fix: cache the loaded model

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

@@ -29,7 +29,6 @@ logger = logging.getLogger(__name__)
 
 def create_hnsw_embedding_server(
     passages_file: Optional[str] = None,
-    passages_data: Optional[Dict[str, str]] = None,
     zmq_port: int = 5555,
     model_name: str = "sentence-transformers/all-mpnet-base-v2",
     distance_metric: str = "mips",
@@ -39,12 +38,6 @@ def create_hnsw_embedding_server(
     Create and start a ZMQ-based embedding server for HNSW backend.
     Simplified version using unified embedding computation module.
     """
-    # Auto-detect mode based on model name if not explicitly set
-    if embedding_mode == "sentence-transformers" and model_name.startswith(
-        "text-embedding-"
-    ):
-        embedding_mode = "openai"
-
     print(f"Starting HNSW server on port {zmq_port} with model {model_name}")
     print(f"Using embedding mode: {embedding_mode}")
 
@@ -64,6 +57,7 @@ def create_hnsw_embedding_server(
     finally:
         sys.path.pop(0)
 
+
     # Check port availability
     import socket
 
@@ -78,13 +72,15 @@ def create_hnsw_embedding_server(
     # Only support metadata file, fail fast for everything else
     if not passages_file or not passages_file.endswith(".meta.json"):
         raise ValueError("Only metadata files (.meta.json) are supported")
-    
+
     # Load metadata to get passage sources
     with open(passages_file, "r") as f:
         meta = json.load(f)
-    
+
     passages = PassageManager(meta["passage_sources"])
-    print(f"Loaded PassageManager with {len(passages.global_offset_map)} passages from metadata")
+    print(
+        f"Loaded PassageManager with {len(passages.global_offset_map)} passages from metadata"
+    )
 
     def zmq_server_thread():
         """ZMQ server thread"""
@@ -112,7 +108,7 @@ def create_hnsw_embedding_server(
                             f"Processing direct text embedding request for {len(request_payload)} texts in {embedding_mode} mode"
                         )
 
-                        # Use unified embedding computation
+                        # Use unified embedding computation (now with model caching)
                         embeddings = compute_embeddings(
                             request_payload, model_name, mode=embedding_mode
                         )
@@ -148,15 +144,15 @@ def create_hnsw_embedding_server(
                             texts.append(txt)
                         except KeyError:
                             print(f"ERROR: Passage ID {nid} not found")
-                            raise RuntimeError(f"FATAL: Passage with ID {nid} not found")
+                            raise RuntimeError(
+                                f"FATAL: Passage with ID {nid} not found"
+                            )
                         except Exception as e:
                             print(f"ERROR: Exception looking up passage ID {nid}: {e}")
                             raise
 
                     # Process embeddings
-                    embeddings = compute_embeddings(
-                        texts, model_name, mode=embedding_mode
-                    )
+                    embeddings = compute_embeddings(texts, model_name, mode=embedding_mode)
                     print(
                         f"INFO: Computed embeddings for {len(texts)} texts, shape: {embeddings.shape}"
                     )
@@ -204,7 +200,9 @@ def create_hnsw_embedding_server(
                         passage_data = passages.get_passage(str(nid))
                         txt = passage_data["text"]
                         if not txt:
-                            raise RuntimeError(f"FATAL: Empty text for passage ID {nid}")
+                            raise RuntimeError(
+                                f"FATAL: Empty text for passage ID {nid}"
+                            )
                         texts.append(txt)
                     except KeyError:
                         raise RuntimeError(f"FATAL: Passage with ID {nid} not found")