fix: build with direct embedding

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

@@ -141,6 +141,14 @@ class HNSWSearcher(BaseSearcher):
             raise RuntimeError("Index is pruned but recompute is disabled.")
 
         self._index = faiss.read_index(str(index_file), faiss.IO_FLAG_MMAP, hnsw_config)
+        
+        # Load label mapping
+        label_map_file = self.index_dir / "leann.labels.map"
+        if not label_map_file.exists():
+            raise FileNotFoundError(f"Label map file not found at {label_map_file}")
+        
+        with open(label_map_file, "rb") as f:
+            self.label_map = pickle.load(f)
 
     def search(
         self,

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

@@ -20,7 +20,8 @@ from .chat import get_llm
 def compute_embeddings(
     chunks: List[str], 
     model_name: str, 
-    mode: str = "sentence-transformers"
+    mode: str = "sentence-transformers",
+    use_server: bool = True
 ) -> np.ndarray:
     """
     Computes embeddings using different backends.
@@ -32,6 +33,7 @@ def compute_embeddings(
             - "sentence-transformers": Use sentence-transformers library (default)
             - "mlx": Use MLX backend for Apple Silicon
             - "openai": Use OpenAI embedding API
+        use_server: Whether to use embedding server (True for search, False for build)
     
     Returns:
         numpy array of embeddings
@@ -45,13 +47,23 @@ def compute_embeddings(
     elif mode == "openai":
         return compute_embeddings_openai(chunks, model_name)
     elif mode == "sentence-transformers":
-        return compute_embeddings_sentence_transformers(chunks, model_name)
+        return compute_embeddings_sentence_transformers(chunks, model_name, use_server=use_server)
     else:
         raise ValueError(f"Unsupported embedding mode: {mode}. Supported modes: sentence-transformers, mlx, openai")
 
 
-def compute_embeddings_sentence_transformers(chunks: List[str], model_name: str) -> np.ndarray:
+def compute_embeddings_sentence_transformers(chunks: List[str], model_name: str, use_server: bool = True) -> np.ndarray:
-    """Computes embeddings using sentence-transformers via embedding server."""
+    """Computes embeddings using sentence-transformers.
+    
+    Args:
+        chunks: List of text chunks to embed
+        model_name: Name of the sentence transformer model
+        use_server: If True, use embedding server (good for search). If False, use direct computation (good for build).
+    """
+    if not use_server:
+        print(f"INFO: Computing embeddings for {len(chunks)} chunks using SentenceTransformer model '{model_name}' (direct)...")
+        return _compute_embeddings_sentence_transformers_direct(chunks, model_name)
+    
     print(
         f"INFO: Computing embeddings for {len(chunks)} chunks using SentenceTransformer model '{model_name}' (via embedding server)..."
     )
@@ -296,7 +308,7 @@ class LeannBuilder:
             raise ValueError("No chunks added.")
         if self.dimensions is None:
             self.dimensions = len(
-                compute_embeddings(["dummy"], self.embedding_model, self.embedding_mode)[0]
+                compute_embeddings(["dummy"], self.embedding_model, self.embedding_mode, use_server=False)[0]
             )
         path = Path(index_path)
         index_dir = path.parent
@@ -323,7 +335,7 @@ class LeannBuilder:
             pickle.dump(offset_map, f)
         texts_to_embed = [c["text"] for c in self.chunks]
         embeddings = compute_embeddings(
-            texts_to_embed, self.embedding_model, self.embedding_mode
+            texts_to_embed, self.embedding_model, self.embedding_mode, use_server=False
         )
         string_ids = [chunk["id"] for chunk in self.chunks]
         current_backend_kwargs = {**self.backend_kwargs, "dimensions": self.dimensions}