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

apps/base_rag_example.py

@@ -268,7 +268,6 @@ 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 +197,19 @@ def main():
     parser.add_argument(
         "--ef-search", type=int, default=120, help="The 'efSearch' parameter for HNSW."
     )
+    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 +331,14 @@ 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)
             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)
+            print(f"Answer: {answer}")
             # Correct Recall Calculation: Based on TEXT content
             new_texts = {result.text for result in new_results}
 

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

@@ -613,9 +613,9 @@ class LeannSearcher:
             use_server_if_available=recompute_embeddings,
             zmq_port=zmq_port,
         )
-        # logger.info(f"  Generated embedding shape: {query_embedding.shape}")
+        logger.info(f"  Generated embedding shape: {query_embedding.shape}")
-        # time.time() - start_time
+        embedding_time = time.time() - start_time
-        # logger.info(f"  Embedding time: {embedding_time} seconds")
+        logger.info(f"  Embedding time: {embedding_time} seconds")
 
         start_time = time.time()
         results = self.backend_impl.search(
@@ -629,7 +629,8 @@ class LeannSearcher:
             zmq_port=zmq_port,
             **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 = []
@@ -708,9 +709,15 @@ 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(
@@ -741,7 +748,7 @@ class LeannChat:
             **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"
@@ -777,7 +784,9 @@ 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