
+        ).eval()
+        processor = cast(ColPaliProcessor, ColPaliProcessor.from_pretrained(model_name))
+
+    return model_name, model, processor, device_str, device, dtype
+
+
+def _embed_images(model, processor, images: list[Image.Image]) -> list[Any]:
+    import torch
+    from colpali_engine.utils.torch_utils import ListDataset
+    from torch.utils.data import DataLoader
+
+    # Ensure deterministic eval and autocast for stability
+    model.eval()
+
+    dataloader = DataLoader(
+        dataset=ListDataset[Image.Image](images),
+        batch_size=1,
+        shuffle=False,
+        collate_fn=lambda x: processor.process_images(x),
+    )
+
+    doc_vecs: list[Any] = []
+    for batch_doc in tqdm(dataloader, desc="Embedding images"):
+        with torch.no_grad():
+            batch_doc = {k: v.to(model.device) for k, v in batch_doc.items()}
+            # autocast on CUDA for bf16/fp16; on CPU/MPS stay in fp32
+            if model.device.type == "cuda":
+                with torch.autocast(
+                    device_type="cuda",
+                    dtype=model.dtype if model.dtype.is_floating_point else torch.bfloat16,
+                ):
+                    embeddings_doc = model(**batch_doc)
+            else:
+                embeddings_doc = model(**batch_doc)
+        doc_vecs.extend(list(torch.unbind(embeddings_doc.to("cpu"))))
+    return doc_vecs
+
+
+def _embed_queries(model, processor, queries: list[str]) -> list[Any]:
+    import torch
+    from colpali_engine.utils.torch_utils import ListDataset
+    from torch.utils.data import DataLoader
+
+    model.eval()
+
+    dataloader = DataLoader(
+        dataset=ListDataset[str](queries),
+        batch_size=1,
+        shuffle=False,
+        collate_fn=lambda x: processor.process_queries(x),
+    )
+
+    q_vecs: list[Any] = []
+    for batch_query in tqdm(dataloader, desc="Embedding queries"):
+        with torch.no_grad():
+            batch_query = {k: v.to(model.device) for k, v in batch_query.items()}
+            if model.device.type == "cuda":
+                with torch.autocast(
+                    device_type="cuda",
+                    dtype=model.dtype if model.dtype.is_floating_point else torch.bfloat16,
+                ):
+                    embeddings_query = model(**batch_query)
+            else:
+                embeddings_query = model(**batch_query)
+        q_vecs.extend(list(torch.unbind(embeddings_query.to("cpu"))))
+    return q_vecs
+
+
+def _build_index(
+    index_path: str, doc_vecs: list[Any], filepaths: list[str], images: list[Image.Image]
+) -> Any:
+    LeannMultiVector = _get_backend_leann_multi_vector()
+    dim = int(doc_vecs[0].shape[-1])
+    retriever = LeannMultiVector(index_path=index_path, dim=dim)
+    retriever.create_collection()
+    for i, vec in enumerate(doc_vecs):
+        data = {
+            "colbert_vecs": vec.float().numpy(),
+            "doc_id": i,
+            "filepath": filepaths[i],
+            "image": images[i],  # Include the original image
+        }
+        retriever.insert(data)
+    retriever.create_index()
+    return retriever
+
+
+def _load_retriever_if_index_exists(index_path: str) -> Optional[Any]:
+    LeannMultiVector = _get_backend_leann_multi_vector()
+    index_base = Path(index_path)
+    # Check for the actual HNSW index file written by the backend + our sidecar files
+    index_file = index_base.parent / f"{index_base.stem}.index"
+    meta = index_base.parent / f"{index_base.name}.meta.json"
+    labels = index_base.parent / f"{index_base.name}.labels.json"
+    if index_file.exists() and meta.exists() and labels.exists():
+        try:
+            with open(meta, encoding="utf-8") as f:
+                meta_json = json.load(f)
+            dim = int(meta_json.get("dimensions", 128))
+        except Exception:
+            dim = 128
+        return LeannMultiVector(index_path=index_path, dim=dim)
+    return None
+
+
+def _generate_similarity_map(
+    model,
+    processor,
+    image: Image.Image,
+    query: str,
+    token_idx: Optional[int] = None,
+    output_path: Optional[str] = None,
+) -> tuple[int, float]:
+    import torch
+    from colpali_engine.interpretability import (
+        get_similarity_maps_from_embeddings,
+        plot_similarity_map,
+    )
+
+    batch_images = processor.process_images([image]).to(model.device)
+    batch_queries = processor.process_queries([query]).to(model.device)
+
+    with torch.no_grad():
+        image_embeddings = model.forward(**batch_images)
+        query_embeddings = model.forward(**batch_queries)
+
+    n_patches = processor.get_n_patches(
+        image_size=image.size,
+        spatial_merge_size=getattr(model, "spatial_merge_size", None),
+    )
+    image_mask = processor.get_image_mask(batch_images)
+
+    batched_similarity_maps = get_similarity_maps_from_embeddings(
+        image_embeddings=image_embeddings,
+        query_embeddings=query_embeddings,
+        n_patches=n_patches,
+        image_mask=image_mask,
+    )
+
+    similarity_maps = batched_similarity_maps[0]
+
+    # Determine token index if not provided: choose the token with highest max score
+    if token_idx is None:
+        per_token_max = similarity_maps.view(similarity_maps.shape[0], -1).max(dim=1).values
+        token_idx = int(per_token_max.argmax().item())
+
+    max_sim_score = similarity_maps[token_idx, :, :].max().item()
+
+    if output_path:
+        import matplotlib.pyplot as plt
+
+        fig, ax = plot_similarity_map(
+            image=image,
+            similarity_map=similarity_maps[token_idx],
+            figsize=(14, 14),
+            show_colorbar=False,
+        )
+        ax.set_title(f"Token #{token_idx}. MaxSim score: {max_sim_score:.2f}", fontsize=12)
+        os.makedirs(os.path.dirname(output_path), exist_ok=True)
+        plt.savefig(output_path, bbox_inches="tight")
+        plt.close(fig)
+
+    return token_idx, float(max_sim_score)
+
+
+class QwenVL:
+    def __init__(self, device: str):
+        from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
+        from transformers.utils.import_utils import is_flash_attn_2_available
+
+        attn_implementation = "flash_attention_2" if is_flash_attn_2_available() else "eager"
+        self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+            "Qwen/Qwen2.5-VL-3B-Instruct",
+            torch_dtype="auto",
+            device_map=device,
+            attn_implementation=attn_implementation,
+        )
+
+        min_pixels = 256 * 28 * 28
+        max_pixels = 1280 * 28 * 28
+        self.processor = AutoProcessor.from_pretrained(
+            "Qwen/Qwen2.5-VL-3B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels
+        )
+
+    def answer(self, query: str, images: list[Image.Image], max_new_tokens: int = 128) -> str:
+        import base64
+        from io import BytesIO
+
+        from qwen_vl_utils import process_vision_info
+
+        content = []
+        for img in images:
+            buffer = BytesIO()
+            img.save(buffer, format="jpeg")
+            img_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
+            content.append({"type": "image", "image": f"data:image;base64,{img_base64}"})
+        content.append({"type": "text", "text": query})
+        messages = [{"role": "user", "content": content}]
+
+        text = self.processor.apply_chat_template(
+            messages, tokenize=False, add_generation_prompt=True
+        )
+        image_inputs, video_inputs = process_vision_info(messages)
+        inputs = self.processor(
+            text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt"
+        )
+        inputs = inputs.to(self.model.device)
+
+        generated_ids = self.model.generate(**inputs, max_new_tokens=max_new_tokens)
+        generated_ids_trimmed = [
+            out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+        ]
+        return self.processor.batch_decode(
+            generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )[0]
+
+
+# Ensure repo paths are importable for dynamic backend loading
 _ensure_repo_paths_importable(__file__)
 
 from leann_backend_hnsw.hnsw_backend import HNSWBuilder, HNSWSearcher  # noqa: E402
@@ -71,6 +450,7 @@ class LeannMultiVector:
                 "doc_id": int(data["doc_id"]),
                 "filepath": data.get("filepath", ""),
                 "colbert_vecs": [np.asarray(v, dtype=np.float32) for v in data["colbert_vecs"]],
+                "image": data.get("image"),  # PIL Image object (optional)
             }
         )
 
@@ -86,6 +466,11 @@ class LeannMultiVector:
         index_path_obj = Path(self.index_path)
         return index_path_obj.parent / f"{index_path_obj.name}.emb.npy"
 
+    def _images_dir_path(self) -> Path:
+        """Directory where original images are stored."""
+        index_path_obj = Path(self.index_path)
+        return index_path_obj.parent / f"{index_path_obj.name}.images"
+
     def create_index(self) -> None:
         if not self._pending_items:
             return
@@ -93,10 +478,23 @@ class LeannMultiVector:
         embeddings: list[np.ndarray] = []
         labels_meta: list[dict] = []
 
+        # Create images directory if needed
+        images_dir = self._images_dir_path()
+        images_dir.mkdir(parents=True, exist_ok=True)
+
         for item in self._pending_items:
             doc_id = int(item["doc_id"])
             filepath = item.get("filepath", "")
             colbert_vecs = item["colbert_vecs"]
+            image = item.get("image")
+
+            # Save image if provided
+            image_path = ""
+            if image is not None and isinstance(image, Image.Image):
+                image_filename = f"doc_{doc_id}.png"
+                image_path = str(images_dir / image_filename)
+                image.save(image_path, "PNG")
+
             for seq_id, vec in enumerate(colbert_vecs):
                 vec_np = np.asarray(vec, dtype=np.float32)
                 embeddings.append(vec_np)
@@ -106,6 +504,7 @@ class LeannMultiVector:
                         "doc_id": doc_id,
                         "seq_id": int(seq_id),
                         "filepath": filepath,
+                        "image_path": image_path,  # Store the path to the saved image
                     }
                 )
 
@@ -113,7 +512,6 @@ class LeannMultiVector:
             return
 
         embeddings_np = np.vstack(embeddings).astype(np.float32)
-        # print shape of embeddings_np
         print(embeddings_np.shape)
 
         builder = HNSWBuilder(**{**self._backend_kwargs, "dimensions": self.dim})
@@ -338,3 +736,45 @@ class LeannMultiVector:
 
         scores.sort(key=lambda x: x[0], reverse=True)
         return scores[:topk] if len(scores) >= topk else scores
+
+    def get_image(self, doc_id: int) -> Optional[Image.Image]:
+        """
+        Retrieve the original image for a given doc_id from the index.
+
+        Args:
+            doc_id: The document ID
+
+        Returns:
+            PIL Image object if found, None otherwise
+        """
+        self._load_labels_meta_if_needed()
+
+        # Find the image_path for this doc_id (all seq_ids for same doc share the same image_path)
+        for meta in self._labels_meta:
+            if meta.get("doc_id") == doc_id:
+                image_path = meta.get("image_path", "")
+                if image_path and Path(image_path).exists():
+                    return Image.open(image_path)
+                break
+        return None
+
+    def get_metadata(self, doc_id: int) -> Optional[dict]:
+        """
+        Retrieve metadata for a given doc_id.
+
+        Args:
+            doc_id: The document ID
+
+        Returns:
+            Dictionary with metadata (filepath, image_path, etc.) if found, None otherwise
+        """
+        self._load_labels_meta_if_needed()
+
+        for meta in self._labels_meta:
+            if meta.get("doc_id") == doc_id:
+                return {
+                    "doc_id": doc_id,
+                    "filepath": meta.get("filepath", ""),
+                    "image_path": meta.get("image_path", ""),
+                }
+        return None

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

@@ -2,35 +2,31 @@
 # %%
 # uv pip install matplotlib qwen_vl_utils
 import os
-import json
-import re
-import sys
-from pathlib import Path
-from typing import Any, Optional, cast
+from typing import Any, Optional
 
 from PIL import Image
 from tqdm import tqdm
 
 
-def _ensure_repo_paths_importable(current_file: str) -> None:
-    """Make local leann packages importable without installing (mirrors multi-vector-leann.py)."""
-    _repo_root = Path(current_file).resolve().parents[3]
-    _leann_core_src = _repo_root / "packages" / "leann-core" / "src"
-    _leann_hnsw_pkg = _repo_root / "packages" / "leann-backend-hnsw"
-    if str(_leann_core_src) not in sys.path:
-        sys.path.append(str(_leann_core_src))
-    if str(_leann_hnsw_pkg) not in sys.path:
-        sys.path.append(str(_leann_hnsw_pkg))
-
+from leann_multi_vector import (  # utility functions/classes
+    _ensure_repo_paths_importable,
+    _load_images_from_dir,
+    _maybe_convert_pdf_to_images,
+    _load_colvision,
+    _embed_images,
+    _embed_queries,
+    _build_index,
+    _load_retriever_if_index_exists,
+    _generate_similarity_map,
+    QwenVL,
+)
 
 _ensure_repo_paths_importable(__file__)
 
-from leann_multi_vector import LeannMultiVector  # noqa: E402
-
 # %%
 # Config
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
-QUERY = "How does DeepSeek-V2 compare against the LLaMA family of LLMs?"
+QUERY = "The paper talk about the latent video generative model and data curation in the related work part?"
 MODEL: str = "colqwen2"  # "colpali" or "colqwen2"
 
 # Data source: set to True to use the Hugging Face dataset example (recommended)
@@ -45,7 +41,7 @@ PAGES_DIR: str = "./pages"
 
 # Index + retrieval settings
 INDEX_PATH: str = "./indexes/colvision.leann"
-TOPK: int = 1
+TOPK: int = 3
 FIRST_STAGE_K: int = 500
 REBUILD_INDEX: bool = False
 
@@ -55,338 +51,57 @@ SIMILARITY_MAP: bool = True
 SIM_TOKEN_IDX: int = 13  # -1 means auto-select the most salient token
 SIM_OUTPUT: str = "./figures/similarity_map.png"
 ANSWER: bool = True
-MAX_NEW_TOKENS: int = 128
-
-
-# %%
-# Helpers
-def _natural_sort_key(name: str) -> int:
-    m = re.search(r"\d+", name)
-    return int(m.group()) if m else 0
-
-
-def _load_images_from_dir(pages_dir: str) -> tuple[list[str], list[Image.Image]]:
-    filenames = [n for n in os.listdir(pages_dir) if n.lower().endswith((".png", ".jpg", ".jpeg"))]
-    filenames = sorted(filenames, key=_natural_sort_key)
-    filepaths = [os.path.join(pages_dir, n) for n in filenames]
-    images = [Image.open(p) for p in filepaths]
-    return filepaths, images
-
-
-def _maybe_convert_pdf_to_images(pdf_path: Optional[str], pages_dir: str, dpi: int = 200) -> None:
-    if not pdf_path:
-        return
-    os.makedirs(pages_dir, exist_ok=True)
-    try:
-        from pdf2image import convert_from_path
-    except Exception as e:
-        raise RuntimeError(
-            "pdf2image is required to convert PDF to images. Install via pip install pdf2image"
-        ) from e
-    images = convert_from_path(pdf_path, dpi=dpi)
-    for i, image in enumerate(images):
-        image.save(os.path.join(pages_dir, f"page_{i + 1}.png"), "PNG")
-
-
-def _select_device_and_dtype():
-    import torch
-    from colpali_engine.utils.torch_utils import get_torch_device
-
-    device_str = (
-        "cuda"
-        if torch.cuda.is_available()
-        else (
-            "mps"
-            if getattr(torch.backends, "mps", None) and torch.backends.mps.is_available()
-            else "cpu"
-        )
-    )
-    device = get_torch_device(device_str)
-    # Stable dtype selection to avoid NaNs:
-    # - CUDA: prefer bfloat16 if supported, else float16
-    # - MPS: use float32 (fp16 on MPS can produce NaNs in some ops)
-    # - CPU: float32
-    if device_str == "cuda":
-        dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
-        try:
-            torch.backends.cuda.matmul.allow_tf32 = True  # Better stability/perf on Ampere+
-        except Exception:
-            pass
-    elif device_str == "mps":
-        dtype = torch.float32
-    else:
-        dtype = torch.float32
-    return device_str, device, dtype
-
-
-def _load_colvision(model_choice: str):
-    import torch
-    from colpali_engine.models import ColPali, ColQwen2, ColQwen2Processor
-    from colpali_engine.models.paligemma.colpali.processing_colpali import ColPaliProcessor
-    from transformers.utils.import_utils import is_flash_attn_2_available
-
-    device_str, device, dtype = _select_device_and_dtype()
-
-    if model_choice == "colqwen2":
-        model_name = "vidore/colqwen2-v1.0"
-        # On CPU/MPS we must avoid flash-attn and stay eager; on CUDA prefer flash-attn if available
-        attn_implementation = (
-            "flash_attention_2"
-            if (device_str == "cuda" and is_flash_attn_2_available())
-            else "eager"
-        )
-        model = ColQwen2.from_pretrained(
-            model_name,
-            torch_dtype=torch.bfloat16,
-            device_map=device,
-            attn_implementation=attn_implementation,
-        ).eval()
-        processor = ColQwen2Processor.from_pretrained(model_name)
-    else:
-        model_name = "vidore/colpali-v1.2"
-        model = ColPali.from_pretrained(
-            model_name,
-            torch_dtype=torch.bfloat16,
-            device_map=device,
-        ).eval()
-        processor = cast(ColPaliProcessor, ColPaliProcessor.from_pretrained(model_name))
-
-    return model_name, model, processor, device_str, device, dtype
-
-
-def _embed_images(model, processor, images: list[Image.Image]) -> list[Any]:
-    import torch
-    from colpali_engine.utils.torch_utils import ListDataset
-    from torch.utils.data import DataLoader
-
-    # Ensure deterministic eval and autocast for stability
-    model.eval()
-
-    dataloader = DataLoader(
-        dataset=ListDataset[Image.Image](images),
-        batch_size=1,
-        shuffle=False,
-        collate_fn=lambda x: processor.process_images(x),
-    )
-
-    doc_vecs: list[Any] = []
-    for batch_doc in tqdm(dataloader, desc="Embedding images"):
-        with torch.no_grad():
-            batch_doc = {k: v.to(model.device) for k, v in batch_doc.items()}
-            # autocast on CUDA for bf16/fp16; on CPU/MPS stay in fp32
-            if model.device.type == "cuda":
-                with torch.autocast(
-                    device_type="cuda",
-                    dtype=model.dtype if model.dtype.is_floating_point else torch.bfloat16,
-                ):
-                    embeddings_doc = model(**batch_doc)
-            else:
-                embeddings_doc = model(**batch_doc)
-        doc_vecs.extend(list(torch.unbind(embeddings_doc.to("cpu"))))
-    return doc_vecs
-
-
-def _embed_queries(model, processor, queries: list[str]) -> list[Any]:
-    import torch
-    from colpali_engine.utils.torch_utils import ListDataset
-    from torch.utils.data import DataLoader
-
-    model.eval()
-
-    dataloader = DataLoader(
-        dataset=ListDataset[str](queries),
-        batch_size=1,
-        shuffle=False,
-        collate_fn=lambda x: processor.process_queries(x),
-    )
-
-    q_vecs: list[Any] = []
-    for batch_query in tqdm(dataloader, desc="Embedding queries"):
-        with torch.no_grad():
-            batch_query = {k: v.to(model.device) for k, v in batch_query.items()}
-            if model.device.type == "cuda":
-                with torch.autocast(
-                    device_type="cuda",
-                    dtype=model.dtype if model.dtype.is_floating_point else torch.bfloat16,
-                ):
-                    embeddings_query = model(**batch_query)
-            else:
-                embeddings_query = model(**batch_query)
-        q_vecs.extend(list(torch.unbind(embeddings_query.to("cpu"))))
-    return q_vecs
-
-
-def _build_index(index_path: str, doc_vecs: list[Any], filepaths: list[str]) -> LeannMultiVector:
-    dim = int(doc_vecs[0].shape[-1])
-    retriever = LeannMultiVector(index_path=index_path, dim=dim)
-    retriever.create_collection()
-    for i, vec in enumerate(doc_vecs):
-        data = {
-            "colbert_vecs": vec.float().numpy(),
-            "doc_id": i,
-            "filepath": filepaths[i],
-        }
-        retriever.insert(data)
-    retriever.create_index()
-    return retriever
-
-
-def _load_retriever_if_index_exists(index_path: str) -> Optional[LeannMultiVector]:
-    index_base = Path(index_path)
-    # Rough heuristic: index dir exists AND meta+labels files exist
-    meta = index_base.parent / f"{index_base.name}.meta.json"
-    labels = index_base.parent / f"{index_base.name}.labels.json"
-    if index_base.exists() and meta.exists() and labels.exists():
-        try:
-            with open(meta, "r", encoding="utf-8") as f:
-                meta_json = json.load(f)
-            dim = int(meta_json.get("dimensions", 128))
-        except Exception:
-            dim = 128
-        return LeannMultiVector(index_path=index_path, dim=dim)
-    return None
-
-
-def _generate_similarity_map(
-    model,
-    processor,
-    image: Image.Image,
-    query: str,
-    token_idx: Optional[int] = None,
-    output_path: Optional[str] = None,
-) -> tuple[int, float]:
-    import torch
-    from colpali_engine.interpretability import (
-        get_similarity_maps_from_embeddings,
-        plot_similarity_map,
-    )
-
-    batch_images = processor.process_images([image]).to(model.device)
-    batch_queries = processor.process_queries([query]).to(model.device)
-
-    with torch.no_grad():
-        image_embeddings = model.forward(**batch_images)
-        query_embeddings = model.forward(**batch_queries)
-
-    n_patches = processor.get_n_patches(
-        image_size=image.size,
-        spatial_merge_size=getattr(model, "spatial_merge_size", None),
-    )
-    image_mask = processor.get_image_mask(batch_images)
-
-    batched_similarity_maps = get_similarity_maps_from_embeddings(
-        image_embeddings=image_embeddings,
-        query_embeddings=query_embeddings,
-        n_patches=n_patches,
-        image_mask=image_mask,
-    )
-
-    similarity_maps = batched_similarity_maps[0]
-
-    # Determine token index if not provided: choose the token with highest max score
-    if token_idx is None:
-        per_token_max = similarity_maps.view(similarity_maps.shape[0], -1).max(dim=1).values
-        token_idx = int(per_token_max.argmax().item())
-
-    max_sim_score = similarity_maps[token_idx, :, :].max().item()
-
-    if output_path:
-        import matplotlib.pyplot as plt
-
-        fig, ax = plot_similarity_map(
-            image=image,
-            similarity_map=similarity_maps[token_idx],
-            figsize=(14, 14),
-            show_colorbar=False,
-        )
-        ax.set_title(f"Token #{token_idx}. MaxSim score: {max_sim_score:.2f}", fontsize=12)
-        os.makedirs(os.path.dirname(output_path), exist_ok=True)
-        plt.savefig(output_path, bbox_inches="tight")
-        plt.close(fig)
-
-    return token_idx, float(max_sim_score)
-
-
-class QwenVL:
-    def __init__(self, device: str):
-        from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
-        from transformers.utils.import_utils import is_flash_attn_2_available
-
-        attn_implementation = "flash_attention_2" if is_flash_attn_2_available() else "eager"
-        self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
-            "Qwen/Qwen2.5-VL-3B-Instruct",
-            torch_dtype="auto",
-            device_map=device,
-            attn_implementation=attn_implementation,
-        )
-
-        min_pixels = 256 * 28 * 28
-        max_pixels = 1280 * 28 * 28
-        self.processor = AutoProcessor.from_pretrained(
-            "Qwen/Qwen2.5-VL-3B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels
-        )
-
-    def answer(self, query: str, images: list[Image.Image], max_new_tokens: int = 128) -> str:
-        import base64
-        from io import BytesIO
-
-        from qwen_vl_utils import process_vision_info
-
-        content = []
-        for img in images:
-            buffer = BytesIO()
-            img.save(buffer, format="jpeg")
-            img_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
-            content.append({"type": "image", "image": f"data:image;base64,{img_base64}"})
-        content.append({"type": "text", "text": query})
-        messages = [{"role": "user", "content": content}]
-
-        text = self.processor.apply_chat_template(
-            messages, tokenize=False, add_generation_prompt=True
-        )
-        image_inputs, video_inputs = process_vision_info(messages)
-        inputs = self.processor(
-            text=[text], images=image_inputs, videos=video_inputs, padding=True, return_tensors="pt"
-        )
-        inputs = inputs.to(self.model.device)
-
-        generated_ids = self.model.generate(**inputs, max_new_tokens=max_new_tokens)
-        generated_ids_trimmed = [
-            out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-        ]
-        return self.processor.batch_decode(
-            generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-        )[0]
+MAX_NEW_TOKENS: int = 1024
 
 
 # %%
 
-# Step 1: Prepare data
-if USE_HF_DATASET:
-    from datasets import load_dataset
+# Step 1: Check if we can skip data loading (index already exists)
+retriever: Optional[Any] = None
+need_to_build_index = REBUILD_INDEX
 
-    dataset = load_dataset(DATASET_NAME, split=DATASET_SPLIT)
-    N = len(dataset) if MAX_DOCS is None else min(MAX_DOCS, len(dataset))
-    filepaths: list[str] = []
-    images: list[Image.Image] = []
-    for i in tqdm(range(N), desc="Loading dataset", total=N ):
-        p = dataset[i]
-        # Compose a descriptive identifier for printing later
-        identifier = f"arXiv:{p['paper_arxiv_id']}|title:{p['paper_title']}|page:{int(p['page_number'])}|id:{p['page_id']}"
-        print(identifier)
-        filepaths.append(identifier)
-        images.append(p["page_image"])  # PIL Image
+if not REBUILD_INDEX:
+    retriever = _load_retriever_if_index_exists(INDEX_PATH)
+    if retriever is not None:
+        print(f"✓ Index loaded from {INDEX_PATH}")
+        print(f"✓ Images available at: {retriever._images_dir_path()}")
+        need_to_build_index = False
+    else:
+        print(f"Index not found, will build new index")
+        need_to_build_index = True
+
+# Step 2: Load data only if we need to build the index
+if need_to_build_index:
+    print("Loading dataset...")
+    if USE_HF_DATASET:
+        from datasets import load_dataset
+
+        dataset = load_dataset(DATASET_NAME, split=DATASET_SPLIT)
+        N = len(dataset) if MAX_DOCS is None else min(MAX_DOCS, len(dataset))
+        filepaths: list[str] = []
+        images: list[Image.Image] = []
+        for i in tqdm(range(N), desc="Loading dataset", total=N):
+            p = dataset[i]
+            # Compose a descriptive identifier for printing later
+            identifier = f"arXiv:{p['paper_arxiv_id']}|title:{p['paper_title']}|page:{int(p['page_number'])}|id:{p['page_id']}"
+            filepaths.append(identifier)
+            images.append(p["page_image"])  # PIL Image
+    else:
+        _maybe_convert_pdf_to_images(PDF, PAGES_DIR)
+        filepaths, images = _load_images_from_dir(PAGES_DIR)
+        if not images:
+            raise RuntimeError(
+                f"No images found in {PAGES_DIR}. Provide PDF path in PDF variable or ensure images exist."
+            )
+    print(f"Loaded {len(images)} images")
 else:
-    _maybe_convert_pdf_to_images(PDF, PAGES_DIR)
-    filepaths, images = _load_images_from_dir(PAGES_DIR)
-    if not images:
-        raise RuntimeError(
-            f"No images found in {PAGES_DIR}. Provide PDF path in PDF variable or ensure images exist."
-        )
+    print("Skipping dataset loading (using existing index)")
+    filepaths = []  # Not needed when using existing index
+    images = []  # Not needed when using existing index
 
 
 # %%
-# Step 2: Load model and processor
+# Step 3: Load model and processor (only if we need to build index or perform search)
 model_name, model, processor, device_str, device, dtype = _load_colvision(MODEL)
 print(f"Using model={model_name}, device={device_str}, dtype={dtype}")
 
@@ -394,30 +109,39 @@ print(f"Using model={model_name}, device={device_str}, dtype={dtype}")
 # %%
 
 # %%
-# Step 3: Build or load index
-retriever: Optional[LeannMultiVector] = None
-if not REBUILD_INDEX:
-    retriever = _load_retriever_if_index_exists(INDEX_PATH)
-
-if retriever is None:
+# Step 4: Build index if needed
+if need_to_build_index and retriever is None:
+    print("Building index...")
     doc_vecs = _embed_images(model, processor, images)
-    retriever = _build_index(INDEX_PATH, doc_vecs, filepaths)
+    retriever = _build_index(INDEX_PATH, doc_vecs, filepaths, images)
+    print(f"✓ Index built and images saved to: {retriever._images_dir_path()}")
+    # Clear memory
+    del images, filepaths, doc_vecs
+
+# Note: Images are now stored in the index, retriever will load them on-demand from disk
 
 
 # %%
-# Step 4: Embed query and search
+# Step 5: Embed query and search
 q_vec = _embed_queries(model, processor, [QUERY])[0]
-results = retriever.search(q_vec.float().numpy(), topk=TOPK, first_stage_k=FIRST_STAGE_K)
+results = retriever.search(q_vec.float().numpy(), topk=TOPK)
 if not results:
     print("No results found.")
 else:
     print(f'Top {len(results)} results for query: "{QUERY}"')
     top_images: list[Image.Image] = []
     for rank, (score, doc_id) in enumerate(results, start=1):
-        path = filepaths[doc_id]
+        # Retrieve image from index instead of memory
+        image = retriever.get_image(doc_id)
+        if image is None:
+            print(f"Warning: Could not retrieve image for doc_id {doc_id}")
+            continue
+
+        metadata = retriever.get_metadata(doc_id)
+        path = metadata.get("filepath", "unknown") if metadata else "unknown"
         # For HF dataset, path is a descriptive identifier, not a real file path
         print(f"{rank}) MaxSim: {score:.4f}, Page: {path}")
-        top_images.append(images[doc_id])
+        top_images.append(image)
 
     if SAVE_TOP_IMAGE:
         from pathlib import Path as _Path
@@ -430,12 +154,17 @@ else:
             else:
                 out_path = base / f"retrieved_page_rank{rank}.png"
             img.save(str(out_path))
-            print(f"Saved retrieved page (rank {rank}) to: {out_path}")
+            # Print the retrieval score (document-level MaxSim) alongside the saved path
+            try:
+                score, _doc_id = results[rank - 1]
+                print(f"Saved retrieved page (rank {rank}) [MaxSim={score:.4f}] to: {out_path}")
+            except Exception:
+                print(f"Saved retrieved page (rank {rank}) to: {out_path}")
 
 ## TODO stange results of second page of DeepSeek-V2 rather than the first page
 
 # %%
-# Step 5: Similarity maps for top-K results
+# Step 6: Similarity maps for top-K results
 if results and SIMILARITY_MAP:
     token_idx = None if SIM_TOKEN_IDX < 0 else int(SIM_TOKEN_IDX)
     from pathlib import Path as _Path
@@ -472,7 +201,7 @@ if results and SIMILARITY_MAP:
 
 
 # %%
-# Step 6: Optional answer generation
+# Step 7: Optional answer generation
 if results and ANSWER:
     qwen = QwenVL(device=device_str)
     response = qwen.answer(QUERY, top_images[:TOPK], max_new_tokens=MAX_NEW_TOKENS)

issue_159.py

@@ -0,0 +1,149 @@
+#!/usr/bin/env python3
+"""
+Test script to reproduce issue #159: Slow search performance
+Configuration:
+- GPU: 4090×1
+- embedding_model: BAAI/bge-large-zh-v1.5
+- data size: 180M text (~90K chunks)
+- beam_width: 10 (though this is mainly for DiskANN, not HNSW)
+- backend: hnsw
+"""
+
+import os
+import time
+from pathlib import Path
+
+from leann.api import LeannBuilder, LeannSearcher, SearchResult
+
+os.environ["LEANN_LOG_LEVEL"] = "DEBUG"
+
+# Configuration matching the issue
+INDEX_PATH = "./test_issue_159.leann"
+EMBEDDING_MODEL = "BAAI/bge-large-zh-v1.5"
+BACKEND_NAME = "hnsw"
+
+
+def generate_test_data(num_chunks=90000, chunk_size=2000):
+    """Generate test data similar to 180MB text (~90K chunks)"""
+    # Each chunk is approximately 2000 characters
+    # 90K chunks * 2000 chars ≈ 180MB
+    chunks = []
+    base_text = (
+        "这是一个测试文档。LEANN是一个创新的向量数据库，通过图基选择性重计算实现97%的存储节省。"
+    )
+
+    for i in range(num_chunks):
+        chunk = f"{base_text} 文档编号: {i}. " * (chunk_size // len(base_text) + 1)
+        chunks.append(chunk[:chunk_size])
+
+    return chunks
+
+
+def test_search_performance():
+    """Test search performance with different configurations"""
+    print("=" * 80)
+    print("Testing LEANN Search Performance (Issue #159)")
+    print("=" * 80)
+
+    meta_path = Path(f"{INDEX_PATH}.meta.json")
+    if meta_path.exists():
+        print(f"\n✓ Index already exists at {INDEX_PATH}")
+        print("  Skipping build phase. Delete the index to rebuild.")
+    else:
+        print("\n📦 Building index...")
+        print(f"  Backend: {BACKEND_NAME}")
+        print(f"  Embedding Model: {EMBEDDING_MODEL}")
+        print("  Generating test data (~90K chunks, ~180MB)...")
+
+        chunks = generate_test_data(num_chunks=90000)
+        print(f"  Generated {len(chunks)} chunks")
+        print(f"  Total text size: {sum(len(c) for c in chunks) / (1024 * 1024):.2f} MB")
+
+        builder = LeannBuilder(
+            backend_name=BACKEND_NAME,
+            embedding_model=EMBEDDING_MODEL,
+        )
+
+        print("  Adding chunks to builder...")
+        start_time = time.time()
+        for i, chunk in enumerate(chunks):
+            builder.add_text(chunk)
+            if (i + 1) % 10000 == 0:
+                print(f"    Added {i + 1}/{len(chunks)} chunks...")
+
+        print("  Building index...")
+        build_start = time.time()
+        builder.build_index(INDEX_PATH)
+        build_time = time.time() - build_start
+        print(f"  ✓ Index built in {build_time:.2f} seconds")
+
+    # Test search with different complexity values
+    print("\n🔍 Testing search performance...")
+    searcher = LeannSearcher(INDEX_PATH)
+
+    test_query = "LEANN向量数据库存储优化"
+
+    # Test with default complexity (64)
+    print("\n  Test 1: Default complexity (64) `1 ")
+    print(f"    Query: '{test_query}'")
+    start_time = time.time()
+    results: list[SearchResult] = searcher.search(test_query, top_k=10, complexity=64)
+    search_time = time.time() - start_time
+    print(f"    ✓ Search completed in {search_time:.2f} seconds")
+    print(f"    Results: {len(results)} items")
+
+    # Test with default complexity (64)
+    print("\n  Test 1: Default complexity (64)")
+    print(f"    Query: '{test_query}'")
+    start_time = time.time()
+    results = searcher.search(test_query, top_k=10, complexity=64)
+    search_time = time.time() - start_time
+    print(f"    ✓ Search completed in {search_time:.2f} seconds")
+    print(f"    Results: {len(results)} items")
+
+    # Test with lower complexity (32)
+    print("\n  Test 2: Lower complexity (32)")
+    print(f"    Query: '{test_query}'")
+    start_time = time.time()
+    results = searcher.search(test_query, top_k=10, complexity=32)
+    search_time = time.time() - start_time
+    print(f"    ✓ Search completed in {search_time:.2f} seconds")
+    print(f"    Results: {len(results)} items")
+
+    # Test with even lower complexity (16)
+    print("\n  Test 3: Lower complexity (16)")
+    print(f"    Query: '{test_query}'")
+    start_time = time.time()
+    results = searcher.search(test_query, top_k=10, complexity=16)
+    search_time = time.time() - start_time
+    print(f"    ✓ Search completed in {search_time:.2f} seconds")
+    print(f"    Results: {len(results)} items")
+
+    # Test with minimal complexity (8)
+    print("\n  Test 4: Minimal complexity (8)")
+    print(f"    Query: '{test_query}'")
+    start_time = time.time()
+    results = searcher.search(test_query, top_k=10, complexity=8)
+    search_time = time.time() - start_time
+    print(f"    ✓ Search completed in {search_time:.2f} seconds")
+    print(f"    Results: {len(results)} items")
+
+    print("\n" + "=" * 80)
+    print("Performance Analysis:")
+    print("=" * 80)
+    print("\nKey Findings:")
+    print("1. beam_width parameter is mainly for DiskANN backend, not HNSW")
+    print("2. For HNSW, the main parameter affecting search speed is 'complexity'")
+    print("3. Lower complexity values (16-32) should provide faster search")
+    print("4. The paper mentions ~2 seconds, which likely uses:")
+    print("   - Smaller embedding model (~100M params vs 300M for bge-large)")
+    print("   - Lower complexity (16-32)")
+    print("   - Possibly DiskANN backend for better performance")
+    print("\nRecommendations:")
+    print("- Try complexity=16 or complexity=32 for faster search")
+    print("- Consider using DiskANN backend for better performance on large datasets")
+    print("- Or use a smaller embedding model if speed is critical")
+
+
+if __name__ == "__main__":
+    test_search_performance()

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

@@ -143,8 +143,6 @@ def create_hnsw_embedding_server(
             pass
         return str(nid)
 
-    # (legacy ZMQ thread removed; using shutdown-capable server only)
-
     def zmq_server_thread_with_shutdown(shutdown_event):
         """ZMQ server thread that respects shutdown signal.
 
@@ -158,225 +156,238 @@ def create_hnsw_embedding_server(
         rep_socket.bind(f"tcp://*:{zmq_port}")
         logger.info(f"HNSW ZMQ REP server listening on port {zmq_port}")
         rep_socket.setsockopt(zmq.RCVTIMEO, 1000)
-        # Keep sends from blocking during shutdown; fail fast and drop on close
         rep_socket.setsockopt(zmq.SNDTIMEO, 1000)
         rep_socket.setsockopt(zmq.LINGER, 0)
 
-        # Track last request type/length for shape-correct fallbacks
-        last_request_type = "unknown"  # 'text' | 'distance' | 'embedding' | 'unknown'
+        last_request_type = "unknown"
         last_request_length = 0
 
+        def _build_safe_fallback():
+            if last_request_type == "distance":
+                large_distance = 1e9
+                fallback_len = max(0, int(last_request_length))
+                return [[large_distance] * fallback_len]
+            if last_request_type == "embedding":
+                bsz = max(0, int(last_request_length))
+                dim = max(0, int(embedding_dim))
+                if dim > 0:
+                    return [[bsz, dim], [0.0] * (bsz * dim)]
+                return [[0, 0], []]
+            if last_request_type == "text":
+                return []
+            return [[0, int(embedding_dim) if embedding_dim > 0 else 0], []]
+
+        def _handle_text_embedding(request: list[str]) -> None:
+            nonlocal last_request_type, last_request_length
+
+            e2e_start = time.time()
+            last_request_type = "text"
+            last_request_length = len(request)
+            embeddings = compute_embeddings(
+                request,
+                model_name,
+                mode=embedding_mode,
+                provider_options=PROVIDER_OPTIONS,
+            )
+            rep_socket.send(msgpack.packb(embeddings.tolist()))
+            e2e_end = time.time()
+            logger.info(f"⏱️  Direct text embedding E2E time: {e2e_end - e2e_start:.6f}s")
+
+        def _handle_distance_request(request: list[Any]) -> None:
+            nonlocal last_request_type, last_request_length
+
+            e2e_start = time.time()
+            node_ids = request[0]
+            if len(node_ids) == 1 and isinstance(node_ids[0], list):
+                node_ids = node_ids[0]
+            query_vector = np.array(request[1], dtype=np.float32)
+            last_request_type = "distance"
+            last_request_length = len(node_ids)
+
+            logger.debug("Distance calculation request received")
+            logger.debug(f"    Node IDs: {node_ids}")
+            logger.debug(f"    Query vector dim: {len(query_vector)}")
+
+            texts: list[str] = []
+            found_indices: list[int] = []
+            for idx, nid in enumerate(node_ids):
+                try:
+                    passage_id = _map_node_id(nid)
+                    passage_data = passages.get_passage(passage_id)
+                    txt = passage_data.get("text", "")
+                    if isinstance(txt, str) and len(txt) > 0:
+                        texts.append(txt)
+                        found_indices.append(idx)
+                    else:
+                        logger.error(f"Empty text for passage ID {passage_id}")
+                except KeyError:
+                    logger.error(f"Passage ID {nid} not found")
+                except Exception as exc:
+                    logger.error(f"Exception looking up passage ID {nid}: {exc}")
+
+            large_distance = 1e9
+            response_distances = [large_distance] * len(node_ids)
+
+            if texts:
+                try:
+                    embeddings = compute_embeddings(
+                        texts,
+                        model_name,
+                        mode=embedding_mode,
+                        provider_options=PROVIDER_OPTIONS,
+                    )
+                    logger.info(
+                        f"Computed embeddings for {len(texts)} texts, shape: {embeddings.shape}"
+                    )
+                    if distance_metric == "l2":
+                        partial = np.sum(
+                            np.square(embeddings - query_vector.reshape(1, -1)), axis=1
+                        )
+                    else:
+                        partial = -np.dot(embeddings, query_vector)
+
+                    for pos, dval in zip(found_indices, partial.flatten().tolist()):
+                        response_distances[pos] = float(dval)
+                except Exception as exc:
+                    logger.error(f"Distance computation error, using sentinels: {exc}")
+
+            rep_socket.send(msgpack.packb([response_distances], use_single_float=True))
+            e2e_end = time.time()
+            logger.info(f"⏱️  Distance calculation E2E time: {e2e_end - e2e_start:.6f}s")
+
+        def _handle_embedding_by_id(request: Any) -> None:
+            nonlocal last_request_type, last_request_length
+
+            if isinstance(request, list) and len(request) == 1 and isinstance(request[0], list):
+                node_ids = request[0]
+            elif isinstance(request, list):
+                node_ids = request
+            else:
+                node_ids = []
+
+            e2e_start = time.time()
+            last_request_type = "embedding"
+            last_request_length = len(node_ids)
+            logger.info(f"ZMQ received {len(node_ids)} node IDs for embedding fetch")
+
+            if embedding_dim <= 0:
+                dims = [0, 0]
+                flat_data: list[float] = []
+            else:
+                dims = [len(node_ids), embedding_dim]
+                flat_data = [0.0] * (dims[0] * dims[1])
+
+            texts: list[str] = []
+            found_indices: list[int] = []
+            for idx, nid in enumerate(node_ids):
+                try:
+                    passage_id = _map_node_id(nid)
+                    passage_data = passages.get_passage(passage_id)
+                    txt = passage_data.get("text", "")
+                    if isinstance(txt, str) and len(txt) > 0:
+                        texts.append(txt)
+                        found_indices.append(idx)
+                    else:
+                        logger.error(f"Empty text for passage ID {passage_id}")
+                except KeyError:
+                    logger.error(f"Passage with ID {nid} not found")
+                except Exception as exc:
+                    logger.error(f"Exception looking up passage ID {nid}: {exc}")
+
+            if texts:
+                try:
+                    embeddings = compute_embeddings(
+                        texts,
+                        model_name,
+                        mode=embedding_mode,
+                        provider_options=PROVIDER_OPTIONS,
+                    )
+                    logger.info(
+                        f"Computed embeddings for {len(texts)} texts, shape: {embeddings.shape}"
+                    )
+
+                    if np.isnan(embeddings).any() or np.isinf(embeddings).any():
+                        logger.error(
+                            f"NaN or Inf detected in embeddings! Requested IDs: {node_ids[:5]}..."
+                        )
+                        dims = [0, embedding_dim]
+                        flat_data = []
+                    else:
+                        emb_f32 = np.ascontiguousarray(embeddings, dtype=np.float32)
+                        flat = emb_f32.flatten().tolist()
+                        for j, pos in enumerate(found_indices):
+                            start = pos * embedding_dim
+                            end = start + embedding_dim
+                            if end <= len(flat_data):
+                                flat_data[start:end] = flat[
+                                    j * embedding_dim : (j + 1) * embedding_dim
+                                ]
+                except Exception as exc:
+                    logger.error(f"Embedding computation error, returning zeros: {exc}")
+
+            response_payload = [dims, flat_data]
+            rep_socket.send(msgpack.packb(response_payload, use_single_float=True))
+            e2e_end = time.time()
+            logger.info(f"⏱️ Fallback Embed by Id E2E time: {e2e_end - e2e_start:.6f}s")
+
         try:
             while not shutdown_event.is_set():
                 try:
-                    e2e_start = time.time()
                     logger.debug("🔍 Waiting for ZMQ message...")
                     request_bytes = rep_socket.recv()
+                except zmq.Again:
+                    continue
 
-                    # Rest of the processing logic (same as original)
+                try:
                     request = msgpack.unpackb(request_bytes)
+                except Exception as exc:
+                    if shutdown_event.is_set():
+                        logger.info("Shutdown in progress, ignoring ZMQ error")
+                        break
+                    logger.error(f"Error unpacking ZMQ message: {exc}")
+                    try:
+                        safe = _build_safe_fallback()
+                        rep_socket.send(msgpack.packb(safe, use_single_float=True))
+                    except Exception:
+                        pass
+                    continue
 
-                    if len(request) == 1 and request[0] == "__QUERY_MODEL__":
-                        response_bytes = msgpack.packb([model_name])
-                        rep_socket.send(response_bytes)
-                        continue
-
-                    # Handle direct text embedding request
+                try:
+                    # Model query
                     if (
+                        isinstance(request, list)
+                        and len(request) == 1
+                        and request[0] == "__QUERY_MODEL__"
+                    ):
+                        rep_socket.send(msgpack.packb([model_name]))
+                    # Direct text embedding
+                    elif (
                         isinstance(request, list)
                         and request
                         and all(isinstance(item, str) for item in request)
                     ):
-                        last_request_type = "text"
-                        last_request_length = len(request)
-                        embeddings = compute_embeddings(
-                            request,
-                            model_name,
-                            mode=embedding_mode,
-                            provider_options=PROVIDER_OPTIONS,
-                        )
-                        rep_socket.send(msgpack.packb(embeddings.tolist()))
-                        e2e_end = time.time()
-                        logger.info(f"⏱️  Text embedding E2E time: {e2e_end - e2e_start:.6f}s")
-                        continue
-
-                    # Handle distance calculation request: [[ids], [query_vector]]
-                    if (
+                        _handle_text_embedding(request)
+                    # Distance calculation: [[ids], [query_vector]]
+                    elif (
                         isinstance(request, list)
                         and len(request) == 2
                         and isinstance(request[0], list)
                         and isinstance(request[1], list)
                     ):
-                        node_ids = request[0]
-                        # Handle nested [[ids]] shape defensively
-                        if len(node_ids) == 1 and isinstance(node_ids[0], list):
-                            node_ids = node_ids[0]
-                        query_vector = np.array(request[1], dtype=np.float32)
-                        last_request_type = "distance"
-                        last_request_length = len(node_ids)
-
-                        logger.debug("Distance calculation request received")
-                        logger.debug(f"    Node IDs: {node_ids}")
-                        logger.debug(f"    Query vector dim: {len(query_vector)}")
-
-                        # Gather texts for found ids
-                        texts: list[str] = []
-                        found_indices: list[int] = []
-                        for idx, nid in enumerate(node_ids):
-                            try:
-                                passage_id = _map_node_id(nid)
-                                passage_data = passages.get_passage(passage_id)
-                                txt = passage_data.get("text", "")
-                                if isinstance(txt, str) and len(txt) > 0:
-                                    texts.append(txt)
-                                    found_indices.append(idx)
-                                else:
-                                    logger.error(f"Empty text for passage ID {passage_id}")
-                            except KeyError:
-                                logger.error(f"Passage ID {nid} not found")
-                            except Exception as e:
-                                logger.error(f"Exception looking up passage ID {nid}: {e}")
-
-                        # Prepare full-length response with large sentinel values
-                        large_distance = 1e9
-                        response_distances = [large_distance] * len(node_ids)
-
-                        if texts:
-                            try:
-                                embeddings = compute_embeddings(
-                                    texts,
-                                    model_name,
-                                    mode=embedding_mode,
-                                    provider_options=PROVIDER_OPTIONS,
-                                )
-                                logger.info(
-                                    f"Computed embeddings for {len(texts)} texts, shape: {embeddings.shape}"
-                                )
-                                if distance_metric == "l2":
-                                    partial = np.sum(
-                                        np.square(embeddings - query_vector.reshape(1, -1)), axis=1
-                                    )
-                                else:  # mips or cosine
-                                    partial = -np.dot(embeddings, query_vector)
-
-                                for pos, dval in zip(found_indices, partial.flatten().tolist()):
-                                    response_distances[pos] = float(dval)
-                            except Exception as e:
-                                logger.error(f"Distance computation error, using sentinels: {e}")
-
-                        # Send response in expected shape [[distances]]
-                        rep_socket.send(msgpack.packb([response_distances], use_single_float=True))
-                        e2e_end = time.time()
-                        logger.info(f"⏱️  Distance calculation E2E time: {e2e_end - e2e_start:.6f}s")
-                        continue
-
-                    # Fallback: treat as embedding-by-id request
-                    if (
-                        isinstance(request, list)
-                        and len(request) == 1
-                        and isinstance(request[0], list)
-                    ):
-                        node_ids = request[0]
-                    elif isinstance(request, list):
-                        node_ids = request
-                    else:
-                        node_ids = []
-                    last_request_type = "embedding"
-                    last_request_length = len(node_ids)
-                    logger.info(f"ZMQ received {len(node_ids)} node IDs for embedding fetch")
-
-                    # Preallocate zero-filled flat data for robustness
-                    if embedding_dim <= 0:
-                        dims = [0, 0]
-                        flat_data: list[float] = []
-                    else:
-                        dims = [len(node_ids), embedding_dim]
-                        flat_data = [0.0] * (dims[0] * dims[1])
-
-                    # Collect texts for found ids
-                    texts: list[str] = []
-                    found_indices: list[int] = []
-                    for idx, nid in enumerate(node_ids):
-                        try:
-                            passage_id = _map_node_id(nid)
-                            passage_data = passages.get_passage(passage_id)
-                            txt = passage_data.get("text", "")
-                            if isinstance(txt, str) and len(txt) > 0:
-                                texts.append(txt)
-                                found_indices.append(idx)
-                            else:
-                                logger.error(f"Empty text for passage ID {passage_id}")
-                        except KeyError:
-                            logger.error(f"Passage with ID {nid} not found")
-                        except Exception as e:
-                            logger.error(f"Exception looking up passage ID {nid}: {e}")
-
-                    if texts:
-                        try:
-                            embeddings = compute_embeddings(
-                                texts,
-                                model_name,
-                                mode=embedding_mode,
-                                provider_options=PROVIDER_OPTIONS,
-                            )
-                            logger.info(
-                                f"Computed embeddings for {len(texts)} texts, shape: {embeddings.shape}"
-                            )
-
-                            if np.isnan(embeddings).any() or np.isinf(embeddings).any():
-                                logger.error(
-                                    f"NaN or Inf detected in embeddings! Requested IDs: {node_ids[:5]}..."
-                                )
-                                dims = [0, embedding_dim]
-                                flat_data = []
-                            else:
-                                emb_f32 = np.ascontiguousarray(embeddings, dtype=np.float32)
-                                flat = emb_f32.flatten().tolist()
-                                for j, pos in enumerate(found_indices):
-                                    start = pos * embedding_dim
-                                    end = start + embedding_dim
-                                    if end <= len(flat_data):
-                                        flat_data[start:end] = flat[
-                                            j * embedding_dim : (j + 1) * embedding_dim
-                                        ]
-                        except Exception as e:
-                            logger.error(f"Embedding computation error, returning zeros: {e}")
-
-                    response_payload = [dims, flat_data]
-                    response_bytes = msgpack.packb(response_payload, use_single_float=True)
-
-                    rep_socket.send(response_bytes)
-                    e2e_end = time.time()
-                    logger.info(f"⏱️  ZMQ E2E time: {e2e_end - e2e_start:.6f}s")
-
-                except zmq.Again:
-                    # Timeout - check shutdown_event and continue
-                    continue
-                except Exception as e:
-                    if not shutdown_event.is_set():
-                        logger.error(f"Error in ZMQ server loop: {e}")
-                        # Shape-correct fallback
-                        try:
-                            if last_request_type == "distance":
-                                large_distance = 1e9
-                                fallback_len = max(0, int(last_request_length))
-                                safe = [[large_distance] * fallback_len]
-                            elif last_request_type == "embedding":
-                                bsz = max(0, int(last_request_length))
-                                dim = max(0, int(embedding_dim))
-                                safe = (
-                                    [[bsz, dim], [0.0] * (bsz * dim)] if dim > 0 else [[0, 0], []]
-                                )
-                            elif last_request_type == "text":
-                                safe = []  # direct text embeddings expectation is a flat list
-                            else:
-                                safe = [[0, int(embedding_dim) if embedding_dim > 0 else 0], []]
-                            rep_socket.send(msgpack.packb(safe, use_single_float=True))
-                        except Exception:
-                            pass
+                        _handle_distance_request(request)
+                    # Embedding-by-id fallback
                     else:
+                        _handle_embedding_by_id(request)
+                except Exception as exc:
+                    if shutdown_event.is_set():
                         logger.info("Shutdown in progress, ignoring ZMQ error")
                         break
+                    logger.error(f"Error in ZMQ server loop: {exc}")
+                    try:
+                        safe = _build_safe_fallback()
+                        rep_socket.send(msgpack.packb(safe, use_single_float=True))
+                    except Exception:
+                        pass
         finally:
             try:
                 rep_socket.close(0)

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

@@ -864,7 +864,13 @@ class LeannBuilder:
 
 
 class LeannSearcher:
-    def __init__(self, index_path: str, enable_warmup: bool = False, **backend_kwargs):
+    def __init__(
+        self,
+        index_path: str,
+        enable_warmup: bool = True,
+        recompute_embeddings: bool = True,
+        **backend_kwargs,
+    ):
         # Fix path resolution for Colab and other environments
         if not Path(index_path).is_absolute():
             index_path = str(Path(index_path).resolve())
@@ -895,14 +901,32 @@ class LeannSearcher:
         backend_factory = BACKEND_REGISTRY.get(backend_name)
         if backend_factory is None:
             raise ValueError(f"Backend '{backend_name}' not found.")
+
+        # Global recompute flag for this searcher (explicit knob, default True)
+        self.recompute_embeddings: bool = bool(recompute_embeddings)
+
+        # Warmup flag: keep using the existing enable_warmup parameter,
+        # but default it to True so cold-start happens earlier.
+        self._warmup: bool = bool(enable_warmup)
+
         final_kwargs = {**self.meta_data.get("backend_kwargs", {}), **backend_kwargs}
-        final_kwargs["enable_warmup"] = enable_warmup
+        final_kwargs["enable_warmup"] = self._warmup
         if self.embedding_options:
             final_kwargs.setdefault("embedding_options", self.embedding_options)
         self.backend_impl: LeannBackendSearcherInterface = backend_factory.searcher(
             index_path, **final_kwargs
         )
 
+        # Optional one-shot warmup at construction time to hide cold-start latency.
+        if self._warmup:
+            try:
+                _ = self.backend_impl.compute_query_embedding(
+                    "__LEANN_WARMUP__",
+                    use_server_if_available=self.recompute_embeddings,
+                )
+            except Exception as exc:
+                logger.warning(f"Warmup embedding failed (ignored): {exc}")
+
     def search(
         self,
         query: str,
@@ -910,7 +934,7 @@ class LeannSearcher:
         complexity: int = 64,
         beam_width: int = 1,
         prune_ratio: float = 0.0,
-        recompute_embeddings: bool = True,
+        recompute_embeddings: Optional[bool] = None,
         pruning_strategy: Literal["global", "local", "proportional"] = "global",
         expected_zmq_port: int = 5557,
         metadata_filters: Optional[dict[str, dict[str, Union[str, int, float, bool, list]]]] = None,
@@ -927,7 +951,8 @@ class LeannSearcher:
             complexity: Search complexity/candidate list size, higher = more accurate but slower
             beam_width: Number of parallel search paths/IO requests per iteration
             prune_ratio: Ratio of neighbors to prune via approximate distance (0.0-1.0)
-            recompute_embeddings: Whether to fetch fresh embeddings from server vs use stored codes
+            recompute_embeddings: (Deprecated) Per-call override for recompute mode.
+                Configure this at LeannSearcher(..., recompute_embeddings=...) instead.
             pruning_strategy: Candidate selection strategy - "global" (default), "local", or "proportional"
             expected_zmq_port: ZMQ port for embedding server communication
             metadata_filters: Optional filters to apply to search results based on metadata.
@@ -966,8 +991,19 @@ class LeannSearcher:
 
         zmq_port = None
 
+        # Resolve effective recompute flag for this search.
+        if recompute_embeddings is not None:
+            logger.warning(
+                "LeannSearcher.search(..., recompute_embeddings=...) is deprecated and "
+                "will be removed in a future version. Configure recompute at "
+                "LeannSearcher(..., recompute_embeddings=...) instead."
+            )
+            effective_recompute = bool(recompute_embeddings)
+        else:
+            effective_recompute = self.recompute_embeddings
+
         start_time = time.time()
-        if recompute_embeddings:
+        if effective_recompute:
             zmq_port = self.backend_impl._ensure_server_running(
                 self.meta_path_str,
                 port=expected_zmq_port,
@@ -981,7 +1017,7 @@ class LeannSearcher:
 
         query_embedding = self.backend_impl.compute_query_embedding(
             query,
-            use_server_if_available=recompute_embeddings,
+            use_server_if_available=effective_recompute,
             zmq_port=zmq_port,
         )
         logger.info(f"  Generated embedding shape: {query_embedding.shape}")
@@ -993,7 +1029,7 @@ class LeannSearcher:
             "complexity": complexity,
             "beam_width": beam_width,
             "prune_ratio": prune_ratio,
-            "recompute_embeddings": recompute_embeddings,
+            "recompute_embeddings": effective_recompute,
             "pruning_strategy": pruning_strategy,
             "zmq_port": zmq_port,
         }

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

@@ -5,12 +5,15 @@ Packaged within leann-core so installed wheels can import it reliably.
 
 import logging
 from pathlib import Path
-from typing import Optional
+from typing import Any, 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:
     """
@@ -174,37 +177,44 @@ def create_ast_chunks(
     max_chunk_size: int = 512,
     chunk_overlap: int = 64,
     metadata_template: str = "default",
-) -> list[str]:
+) -> list[dict[str, Any]]:
     """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 create_traditional_chunks(documents, max_chunk_size, chunk_overlap)
+        return _traditional_chunks_as_dicts(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(create_traditional_chunks([doc], max_chunk_size, chunk_overlap))
+            all_chunks.extend(_traditional_chunks_as_dicts([doc], max_chunk_size, chunk_overlap))
             continue
 
         try:
-            # Warn if AST chunk size + overlap might exceed common token limits
+            # Warn once if AST chunk size + overlap might exceed common token limits
+            # Note: Actual truncation happens at embedding time with dynamic model limits
+            global _ast_token_warning_shown
             estimated_max_tokens = int(
                 (max_chunk_size + chunk_overlap) * 1.2
             )  # Conservative estimate
-            if estimated_max_tokens > 512:
+            if estimated_max_tokens > 512 and not _ast_token_warning_shown:
                 logger.warning(
                     f"AST chunk size ({max_chunk_size}) + overlap ({chunk_overlap}) = {max_chunk_size + chunk_overlap} chars "
                     f"may exceed 512 token limit (~{estimated_max_tokens} tokens estimated). "
-                    f"Consider reducing --ast-chunk-size to {int(400 / 1.2)} or --ast-chunk-overlap to {int(50 / 1.2)}"
+                    f"Consider reducing --ast-chunk-size to {int(400 / 1.2)} or --ast-chunk-overlap to {int(50 / 1.2)}. "
+                    f"Note: Chunks will be auto-truncated at embedding time based on your model's actual token limit."
                 )
+                _ast_token_warning_shown = True
 
             configs = {
                 "max_chunk_size": max_chunk_size,
@@ -229,17 +239,40 @@ 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():
-                    all_chunks.append(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})
 
             logger.info(
                 f"Created {len(chunks)} AST chunks from {language} file: {doc.metadata.get('file_name', 'unknown')}"
@@ -247,15 +280,19 @@ 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(create_traditional_chunks([doc], max_chunk_size, chunk_overlap))
+            all_chunks.extend(_traditional_chunks_as_dicts([doc], max_chunk_size, chunk_overlap))
 
     return all_chunks
 
 
 def create_traditional_chunks(
     documents, chunk_size: int = 256, chunk_overlap: int = 128
-) -> list[str]:
-    """Create traditional text chunks using LlamaIndex SentenceSplitter."""
+) -> list[dict[str, Any]]:
+    """Create traditional text chunks using LlamaIndex SentenceSplitter.
+
+    Returns:
+        List of dicts with {"text": str, "metadata": dict}
+    """
     if chunk_size <= 0:
         logger.warning(f"Invalid chunk_size={chunk_size}, using default value of 256")
         chunk_size = 256
@@ -271,19 +308,40 @@ def create_traditional_chunks(
         paragraph_separator="\n\n",
     )
 
-    all_texts = []
+    result = []
     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:
-                all_texts.extend(node.get_content() for node in nodes)
+                for node in nodes:
+                    result.append({"text": node.get_content(), "metadata": doc_metadata})
         except Exception as e:
             logger.error(f"Traditional chunking failed for document: {e}")
             content = doc.get_content()
             if content and content.strip():
-                all_texts.append(content.strip())
+                result.append({"text": content.strip(), "metadata": doc_metadata})
 
-    return all_texts
+    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)
 
 
 def create_text_chunks(
@@ -295,8 +353,12 @@ def create_text_chunks(
     ast_chunk_overlap: int = 64,
     code_file_extensions: Optional[list[str]] = None,
     ast_fallback_traditional: bool = True,
-) -> list[str]:
-    """Create text chunks from documents with optional AST support for code files."""
+) -> 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}
+    """
     if not documents:
         logger.warning("No documents provided for chunking")
         return []
@@ -331,24 +393,17 @@ def create_text_chunks(
                 logger.error(f"AST chunking failed: {e}")
                 if ast_fallback_traditional:
                     all_chunks.extend(
-                        create_traditional_chunks(code_docs, chunk_size, chunk_overlap)
+                        _traditional_chunks_as_dicts(code_docs, chunk_size, chunk_overlap)
                     )
                 else:
                     raise
         if text_docs:
-            all_chunks.extend(create_traditional_chunks(text_docs, chunk_size, chunk_overlap))
+            all_chunks.extend(_traditional_chunks_as_dicts(text_docs, chunk_size, chunk_overlap))
     else:
-        all_chunks = create_traditional_chunks(documents, chunk_size, chunk_overlap)
+        all_chunks = _traditional_chunks_as_dicts(documents, chunk_size, chunk_overlap)
 
     logger.info(f"Total chunks created: {len(all_chunks)}")
 
-    # Validate chunk token limits (default to 512 for safety)
-    # This provides a safety net for embedding models with token limits
-    validated_chunks, num_truncated = validate_chunk_token_limits(all_chunks, max_tokens=512)
-
-    if num_truncated > 0:
-        logger.info(
-            f"Post-chunking validation: {num_truncated} chunks were truncated to fit 512 token limit"
-        )
-
-    return validated_chunks
+    # Note: Token truncation is now handled at embedding time with dynamic model limits
+    # See get_model_token_limit() and truncate_to_token_limit() in embedding_compute.py
+    return all_chunks

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

@@ -1279,13 +1279,8 @@ Examples:
                     ast_fallback_traditional=getattr(args, "ast_fallback_traditional", True),
                 )
 
-                # Note: AST chunking currently returns plain text chunks without metadata
-                # We preserve basic file info by associating chunks with their source documents
-                # For better metadata preservation, documents list order should be maintained
-                for chunk_text in chunk_texts:
-                    # TODO: Enhance create_text_chunks to return metadata alongside text
-                    # For now, we store chunks with empty metadata
-                    all_texts.append({"text": chunk_text, "metadata": {}})
+                # create_text_chunks now returns list[dict] with metadata preserved
+                all_texts.extend(chunk_texts)
 
             except ImportError as e:
                 print(

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

@@ -10,72 +10,63 @@ 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
 
+# 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)
 
-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
+# Token limit registry for embedding models
+# Used as fallback when dynamic discovery fails (e.g., LM Studio, OpenAI)
+# Ollama models use dynamic discovery via /api/show
+EMBEDDING_MODEL_LIMITS = {
+    # Nomic models (common across servers)
+    "nomic-embed-text": 2048,  # Corrected from 512 - verified via /api/show
+    "nomic-embed-text-v1.5": 2048,
+    "nomic-embed-text-v2": 512,
+    # Other embedding models
+    "mxbai-embed-large": 512,
+    "all-minilm": 512,
+    "bge-m3": 8192,
+    "snowflake-arctic-embed": 512,
+    # OpenAI models
+    "text-embedding-3-small": 8192,
+    "text-embedding-3-large": 8192,
+    "text-embedding-ada-002": 8192,
+}
 
 
-def get_model_token_limit(model_name: 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.
 
     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, defaults to 512 if unknown
+        Token limit for the model in tokens
     """
+    # 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:
+                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]
 
@@ -92,31 +83,111 @@ def get_model_token_limit(model_name: str) -> int:
         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
+    # Default fallback
+    logger.warning(f"Unknown model '{model_name}', using default {default} token limit")
+    return default
 
 
-# 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)
+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
+
 
 # Global model cache to avoid repeated loading
 _model_cache: dict[str, Any] = {}
 
-# Known embedding model token limits
-EMBEDDING_MODEL_LIMITS = {
-    "nomic-embed-text": 512,
-    "nomic-embed-text-v2": 512,
-    "mxbai-embed-large": 512,
-    "all-minilm": 512,
-    "bge-m3": 8192,
-    "snowflake-arctic-embed": 512,
-    # Add more models as needed
-}
-
 
 def compute_embeddings(
     texts: list[str],
@@ -144,9 +215,14 @@ def compute_embeddings(
         Normalized embeddings array, shape: (len(texts), embedding_dim)
     """
     provider_options = provider_options or {}
+    wrapper_start_time = time.time()
+    logger.debug(
+        f"[compute_embeddings] entry: mode={mode}, model='{model_name}', text_count={len(texts)}"
+    )
 
     if mode == "sentence-transformers":
-        return compute_embeddings_sentence_transformers(
+        inner_start_time = time.time()
+        result = compute_embeddings_sentence_transformers(
             texts,
             model_name,
             is_build=is_build,
@@ -155,6 +231,14 @@ def compute_embeddings(
             manual_tokenize=manual_tokenize,
             max_length=max_length,
         )
+        inner_end_time = time.time()
+        wrapper_end_time = time.time()
+        logger.debug(
+            "[compute_embeddings] sentence-transformers timings: "
+            f"inner={inner_end_time - inner_start_time:.6f}s, "
+            f"wrapper_total={wrapper_end_time - wrapper_start_time:.6f}s"
+        )
+        return result
     elif mode == "openai":
         return compute_embeddings_openai(
             texts,
@@ -200,6 +284,7 @@ def compute_embeddings_sentence_transformers(
         is_build: Whether this is a build operation (shows progress bar)
         adaptive_optimization: Whether to use adaptive optimization based on batch size
     """
+    outer_start_time = time.time()
     # Handle empty input
     if not texts:
         raise ValueError("Cannot compute embeddings for empty text list")
@@ -230,7 +315,14 @@ def compute_embeddings_sentence_transformers(
     # Create cache key
     cache_key = f"sentence_transformers_{model_name}_{device}_{use_fp16}_optimized"
 
+    pre_model_init_end_time = time.time()
+    logger.debug(
+        "compute_embeddings_sentence_transformers pre-model-init time "
+        f"(device/batch selection etc.): {pre_model_init_end_time - outer_start_time:.6f}s"
+    )
+
     # Check if model is already cached
+    start_time = time.time()
     if cache_key in _model_cache:
         logger.info(f"Using cached optimized model: {model_name}")
         model = _model_cache[cache_key]
@@ -370,10 +462,13 @@ def compute_embeddings_sentence_transformers(
         _model_cache[cache_key] = model
         logger.info(f"Model cached: {cache_key}")
 
-    # Compute embeddings with optimized inference mode
-    logger.info(
-        f"Starting embedding computation... (batch_size: {batch_size}, manual_tokenize={manual_tokenize})"
-    )
+        end_time = time.time()
+
+        # Compute embeddings with optimized inference mode
+        logger.info(
+            f"Starting embedding computation... (batch_size: {batch_size}, manual_tokenize={manual_tokenize})"
+        )
+        logger.info(f"start sentence transformers {model} takes {end_time - start_time}")
 
     start_time = time.time()
     if not manual_tokenize:
@@ -394,32 +489,46 @@ def compute_embeddings_sentence_transformers(
         except Exception:
             pass
     else:
-        # Manual tokenization + forward pass using HF AutoTokenizer/AutoModel
+        # Manual tokenization + forward pass using HF AutoTokenizer/AutoModel.
+        # This path is reserved for an aggressively optimized FP pipeline
+        # (no quantization), mainly for experimentation.
         try:
             from transformers import AutoModel, AutoTokenizer  # type: ignore
         except Exception as e:
             raise ImportError(f"transformers is required for manual_tokenize=True: {e}")
 
-        # Cache tokenizer and model
         tok_cache_key = f"hf_tokenizer_{model_name}"
-        mdl_cache_key = f"hf_model_{model_name}_{device}_{use_fp16}"
+        mdl_cache_key = f"hf_model_{model_name}_{device}_{use_fp16}_fp"
+
         if tok_cache_key in _model_cache and mdl_cache_key in _model_cache:
             hf_tokenizer = _model_cache[tok_cache_key]
             hf_model = _model_cache[mdl_cache_key]
-            logger.info("Using cached HF tokenizer/model for manual path")
+            logger.info("Using cached HF tokenizer/model for manual FP path")
         else:
-            logger.info("Loading HF tokenizer/model for manual tokenization path")
+            logger.info("Loading HF tokenizer/model for manual FP path")
             hf_tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+
             torch_dtype = torch.float16 if (use_fp16 and device == "cuda") else torch.float32
-            hf_model = AutoModel.from_pretrained(model_name, torch_dtype=torch_dtype)
+            hf_model = AutoModel.from_pretrained(
+                model_name,
+                torch_dtype=torch_dtype,
+            )
             hf_model.to(device)
+
             hf_model.eval()
             # Optional compile on supported devices
             if device in ["cuda", "mps"]:
                 try:
-                    hf_model = torch.compile(hf_model, mode="reduce-overhead", dynamic=True)  # type: ignore
-                except Exception:
-                    pass
+                    hf_model = torch.compile(  # type: ignore
+                        hf_model, mode="reduce-overhead", dynamic=True
+                    )
+                    logger.info(
+                        f"Applied torch.compile to HF model for {model_name} "
+                        f"(device={device}, dtype={torch_dtype})"
+                    )
+                except Exception as exc:
+                    logger.warning(f"torch.compile optimization failed: {exc}")
+
             _model_cache[tok_cache_key] = hf_tokenizer
             _model_cache[mdl_cache_key] = hf_model
 
@@ -445,7 +554,6 @@ def compute_embeddings_sentence_transformers(
             for start_index in batch_iter:
                 end_index = min(start_index + batch_size, len(texts))
                 batch_texts = texts[start_index:end_index]
-                tokenize_start_time = time.time()
                 inputs = hf_tokenizer(
                     batch_texts,
                     padding=True,
@@ -453,34 +561,17 @@ def compute_embeddings_sentence_transformers(
                     max_length=max_length,
                     return_tensors="pt",
                 )
-                tokenize_end_time = time.time()
-                logger.info(
-                    f"Tokenize time taken: {tokenize_end_time - tokenize_start_time} seconds"
-                )
-                # Print shapes of all input tensors for debugging
-                for k, v in inputs.items():
-                    print(f"inputs[{k!r}] shape: {getattr(v, 'shape', type(v))}")
-                to_device_start_time = time.time()
                 inputs = {k: v.to(device) for k, v in inputs.items()}
-                to_device_end_time = time.time()
-                logger.info(
-                    f"To device time taken: {to_device_end_time - to_device_start_time} seconds"
-                )
-                forward_start_time = time.time()
                 outputs = hf_model(**inputs)
-                forward_end_time = time.time()
-                logger.info(f"Forward time taken: {forward_end_time - forward_start_time} seconds")
                 last_hidden_state = outputs.last_hidden_state  # (B, L, H)
                 attention_mask = inputs.get("attention_mask")
                 if attention_mask is None:
-                    # Fallback: assume all tokens are valid
                     pooled = last_hidden_state.mean(dim=1)
                 else:
                     mask = attention_mask.unsqueeze(-1).to(last_hidden_state.dtype)
                     masked = last_hidden_state * mask
                     lengths = mask.sum(dim=1).clamp(min=1)
                     pooled = masked.sum(dim=1) / lengths
-                # Move to CPU float32
                 batch_embeddings = pooled.detach().to("cpu").float().numpy()
                 all_embeddings.append(batch_embeddings)
 
@@ -500,6 +591,12 @@ def compute_embeddings_sentence_transformers(
     if np.isnan(embeddings).any() or np.isinf(embeddings).any():
         raise RuntimeError(f"Detected NaN or Inf values in embeddings, model: {model_name}")
 
+    outer_end_time = time.time()
+    logger.debug(
+        "compute_embeddings_sentence_transformers total time "
+        f"(function entry -> return): {outer_end_time - outer_start_time:.6f}s"
+    )
+
     return embeddings
 
 
@@ -814,15 +911,13 @@ def compute_embeddings_ollama(
 
     logger.info(f"Using batch size: {batch_size} for true batch processing")
 
-    # Get model token limit and apply truncation
-    token_limit = get_model_token_limit(model_name)
+    # Get model token limit and apply truncation before batching
+    token_limit = get_model_token_limit(model_name, base_url=resolved_host)
     logger.info(f"Model '{model_name}' token limit: {token_limit}")
 
-    # Apply 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
+    # Apply truncation to all texts before batch processing
+    # Function logs truncation details internally
+    texts = truncate_to_token_limit(texts, token_limit)
 
     def get_batch_embeddings(batch_texts):
         """Get embeddings for a batch of texts using /api/embed endpoint."""
@@ -880,12 +975,12 @@ def compute_embeddings_ollama(
 
         return None, list(range(len(batch_texts)))
 
-    # Process truncated texts in batches
+    # Process texts in batches
     all_embeddings = []
     all_failed_indices = []
 
     # Setup progress bar if needed
-    show_progress = is_build or len(truncated_texts) > 10
+    show_progress = is_build or len(texts) > 10
     try:
         if show_progress:
             from tqdm import tqdm
@@ -893,7 +988,7 @@ def compute_embeddings_ollama(
         show_progress = False
 
     # Process batches
-    num_batches = (len(truncated_texts) + batch_size - 1) // batch_size
+    num_batches = (len(texts) + batch_size - 1) // batch_size
 
     if show_progress:
         batch_iterator = tqdm(range(num_batches), desc="Computing Ollama embeddings (batched)")
@@ -902,8 +997,8 @@ def compute_embeddings_ollama(
 
     for batch_idx in batch_iterator:
         start_idx = batch_idx * batch_size
-        end_idx = min(start_idx + batch_size, len(truncated_texts))
-        batch_texts = truncated_texts[start_idx:end_idx]
+        end_idx = min(start_idx + batch_size, len(texts))
+        batch_texts = texts[start_idx:end_idx]
 
         batch_embeddings, batch_failed = get_batch_embeddings(batch_texts)
 
@@ -918,11 +1013,11 @@ def compute_embeddings_ollama(
 
     # Handle failed embeddings
     if all_failed_indices:
-        if len(all_failed_indices) == len(truncated_texts):
+        if len(all_failed_indices) == len(texts):
             raise RuntimeError("Failed to compute any embeddings")
 
         logger.warning(
-            f"Failed to compute embeddings for {len(all_failed_indices)}/{len(truncated_texts)} texts"
+            f"Failed to compute embeddings for {len(all_failed_indices)}/{len(texts)} texts"
         )
 
         # Use zero embeddings as fallback for failed ones

pyproject.toml

@@ -57,6 +57,8 @@ dependencies = [
     "tree-sitter-c-sharp>=0.20.0",
     "tree-sitter-typescript>=0.20.0",
     "torchvision>=0.23.0",
+    "einops",
+    "seaborn",
 ]
 
 [project.optional-dependencies]

tests/test_astchunk_integration.py

@@ -8,7 +8,7 @@ import subprocess
 import sys
 import tempfile
 from pathlib import Path
-from unittest.mock import patch
+from unittest.mock import Mock, patch
 
 import pytest
 
@@ -116,8 +116,10 @@ class TestChunkingFunctions:
         chunks = create_traditional_chunks(docs, chunk_size=50, chunk_overlap=10)
 
         assert len(chunks) > 0
-        assert all(isinstance(chunk, str) for chunk in chunks)
-        assert all(len(chunk.strip()) > 0 for chunk in chunks)
+        # 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)
 
     def test_create_traditional_chunks_empty_docs(self):
         """Test traditional chunking with empty documents."""
@@ -158,11 +160,22 @@ class Calculator:
 
             # Should have multiple chunks due to different functions/classes
             assert len(chunks) > 0
-            assert all(isinstance(chunk, str) for chunk in chunks)
-            assert all(len(chunk.strip()) > 0 for chunk in chunks)
+            # 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"
+            )
 
             # Check that code structure is somewhat preserved
-            combined_content = " ".join(chunks)
+            combined_content = " ".join([c["text"] for c in chunks])
             assert "def hello_world" in combined_content
             assert "class Calculator" in combined_content
 
@@ -194,7 +207,11 @@ class Calculator:
         chunks = create_text_chunks(docs, use_ast_chunking=False, chunk_size=50, chunk_overlap=10)
 
         assert len(chunks) > 0
-        assert all(isinstance(chunk, str) for chunk in chunks)
+        # 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"
+        )
 
     def test_create_text_chunks_ast_mode(self):
         """Test text chunking in AST mode."""
@@ -213,7 +230,11 @@ class Calculator:
         )
 
         assert len(chunks) > 0
-        assert all(isinstance(chunk, str) for chunk in chunks)
+        # 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"
+        )
 
     def test_create_text_chunks_custom_extensions(self):
         """Test text chunking with custom code file extensions."""
@@ -353,6 +374,552 @@ 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_token_truncation.py

@@ -0,0 +1,268 @@
+"""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}"
+        )