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| from typing import List, Dict, Optional | |
| from langchain.schema import Document | |
| from langchain.vectorstores.milvus import Milvus | |
| from configs import kbs_config | |
| from server.knowledge_base.kb_service.base import KBService, SupportedVSType, EmbeddingsFunAdapter, \ | |
| score_threshold_process | |
| from server.knowledge_base.utils import KnowledgeFile | |
| class MilvusKBService(KBService): | |
| milvus: Milvus | |
| def get_collection(milvus_name): | |
| from pymilvus import Collection | |
| return Collection(milvus_name) | |
| def get_doc_by_ids(self, ids: List[str]) -> List[Document]: | |
| result = [] | |
| if self.milvus.col: | |
| # ids = [int(id) for id in ids] # for milvus if needed #pr 2725 | |
| data_list = self.milvus.col.query(expr=f'pk in {ids}', output_fields=["*"]) | |
| for data in data_list: | |
| text = data.pop("text") | |
| result.append(Document(page_content=text, metadata=data)) | |
| return result | |
| def del_doc_by_ids(self, ids: List[str]) -> bool: | |
| self.milvus.col.delete(expr=f'pk in {ids}') | |
| def search(milvus_name, content, limit=3): | |
| search_params = { | |
| "metric_type": "L2", | |
| "params": {"nprobe": 10}, | |
| } | |
| c = MilvusKBService.get_collection(milvus_name) | |
| return c.search(content, "embeddings", search_params, limit=limit, output_fields=["content"]) | |
| def do_create_kb(self): | |
| pass | |
| def vs_type(self) -> str: | |
| return SupportedVSType.MILVUS | |
| def _load_milvus(self): | |
| self.milvus = Milvus(embedding_function=EmbeddingsFunAdapter(self.embed_model), | |
| collection_name=self.kb_name, | |
| connection_args=kbs_config.get("milvus"), | |
| index_params=kbs_config.get("milvus_kwargs")["index_params"], | |
| search_params=kbs_config.get("milvus_kwargs")["search_params"] | |
| ) | |
| def do_init(self): | |
| self._load_milvus() | |
| def do_drop_kb(self): | |
| if self.milvus.col: | |
| self.milvus.col.release() | |
| self.milvus.col.drop() | |
| def do_search(self, query: str, top_k: int, score_threshold: float): | |
| self._load_milvus() | |
| embed_func = EmbeddingsFunAdapter(self.embed_model) | |
| embeddings = embed_func.embed_query(query) | |
| docs = self.milvus.similarity_search_with_score_by_vector(embeddings, top_k) | |
| return score_threshold_process(score_threshold, top_k, docs) | |
| def do_add_doc(self, docs: List[Document], **kwargs) -> List[Dict]: | |
| for doc in docs: | |
| for k, v in doc.metadata.items(): | |
| doc.metadata[k] = str(v) | |
| for field in self.milvus.fields: | |
| doc.metadata.setdefault(field, "") | |
| doc.metadata.pop(self.milvus._text_field, None) | |
| doc.metadata.pop(self.milvus._vector_field, None) | |
| ids = self.milvus.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): | |
| if self.milvus.col: | |
| filepath = kb_file.filepath.replace('\\', '\\\\') | |
| delete_list = [item.get("pk") for item in | |
| self.milvus.col.query(expr=f'source == "{filepath}"', output_fields=["pk"])] | |
| self.milvus.col.delete(expr=f'pk in {delete_list}') | |
| def do_clear_vs(self): | |
| if self.milvus.col: | |
| self.do_drop_kb() | |
| self.do_init() | |
| if __name__ == '__main__': | |
| # 测试建表使用 | |
| from server.db.base import Base, engine | |
| Base.metadata.create_all(bind=engine) | |
| milvusService = MilvusKBService("test") | |
| # milvusService.add_doc(KnowledgeFile("README.md", "test")) | |
| print(milvusService.get_doc_by_ids(["444022434274215486"])) | |
| # milvusService.delete_doc(KnowledgeFile("README.md", "test")) | |
| # milvusService.do_drop_kb() | |
| # print(milvusService.search_docs("如何启动api服务")) | |