321 lines
11 KiB
Python
321 lines
11 KiB
Python
import os
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import time
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import asyncio
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from typing import Any, final
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import json
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import numpy as np
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from dataclasses import dataclass
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import pipmaster as pm
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from lightrag.utils import (
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logger,
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compute_mdhash_id,
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)
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from lightrag.base import (
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BaseVectorStorage,
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)
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if not pm.is_installed("faiss"):
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pm.install("faiss")
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try:
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import faiss
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except ImportError as e:
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raise ImportError(
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"`faiss` library is not installed. Please install it via pip: `pip install faiss`."
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) from e
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@final
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@dataclass
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class FaissVectorDBStorage(BaseVectorStorage):
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"""
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A Faiss-based Vector DB Storage for LightRAG.
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Uses cosine similarity by storing normalized vectors in a Faiss index with inner product search.
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"""
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def __post_init__(self):
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# Grab config values if available
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kwargs = self.global_config.get("vector_db_storage_cls_kwargs", {})
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cosine_threshold = kwargs.get("cosine_better_than_threshold")
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if cosine_threshold is None:
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raise ValueError(
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"cosine_better_than_threshold must be specified in vector_db_storage_cls_kwargs"
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)
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self.cosine_better_than_threshold = cosine_threshold
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# Where to save index file if you want persistent storage
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self._faiss_index_file = os.path.join(
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self.global_config["working_dir"], f"faiss_index_{self.namespace}.index"
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)
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self._meta_file = self._faiss_index_file + ".meta.json"
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self._max_batch_size = self.global_config["embedding_batch_num"]
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# Embedding dimension (e.g. 768) must match your embedding function
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self._dim = self.embedding_func.embedding_dim
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# Create an empty Faiss index for inner product (useful for normalized vectors = cosine similarity).
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# If you have a large number of vectors, you might want IVF or other indexes.
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# For demonstration, we use a simple IndexFlatIP.
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self._index = faiss.IndexFlatIP(self._dim)
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# Keep a local store for metadata, IDs, etc.
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# Maps <int faiss_id> → metadata (including your original ID).
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self._id_to_meta = {}
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# Attempt to load an existing index + metadata from disk
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self._load_faiss_index()
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async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
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"""
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Insert or update vectors in the Faiss index.
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data: {
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"custom_id_1": {
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"content": <text>,
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...metadata...
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},
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"custom_id_2": {
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"content": <text>,
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...metadata...
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},
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...
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}
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"""
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logger.info(f"Inserting {len(data)} vectors to {self.namespace}")
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if not data:
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logger.warning("You are inserting empty data to the vector DB")
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return []
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current_time = time.time()
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# Prepare data for embedding
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list_data = []
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contents = []
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for k, v in data.items():
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# Store only known meta fields if needed
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meta = {mf: v[mf] for mf in self.meta_fields if mf in v}
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meta["__id__"] = k
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meta["__created_at__"] = current_time
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list_data.append(meta)
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contents.append(v["content"])
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# Split into batches for embedding if needed
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batches = [
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contents[i : i + self._max_batch_size]
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for i in range(0, len(contents), self._max_batch_size)
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]
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embedding_tasks = [self.embedding_func(batch) for batch in batches]
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embeddings_list = await asyncio.gather(*embedding_tasks)
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# Flatten the list of arrays
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embeddings = np.concatenate(embeddings_list, axis=0)
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if len(embeddings) != len(list_data):
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logger.error(
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f"Embedding size mismatch. Embeddings: {len(embeddings)}, Data: {len(list_data)}"
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)
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return []
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# Normalize embeddings for cosine similarity (in-place)
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faiss.normalize_L2(embeddings)
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# Upsert logic:
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# 1. Identify which vectors to remove if they exist
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# 2. Remove them
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# 3. Add the new vectors
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existing_ids_to_remove = []
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for meta, emb in zip(list_data, embeddings):
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faiss_internal_id = self._find_faiss_id_by_custom_id(meta["__id__"])
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if faiss_internal_id is not None:
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existing_ids_to_remove.append(faiss_internal_id)
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if existing_ids_to_remove:
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self._remove_faiss_ids(existing_ids_to_remove)
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# Step 2: Add new vectors
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start_idx = self._index.ntotal
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self._index.add(embeddings)
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# Step 3: Store metadata + vector for each new ID
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for i, meta in enumerate(list_data):
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fid = start_idx + i
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# Store the raw vector so we can rebuild if something is removed
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meta["__vector__"] = embeddings[i].tolist()
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self._id_to_meta[fid] = meta
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logger.info(f"Upserted {len(list_data)} vectors into Faiss index.")
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return [m["__id__"] for m in list_data]
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async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
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"""
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Search by a textual query; returns top_k results with their metadata + similarity distance.
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"""
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embedding = await self.embedding_func([query])
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# embedding is shape (1, dim)
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embedding = np.array(embedding, dtype=np.float32)
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faiss.normalize_L2(embedding) # we do in-place normalization
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logger.info(
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f"Query: {query}, top_k: {top_k}, threshold: {self.cosine_better_than_threshold}"
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)
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# Perform the similarity search
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distances, indices = self._index.search(embedding, top_k)
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distances = distances[0]
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indices = indices[0]
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results = []
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for dist, idx in zip(distances, indices):
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if idx == -1:
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# Faiss returns -1 if no neighbor
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continue
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# Cosine similarity threshold
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if dist < self.cosine_better_than_threshold:
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continue
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meta = self._id_to_meta.get(idx, {})
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results.append(
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{
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**meta,
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"id": meta.get("__id__"),
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"distance": float(dist),
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"created_at": meta.get("__created_at__"),
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}
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)
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return results
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@property
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def client_storage(self):
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# Return whatever structure LightRAG might need for debugging
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return {"data": list(self._id_to_meta.values())}
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async def delete(self, ids: list[str]):
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"""
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Delete vectors for the provided custom IDs.
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"""
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logger.info(f"Deleting {len(ids)} vectors from {self.namespace}")
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to_remove = []
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for cid in ids:
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fid = self._find_faiss_id_by_custom_id(cid)
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if fid is not None:
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to_remove.append(fid)
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if to_remove:
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self._remove_faiss_ids(to_remove)
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logger.info(
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f"Successfully deleted {len(to_remove)} vectors from {self.namespace}"
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)
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async def delete_entity(self, entity_name: str) -> None:
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entity_id = compute_mdhash_id(entity_name, prefix="ent-")
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logger.debug(f"Attempting to delete entity {entity_name} with ID {entity_id}")
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await self.delete([entity_id])
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async def delete_entity_relation(self, entity_name: str) -> None:
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"""
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Delete relations for a given entity by scanning metadata.
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"""
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logger.debug(f"Searching relations for entity {entity_name}")
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relations = []
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for fid, meta in self._id_to_meta.items():
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if meta.get("src_id") == entity_name or meta.get("tgt_id") == entity_name:
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relations.append(fid)
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logger.debug(f"Found {len(relations)} relations for {entity_name}")
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if relations:
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self._remove_faiss_ids(relations)
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logger.debug(f"Deleted {len(relations)} relations for {entity_name}")
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async def index_done_callback(self) -> None:
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self._save_faiss_index()
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# --------------------------------------------------------------------------------
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# Internal helper methods
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# --------------------------------------------------------------------------------
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def _find_faiss_id_by_custom_id(self, custom_id: str):
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"""
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Return the Faiss internal ID for a given custom ID, or None if not found.
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"""
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for fid, meta in self._id_to_meta.items():
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if meta.get("__id__") == custom_id:
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return fid
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return None
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def _remove_faiss_ids(self, fid_list):
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"""
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Remove a list of internal Faiss IDs from the index.
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Because IndexFlatIP doesn't support 'removals',
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we rebuild the index excluding those vectors.
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"""
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keep_fids = [fid for fid in self._id_to_meta if fid not in fid_list]
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# Rebuild the index
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vectors_to_keep = []
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new_id_to_meta = {}
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for new_fid, old_fid in enumerate(keep_fids):
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vec_meta = self._id_to_meta[old_fid]
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vectors_to_keep.append(vec_meta["__vector__"]) # stored as list
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new_id_to_meta[new_fid] = vec_meta
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# Re-init index
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self._index = faiss.IndexFlatIP(self._dim)
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if vectors_to_keep:
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arr = np.array(vectors_to_keep, dtype=np.float32)
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self._index.add(arr)
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self._id_to_meta = new_id_to_meta
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def _save_faiss_index(self):
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"""
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Save the current Faiss index + metadata to disk so it can persist across runs.
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"""
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faiss.write_index(self._index, self._faiss_index_file)
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# Save metadata dict to JSON. Convert all keys to strings for JSON storage.
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# _id_to_meta is { int: { '__id__': doc_id, '__vector__': [float,...], ... } }
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# We'll keep the int -> dict, but JSON requires string keys.
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serializable_dict = {}
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for fid, meta in self._id_to_meta.items():
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serializable_dict[str(fid)] = meta
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with open(self._meta_file, "w", encoding="utf-8") as f:
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json.dump(serializable_dict, f)
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def _load_faiss_index(self):
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"""
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Load the Faiss index + metadata from disk if it exists,
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and rebuild in-memory structures so we can query.
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"""
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if not os.path.exists(self._faiss_index_file):
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logger.warning("No existing Faiss index file found. Starting fresh.")
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return
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try:
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# Load the Faiss index
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self._index = faiss.read_index(self._faiss_index_file)
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# Load metadata
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with open(self._meta_file, "r", encoding="utf-8") as f:
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stored_dict = json.load(f)
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# Convert string keys back to int
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self._id_to_meta = {}
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for fid_str, meta in stored_dict.items():
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fid = int(fid_str)
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self._id_to_meta[fid] = meta
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logger.info(
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f"Faiss index loaded with {self._index.ntotal} vectors from {self._faiss_index_file}"
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)
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except Exception as e:
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logger.error(f"Failed to load Faiss index or metadata: {e}")
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logger.warning("Starting with an empty Faiss index.")
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self._index = faiss.IndexFlatIP(self._dim)
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self._id_to_meta = {}
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