Add faiss integration for storage

README.md

@@ -465,7 +465,36 @@ For production level scenarios you will most likely want to leverage an enterpri
   >
   > You can Compile the AGE from source code and fix it.
 
+### Using Faiss for Storage
+- Install the required dependencies:
+```
+pip install faiss-cpu
+```
+You can also install `faiss-gpu` if you have GPU support.
 
+- Here we are using `sentence-transformers` but you can also use `OpenAIEmbedding` model with `3072` dimensions.
+
+```
+async def embedding_func(texts: list[str]) -> np.ndarray:
+    model = SentenceTransformer('all-MiniLM-L6-v2')
+    embeddings = model.encode(texts, convert_to_numpy=True)
+    return embeddings
+
+# Initialize LightRAG with the LLM model function and embedding function
+    rag = LightRAG(
+        working_dir=WORKING_DIR,
+        llm_model_func=llm_model_func,
+        embedding_func=EmbeddingFunc(
+            embedding_dim=384,
+            max_token_size=8192,
+            func=embedding_func,
+        ),
+        vector_storage="FaissVectorDBStorage",
+        vector_db_storage_cls_kwargs={
+            "cosine_better_than_threshold": 0.3  # Your desired threshold
+        }
+    )
+```
 
 ### Insert Custom KG
 

examples/test_faiss.py

@@ -0,0 +1,104 @@
+import os
+import logging
+import numpy as np
+
+from dotenv import load_dotenv
+from sentence_transformers import SentenceTransformer
+
+from openai import AzureOpenAI
+from lightrag import LightRAG, QueryParam
+from lightrag.utils import EmbeddingFunc
+from lightrag.kg.faiss_impl import FaissVectorDBStorage
+
+# Configure Logging
+logging.basicConfig(level=logging.INFO)
+
+# Load environment variables from .env file
+load_dotenv()
+AZURE_OPENAI_API_VERSION = os.getenv("AZURE_OPENAI_API_VERSION")
+AZURE_OPENAI_DEPLOYMENT = os.getenv("AZURE_OPENAI_DEPLOYMENT")
+AZURE_OPENAI_API_KEY = os.getenv("AZURE_OPENAI_API_KEY")
+AZURE_OPENAI_ENDPOINT = os.getenv("AZURE_OPENAI_ENDPOINT")
+
+async def llm_model_func(
+    prompt,
+    system_prompt=None,
+    history_messages=[],
+    keyword_extraction=False,
+    **kwargs
+) -> str:
+    
+    # Create a client for AzureOpenAI
+    client = AzureOpenAI(
+        api_key=AZURE_OPENAI_API_KEY,
+        api_version=AZURE_OPENAI_API_VERSION,
+        azure_endpoint=AZURE_OPENAI_ENDPOINT,
+    )
+
+    # Build the messages list for the conversation
+    messages = []
+    if system_prompt:
+        messages.append({"role": "system", "content": system_prompt})
+    if history_messages:
+        messages.extend(history_messages)
+    messages.append({"role": "user", "content": prompt})
+
+    # Call the LLM
+    chat_completion = client.chat.completions.create(
+        model=AZURE_OPENAI_DEPLOYMENT,
+        messages=messages,
+        temperature=kwargs.get("temperature", 0),
+        top_p=kwargs.get("top_p", 1),
+        n=kwargs.get("n", 1),
+    )
+
+    return chat_completion.choices[0].message.content
+
+
+async def embedding_func(texts: list[str]) -> np.ndarray:
+    model = SentenceTransformer('all-MiniLM-L6-v2')
+    embeddings = model.encode(texts, convert_to_numpy=True)
+    return embeddings
+
+def main():
+    
+    WORKING_DIR = "./dickens"
+
+    # Initialize LightRAG with the LLM model function and embedding function
+    rag = LightRAG(
+        working_dir=WORKING_DIR,
+        llm_model_func=llm_model_func,
+        embedding_func=EmbeddingFunc(
+            embedding_dim=384,
+            max_token_size=8192,
+            func=embedding_func,
+        ),
+        vector_storage="FaissVectorDBStorage",
+        vector_db_storage_cls_kwargs={
+            "cosine_better_than_threshold": 0.3  # Your desired threshold
+        }
+    )
+
+    # Insert the custom chunks into LightRAG
+    book1 = open("./book_1.txt", encoding="utf-8")
+    book2 = open("./book_2.txt", encoding="utf-8")
+
+    rag.insert([book1.read(), book2.read()])
+
+    query_text = "What are the main themes?"
+
+    print("Result (Naive):")
+    print(rag.query(query_text, param=QueryParam(mode="naive")))
+
+    print("\nResult (Local):")
+    print(rag.query(query_text, param=QueryParam(mode="local")))
+
+    print("\nResult (Global):")
+    print(rag.query(query_text, param=QueryParam(mode="global")))
+
+    print("\nResult (Hybrid):")
+    print(rag.query(query_text, param=QueryParam(mode="hybrid")))
+
+
+if __name__ == "__main__":
+    main()

lightrag/kg/faiss_impl.py

@@ -0,0 +1,318 @@
+import os
+import time
+import asyncio
+import faiss
+import json
+import numpy as np
+from tqdm.asyncio import tqdm as tqdm_async
+from dataclasses import dataclass
+
+from lightrag.utils import (
+    logger,
+    compute_mdhash_id,
+)
+from lightrag.base import (
+    BaseVectorStorage,
+)
+
+
+@dataclass
+class FaissVectorDBStorage(BaseVectorStorage):
+    """
+    A Faiss-based Vector DB Storage for LightRAG.
+    Uses cosine similarity by storing normalized vectors in a Faiss index with inner product search.
+    """
+    cosine_better_than_threshold: float = float(os.getenv("COSINE_THRESHOLD", "0.2"))
+
+    def __post_init__(self):
+        # Grab config values if available
+        config = self.global_config.get("vector_db_storage_cls_kwargs", {})
+        self.cosine_better_than_threshold = config.get(
+            "cosine_better_than_threshold", self.cosine_better_than_threshold
+        )
+
+        # Where to save index file if you want persistent storage
+        self._faiss_index_file = os.path.join(
+            self.global_config["working_dir"], f"faiss_index_{self.namespace}.index"
+        )
+        self._meta_file = self._faiss_index_file + ".meta.json"
+
+        self._max_batch_size = self.global_config["embedding_batch_num"]
+        # 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).
+        # If you have a large number of vectors, you might want IVF or other indexes.
+        # For demonstration, we use a simple IndexFlatIP.
+        self._index = faiss.IndexFlatIP(self._dim)
+
+        # Keep a local store for metadata, IDs, etc. 
+        # Maps <int faiss_id> → metadata (including your original ID).
+        self._id_to_meta = {}
+
+        # Attempt to load an existing index + metadata from disk
+        self._load_faiss_index()
+
+    async def upsert(self, data: dict[str, dict]):
+        """
+        Insert or update vectors in the Faiss index.
+
+        data: {
+           "custom_id_1": {
+               "content": <text>,
+               ...metadata...
+           },
+           "custom_id_2": {
+               "content": <text>,
+               ...metadata...
+           },
+           ...
+        }
+        """
+        logger.info(f"Inserting {len(data)} vectors to {self.namespace}")
+        if not data:
+            logger.warning("You are inserting empty data to the vector DB")
+            return []
+
+        current_time = time.time()
+
+        # Prepare data for embedding
+        list_data = []
+        contents = []
+        for k, v in data.items():
+            # Store only known meta fields if needed
+            meta = {mf: v[mf] for mf in self.meta_fields if mf in v}
+            meta["__id__"] = k
+            meta["__created_at__"] = current_time
+            list_data.append(meta)
+            contents.append(v["content"])
+
+        # Split into batches for embedding if needed
+        batches = [
+            contents[i : i + self._max_batch_size]
+            for i in range(0, len(contents), self._max_batch_size)
+        ]
+
+        pbar = tqdm_async(total=len(batches), desc="Generating embeddings", unit="batch")
+
+        async def wrapped_task(batch):
+            result = await self.embedding_func(batch)
+            pbar.update(1)
+            return result
+
+        embedding_tasks = [wrapped_task(batch) for batch in batches]
+        embeddings_list = await asyncio.gather(*embedding_tasks)
+
+        # Flatten the list of arrays
+        embeddings = np.concatenate(embeddings_list, axis=0)
+        if len(embeddings) != len(list_data):
+            logger.error(
+                f"Embedding size mismatch. Embeddings: {len(embeddings)}, Data: {len(list_data)}"
+            )
+            return []
+
+        # Normalize embeddings for cosine similarity (in-place)
+        faiss.normalize_L2(embeddings)
+
+        # Upsert logic:
+        # 1. Identify which vectors to remove if they exist
+        # 2. Remove them
+        # 3. Add the new vectors
+        existing_ids_to_remove = []
+        for meta, emb in zip(list_data, embeddings):
+            faiss_internal_id = self._find_faiss_id_by_custom_id(meta["__id__"])
+            if faiss_internal_id is not None:
+                existing_ids_to_remove.append(faiss_internal_id)
+
+        if existing_ids_to_remove:
+            self._remove_faiss_ids(existing_ids_to_remove)
+
+        # Step 2: Add new vectors
+        start_idx = self._index.ntotal
+        self._index.add(embeddings)
+
+        # Step 3: Store metadata + vector for each new ID
+        for i, meta in enumerate(list_data):
+            fid = start_idx + i
+            # Store the raw vector so we can rebuild if something is removed
+            meta["__vector__"] = embeddings[i].tolist()
+            self._id_to_meta[fid] = meta
+
+        logger.info(f"Upserted {len(list_data)} vectors into Faiss index.")
+        return [m["__id__"] for m in list_data]
+
+    async def query(self, query: str, top_k=5):
+        """
+        Search by a textual query; returns top_k results with their metadata + similarity distance.
+        """
+        embedding = await self.embedding_func([query])
+        # embedding is shape (1, dim)
+        embedding = np.array(embedding, dtype=np.float32)
+        faiss.normalize_L2(embedding)  # we do in-place normalization
+
+        logger.info(
+            f"Query: {query}, top_k: {top_k}, threshold: {self.cosine_better_than_threshold}"
+        )
+
+        # Perform the similarity search
+        distances, indices = self._index.search(embedding, top_k)
+
+        distances = distances[0]
+        indices = indices[0]
+
+        results = []
+        for dist, idx in zip(distances, indices):
+            if idx == -1:
+                # Faiss returns -1 if no neighbor
+                continue
+
+            # Cosine similarity threshold
+            if dist < self.cosine_better_than_threshold:
+                continue
+
+            meta = self._id_to_meta.get(idx, {})
+            results.append(
+                {
+                    **meta,
+                    "id": meta.get("__id__"),
+                    "distance": float(dist),
+                    "created_at": meta.get("__created_at__"),
+                }
+            )
+
+        return results
+
+    @property
+    def client_storage(self):
+        # Return whatever structure LightRAG might need for debugging
+        return {"data": list(self._id_to_meta.values())}
+
+    async def delete(self, ids: list[str]):
+        """
+        Delete vectors for the provided custom IDs.
+        """
+        logger.info(f"Deleting {len(ids)} vectors from {self.namespace}")
+        to_remove = []
+        for cid in ids:
+            fid = self._find_faiss_id_by_custom_id(cid)
+            if fid is not None:
+                to_remove.append(fid)
+
+        if to_remove:
+            self._remove_faiss_ids(to_remove)
+        logger.info(f"Successfully deleted {len(to_remove)} vectors from {self.namespace}")
+
+    async def delete_entity(self, entity_name: str):
+        """
+        Delete a single entity by computing its hashed ID
+        the same way your code does it with `compute_mdhash_id`.
+        """
+        entity_id = compute_mdhash_id(entity_name, prefix="ent-")
+        logger.debug(f"Attempting to delete entity {entity_name} with ID {entity_id}")
+        await self.delete([entity_id])
+
+    async def delete_entity_relation(self, entity_name: str):
+        """
+        Delete relations for a given entity by scanning metadata.
+        """
+        logger.debug(f"Searching relations for entity {entity_name}")
+        relations = []
+        for fid, meta in self._id_to_meta.items():
+            if meta.get("src_id") == entity_name or meta.get("tgt_id") == entity_name:
+                relations.append(fid)
+
+        logger.debug(f"Found {len(relations)} relations for {entity_name}")
+        if relations:
+            self._remove_faiss_ids(relations)
+            logger.debug(f"Deleted {len(relations)} relations for {entity_name}")
+
+    async def index_done_callback(self):
+        """
+        Called after indexing is done (save Faiss index + metadata).
+        """
+        self._save_faiss_index()
+        logger.info("Faiss index saved successfully.")
+
+    # --------------------------------------------------------------------------------
+    # Internal helper methods
+    # --------------------------------------------------------------------------------
+
+    def _find_faiss_id_by_custom_id(self, custom_id: str):
+        """
+        Return the Faiss internal ID for a given custom ID, or None if not found.
+        """
+        for fid, meta in self._id_to_meta.items():
+            if meta.get("__id__") == custom_id:
+                return fid
+        return None
+
+    def _remove_faiss_ids(self, fid_list):
+        """
+        Remove a list of internal Faiss IDs from the index.
+        Because IndexFlatIP doesn't support 'removals',
+        we rebuild the index excluding those vectors.
+        """
+        keep_fids = [fid for fid in self._id_to_meta if fid not in fid_list]
+
+        # Rebuild the index
+        vectors_to_keep = []
+        new_id_to_meta = {}
+        for new_fid, old_fid in enumerate(keep_fids):
+            vec_meta = self._id_to_meta[old_fid]
+            vectors_to_keep.append(vec_meta["__vector__"])  # stored as list
+            new_id_to_meta[new_fid] = vec_meta
+
+        # Re-init index
+        self._index = faiss.IndexFlatIP(self._dim)
+        if vectors_to_keep:
+            arr = np.array(vectors_to_keep, dtype=np.float32)
+            self._index.add(arr)
+
+        self._id_to_meta = new_id_to_meta
+
+    def _save_faiss_index(self):
+        """
+        Save the current Faiss index + metadata to disk so it can persist across runs.
+        """
+        faiss.write_index(self._index, self._faiss_index_file)
+
+        # Save metadata dict to JSON. Convert all keys to strings for JSON storage.
+        # _id_to_meta is { int: { '__id__': doc_id, '__vector__': [float,...], ... } }
+        # We'll keep the int -> dict, but JSON requires string keys.
+        serializable_dict = {}
+        for fid, meta in self._id_to_meta.items():
+            serializable_dict[str(fid)] = meta
+
+        with open(self._meta_file, "w", encoding="utf-8") as f:
+            json.dump(serializable_dict, f)
+
+    def _load_faiss_index(self):
+        """
+        Load the Faiss index + metadata from disk if it exists, 
+        and rebuild in-memory structures so we can query.
+        """
+        if not os.path.exists(self._faiss_index_file):
+            logger.warning("No existing Faiss index file found. Starting fresh.")
+            return
+
+        try:
+            # Load the Faiss index
+            self._index = faiss.read_index(self._faiss_index_file)
+            # Load metadata
+            with open(self._meta_file, "r", encoding="utf-8") as f:
+                stored_dict = json.load(f)
+
+            # Convert string keys back to int
+            self._id_to_meta = {}
+            for fid_str, meta in stored_dict.items():
+                fid = int(fid_str)
+                self._id_to_meta[fid] = meta
+
+            logger.info(
+                f"Faiss index loaded with {self._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.")
+            self._index = faiss.IndexFlatIP(self._dim)
+            self._id_to_meta = {}

lightrag/lightrag.py

@@ -60,6 +60,7 @@ STORAGES = {
     "PGGraphStorage": ".kg.postgres_impl",
     "GremlinStorage": ".kg.gremlin_impl",
     "PGDocStatusStorage": ".kg.postgres_impl",
+    "FaissVectorDBStorage": ".kg.faiss_impl",
 }