209 lines
9.9 KiB
Python
209 lines
9.9 KiB
Python
import uuid
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from fastapi import Body
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from langchain.memory import (
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CombinedMemory,
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ConversationBufferMemory,
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ConversationSummaryMemory,
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ConversationBufferWindowMemory
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)
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from configs.model_config import MAX_TOKENS
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from configs.outline_config import outlines
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from langchain.chains.question_answering import load_qa_chain
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from typing import Any
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from sse_starlette.sse import EventSourceResponse
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from configs import LLM_MODELS, TEMPERATURE, HISTORY_LEN
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from configs.kb_config import CH_BASE_NAME
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from server.agent.tools.search_tool import search_tool
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from server.chat import utils
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from server.chat.agent_chat_test import agent_chat_test
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from server.chat.policy_fun_iast import get_llm_model_response
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from server.chat.solve_problem import solve_problem
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from server.knowledge_base.kb_service.base import TextRank
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from server.knowledge_base.model.kb_document_model import DocumentWithVSId
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from server.utils import wrap_done, get_ChatOpenAI
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from langchain.chains import LLMChain, ConversationChain
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from langchain.callbacks import AsyncIteratorCallbackHandler
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from typing import AsyncIterable
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import asyncio
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import json
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from langchain.prompts.chat import ChatPromptTemplate
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from typing import List, Optional, Union
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from server.chat.utils import History, compute_lps, remove_after_and_including,remove_before_and_including
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from langchain.prompts import PromptTemplate
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from server.utils import get_prompt_template, get_format_template
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from server.memory.conversation_db_buffer_memory import ConversationBufferDBMemory
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from server.db.repository import add_message_to_db
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from server.callback_handler.conversation_callback_handler import ConversationCallbackHandler
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from datetime import datetime
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from langchain_core.messages import SystemMessage
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import time as t
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from configs.basic_config import *
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async def process_task(task):
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results = []
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async for result in task:
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results.append(result)
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return results
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async def knowledge_chat_test(
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uid: Optional[Any] = Body(None, description="用户ID"),
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rag: bool = Body(False, description="增强标识, 当knowledge_base_list有值时，需要传True"),
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knowledge_base_list: Optional[List[str]] = Body(None, description="知识库名称列表"),
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query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
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conversation_id: str = Body("", description="对话框ID"),
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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=2.0),
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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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# top_p: float = Body(TOP_P, description="LLM 核采样。勿与temperature 同时设置", gt=0.0, lt=1.0),
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prompt_name: str = Body("default", 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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async def chat_iterator() -> AsyncIterable[str]:
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nonlocal rag,max_tokens, uid, model_name,knowledge_base_list
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# rag = True
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max_tokens = MAX_TOKENS
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history_summary = ""
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memory = None
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message_id = str(uuid.uuid1())+"q"
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think_type = {"text": "", "message_id": message_id}
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user_prompt_name = prompt_name
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outline_num = ""
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res = ""
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callback = AsyncIteratorCallbackHandler()
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callbacks = [callback]
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conversation_callback = ConversationCallbackHandler(conversation_id=conversation_id, message_id=message_id,
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chat_type="llm_chat",
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query=query)
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callbacks.append(conversation_callback)
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time_based_uuid = str(uuid.uuid1())+"q"
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tip ={}
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tip["END"]=""
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tip["source_docs"]=[]
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tip["num"] = 0
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tip["title"]=[]
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utils.set_shared_variable(time_based_uuid,tip)
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# 先判断需不需要使用知识库或者联网检索同时需要获取大纲模板
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task1 = solve_problem(user_prompt_name= user_prompt_name,query=query, conversation_id="", history=[], model_name=model_name,temperature=temperature,max_tokens=max_tokens,prompt_name="think_route",stream=True)
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task2 = solve_problem(user_prompt_name= user_prompt_name,query=query, conversation_id="", history=[], model_name=model_name,temperature=temperature,max_tokens=max_tokens,prompt_name="outlines_route",stream=True)
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results = await asyncio.gather(process_task(task1), process_task(task2))
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for result0 in results[0]:
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think_type["text"] += json.loads(result0)["text"].strip()
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break
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for result1 in results[1]:
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outline_num += json.loads(result1)["text"]
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break
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if(str(think_type["text"]) == "2") or rag == True:
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yield json.dumps({"text": "思考中..."}, ensure_ascii=False)
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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=callbacks,
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)
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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=LLM_MODELS[0],
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template_prompt_name="query_rewrite",
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prompt_param_dict={"query": query, "history": [], "time": datetime.now().strftime("%Y%m%d")},
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temperature=0.01,
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max_tokens=512
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)
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# 第一个 JSON 对象
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keys = json.loads(search_query).keys()
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keys_list = list(keys)
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if rag:
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knowledge_base_list = list(set(CH_BASE_NAME).union(knowledge_base_list))
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logger.info(f"大纲撰写参考的个人知识库名称：{knowledge_base_list}")
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first_json = {
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"query": json.loads(search_query)[keys_list[0]],
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"knowledge_name": knowledge_base_list,
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"keywords": []
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}
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else:
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first_json = {
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"query": json.loads(search_query)[keys_list[0]],
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"knowledge_name": CH_BASE_NAME,
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"keywords": []
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}
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# 第二个 JSON 对象
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second_json = {
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"uuid": time_based_uuid
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}
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origin_query=json.dumps(first_json) + json.dumps(second_json)
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# 截断检索资料，避免超token
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res += search_tool(origin_query)
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if len(res) >= 7000:
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res = res[:7000]
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else:
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model = get_ChatOpenAI(
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model_name=LLM_MODELS[0],
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temperature=temperature,
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max_tokens=max_tokens,
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callbacks=callbacks,
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)
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prompt_template = get_prompt_template("knowledge_base_chat", "default_outlines")
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input_prompt = History(role="system", content=prompt_template).to_msg_template(False)
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# input_msg = History(role="user", content=query).to_msg_template(False)
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chat_prompt = ChatPromptTemplate.from_messages([input_prompt])
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chain = LLMChain(prompt=chat_prompt, llm=model)
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input_prompt = History(role="system", content=prompt_template).to_msg_template(False)
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chat_prompt = ChatPromptTemplate.from_messages([input_prompt])
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format_template = get_format_template("knowledge_base_chat", "default_outlines")
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chain = load_qa_chain(
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model, chain_type="stuff", memory=memory, prompt=chat_prompt, verbose=True
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)
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# docs = list(itertools.chain(policydocs, reportdocs, journaldocs, personaldocs))
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if not outline_num or outline_num == "" or "无" in outline_num:
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outline_num = len(outlines)-1
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if res =="":
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res+="根据你的自身能力回答"
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results1=[]
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results1.append(DocumentWithVSId(page_content=res))
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logger.info(f"outline_num:{outline_num}")
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task = asyncio.create_task(wrap_done(
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chain.acall({
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# "context": res,
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"input_documents": results1,
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"self_knowledge":outlines[int(outline_num)],
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"history": [],
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"question": query,
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"format_template": format_template,
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"time": datetime.now().strftime("%Y%m%d")
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}),
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callback.done),
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)
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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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history_summary += token
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yield json.dumps(
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{"text": token, "message_id": message_id},
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ensure_ascii=False)
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# try:
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# source = utils.get_shared_variable(time_based_uuid)
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# source_docs = source["source_docs"]
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# docs_string = "\n"+"\n".join(f"{str(doc)}\n" for doc in source_docs)
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# yield json.dumps({"docs": docs_string}, ensure_ascii=False)
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# except Exception as e:
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# logging.error(f"获取知识库联想信息失败：{e}")
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# print("知识库联想存在异常或没有进行知识库联想")
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# pass
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#summary = TextRank(history_summary, 80)
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#yield json.dumps({"summary":summary}, ensure_ascii=False)
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# del question_history
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await task
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callback = AsyncIteratorCallbackHandler()
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return EventSourceResponse(chat_iterator())
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