mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-26 00:33:35 +08:00
f7c73b842a
* remove llm_model_dict * optimize configs * fix get_model_path * 更改一些默认参数，添加千帆的默认配置 * Update server_config.py.example
95 lines
3.9 KiB
Python
95 lines
3.9 KiB
Python
import json
|
|
from typing import List, Dict, Optional
|
|
|
|
from langchain.embeddings.base import Embeddings
|
|
from langchain.schema import Document
|
|
from langchain.vectorstores import PGVector
|
|
from langchain.vectorstores.pgvector import DistanceStrategy
|
|
from sqlalchemy import text
|
|
|
|
from configs import kbs_config
|
|
|
|
from server.knowledge_base.kb_service.base import SupportedVSType, KBService, EmbeddingsFunAdapter, \
|
|
score_threshold_process
|
|
from server.knowledge_base.utils import load_embeddings, KnowledgeFile
|
|
from server.utils import embedding_device as get_embedding_device
|
|
|
|
|
|
class PGKBService(KBService):
|
|
pg_vector: PGVector
|
|
|
|
def _load_pg_vector(self, embedding_device: str = get_embedding_device(), embeddings: Embeddings = None):
|
|
_embeddings = embeddings
|
|
if _embeddings is None:
|
|
_embeddings = load_embeddings(self.embed_model, embedding_device)
|
|
self.pg_vector = PGVector(embedding_function=EmbeddingsFunAdapter(_embeddings),
|
|
collection_name=self.kb_name,
|
|
distance_strategy=DistanceStrategy.EUCLIDEAN,
|
|
connection_string=kbs_config.get("pg").get("connection_uri"))
|
|
|
|
def get_doc_by_id(self, id: str) -> Optional[Document]:
|
|
with self.pg_vector.connect() as connect:
|
|
stmt = text("SELECT document, cmetadata FROM langchain_pg_embedding WHERE collection_id=:id")
|
|
results = [Document(page_content=row[0], metadata=row[1]) for row in
|
|
connect.execute(stmt, parameters={'id': id}).fetchall()]
|
|
if len(results) > 0:
|
|
return results[0]
|
|
|
|
def do_init(self):
|
|
self._load_pg_vector()
|
|
|
|
def do_create_kb(self):
|
|
pass
|
|
|
|
def vs_type(self) -> str:
|
|
return SupportedVSType.PG
|
|
|
|
def do_drop_kb(self):
|
|
with self.pg_vector.connect() as connect:
|
|
connect.execute(text(f'''
|
|
-- 删除 langchain_pg_embedding 表中关联到 langchain_pg_collection 表中 的记录
|
|
DELETE FROM langchain_pg_embedding
|
|
WHERE collection_id IN (
|
|
SELECT uuid FROM langchain_pg_collection WHERE name = '{self.kb_name}'
|
|
);
|
|
-- 删除 langchain_pg_collection 表中 记录
|
|
DELETE FROM langchain_pg_collection WHERE name = '{self.kb_name}';
|
|
'''))
|
|
connect.commit()
|
|
|
|
def do_search(self, query: str, top_k: int, score_threshold: float, embeddings: Embeddings):
|
|
self._load_pg_vector(embeddings=embeddings)
|
|
return score_threshold_process(score_threshold, top_k,
|
|
self.pg_vector.similarity_search_with_score(query, top_k))
|
|
|
|
def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]:
|
|
ids = self.pg_vector.add_documents(docs)
|
|
doc_infos = [{"id": id, "metadata": doc.metadata} for id, doc in zip(ids, docs)]
|
|
return doc_infos
|
|
|
|
def do_delete_doc(self, kb_file: KnowledgeFile, **kwargs):
|
|
with self.pg_vector.connect() as connect:
|
|
filepath = kb_file.filepath.replace('\\', '\\\\')
|
|
connect.execute(
|
|
text(
|
|
''' DELETE FROM langchain_pg_embedding WHERE cmetadata::jsonb @> '{"source": "filepath"}'::jsonb;'''.replace(
|
|
"filepath", filepath)))
|
|
connect.commit()
|
|
|
|
def do_clear_vs(self):
|
|
self.pg_vector.delete_collection()
|
|
self.pg_vector.create_collection()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
from server.db.base import Base, engine
|
|
|
|
# Base.metadata.create_all(bind=engine)
|
|
pGKBService = PGKBService("test")
|
|
# pGKBService.create_kb()
|
|
# pGKBService.add_doc(KnowledgeFile("README.md", "test"))
|
|
# pGKBService.delete_doc(KnowledgeFile("README.md", "test"))
|
|
# pGKBService.drop_kb()
|
|
print(pGKBService.get_doc_by_id("f1e51390-3029-4a19-90dc-7118aaa25772"))
|
|
# print(pGKBService.search_docs("如何启动api服务"))
|