[全量] 初始化项目代码、配置、文档及Agent协同harness
This commit is contained in:
564
langchain-chat/server/chat/knowledge_base_chat.py
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564
langchain-chat/server/chat/knowledge_base_chat.py
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from fastapi import Body, Request
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from langchain.chains.question_answering import load_qa_chain
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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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POLICY_KNOWLEDGE_BASE,
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REPORT_KNOWLEDGE_BASE,
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JOURNAL_KNOWLEDGE_BASE,
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OLD_POLICY_BASE
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)
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from configs.kb_config import OLD_JOURNAL_BASE
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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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from server.chat.policy_fun_iast import get_llm_model_response
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import json
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from langchain.memory import ConversationSummaryBufferMemory, ConversationBufferWindowMemory, ConversationBufferMemory
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from langchain_core.prompts import PromptTemplate
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import itertools
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from datetime import datetime
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import time
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from langchain.schema import Document
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REPLACEMENT_RULES = [
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(OLD_POLICY_BASE, "t_policy_total_bge_new_v2"),
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(OLD_JOURNAL_BASE, "t_journal_article_bge_v1")
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]
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async def knowledge_base_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=[["123.txt"]]),
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knowledge_base_name_list: list = Body(..., description="多种知识库名称",
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examples=[[ "t_policy_total_bge_v1","t_strategy_report_20_bge_v2","t_journal_article_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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{"role": "user",
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"content": "我们来玩成语接龙，我先来，生龙活虎"},
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{"role": "assistant",
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"content": "虎头虎脑"}]]
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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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use_model_self_response = 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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# 创建集合提高查找效率
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original_kb_set = set(knowledge_base_name_list)
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new_elements_added = []
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# 批量处理替换规则
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for old_bases, new_base in REPLACEMENT_RULES:
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# 使用集合运算快速找到需要移除的元素
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to_remove = original_kb_set & set(old_bases)
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if to_remove:
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# 使用列表推导式生成新列表（保持原有顺序）
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knowledge_base_name_list = [
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elem for elem in knowledge_base_name_list
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if elem not in to_remove
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]
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new_elements_added.append(new_base)
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# 去重后添加新元素（如果原列表已存在则不添加）
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for new_base in new_elements_added:
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if new_base not in knowledge_base_name_list:
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knowledge_base_name_list.append(new_base)
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print(f'========== 当前检索的知识库：{knowledge_base_name_list} ==========')
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new_knowledge_base_name_list = knowledge_base_name_list[:]
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for knowledge_base_name in knowledge_base_name_list:
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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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# 记录开始时间
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start_time = time.time()
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history = [History.from_data(h) for h in history]
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print(f"========== 当前的对话历史为==========\n{history}")
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# 获取当前时间并格式化为YYYYMMDD
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current_time = datetime.now().strftime("%Y%m%d")
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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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memory = None
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policydocs = []
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reportdocs = []
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journaldocs = []
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personaldocs = []
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docs = []
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if isinstance(max_tokens, int) and max_tokens <= 0:
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max_tokens = None
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if prompt_name == "policy_chat":
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model_name = LLM_MODELS[1]
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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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knowledge = []
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self_knowledge = []
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user_queries = [] # 初始化列表来收集用户消息
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if use_model_self_response:
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# 获取大模型本身对用户问题的回答
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modelself_response=get_llm_model_response(
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strategy_name="self response",
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llm_model_name=query_rewrite_model_name,
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template_prompt_name="self_response",
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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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self_knowledge.append(f"""{modelself_response}""")
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if len(knowledge_base_name_list) != 0:
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# 政策库
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if POLICY_KNOWLEDGE_BASE in knowledge_base_name_list:
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# 遍历历史消息并收集用户消息
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for message in history:
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if message.role == 'user':
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user_queries.append(message.content)
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#改写原问题
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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_policy",
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prompt_param_dict={"query": query, "history": user_queries, "time": current_time},
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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: ", query)
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print("search_history: ", user_queries)
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json_string = search_query.strip("```json\n").strip("```")
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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['policies']
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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('policy search query', search_query)
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#搜索政策相关的docs
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policydocs = await run_in_threadpool(search_docs,
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fileName=fileName,
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query=search_query,
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usr_query=query,
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knowledge_base_name=POLICY_KNOWLEDGE_BASE,
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top_k=top_k,
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score_threshold=score_threshold)
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# print('政策数据库共搜索出：',len(policydocs))
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#使用概括将只有文章标题的内容总结成段落
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if use_summary:
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# policydocs = await add_summary_retrieved_results(policydocs, query, 512,chunk_size,min_chunk_size,summary_model_name)
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seen_docs = set() # 用于跟踪已见过的标题和内容组合
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duplicate_indices = [] # 用于跟踪重复文档的索引
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for inum,doc in enumerate(policydocs):
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if len(doc.metadata['summary'])>15:
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doc_identifier = (doc.metadata['title'], doc.page_content)
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# 检查此标识符是否已存在于集合中
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if doc_identifier not in seen_docs:
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# 如果不存在，将其添加到集合中
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seen_docs.add(doc_identifier)
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knowledge.append(f"""参考资料[{len(knowledge) + 1}] 文章标题： {doc.metadata['title']} \n文章内容： {doc.metadata['summary']}""")
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else:
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# 如果存在，将当前索引添加到重复索引列表中
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duplicate_indices.append(inum)
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else:
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duplicate_indices.append(inum)
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# 从policydocs中删除重复的文档（从后往前删除以防止索引错位）
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for index in sorted(duplicate_indices, reverse=True):
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del policydocs[index]
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else:
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for inum,doc in enumerate(policydocs):
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if doc.metadata["_type"] == "title":
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knowledge.append(f"""参考资料[{inum + 1}] 文章标题 {doc.page_content} \n文章内容 {doc.metadata['content']}""")
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if doc.metadata["_type"] == "content":
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knowledge.append(f"""参考资料[{inum + 1}] 文章标题 {doc.metadata['title']} \n文章内容 {doc.page_content}""")
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new_knowledge_base_name_list.remove(POLICY_KNOWLEDGE_BASE)
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# print('政策数据库剩下：',len(policydocs))
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# 报告库
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if REPORT_KNOWLEDGE_BASE in knowledge_base_name_list:
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# 遍历历史消息并收集用户消息
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for message in history:
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if message.role == 'user':
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user_queries.append(message.content)
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#先改写原问题
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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, "history": user_queries + [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: ", query)
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print("search_history: ", user_queries)
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json_string = search_query.strip("```json\n").strip("```")
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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('report search query', search_query)
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reportdocs = 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=REPORT_KNOWLEDGE_BASE,
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top_k=top_k,
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score_threshold=score_threshold,
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expr = " _type == 'content'")
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# print('报告数据库共搜索出：',len(reportdocs))
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seen_docs = set() # 用于跟踪已见过的标题和内容组合
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duplicate_indices = [] # 用于跟踪重复文档的索引
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for inum,doc in enumerate(reportdocs):
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doc_identifier = (doc.metadata['source'], doc.page_content)
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# 检查此标识符是否已存在于集合中
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if doc_identifier not in seen_docs:
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# 如果不存在，将其添加到集合中
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seen_docs.add(doc_identifier)
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# 并将文档信息添加到knowledge列表中
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knowledge.append(f"""参考资料[{len(knowledge) + 1}] 报告来源： {doc.metadata['source'].replace('.pdf','')} \n报告内容： {doc.page_content}""")
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else:
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duplicate_indices.append(inum)
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# print('重复报告',doc_identifier)
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# 从reportdocs中删除重复的文档（从后往前删除以防止索引错位）
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for index in sorted(duplicate_indices, reverse=True):
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del reportdocs[index]
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new_knowledge_base_name_list.remove(REPORT_KNOWLEDGE_BASE)
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# 期刊库
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if JOURNAL_KNOWLEDGE_BASE in knowledge_base_name_list:
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# 遍历历史消息并收集用户消息
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for message in history:
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if message.role == 'user':
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user_queries.append(message.content)
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#先改写原问题
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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",
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prompt_param_dict={"query": query, "history": user_queries + [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: ", query)
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print("search_history: ", user_queries)
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json_string = search_query.strip("```json\n").strip("```")
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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('journal search query', search_query)
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journaldocs = 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=JOURNAL_KNOWLEDGE_BASE,
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top_k=top_k,
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score_threshold=score_threshold)
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# print('期刊数据库共搜索出：',len(journaldocs))
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seen_docs = set() # 用于跟踪已见过的标题和内容组合
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duplicate_indices = [] # 用于跟踪重复文档的索引
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for inum,doc in enumerate(journaldocs):
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doc_identifier = (doc.metadata['title'], doc.metadata['abstract'])
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# 检查此标识符是否已存在于集合中
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if doc_identifier not in seen_docs:
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# 如果不存在，将其添加到集合中
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seen_docs.add(doc_identifier)
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# 并将文档信息添加到knowledge列表中
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knowledge.append(f"""参考资料[{len(knowledge) + 1}] 论文标题： {doc.metadata['title']} \n论文摘要： {doc.metadata['abstract']}""")
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else:
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duplicate_indices.append(inum)
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# print('重复期刊',doc_identifier)
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# 从journaldocs中删除重复的文档（从后往前删除以防止索引错位）
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for index in sorted(duplicate_indices, reverse=True):
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del journaldocs[index]
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new_knowledge_base_name_list.remove(JOURNAL_KNOWLEDGE_BASE)
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if len(new_knowledge_base_name_list)>0:
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# 个人知识库
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for knowledge_base_name in new_knowledge_base_name_list:
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if knowledge_base_name == 'yj_oa_journal_bge_v2_yejinbak':
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knowledge_base_name = 'yj_oa_article_v1_yejinbak' #采集数据代替oa资源
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personaldocs = await run_in_threadpool(search_docs,
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fileName=fileName,
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query=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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seen_docs = set() # 用于跟踪已见过的标题和内容组合
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for inum,doc in enumerate(personaldocs):
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doc_identifier = (doc.page_content)
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# 检查此标识符是否已存在于集合中
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if doc_identifier not in seen_docs:
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# 如果不存在，将其添加到集合中
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seen_docs.add(doc_identifier)
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# 并将文档信息添加到knowledge列表中
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knowledge.append(f"""参考资料[{len(knowledge) + 1}] {doc.page_content}""")
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else:
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personaldocs = await run_in_threadpool(search_docs,
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fileName=fileName,
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query=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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seen_docs = set() # 用于跟踪已见过的标题和内容组合
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for inum,doc in enumerate(personaldocs):
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doc_identifier = (doc.page_content)
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# 检查此标识符是否已存在于集合中
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if doc_identifier not in seen_docs:
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# 如果不存在，将其添加到集合中
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seen_docs.add(doc_identifier)
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# 并将文档信息添加到knowledge列表中
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knowledge.append(f"""参考资料[{len(knowledge) + 1}] {doc.page_content}""")
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# context = "\n\n".join(knowledge)
|
||||
docs = [Document(page_content=k) for k in knowledge]
|
||||
# print(f"=========================知识库问答参考资料====================\n{docs}\n====================知识库问答参考资料====================")
|
||||
format_list = ["Abstract Assistant", "Outline Assistant"]
|
||||
if prompt_name in format_list:
|
||||
format_template = get_format_template("knowledge_base_chat", "abstract_format")
|
||||
else:
|
||||
format_template = get_format_template("knowledge_base_chat", "default")
|
||||
|
||||
# 政策知识库
|
||||
# 相关信息把标题和内容进行整合
|
||||
if len(knowledge) == 0 and not fileName and prompt_name != "Abstract Assistant":
|
||||
prompt_template = get_prompt_template("knowledge_base_chat", "empty")
|
||||
# elif prompt_name == 'default' and "t_policy_total_bge_v1" in knowledge_base_name_list:
|
||||
# if len(knowledge_base_name_list) == 1: # 如果是科学研究院policy推荐功能，则使用如下模板
|
||||
# prompt_template = get_strategy_prompt_template("knowledge_base_chat", 'iast_policy_chat')
|
||||
# else:
|
||||
# prompt_template = get_prompt_template("knowledge_base_chat", prompt_name)
|
||||
else:
|
||||
prompt_template = get_prompt_template("knowledge_base_chat", prompt_name)
|
||||
print("prompt_name(no history):", prompt_name)
|
||||
if history and prompt_name not in ["Question Assistant"]:
|
||||
prompt_template = get_prompt_template("knowledge_base_chat", prompt_name)
|
||||
print("prompt_name(with history):", prompt_name)
|
||||
chat_prompt = PromptTemplate.from_template(template=prompt_template, template_format='jinja2')
|
||||
# 把history转成memory
|
||||
memory = ConversationBufferMemory(memory_key="history", input_key="question")
|
||||
for message in history:
|
||||
# 检查消息的角色
|
||||
if message.role == 'user':
|
||||
# 添加用户消息
|
||||
memory.chat_memory.add_user_message(message.content)
|
||||
elif message.role == 'assistant':
|
||||
# 添加AI消息
|
||||
memory.chat_memory.add_ai_message(message.content)
|
||||
else:
|
||||
input_prompt = History(role="system", content=prompt_template).to_msg_template(False)
|
||||
chat_prompt = ChatPromptTemplate.from_messages([input_prompt])
|
||||
|
||||
query = query.replace("原文", "")
|
||||
chain = load_qa_chain(
|
||||
model, chain_type="stuff", memory=memory, prompt=chat_prompt, verbose=True
|
||||
)
|
||||
# docs = list(itertools.chain(policydocs, reportdocs, journaldocs, personaldocs))
|
||||
task = asyncio.create_task(wrap_done(
|
||||
chain.acall({
|
||||
# "context": context,
|
||||
"input_documents": docs,
|
||||
"self_knowledge":self_knowledge,
|
||||
"history": history,
|
||||
"question": query,
|
||||
"file_name": str(fileName),
|
||||
"format_template": format_template,
|
||||
"time": current_time
|
||||
}),
|
||||
callback.done),
|
||||
)
|
||||
source_documents = []
|
||||
|
||||
if len(knowledge_base_name_list) != 0:
|
||||
# 政策库
|
||||
if POLICY_KNOWLEDGE_BASE in knowledge_base_name_list:
|
||||
for inum, doc in enumerate(policydocs):
|
||||
# 获取标题以及详情地址（url）
|
||||
filename = doc.metadata.get("title")
|
||||
# detail_url = doc.metadata.get("source")
|
||||
detail_url = "https://policy.ckcest.cn/detail/" + doc.metadata.get("primary_key") + ".html"
|
||||
# parameters = urlencode({"knowledge_base_name": knowledge_base_name, "file_name": filename})
|
||||
# base_url = request.base_url
|
||||
# url = f"{base_url}knowledge_base/download_doc?" + parameters
|
||||
# text = f"""出处 [{inum + 1}] [{filename}]({url}) \n\n{doc.page_content}\n\n"""
|
||||
if filename:
|
||||
# print(doc.metadata.get('_type'), detail_url)
|
||||
# if doc.metadata.get('_type') == 'title':
|
||||
filename = filename.replace('\r', '').replace('\n', '')
|
||||
text = f"""_政策[{len(source_documents) + 1}] [{filename}]({detail_url})_\n"""
|
||||
# else:
|
||||
# text = f"""政策: [{len(source_documents) + 1}][{filename}]({detail_url})\n\n{doc.page_content} \n\n"""
|
||||
else:
|
||||
# if doc.metadata.get('_type') == 'title':
|
||||
text = f"""_政策[{len(source_documents) + 1}] [{"原文地址"}]({detail_url})_"""
|
||||
# else:
|
||||
# text = f"""政策: [{len(source_documents) + 1}][{"原文地址"}]({detail_url})\n\n{doc.page_content}\n\n"""
|
||||
source_documents.append(text)
|
||||
# 报告库
|
||||
if REPORT_KNOWLEDGE_BASE in knowledge_base_name_list:
|
||||
for inum, doc in enumerate(reportdocs):
|
||||
text = f"""_报告[{len(source_documents) + 1}] [{doc.metadata.get("source").replace('.pdf','')}](https://kgo.ckcest.cn/kgo/list?dbId=1010&word=&shortName=ALL&page=1&order=1)_"""
|
||||
source_documents.append(text)
|
||||
# 期刊库
|
||||
if JOURNAL_KNOWLEDGE_BASE in knowledge_base_name_list:
|
||||
for inum, doc in enumerate(journaldocs):
|
||||
text = f"""_期刊论文[{len(source_documents) + 1}] [{doc.metadata.get("title")}](https://kgo.ckcest.cn/kgo/detail/1002/ads_journal_article/{doc.metadata.get("ID")}.html)_"""
|
||||
source_documents.append(text)
|
||||
|
||||
|
||||
#个人知识库
|
||||
if len(new_knowledge_base_name_list)>0:
|
||||
for knowledge_base_name in new_knowledge_base_name_list:
|
||||
if knowledge_base_name == 'yj_oa_journal_bge_v2_yejinbak':
|
||||
knowledge_base_name = 'yj_oa_article_v1_yejinbak' #采集数据代替oa资源
|
||||
docs = await run_in_threadpool(search_docs,
|
||||
fileName=fileName,
|
||||
query=query,
|
||||
knowledge_base_name=knowledge_base_name,
|
||||
top_k=top_k,
|
||||
score_threshold=score_threshold)
|
||||
|
||||
seen_docs = set() # 用于跟踪已见过的内容组合
|
||||
for inum,doc in enumerate(docs):
|
||||
doc_identifier = (doc.page_content)
|
||||
hasSummary = doc.metadata.get("summary")
|
||||
if doc_identifier not in seen_docs:
|
||||
# 如果不存在，将其添加到集合中
|
||||
seen_docs.add(doc_identifier)
|
||||
if doc.metadata.get('_type') == 'title' and hasSummary and knowledge_base_name in ["yj_policys_bge_v1_yejinbak","yj_oa_journal_bge_v2_yejinbak","yj_for_journal_bge_v1_yejinbak","yj_ch_journal_bge_v1_yejinbak"]:
|
||||
text = f"""[{len(source_documents) + 1}] 《{doc.page_content}》\n{doc.metadata.get("summary")}\n资料年份：{doc.metadata.get("publish_year")}\n\n"""
|
||||
elif doc.metadata.get('_type') == 'title' and knowledge_base_name in ["yj_policys_bge_v1_yejinbak","yj_oa_journal_bge_v2_yejinbak","yj_for_journal_bge_v1_yejinbak","yj_ch_journal_bge_v1_yejinbak"]:
|
||||
text = f"""[{len(source_documents) + 1}] 《{doc.page_content}》\n资料年份：{doc.metadata.get("publish_year")}\n\n"""
|
||||
elif knowledge_base_name in ["yj_policys_bge_v1_yejinbak","yj_oa_journal_bge_v2_yejinbak","yj_for_journal_bge_v1_yejinbak","yj_ch_journal_bge_v1_yejinbak"]:
|
||||
text = f"""[{len(source_documents) + 1}] 《{doc.metadata.get("title")}》\n资料年份：{doc.metadata.get("publish_year")}\n\n"""
|
||||
else:
|
||||
# text = f"""参考文档[{len(source_documents) + 1}]： 《{doc.metadata.get("source", "").split('.')[0]}》"""
|
||||
text = f"""参考文档[{len(source_documents) + 1}] [{doc.metadata.get("source")}]()\n"""
|
||||
source_documents.append(text)
|
||||
else:
|
||||
docs = await run_in_threadpool(search_docs,
|
||||
fileName=fileName,
|
||||
query=query,
|
||||
knowledge_base_name=knowledge_base_name,
|
||||
top_k=top_k,
|
||||
score_threshold=score_threshold)
|
||||
|
||||
seen_docs = set() # 用于跟踪已见过的内容组合
|
||||
for inum,doc in enumerate(docs):
|
||||
doc_identifier = (doc.page_content)
|
||||
hasSummary = doc.metadata.get("summary")
|
||||
if doc_identifier not in seen_docs:
|
||||
# 如果不存在，将其添加到集合中
|
||||
seen_docs.add(doc_identifier)
|
||||
if doc.metadata.get('_type') == 'title' and hasSummary and knowledge_base_name in ["yj_policys_bge_v1_yejinbak","yj_oa_journal_bge_v2_yejinbak","yj_for_journal_bge_v1_yejinbak","yj_ch_journal_bge_v1_yejinbak"]:
|
||||
text = f"""[{len(source_documents) + 1}] 《{doc.page_content}》\n{doc.metadata.get("summary")}\n资料年份：{doc.metadata.get("publish_year")}\n\n"""
|
||||
elif doc.metadata.get('_type') == 'title' and knowledge_base_name in ["yj_policys_bge_v1_yejinbak","yj_oa_journal_bge_v2_yejinbak","yj_for_journal_bge_v1_yejinbak","yj_ch_journal_bge_v1_yejinbak"]:
|
||||
text = f"""[{len(source_documents) + 1}] 《{doc.page_content}》\n资料年份：{doc.metadata.get("publish_year")}\n\n"""
|
||||
elif knowledge_base_name in ["yj_policys_bge_v1_yejinbak","yj_oa_journal_bge_v2_yejinbak","yj_for_journal_bge_v1_yejinbak","yj_ch_journal_bge_v1_yejinbak"]:
|
||||
text = f"""[{len(source_documents) + 1}] 《{doc.metadata.get("title")}》\n资料年份：{doc.metadata.get("publish_year")}\n\n"""
|
||||
else:
|
||||
# text = f"""参考文档[{len(source_documents) + 1}]： 《{doc.metadata.get("source", "").split('.')[0]}》"""
|
||||
text = f"""参考文档[{len(source_documents) + 1}] [{doc.metadata.get("source")}]()\n"""
|
||||
source_documents.append(text)
|
||||
# for inum, doc in enumerate(docs):
|
||||
# filename = doc.metadata.get("source")
|
||||
# parameters = urlencode({"knowledge_base_name": knowledge_base_name, "file_name": filename})
|
||||
# base_url = request.base_url
|
||||
# url = f"{base_url}knowledge_base/download_doc?" + parameters
|
||||
# if filename:
|
||||
# text = f"""出处: [{filename}]({url}) \n\n"""
|
||||
# else:
|
||||
# text = f"""出处: [{"原文地址"}]({url}) \n\n"""
|
||||
# source_documents.append(text)
|
||||
|
||||
if len(source_documents) == 0: # 没有找到相关文档
|
||||
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>")
|
||||
|
||||
first_token = True # 记录是否为第一个token
|
||||
if stream:
|
||||
answer = ""
|
||||
async for token in callback.aiter():
|
||||
if first_token:
|
||||
first_token = False
|
||||
# 记录第一个token返回的时间
|
||||
time_elapsed = time.time() - start_time
|
||||
print(f"接收响应到模型吐出第一个字耗时: {time_elapsed:.2f} seconds")
|
||||
# Use server-sent-events to stream the response
|
||||
answer += token
|
||||
yield json.dumps({"answer": token}, ensure_ascii=False)
|
||||
# print(f'====返回结果====\n {answer}')
|
||||
print(f'=====知识库问答模型返回结果=====\n {answer}')
|
||||
else:
|
||||
answer = ""
|
||||
async for token in callback.aiter():
|
||||
if first_token:
|
||||
first_token = False
|
||||
# 记录第一个token返回的时间
|
||||
time_elapsed = time.time() - start_time
|
||||
print(f"接收响应到模型吐出第一个字耗时: {time_elapsed:.2f} seconds")
|
||||
answer += token
|
||||
yield json.dumps({"answer": answer})
|
||||
await task
|
||||
yield json.dumps({"docs": source_documents}, ensure_ascii=False)
|
||||
|
||||
return EventSourceResponse(knowledge_base_chat_iterator(query, top_k, history, model_name, prompt_name))
|
||||
Reference in New Issue
Block a user