from fastapi import Body from configs import (DEFAULT_VS_TYPE, EMBEDDING_MODEL, OVERLAP_SIZE, logger, log_verbose, ) from server.knowledge_base.utils import (list_files_from_folder) from sse_starlette import EventSourceResponse import json from server.knowledge_base.kb_service.base import KBServiceFactory from typing import List, Optional from server.knowledge_base.kb_summary.base import KBSummaryService from server.knowledge_base.kb_summary.summary_chunk import SummaryAdapter from server.utils import wrap_done, get_ChatOpenAI, BaseResponse from configs import LLM_MODELS, TEMPERATURE from server.knowledge_base.model.kb_document_model import DocumentWithVSId def recreate_summary_vector_store( knowledge_base_name: str = Body(..., examples=["samples"]), allow_empty_kb: bool = Body(True), vs_type: str = Body(DEFAULT_VS_TYPE), embed_model: str = Body(EMBEDDING_MODEL), file_description: str = Body(''), model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"), temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0), max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"), ): """ 重建单个知识库文件摘要 :param max_tokens: :param model_name: :param temperature: :param file_description: :param knowledge_base_name: :param allow_empty_kb: :param vs_type: :param embed_model: :return: """ def output(): kb = KBServiceFactory.get_service(knowledge_base_name, vs_type, embed_model) if not kb.exists() and not allow_empty_kb: yield {"code": 404, "msg": f"未找到知识库 ‘{knowledge_base_name}’"} else: # 重新创建知识库 kb_summary = KBSummaryService(knowledge_base_name, embed_model) kb_summary.drop_kb_summary() kb_summary.create_kb_summary() llm = get_ChatOpenAI( model_name=model_name, temperature=temperature, max_tokens=max_tokens, ) reduce_llm = get_ChatOpenAI( model_name=model_name, temperature=temperature, max_tokens=max_tokens, ) # 文本摘要适配器 summary = SummaryAdapter.form_summary(llm=llm, reduce_llm=reduce_llm, overlap_size=OVERLAP_SIZE) files = list_files_from_folder(knowledge_base_name) i = 0 for i, file_name in enumerate(files): doc_infos = kb.list_docs(file_name=file_name) docs = summary.summarize(file_description=file_description, docs=doc_infos) status_kb_summary = kb_summary.add_kb_summary(summary_combine_docs=docs) if status_kb_summary: logger.info(f"({i + 1} / {len(files)}): {file_name} 总结完成") yield json.dumps({ "code": 200, "msg": f"({i + 1} / {len(files)}): {file_name}", "total": len(files), "finished": i + 1, "doc": file_name, }, ensure_ascii=False) else: msg = f"知识库'{knowledge_base_name}'总结文件‘{file_name}’时出错。已跳过。" logger.error(msg) yield json.dumps({ "code": 500, "msg": msg, }) i += 1 return EventSourceResponse(output()) def summary_file_to_vector_store( knowledge_base_name: str = Body(..., examples=["samples"]), file_name: str = Body(..., examples=["test.pdf"]), allow_empty_kb: bool = Body(True), vs_type: str = Body(DEFAULT_VS_TYPE), embed_model: str = Body(EMBEDDING_MODEL), file_description: str = Body(''), model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"), temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0), max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"), ): """ 单个知识库根据文件名称摘要 :param model_name: :param max_tokens: :param temperature: :param file_description: :param file_name: :param knowledge_base_name: :param allow_empty_kb: :param vs_type: :param embed_model: :return: """ def output(): kb = KBServiceFactory.get_service(knowledge_base_name, vs_type, embed_model) if not kb.exists() and not allow_empty_kb: yield {"code": 404, "msg": f"未找到知识库 ‘{knowledge_base_name}’"} else: # 重新创建知识库 kb_summary = KBSummaryService(knowledge_base_name, embed_model) kb_summary.create_kb_summary() llm = get_ChatOpenAI( model_name=model_name, temperature=temperature, max_tokens=max_tokens, ) reduce_llm = get_ChatOpenAI( model_name=model_name, temperature=temperature, max_tokens=max_tokens, ) # 文本摘要适配器 summary = SummaryAdapter.form_summary(llm=llm, reduce_llm=reduce_llm, overlap_size=OVERLAP_SIZE) doc_infos = kb.list_docs(file_name=file_name) docs = summary.summarize(file_description=file_description, docs=doc_infos) status_kb_summary = kb_summary.add_kb_summary(summary_combine_docs=docs) if status_kb_summary: logger.info(f" {file_name} 总结完成") yield json.dumps({ "code": 200, "msg": f"{file_name} 总结完成", "doc": file_name, }, ensure_ascii=False) else: msg = f"知识库'{knowledge_base_name}'总结文件‘{file_name}’时出错。已跳过。" logger.error(msg) yield json.dumps({ "code": 500, "msg": msg, }) return EventSourceResponse(output()) def summary_doc_ids_to_vector_store( knowledge_base_name: str = Body(..., examples=["samples"]), doc_ids: List = Body([], examples=[["uuid"]]), vs_type: str = Body(DEFAULT_VS_TYPE), embed_model: str = Body(EMBEDDING_MODEL), file_description: str = Body(''), model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"), temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0), max_tokens: Optional[int] = Body(None, description="限制LLM生成Token数量，默认None代表模型最大值"), ) -> BaseResponse: """ 单个知识库根据doc_ids摘要 :param knowledge_base_name: :param doc_ids: :param model_name: :param max_tokens: :param temperature: :param file_description: :param vs_type: :param embed_model: :return: """ kb = KBServiceFactory.get_service(knowledge_base_name, vs_type, embed_model) if not kb.exists(): return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}", data={}) else: llm = get_ChatOpenAI( model_name=model_name, temperature=temperature, max_tokens=max_tokens, ) reduce_llm = get_ChatOpenAI( model_name=model_name, temperature=temperature, max_tokens=max_tokens, ) # 文本摘要适配器 summary = SummaryAdapter.form_summary(llm=llm, reduce_llm=reduce_llm, overlap_size=OVERLAP_SIZE) doc_infos = kb.get_doc_by_ids(ids=doc_ids) # doc_infos转换成DocumentWithVSId包装的对象 doc_info_with_ids = [DocumentWithVSId(**doc.dict(), id=with_id) for with_id, doc in zip(doc_ids, doc_infos)] docs = summary.summarize(file_description=file_description, docs=doc_info_with_ids) # 将docs转换成dict resp_summarize = [{**doc.dict()} for doc in docs] return BaseResponse(code=200, msg="总结完成", data={"summarize": resp_summarize})