[EXP] Update the benchmark code (#71)

* chore(hnsw): reorder imports to satisfy ruff I001

* chore: sync changes; fix Ruff import order; update examples, benchmarks, and dependencies

- Fix import order in packages/leann-backend-hnsw/leann_backend_hnsw/hnsw_backend.py (Ruff I001)

- Update benchmarks/run_evaluation.py

- Update apps/base_rag_example.py and leann-core API usage

- Add benchmarks/data/README.md

- Update uv.lock

- Misc cleanup

- Note: added paru-bin as an embedded git repo; consider making it a submodule (git rm --cached paru-bin) if unintended

* chore: remove unintended embedded repo paru-bin and ignore it

Fix CI: avoid missing .gitmodules entry by removing gitlink and adding to .gitignore.

* ci: retrigger after removing unintended gitlink (paru-bin)

* feat(benchmarks): add --batch-size option and plumb through to HNSW search (default 0)

* feat(hnsw): add batch_size to LeannSearcher.search and LeannChat.ask; forward only for HNSW backend

* chore(logging): surface recompute and batching params; enable INFO logging in benchmark

* feat(embeddings): add optional manual tokenization path (HF tokenizer+model) with mean pooling; default remains SentenceTransformer.encode

* fix micro bench and fix pre commit

* update readme

---------

Co-authored-by: yichuan-w <yichuan-w@users.noreply.github.com>

.gitignore

@@ -93,5 +93,6 @@ packages/leann-backend-diskann/third_party/DiskANN/_deps/
 batchtest.py
 tests/__pytest_cache__/
 tests/__pycache__/
+paru-bin/
 
 benchmarks/data/

.pre-commit-config.yaml

@@ -13,4 +13,5 @@ repos:
     rev: v0.12.7  # Fixed version to match pyproject.toml
     hooks:
       - id: ruff
+        args: [--fix, --exit-non-zero-on-fix]
       - id: ruff-format

README.md

@@ -176,8 +176,7 @@ response = chat.ask("How much storage does LEANN save?", top_k=1)
 
 LEANN supports RAG on various data sources including documents (`.pdf`, `.txt`, `.md`), Apple Mail, Google Search History, WeChat, and more.
 
-**AST-Aware Code Chunking** - LEANN also features intelligent code chunking that preserves semantic boundaries (functions, classes, methods) for Python, Java, C#, and TypeScript files, providing improved code understanding compared to traditional text-based approaches.
-📖 Read the [AST Chunking Guide →](docs/ast_chunking_guide.md) to learn more.
+
 
 ### Generation Model Setup
 
@@ -477,6 +476,15 @@ Once the index is built, you can ask questions like:
 
 ### 🚀 Claude Code Integration: Transform Your Development Workflow!
 
+<details>
+<summary><strong>NEW!! AST‑Aware Code Chunking</strong></summary>
+
+LEANN features intelligent code chunking that preserves semantic boundaries (functions, classes, methods) for Python, Java, C#, and TypeScript, improving code understanding compared to text-based chunking.
+
+📖 Read the [AST Chunking Guide →](docs/ast_chunking_guide.md)
+
+</details>
+
 **The future of code assistance is here.** Transform your development workflow with LEANN's native MCP integration for Claude Code. Index your entire codebase and get intelligent code assistance directly in your IDE.
 
 **Key features:**
@@ -697,6 +705,9 @@ MIT License - see [LICENSE](LICENSE) for details.
 
 Core Contributors: [Yichuan Wang](https://yichuan-w.github.io/) & [Zhifei Li](https://github.com/andylizf).
 
+Active Contributors: [Gabriel Dehan](https://github.com/gabriel-dehan)
+
+
 We welcome more contributors! Feel free to open issues or submit PRs.
 
 This work is done at [**Berkeley Sky Computing Lab**](https://sky.cs.berkeley.edu/).

apps/base_rag_example.py

@@ -299,7 +299,6 @@ class BaseRAGExample(ABC):
         chat = LeannChat(
             index_path,
             llm_config=self.get_llm_config(args),
-            system_prompt=f"You are a helpful assistant that answers questions about {self.name} data.",
             complexity=args.search_complexity,
         )
 

benchmarks/data/README.md

@@ -0,0 +1,44 @@
+---
+license: mit
+---
+
+# LEANN-RAG Evaluation Data
+
+This repository contains the necessary data to run the recall evaluation scripts for the [LEANN-RAG](https://huggingface.co/LEANN-RAG) project.
+
+## Dataset Components
+
+This dataset is structured into three main parts:
+
+1.  **Pre-built LEANN Indices**:
+    *   `dpr/`: A pre-built index for the DPR dataset.
+    *   `rpj_wiki/`: A pre-built index for the RPJ-Wiki dataset.
+    These indices were created using the `leann-core` library and are required by the `LeannSearcher`.
+
+2.  **Ground Truth Data**:
+    *   `ground_truth/`: Contains the ground truth files (`flat_results_nq_k3.json`) for both the DPR and RPJ-Wiki datasets. These files map queries to the original passage IDs from the Natural Questions benchmark, evaluated using the Contriever model.
+
+3.  **Queries**:
+    *   `queries/`: Contains the `nq_open.jsonl` file with the Natural Questions queries used for the evaluation.
+
+## Usage
+
+To use this data, you can download it locally using the `huggingface-hub` library. First, install the library:
+
+```bash
+pip install huggingface-hub
+```
+
+Then, you can download the entire dataset to a local directory (e.g., `data/`) with the following Python script:
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="LEANN-RAG/leann-rag-evaluation-data",
+    repo_type="dataset",
+    local_dir="data"
+)
+```
+
+This will download all the necessary files into a local `data` folder, preserving the repository structure. The evaluation scripts in the main [LEANN-RAG Space](https://huggingface.co/LEANN-RAG) are configured to work with this data structure.

benchmarks/run_evaluation.py

@@ -12,7 +12,7 @@ import time
 from pathlib import Path
 
 import numpy as np
-from leann.api import LeannBuilder, LeannSearcher
+from leann.api import LeannBuilder, LeannChat, LeannSearcher
 
 
 def download_data_if_needed(data_root: Path, download_embeddings: bool = False):
@@ -197,6 +197,25 @@ def main():
     parser.add_argument(
         "--ef-search", type=int, default=120, help="The 'efSearch' parameter for HNSW."
     )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=0,
+        help="Batch size for HNSW batched search (0 disables batching)",
+    )
+    parser.add_argument(
+        "--llm-type",
+        type=str,
+        choices=["ollama", "hf", "openai", "gemini", "simulated"],
+        default="ollama",
+        help="LLM backend type to optionally query during evaluation (default: ollama)",
+    )
+    parser.add_argument(
+        "--llm-model",
+        type=str,
+        default="qwen3:1.7b",
+        help="LLM model identifier for the chosen backend (default: qwen3:1.7b)",
+    )
     args = parser.parse_args()
 
     # --- Path Configuration ---
@@ -318,9 +337,24 @@ def main():
 
         for i in range(num_eval_queries):
             start_time = time.time()
-            new_results = searcher.search(queries[i], top_k=args.top_k, ef=args.ef_search)
+            new_results = searcher.search(
+                queries[i],
+                top_k=args.top_k,
+                complexity=args.ef_search,
+                batch_size=args.batch_size,
+            )
             search_times.append(time.time() - start_time)
 
+            # Optional: also call the LLM with configurable backend/model (does not affect recall)
+            llm_config = {"type": args.llm_type, "model": args.llm_model}
+            chat = LeannChat(args.index_path, llm_config=llm_config, searcher=searcher)
+            answer = chat.ask(
+                queries[i],
+                top_k=args.top_k,
+                complexity=args.ef_search,
+                batch_size=args.batch_size,
+            )
+            print(f"Answer: {answer}")
             # Correct Recall Calculation: Based on TEXT content
             new_texts = {result.text for result in new_results}
 

benchmarks/simple_mac_tpt_test.py

@@ -20,7 +20,7 @@ except ImportError:
 
 @dataclass
 class BenchmarkConfig:
-    model_path: str = "facebook/contriever"
+    model_path: str = "facebook/contriever-msmarco"
     batch_sizes: list[int] = None
     seq_length: int = 256
     num_runs: int = 5
@@ -34,7 +34,7 @@ class BenchmarkConfig:
 
     def __post_init__(self):
         if self.batch_sizes is None:
-            self.batch_sizes = [1, 2, 4, 8, 16, 32, 64]
+            self.batch_sizes = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]
 
 
 class MLXBenchmark:
@@ -179,11 +179,14 @@ class Benchmark:
 
     def _run_inference(self, input_ids: torch.Tensor) -> float:
         attention_mask = torch.ones_like(input_ids)
-
+        # print shape of input_ids and attention_mask
+        print(f"input_ids shape: {input_ids.shape}")
+        print(f"attention_mask shape: {attention_mask.shape}")
         start_time = time.time()
         with torch.no_grad():
             self.model(input_ids=input_ids, attention_mask=attention_mask)
-        # mps sync
+        if torch.cuda.is_available():
+            torch.cuda.synchronize()
         if torch.backends.mps.is_available():
             torch.mps.synchronize()
         end_time = time.time()

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

@@ -1,6 +1,7 @@
 import logging
 import os
 import shutil
+import time
 from pathlib import Path
 from typing import Any, Literal, Optional
 
@@ -236,6 +237,7 @@ class HNSWSearcher(BaseSearcher):
         distances = np.empty((batch_size_query, top_k), dtype=np.float32)
         labels = np.empty((batch_size_query, top_k), dtype=np.int64)
 
+        search_time = time.time()
         self._index.search(
             query.shape[0],
             faiss.swig_ptr(query),
@@ -244,7 +246,8 @@ class HNSWSearcher(BaseSearcher):
             faiss.swig_ptr(labels),
             params,
         )
-
+        search_time = time.time() - search_time
+        logger.info(f"  Search time in HNSWSearcher.search() backend: {search_time} seconds")
         string_labels = [[str(int_label) for int_label in batch_labels] for batch_labels in labels]
 
         return {"labels": string_labels, "distances": distances}

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

@@ -578,6 +578,8 @@ class LeannSearcher:
         self.passage_manager = PassageManager(
             self.meta_data.get("passage_sources", []), metadata_file_path=self.meta_path_str
         )
+        # Preserve backend name for conditional parameter forwarding
+        self.backend_name = backend_name
         backend_factory = BACKEND_REGISTRY.get(backend_name)
         if backend_factory is None:
             raise ValueError(f"Backend '{backend_name}' not found.")
@@ -597,6 +599,7 @@ class LeannSearcher:
         recompute_embeddings: bool = True,
         pruning_strategy: Literal["global", "local", "proportional"] = "global",
         expected_zmq_port: int = 5557,
+        batch_size: int = 0,
         **kwargs,
     ) -> list[SearchResult]:
         logger.info("🔍 LeannSearcher.search() called:")
@@ -636,23 +639,33 @@ class LeannSearcher:
             use_server_if_available=recompute_embeddings,
             zmq_port=zmq_port,
         )
-        # logger.info(f"  Generated embedding shape: {query_embedding.shape}")
-        # time.time() - start_time
-        # logger.info(f"  Embedding time: {embedding_time} seconds")
+        logger.info(f"  Generated embedding shape: {query_embedding.shape}")
+        embedding_time = time.time() - start_time
+        logger.info(f"  Embedding time: {embedding_time} seconds")
 
         start_time = time.time()
+        backend_search_kwargs: dict[str, Any] = {
+            "complexity": complexity,
+            "beam_width": beam_width,
+            "prune_ratio": prune_ratio,
+            "recompute_embeddings": recompute_embeddings,
+            "pruning_strategy": pruning_strategy,
+            "zmq_port": zmq_port,
+        }
+        # Only HNSW supports batching; forward conditionally
+        if self.backend_name == "hnsw":
+            backend_search_kwargs["batch_size"] = batch_size
+
+        # Merge any extra kwargs last
+        backend_search_kwargs.update(kwargs)
+
         results = self.backend_impl.search(
             query_embedding,
             top_k,
-            complexity=complexity,
-            beam_width=beam_width,
-            prune_ratio=prune_ratio,
-            recompute_embeddings=recompute_embeddings,
-            pruning_strategy=pruning_strategy,
-            zmq_port=zmq_port,
-            **kwargs,
+            **backend_search_kwargs,
         )
-        # logger.info(f"  Search time: {search_time} seconds")
+        search_time = time.time() - start_time
+        logger.info(f"  Search time in search() LEANN searcher: {search_time} seconds")
         logger.info(f"  Backend returned: labels={len(results.get('labels', [[]])[0])} results")
 
         enriched_results = []
@@ -731,9 +744,15 @@ class LeannChat:
         index_path: str,
         llm_config: Optional[dict[str, Any]] = None,
         enable_warmup: bool = False,
+        searcher: Optional[LeannSearcher] = None,
         **kwargs,
     ):
-        self.searcher = LeannSearcher(index_path, enable_warmup=enable_warmup, **kwargs)
+        if searcher is None:
+            self.searcher = LeannSearcher(index_path, enable_warmup=enable_warmup, **kwargs)
+            self._owns_searcher = True
+        else:
+            self.searcher = searcher
+            self._owns_searcher = False
         self.llm = get_llm(llm_config)
 
     def ask(
@@ -747,6 +766,7 @@ class LeannChat:
         pruning_strategy: Literal["global", "local", "proportional"] = "global",
         llm_kwargs: Optional[dict[str, Any]] = None,
         expected_zmq_port: int = 5557,
+        batch_size: int = 0,
         **search_kwargs,
     ):
         if llm_kwargs is None:
@@ -761,10 +781,11 @@ class LeannChat:
             recompute_embeddings=recompute_embeddings,
             pruning_strategy=pruning_strategy,
             expected_zmq_port=expected_zmq_port,
+            batch_size=batch_size,
             **search_kwargs,
         )
         search_time = time.time() - search_time
-        # logger.info(f"  Search time: {search_time} seconds")
+        logger.info(f"  Search time: {search_time} seconds")
         context = "\n\n".join([r.text for r in results])
         prompt = (
             "Here is some retrieved context that might help answer your question:\n\n"
@@ -800,7 +821,9 @@ class LeannChat:
         This method should be called after you're done using the chat interface,
         especially in test environments or batch processing scenarios.
         """
-        if hasattr(self.searcher, "cleanup"):
+        # Only stop the embedding server if this LeannChat instance created the searcher.
+        # When a shared searcher is passed in, avoid shutting down the server to enable reuse.
+        if getattr(self, "_owns_searcher", False) and hasattr(self.searcher, "cleanup"):
             self.searcher.cleanup()
 
     # Enable automatic cleanup patterns

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

@@ -6,6 +6,7 @@ Preserves all optimization parameters to ensure performance
 
 import logging
 import os
+import time
 from typing import Any
 
 import numpy as np
@@ -28,6 +29,8 @@ def compute_embeddings(
     is_build: bool = False,
     batch_size: int = 32,
     adaptive_optimization: bool = True,
+    manual_tokenize: bool = False,
+    max_length: int = 512,
 ) -> np.ndarray:
     """
     Unified embedding computation entry point
@@ -50,6 +53,8 @@ def compute_embeddings(
             is_build=is_build,
             batch_size=batch_size,
             adaptive_optimization=adaptive_optimization,
+            manual_tokenize=manual_tokenize,
+            max_length=max_length,
         )
     elif mode == "openai":
         return compute_embeddings_openai(texts, model_name)
@@ -71,6 +76,8 @@ def compute_embeddings_sentence_transformers(
     batch_size: int = 32,
     is_build: bool = False,
     adaptive_optimization: bool = True,
+    manual_tokenize: bool = False,
+    max_length: int = 512,
 ) -> np.ndarray:
     """
     Compute embeddings using SentenceTransformer with model caching and adaptive optimization
@@ -214,20 +221,130 @@ def compute_embeddings_sentence_transformers(
         logger.info(f"Model cached: {cache_key}")
 
     # Compute embeddings with optimized inference mode
-    logger.info(f"Starting embedding computation... (batch_size: {batch_size})")
+    logger.info(
+        f"Starting embedding computation... (batch_size: {batch_size}, manual_tokenize={manual_tokenize})"
+    )
 
-    # Use torch.inference_mode for optimal performance
-    with torch.inference_mode():
-        embeddings = model.encode(
-            texts,
-            batch_size=batch_size,
-            show_progress_bar=is_build,  # Don't show progress bar in server environment
-            convert_to_numpy=True,
-            normalize_embeddings=False,
-            device=device,
-        )
+    start_time = time.time()
+    if not manual_tokenize:
+        # Use SentenceTransformer's optimized encode path (default)
+        with torch.inference_mode():
+            embeddings = model.encode(
+                texts,
+                batch_size=batch_size,
+                show_progress_bar=is_build,  # Don't show progress bar in server environment
+                convert_to_numpy=True,
+                normalize_embeddings=False,
+                device=device,
+            )
+        # Synchronize if CUDA to measure accurate wall time
+        try:
+            if torch.cuda.is_available():
+                torch.cuda.synchronize()
+        except Exception:
+            pass
+    else:
+        # Manual tokenization + forward pass using HF AutoTokenizer/AutoModel
+        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}"
+        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")
+        else:
+            logger.info("Loading HF tokenizer/model for manual tokenization 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.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
+            _model_cache[tok_cache_key] = hf_tokenizer
+            _model_cache[mdl_cache_key] = hf_model
+
+        all_embeddings: list[np.ndarray] = []
+        # Progress bar when building or for large inputs
+        show_progress = is_build or len(texts) > 32
+        try:
+            if show_progress:
+                from tqdm import tqdm  # type: ignore
+
+                batch_iter = tqdm(
+                    range(0, len(texts), batch_size),
+                    desc="Embedding (manual)",
+                    unit="batch",
+                )
+            else:
+                batch_iter = range(0, len(texts), batch_size)
+        except Exception:
+            batch_iter = range(0, len(texts), batch_size)
+
+        start_time_manual = time.time()
+        with torch.inference_mode():
+            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,
+                    truncation=True,
+                    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)
+
+        embeddings = np.vstack(all_embeddings).astype(np.float32, copy=False)
+        try:
+            if torch.cuda.is_available():
+                torch.cuda.synchronize()
+        except Exception:
+            pass
+        end_time = time.time()
+        logger.info(f"Manual tokenize time taken: {end_time - start_time_manual} seconds")
+    end_time = time.time()
     logger.info(f"Generated {len(embeddings)} embeddings, dimension: {embeddings.shape[1]}")
+    logger.info(f"Time taken: {end_time - start_time} seconds")
 
     # Validate results
     if np.isnan(embeddings).any() or np.isinf(embeddings).any():

uv.lock

@@ -195,28 +195,7 @@ version = "0.1.4"
 source = { registry = "https://pypi.org/simple" }
 sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170, upload-time = "2024-02-06T09:43:11.258Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321, upload-time = "2024-02-06T09:43:09.663Z" },
-]
-
-[[package]]
-name = "astchunk"
-version = "0.1.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "numpy", version = "2.0.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
-    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version == '3.10.*'" },
-    { name = "numpy", version = "2.3.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
-    { name = "pyrsistent" },
-    { name = "tree-sitter", version = "0.23.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.10'" },
-    { name = "tree-sitter", version = "0.25.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.10'" },
-    { name = "tree-sitter-c-sharp" },
-    { name = "tree-sitter-java" },
-    { name = "tree-sitter-python" },
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