232 lines
10 KiB
Python
232 lines
10 KiB
Python
from fastapi import Body, Request
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from sse_starlette.sse import EventSourceResponse
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from fastapi.concurrency import run_in_threadpool
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from configs import (LLM_MODELS,
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VECTOR_SEARCH_TOP_K,
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SCORE_THRESHOLD,
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TEMPERATURE,
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USE_RERANKER,
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RERANKER_MODEL,
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RERANKER_MAX_LENGTH,
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MODEL_PATH,
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MAX_TOKENS,
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MAX_CUT_TOKENS)
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from server.utils import wrap_done, get_ChatOpenAI
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from server.utils import BaseResponse, get_prompt_template, get_format_template
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from server.utils import get_strategy_prompt_template
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from langchain.chains import LLMChain
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from langchain.callbacks import AsyncIteratorCallbackHandler
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from typing import AsyncIterable, List, Optional
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import asyncio
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from langchain.prompts.chat import ChatPromptTemplate
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from server.chat.utils import History
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from server.knowledge_base.kb_service.base import KBServiceFactory
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import json
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from urllib.parse import urlencode
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from server.knowledge_base.kb_doc_api import search_docs
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from server.reranker.reranker import LangchainReranker
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from server.utils import embedding_device
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from server.chat.policy_fun import add_summary_retrieved_results, get_llm_model_response
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import json
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async def report_chat(query: str = Body(..., description="用户输入", examples=["你好"]),
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fileName: List = Body([], description="文件名称", examples=[[]]),
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knowledge_base_name: str = Body(..., description="知识库名称",
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examples=["t_strategy_report_bge_v1"]),
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top_k: int = Body(VECTOR_SEARCH_TOP_K, description="匹配向量数"),
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score_threshold: float = Body(
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SCORE_THRESHOLD,
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description="知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右",
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ge=0,
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le=2
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),
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history: List[History] = Body(
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[],
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description="历史对话",
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examples=[[]]
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),
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stream: bool = Body(False, description="流式输出"),
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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(
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MAX_TOKENS,
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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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prompt_name: str = Body(
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"default",
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description="使用的prompt模板名称(在configs/prompt_config.py中配置)"
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),
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request: Request = None,
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use_summary = True,
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chunk_size: int = 20000,
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min_chunk_size: int = 2000,
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summary_model_name = LLM_MODELS[0],
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query_rewrite_model_name = LLM_MODELS[0]
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):
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kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
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if kb is None:
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return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
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history = [History.from_data(h) for h in history]
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async def knowledge_base_chat_iterator(
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query: str,
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top_k: int,
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history: Optional[List[History]],
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model_name: str = model_name,
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prompt_name: str = prompt_name,
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) -> AsyncIterable[str]:
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nonlocal max_tokens
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callback = AsyncIteratorCallbackHandler()
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if isinstance(max_tokens, int) and max_tokens <= 0:
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max_tokens = None
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model = 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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callbacks=[callback],
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)
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# print('-------------- debug', query)
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search_query = get_llm_model_response(
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strategy_name="query rewrite",
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llm_model_name=query_rewrite_model_name,
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template_prompt_name="query_rewrite_report",
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prompt_param_dict={"query": query},
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temperature=0.01,
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max_tokens=512
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)
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# print('search query', search_query)
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json_string = search_query.strip("```json\n").strip("```")
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# print('search query----json string', json_string)
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try: # 防止json格式错误
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# 读取改写后的query
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data = json.loads(json_string)
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policies = data['report']
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search_query = ''
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for policy in policies:
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search_query += policy
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except:
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search_query = query
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print('search query', search_query)
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docs = await run_in_threadpool(search_docs,
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fileName=fileName,
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query=search_query,
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knowledge_base_name=knowledge_base_name,
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top_k=top_k,
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score_threshold=score_threshold)
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# print(docs)
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# doc加入metadata的summary字段
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if use_summary:
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docs = await add_summary_retrieved_results(docs, query, 512,chunk_size,min_chunk_size,summary_model_name)
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print(docs)
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# context = "\n".join([doc.page_content for doc in docs])
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# 需要规范格式的prompt_name
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# 默认default，即为空，不用管
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format_list = ["Abstract Assistant", "Outline Assistant"]
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if prompt_name in format_list:
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format_template = get_format_template("knowledge_base_chat", "abstract_format")
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else:
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format_template = get_format_template("knowledge_base_chat", "default")
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# 政策知识库
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# 相关信息把标题和内容进行整合
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if knowledge_base_name == 't_strategy_report_bge_v1':
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knowledge = []
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newdocs =[]
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for inum,doc in enumerate(docs):
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if use_summary :
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if len(doc.metadata['summary'])>15:
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knowledge.append(f"""参考报告[{len(knowledge) + 1}] 报告来源： {doc.metadata['source']} \n报告内容： {doc.metadata['summary']}""")
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newdocs.append(doc)
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else:
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pass
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else:
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knowledge.append(f"""参考报告[{inum + 1}] 报告来源: {doc.metadata['source']} \n报告内容: {doc.page_content}""")
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context = "\n\n".join(knowledge)
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docs = newdocs
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# 非报告知识库
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else:
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context = "\n".join([doc.page_content for doc in docs])
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if len(docs) == 0 and fileName: # 如果没有找到相关文档，使用empty模板
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prompt_template = get_prompt_template("knowledge_base_chat", prompt_name)
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elif len(docs) == 0 and not fileName and prompt_name != "Abstract Assistant":
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prompt_template = get_prompt_template("knowledge_base_chat", "empty")
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elif prompt_name == 'iast_report_chat' or (knowledge_base_name == "t_strategy_report_bge_v1" and prompt_name == 'default'):
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print("use report prompt_template")
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prompt_template = get_strategy_prompt_template("knowledge_base_chat", 'iast_report_chat')
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else:
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prompt_template = get_prompt_template("knowledge_base_chat", prompt_name)
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print("prompt_template", prompt_template)
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input_msg = History(role="user", content=prompt_template).to_msg_template(False)
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chat_prompt = ChatPromptTemplate.from_messages(
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[i.to_msg_template() for i in history] + [input_msg])
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chain = LLMChain(prompt=chat_prompt, llm=model)
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print(
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f"\n知识库问答开始调用：参数：\nkb:{knowledge_base_name}\nquery:{query}\nhistory:{history}\ncontext:{context}\nfile_name:{fileName}\nformat_template:{format_template}\n\n")
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query = query.replace("原文", "")
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task = asyncio.create_task(wrap_done(
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chain.acall({"context": context,
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"history": history,
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"question": query,
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"file_name": str(fileName),
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"format_template": format_template}),
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callback.done),
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)
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source_documents = []
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# 报告知识库
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if knowledge_base_name == 't_strategy_report_bge_v1':
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for inum, doc in enumerate(docs):
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filename = doc.metadata.get("source")
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print("filename", filename)
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if filename:
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text = f"""[{inum + 1}] 报告出处: [{filename}]\n\n{doc.metadata['summary']}\n\n"""
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else:
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text = f"""[{inum + 1}] \n\n{doc.metadata['summary']}\n\n"""
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source_documents.append(text)
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# 非报告知识库
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else:
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for inum, doc in enumerate(docs):
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filename = doc.metadata.get("source")
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parameters = urlencode({"knowledge_base_name": knowledge_base_name, "file_name": filename})
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base_url = request.base_url
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url = f"{base_url}knowledge_base/download_doc?" + parameters
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if filename:
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text = f"""出处: [{filename}]({url}) \n\n"""
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else:
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text = f"""出处: [{"原文地址"}]({url}) \n\n"""
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source_documents.append(text)
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if len(source_documents) == 0: # 没有找到相关文档
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source_documents.append(f"<span style='color:red'>未找到相关文档,该回答为大模型自身能力解答！</span>")
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if stream:
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async for token in callback.aiter():
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# Use server-sent-events to stream the response
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yield json.dumps({"answer": token}, ensure_ascii=False)
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else:
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answer = ""
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async for token in callback.aiter():
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answer += token
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yield json.dumps({"answer": answer})
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await task
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yield json.dumps({"docs": source_documents}, ensure_ascii=False)
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return EventSourceResponse(knowledge_base_chat_iterator(query, top_k, history, model_name, prompt_name))
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