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

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

@@ -557,6 +557,8 @@ 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.")
@@ -576,6 +578,7 @@ 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:")
@@ -618,16 +621,24 @@ class LeannSearcher:
         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,
         )
         search_time = time.time() - start_time
         logger.info(f"  Search time in search() LEANN searcher: {search_time} seconds")
@@ -731,6 +742,7 @@ 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:
@@ -745,6 +757,7 @@ 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