[全量] 初始化项目代码、配置、文档及Agent协同harness
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293
langchain-chat/server/chat/write_article.py
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293
langchain-chat/server/chat/write_article.py
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import base64
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import uuid
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from fastapi import Body
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from fastapi.responses import FileResponse
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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 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 server.chat import utils
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from server.chat.agent_write_test import agent_write_test
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from server.chat.agent_chat_test import agent_chat_test, run_sync
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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.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 chunk_docs(docs, chunk_size=800):
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"""将文档分块"""
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return [docs[i:i + chunk_size] for i in range(0, len(docs), chunk_size)]
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def convert_last_digits(s):
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# 检查字符串最后一位是否是数字
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if s[-1].strip().replace("\n","").isdigit():
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# 如果最后两位都是数字
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if len(s) > 1 and s[-2].strip().replace("\n","").isdigit():
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return int(s[-2:].strip().replace("\n","")),2
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# 如果只有最后一位是数字
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else:
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return int(s[-1].strip().replace("\n","")),1
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# 如果最后一位不是数字，返回None或其他适当的值
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return None
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async def write_article(
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messageId: Optional[Any] = Body(None, description="消息ID", examples=[""]),
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uid: Optional[Any] = Body(None, description="用户ID"),
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styles: Optional[Any] = Body(None, description="风格"),
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query: str = Body(..., description="大纲", examples=["恼羞成怒"]),
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knowledge_base_list: Optional[List[str]] = Body(None, description="个人知识库名称列表"),
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# conversation_id: str = Body("", description="对话框ID"),
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# history_len: int = Body(-1, description="从数据库中取历史消息的数量"),
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# history: Union[int, List] = Body([],
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# description="历史对话，设为一个整数可以从数据库中读取历史消息",
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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", "content": "虎头虎脑","summary":"8989uj9"}]]
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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=2.0),
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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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# 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("complete_outline", description="大纲补全必须使用这个提示词"),
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):
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async def chat_iterator() -> AsyncIterable[str]:
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nonlocal messageId, styles, knowledge_base_list
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res={}
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res["text"]=""
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step=0
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num = 0
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time_based_uuid = messageId if messageId else str(uuid.uuid1())+"q"
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tip ={}
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tip["type"] = "article"
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include_knowldge = False
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if knowledge_base_list:
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tip["database"] = knowledge_base_list
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tip["status"] = True
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tip["source_docs"]=[]
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utils.set_shared_variable(time_based_uuid,tip)
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logger.info(f"生成正文的入参信息：\nquery：{query}\nstyles：{styles}\nmessageId: {messageId}\nknowledge_base_list：{knowledge_base_list}\nprompt_name：{prompt_name}\n")
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# 完成文章分解。
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while True:
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try:
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async for response in solve_problem(query=query, conversation_id="", history=[], model_name=model_name,temperature=temperature,max_tokens=max_tokens,prompt_name="solve_problem_outline",stream=True):
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res["text"]+=json.loads(response)["text"]
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if not utils.get_shared_variable(time_based_uuid)["status"]:
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logging.info("\n==============================STOPPED==============================\n")
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return
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step,num = convert_last_digits(res["text"])
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break
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except:
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continue
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#step,num = convert_last_digits(res["text"])
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res["text"]=res["text"][:-num]
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docs = ""
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finish = []
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i = 0
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nums = 0
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while i < int(step):
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history_summary = ""
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count = 0
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index = 0
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temp = ""
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# time_based_uuid = messageId if messageId else str(uuid.uuid1())+"q"
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answer = ""
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tools = []
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tip["num"]= nums
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tip["END"]=""
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tip['title'] = []
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utils.set_shared_variable(time_based_uuid,tip)
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finish_content=""
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style = ""
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chapter = f"当前撰写的是第{i+1}章"
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try:
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determine_chapter = await run_sync(
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get_llm_model_response,
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strategy_name="default_code",
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llm_model_name=LLM_MODELS[0],
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template_prompt_name="identify_chapters",
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prompt_param_dict={"chapter": chapter, "outline": query},
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temperature=0.01,
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max_tokens=512
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)
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if int(determine_chapter.strip()) == 1:
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i += 1
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await asyncio.sleep(2)
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if docs == "":
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history_summary += f"\n\n## {i}. 参考文献\n\n未参考任何文献"
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yield json.dumps({"text": f"\n\n## {i}. 参考文献\n\n未参考任何文献"}, ensure_ascii=False)
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else:
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# 分块处理文档
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doc_chunks = await chunk_docs(docs)
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total_chunks = len(doc_chunks)
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# 第一块：包含标题
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first_chunk = doc_chunks[0]
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history_summary += f"\n\n## {i}. 参考文献\n\n{first_chunk}"
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yield json.dumps({
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"text": f"\n\n## {i}. 参考文献\n\n{first_chunk}",
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}, ensure_ascii=False)
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# 处理剩余块
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for idx, chunk in enumerate(doc_chunks[1:], 2):
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await asyncio.sleep(2) # 每个块之间等待2秒
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history_summary += chunk
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yield json.dumps({
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"text": chunk,
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}, ensure_ascii=False)
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include_knowldge = True
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finish.append(history_summary)
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continue
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except Exception as e:
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i+=1
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await asyncio.sleep(2)
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if not docs == "":
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history_summary += f"\n\n## {i}. 参考文献\n\n{docs}"
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yield json.dumps({"text": f"\n\n## {i}. 参考文献\n\n{docs}"}, ensure_ascii=False)
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else:
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# 分块处理文档
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doc_chunks = await chunk_docs(docs)
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total_chunks = len(doc_chunks)
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# 第一块：包含标题
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first_chunk = doc_chunks[0]
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history_summary += f"\n\n## {i}. 参考文献\n\n{first_chunk}"
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yield json.dumps({
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"text": f"\n\n## {i}. 参考文献\n\n{first_chunk}",
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}, ensure_ascii=False)
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# 处理剩余块
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for idx, chunk in enumerate(doc_chunks[1:], 2):
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await asyncio.sleep(2) # 每个块之间等待2秒
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history_summary += chunk
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yield json.dumps({
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"text": chunk,
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}, ensure_ascii=False)
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include_knowldge = True
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finish.append(history_summary)
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continue
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if isinstance(styles, str) and styles.strip():
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try:
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# 获取语言风格模板
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result = get_prompt_template("llm_chat", styles)
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style = result if result is not None else styles
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except Exception as e:
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# 使用传入的 styles 作为语言风格
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style = styles
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logger.info(f"当前的语言风格为: {style}")
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search_query = get_llm_model_response(
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strategy_name="find_relation",
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llm_model_name=LLM_MODELS[0],
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template_prompt_name="find_relation",
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prompt_param_dict={"outline":res["text"],"i":i+1},
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temperature=0.01,
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max_tokens=15120
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)
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if "无" in search_query:
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finish_content=""
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else:
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for j in search_query.split(","):
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try:
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if int(j)<i+1 :
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finish_content+=f"已写【第{int(j)}部分】内容:\n{finish[int(j)-1]}\n"
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else:
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finish_content+=""
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except Exception as e:
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if int(j.strip()[-1])<i+1 :
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finish_content+=f"已写【第{int(j)}部分】内容:\n{finish[int(j)-1]}\n"
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else:
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finish_content+=""
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res1 = res["text"]
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#"\n当前使用过的资料名称（如果是参考文献章节，这些资料,必须输出："+(docs if not docs == ""else "无")
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async for response in agent_write_test(user_prompt_name = prompt_name,style = style, query="撰写中间部分章节禁止输出综上所述之类的影响文风的话，！！！\n注意严格按照提示1的部分来，你绝对不能省略任何一个部分.当前需要撰写的是的【第"+str(i+1)+"部分】内容。大纲如下:\n"+query+"\n"+(("可能需要参考的其他部分内容:\n"+finish_content) if not finish_content == "" else ""),uuid=time_based_uuid, history=[], model_name=model_name,temperature=temperature,max_tokens=max_tokens,prompt_name="Write Test",think_content=res1+"\n你只需完成大纲的第"+str(i+1)+"部分撰写补全\n"):
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# print("------------"+response)
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if not utils.get_shared_variable(time_based_uuid)["status"]:
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logging.info("\n==============================STOPPED==============================\n")
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return
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if "answer" in json.loads(response):
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# logging.info(f"answer:{json.loads(response)['answer']}")
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answer = json.loads(response)["answer"]
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history_summary+=answer
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yield json.dumps({"text": answer}, ensure_ascii=False)
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elif "tools" in json.loads(response):
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# print("tools:", json.loads(response)["tools"])
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tools.append(json.loads(response)["tools"])
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# yield json.dumps({"tools": tools}, ensure_ascii=False)
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elif "search_answer" in json.loads(response):
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# search_answer = json.loads(response)["search_answer"]
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pass
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# history_summary+= search_answer
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#yield json.dumps({"texts": search_answer}, ensure_ascii=False)
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elif "docs" in json.loads(response):
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docs = json.loads(response)["docs"]
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elif "pic" in json.loads(response):
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# 获取图片路径
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image_name = json.loads(response)["pic"]
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image_name = f"\n\n\n\n"
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# yield json.dumps({"text": image_name}, ensure_ascii=False)
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else :
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#history_summary += json.loads(response)["final_answer"]
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# yield json.dumps({"texts": json.loads(response)["final_answer"]}, ensure_ascii=False)
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pass
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# if index3 == 0 and not "Action" in answer:
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# history_summary += answer
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# yield json.dumps({"text": answer}, ensure_ascii=False)
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yield json.dumps({"text":"\n"}, ensure_ascii=False)
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if history_summary == "":
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continue
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else:
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temp1 = utils.get_shared_variable(time_based_uuid)
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temp1["END"] = ""
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utils.set_shared_variable(time_based_uuid,temp1)
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i+=1
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nums = utils.get_shared_variable(time_based_uuid)["num"]
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finish.append(history_summary)
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await asyncio.sleep(0)
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if not include_knowldge:
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if not docs == "":
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# 分块处理文档
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doc_chunks = await chunk_docs(docs)
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total_chunks = len(doc_chunks)
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# 第一块：包含标题
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first_chunk = doc_chunks[0]
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history_summary += f"\n\n## {i+1}. 参考文献\n\n{first_chunk}"
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yield json.dumps({
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"text": f"\n\n## {i+1}. 参考文献\n\n{first_chunk}",
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}, ensure_ascii=False)
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# 处理剩余块
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for idx, chunk in enumerate(doc_chunks[1:], 2):
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await asyncio.sleep(2) # 每个块之间等待2秒
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history_summary += chunk
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yield json.dumps({
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"text": chunk,
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}, ensure_ascii=False)
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return EventSourceResponse(chat_iterator())
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