fix: cache the loaded model

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

@@ -5,7 +5,9 @@ with the correct, original embedding logic from the user's reference code.
 
 import json
 import pickle
+from leann.interface import LeannBackendSearcherInterface
 import numpy as np
+import time
 from pathlib import Path
 from typing import List, Dict, Any, Optional, Literal
 from dataclasses import dataclass, field
@@ -126,6 +128,7 @@ class PassageManager:
     def get_passage(self, passage_id: str) -> Dict[str, Any]:
         if passage_id in self.global_offset_map:
             passage_file, offset = self.global_offset_map[passage_id]
+            # Lazy file opening - only open when needed
             with open(passage_file, "r", encoding="utf-8") as f:
                 f.seek(offset)
                 return json.loads(f.readline())
@@ -373,10 +376,12 @@ class LeannBuilder:
 
 class LeannSearcher:
     def __init__(self, index_path: str, enable_warmup: bool = False, **backend_kwargs):
-        meta_path_str = f"{index_path}.meta.json"
-        if not Path(meta_path_str).exists():
-            raise FileNotFoundError(f"Leann metadata file not found at {meta_path_str}")
-        with open(meta_path_str, "r", encoding="utf-8") as f:
+        self.meta_path_str = f"{index_path}.meta.json"
+        if not Path(self.meta_path_str).exists():
+            raise FileNotFoundError(
+                f"Leann metadata file not found at {self.meta_path_str}"
+            )
+        with open(self.meta_path_str, "r", encoding="utf-8") as f:
             self.meta_data = json.load(f)
         backend_name = self.meta_data["backend_name"]
         self.embedding_model = self.meta_data["embedding_model"]
@@ -390,7 +395,9 @@ class LeannSearcher:
             raise ValueError(f"Backend '{backend_name}' not found.")
         final_kwargs = {**self.meta_data.get("backend_kwargs", {}), **backend_kwargs}
         final_kwargs["enable_warmup"] = enable_warmup
-        self.backend_impl = backend_factory.searcher(index_path, **final_kwargs)
+        self.backend_impl: LeannBackendSearcherInterface = backend_factory.searcher(
+            index_path, **final_kwargs
+        )
 
     def search(
         self,
@@ -399,9 +406,9 @@ class LeannSearcher:
         complexity: int = 64,
         beam_width: int = 1,
         prune_ratio: float = 0.0,
-        recompute_embeddings: bool = False,
+        recompute_embeddings: bool = True,
         pruning_strategy: Literal["global", "local", "proportional"] = "global",
-        expected_zmq_port: Optional[int] = None,
+        expected_zmq_port: int = 5557,
         **kwargs,
     ) -> List[SearchResult]:
         print("🔍 DEBUG LeannSearcher.search() called:")
@@ -409,16 +416,21 @@ class LeannSearcher:
         print(f"  Top_k: {top_k}")
         print(f"  Additional kwargs: {kwargs}")
 
-        # Use backend's compute_query_embedding method
-        # This will automatically use embedding server if available and needed
-        import time
-
         start_time = time.time()
 
+        zmq_port = None
+        if recompute_embeddings:
+            zmq_port = self.backend_impl._ensure_server_running(
+                self.meta_path_str,
+                port=expected_zmq_port,
+                **kwargs,
+            )
+            del expected_zmq_port
+
         query_embedding = self.backend_impl.compute_query_embedding(
             query,
-            expected_zmq_port,
             use_server_if_available=recompute_embeddings,
+            zmq_port=zmq_port,
         )
         print(f"  Generated embedding shape: {query_embedding.shape}")
         embedding_time = time.time() - start_time
@@ -433,7 +445,7 @@ class LeannSearcher:
             prune_ratio=prune_ratio,
             recompute_embeddings=recompute_embeddings,
             pruning_strategy=pruning_strategy,
-            expected_zmq_port=expected_zmq_port,
+            expected_zmq_port=zmq_port,
             **kwargs,
         )
         search_time = time.time() - start_time
@@ -488,10 +500,10 @@ class LeannChat:
         complexity: int = 64,
         beam_width: int = 1,
         prune_ratio: float = 0.0,
-        recompute_embeddings: bool = False,
+        recompute_embeddings: bool = True,
         pruning_strategy: Literal["global", "local", "proportional"] = "global",
-        expected_zmq_port: Optional[int] = None,
         llm_kwargs: Optional[Dict[str, Any]] = None,
+        expected_zmq_port: int = 5557,
         **search_kwargs,
     ):
         if llm_kwargs is None: