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