92 lines
4.0 KiB
Python
92 lines
4.0 KiB
Python
from typing import List, Dict, Optional
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from langchain.embeddings.base import Embeddings
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from langchain.schema import Document
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from langchain.vectorstores import PGVector
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from langchain.vectorstores.pgvector import DistanceStrategy
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from sqlalchemy import text
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from configs import kbs_config
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from server.knowledge_base.kb_service.base import SupportedVSType, KBService, EmbeddingsFunAdapter, \
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score_threshold_process
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from server.knowledge_base.utils import load_embeddings, KnowledgeFile
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from server.utils import embedding_device as get_embedding_device
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class PGKBService(KBService):
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pg_vector: PGVector
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def _load_pg_vector(self, embedding_device: str = get_embedding_device(), embeddings: Embeddings = None):
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_embeddings = embeddings
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if _embeddings is None:
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_embeddings = load_embeddings(self.embed_model, embedding_device)
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self.pg_vector = PGVector(embedding_function=EmbeddingsFunAdapter(_embeddings),
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collection_name=self.kb_name,
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distance_strategy=DistanceStrategy.EUCLIDEAN,
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connection_string=kbs_config.get("pg").get("connection_uri"))
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def get_doc_by_id(self, id: str) -> Optional[Document]:
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with self.pg_vector.connect() as connect:
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stmt = text("SELECT document, cmetadata FROM langchain_pg_embedding WHERE collection_id=:id")
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results = [Document(page_content=row[0], metadata=row[1]) for row in
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connect.execute(stmt, parameters={'id': id}).fetchall()]
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if len(results) > 0:
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return results[0]
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def do_init(self):
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self._load_pg_vector()
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def do_create_kb(self):
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pass
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def vs_type(self) -> str:
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return SupportedVSType.PG
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def do_drop_kb(self):
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with self.pg_vector.connect() as connect:
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connect.execute(text(f'''
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-- 删除 langchain_pg_embedding 表中关联到 langchain_pg_collection 表中 的记录
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DELETE FROM langchain_pg_embedding
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WHERE collection_id IN (
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SELECT uuid FROM langchain_pg_collection WHERE name = '{self.kb_name}'
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);
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-- 删除 langchain_pg_collection 表中 记录
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DELETE FROM langchain_pg_collection WHERE name = '{self.kb_name}';
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'''))
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connect.commit()
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def do_search(self, query: str, top_k: int, score_threshold: float, embeddings: Embeddings):
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self._load_pg_vector(embeddings=embeddings)
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return score_threshold_process(score_threshold, top_k,
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self.pg_vector.similarity_search_with_score(query, top_k))
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def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]:
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ids = self.pg_vector.add_documents(docs)
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doc_infos = [{"id": id, "metadata": doc.metadata} for id, doc in zip(ids, docs)]
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return doc_infos
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def do_delete_doc(self, kb_file: KnowledgeFile, **kwargs):
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with self.pg_vector.connect() as connect:
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filepath = kb_file.filepath.replace('\\', '\\\\')
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connect.execute(
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text(
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''' DELETE FROM langchain_pg_embedding WHERE cmetadata::jsonb @> '{"source": "filepath"}'::jsonb;'''.replace(
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"filepath", filepath)))
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connect.commit()
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def do_clear_vs(self):
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self.pg_vector.delete_collection()
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self.pg_vector.create_collection()
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if __name__ == '__main__':
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# Base.metadata.create_all(bind=engine)
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pGKBService = PGKBService("test")
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# pGKBService.create_kb()
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# pGKBService.add_doc(KnowledgeFile("README.md", "test"))
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# pGKBService.delete_doc(KnowledgeFile("README.md", "test"))
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# pGKBService.drop_kb()
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print(pGKBService.get_doc_by_id("f1e51390-3029-4a19-90dc-7118aaa25772"))
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# print(pGKBService.search_docs("如何启动api服务"))
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