Langchain-Chatchat/server/knowledge_base/kb_service/faiss_kb_service.py

import os
import shutil

from configs.model_config import (
    KB_ROOT_PATH,
    CACHED_VS_NUM,
    EMBEDDING_MODEL,
    SCORE_THRESHOLD,
    logger, log_verbose,
)
from server.knowledge_base.kb_service.base import KBService, SupportedVSType
from functools import lru_cache
from server.knowledge_base.utils import get_vs_path, load_embeddings, KnowledgeFile
from langchain.vectorstores import FAISS
from langchain.embeddings.base import Embeddings
from typing import List, Dict, Optional
from langchain.docstore.document import Document
from server.utils import torch_gc, embedding_device


_VECTOR_STORE_TICKS = {}


@lru_cache(CACHED_VS_NUM)
def load_faiss_vector_store(
        knowledge_base_name: str,
        embed_model: str = EMBEDDING_MODEL,
        embed_device: str = embedding_device(),
        embeddings: Embeddings = None,
        tick: int = 0,  # tick will be changed by upload_doc etc. and make cache refreshed.
) -> FAISS:
    logger.info(f"loading vector store in '{knowledge_base_name}'.")
    vs_path = get_vs_path(knowledge_base_name)
    if embeddings is None:
        embeddings = load_embeddings(embed_model, embed_device)

    if not os.path.exists(vs_path):
        os.makedirs(vs_path)

    if "index.faiss" in os.listdir(vs_path):
        search_index = FAISS.load_local(vs_path, embeddings, normalize_L2=True)
    else:
        # create an empty vector store
        doc = Document(page_content="init", metadata={})
        search_index = FAISS.from_documents([doc], embeddings, normalize_L2=True)
        ids = [k for k, v in search_index.docstore._dict.items()]
        search_index.delete(ids)
        search_index.save_local(vs_path)

    if tick == 0: # vector store is loaded first time
        _VECTOR_STORE_TICKS[knowledge_base_name] = 0

    return search_index


def refresh_vs_cache(kb_name: str):
    """
    make vector store cache refreshed when next loading
    """
    _VECTOR_STORE_TICKS[kb_name] = _VECTOR_STORE_TICKS.get(kb_name, 0) + 1
    logger.info(f"知识库 {kb_name} 缓存刷新：{_VECTOR_STORE_TICKS[kb_name]}")


class FaissKBService(KBService):
    vs_path: str
    kb_path: str

    def vs_type(self) -> str:
        return SupportedVSType.FAISS

    def get_vs_path(self):
        return os.path.join(self.get_kb_path(), "vector_store")

    def get_kb_path(self):
        return os.path.join(KB_ROOT_PATH, self.kb_name)

    def load_vector_store(self) -> FAISS:
        return load_faiss_vector_store(
            knowledge_base_name=self.kb_name,
            embed_model=self.embed_model,
            tick=_VECTOR_STORE_TICKS.get(self.kb_name, 0),
        )

    def save_vector_store(self, vector_store: FAISS = None):
        vector_store = vector_store or self.load_vector_store()
        vector_store.save_local(self.vs_path)
        return vector_store

    def refresh_vs_cache(self):
        refresh_vs_cache(self.kb_name)

    def get_doc_by_id(self, id: str) -> Optional[Document]:
        vector_store = self.load_vector_store()
        return vector_store.docstore._dict.get(id)

    def do_init(self):
        self.kb_path = self.get_kb_path()
        self.vs_path = self.get_vs_path()

    def do_create_kb(self):
        if not os.path.exists(self.vs_path):
            os.makedirs(self.vs_path)
        self.load_vector_store()

    def do_drop_kb(self):
        self.clear_vs()
        shutil.rmtree(self.kb_path)

    def do_search(self,
                  query: str,
                  top_k: int,
                  score_threshold: float = SCORE_THRESHOLD,
                  embeddings: Embeddings = None,
                  ) -> List[Document]:
        search_index = self.load_vector_store()
        docs = search_index.similarity_search_with_score(query, k=top_k, score_threshold=score_threshold)
        return docs

    def do_add_doc(self,
                   docs: List[Document],
                   **kwargs,
                   ) -> List[Dict]:
        vector_store = self.load_vector_store()
        ids = vector_store.add_documents(docs)
        doc_infos = [{"id": id, "metadata": doc.metadata} for id, doc in zip(ids, docs)]
        torch_gc()
        if not kwargs.get("not_refresh_vs_cache"):
            vector_store.save_local(self.vs_path)
            self.refresh_vs_cache()
        return doc_infos

    def do_delete_doc(self,
                      kb_file: KnowledgeFile,
                      **kwargs):
        vector_store = self.load_vector_store()

        ids = [k for k, v in vector_store.docstore._dict.items() if v.metadata.get("source") == kb_file.filepath]
        if len(ids) == 0:
            return None

        vector_store.delete(ids)
        if not kwargs.get("not_refresh_vs_cache"):
            vector_store.save_local(self.vs_path)
            self.refresh_vs_cache()

        return vector_store

    def do_clear_vs(self):
        shutil.rmtree(self.vs_path)
        os.makedirs(self.vs_path)
        self.refresh_vs_cache()

    def exist_doc(self, file_name: str):
        if super().exist_doc(file_name):
            return "in_db"

        content_path = os.path.join(self.kb_path, "content")
        if os.path.isfile(os.path.join(content_path, file_name)):
            return "in_folder"
        else:
            return False


if __name__ == '__main__':
    faissService = FaissKBService("test")
    faissService.add_doc(KnowledgeFile("README.md", "test"))
    faissService.delete_doc(KnowledgeFile("README.md", "test"))
    faissService.do_drop_kb()
    print(faissService.search_docs("如何启动api服务"))