feat(storage): Add shared memory support for FAISS

lightrag/kg/faiss_impl.py

@@ -2,6 +2,8 @@ import os
 import time
 import asyncio
 from typing import Any, final
+import threading
+from multiprocessing import Manager
 
 import json
 import numpy as np
@@ -22,6 +24,27 @@ if not pm.is_installed("faiss"):
 
 import faiss
 
+# Global variables for shared memory management
+_init_lock = threading.Lock()
+_manager = None
+_shared_indices = None
+_shared_meta = None
+
+
+def _get_manager():
+    """Get or create the global manager instance"""
+    global _manager, _shared_indices, _shared_meta
+    with _init_lock:
+        if _manager is None:
+            try:
+                _manager = Manager()
+                _shared_indices = _manager.dict()
+                _shared_meta = _manager.dict()
+            except Exception as e:
+                logger.error(f"Failed to initialize shared memory manager: {e}")
+                raise RuntimeError(f"Shared memory initialization failed: {e}")
+    return _manager
+
 
 @final
 @dataclass
@@ -51,17 +74,47 @@ class FaissVectorDBStorage(BaseVectorStorage):
         # Embedding dimension (e.g. 768) must match your embedding function
         self._dim = self.embedding_func.embedding_dim
         
-        # Create an empty Faiss index for inner product (useful for normalized vectors = cosine similarity).
+        # Ensure manager is initialized
-        # If you have a large number of vectors, you might want IVF or other indexes.
+        _get_manager()
-        # For demonstration, we use a simple IndexFlatIP.
-        self._index = faiss.IndexFlatIP(self._dim)
         
-        # Keep a local store for metadata, IDs, etc.
+        # Get or create namespace index and metadata
-        # Maps <int faiss_id> → metadata (including your original ID).
+        if self.namespace not in _shared_indices:
-        self._id_to_meta = {}
+            with _init_lock:
+                if self.namespace not in _shared_indices:
+                    try:
+                        # Create an empty Faiss index for inner product
+                        index = faiss.IndexFlatIP(self._dim)
+                        meta = {}
                         
-        # Attempt to load an existing index + metadata from disk
+                        # Load existing index if available
-        self._load_faiss_index()
+                        if os.path.exists(self._faiss_index_file):
+                            try:
+                                index = faiss.read_index(self._faiss_index_file)
+                                with open(self._meta_file, "r", encoding="utf-8") as f:
+                                    stored_dict = json.load(f)
+                                    # Convert string keys back to int
+                                    meta = {int(k): v for k, v in stored_dict.items()}
+                                logger.info(
+                                    f"Faiss index loaded with {index.ntotal} vectors from {self._faiss_index_file}"
+                                )
+                            except Exception as e:
+                                logger.error(f"Failed to load Faiss index or metadata: {e}")
+                                logger.warning("Starting with an empty Faiss index.")
+                                index = faiss.IndexFlatIP(self._dim)
+                                meta = {}
+                                
+                        _shared_indices[self.namespace] = index
+                        _shared_meta[self.namespace] = meta
+                    except Exception as e:
+                        logger.error(f"Failed to initialize Faiss index for namespace {self.namespace}: {e}")
+                        raise RuntimeError(f"Faiss index initialization failed: {e}")
+        
+        try:
+            self._index = _shared_indices[self.namespace]
+            self._id_to_meta = _shared_meta[self.namespace]
+        except Exception as e:
+            logger.error(f"Failed to access shared memory: {e}")
+            raise RuntimeError(f"Cannot access shared memory: {e}")
 
     async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
         """