113 lines
4.7 KiB
Python
113 lines
4.7 KiB
Python
import asyncio
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import json
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from typing import AsyncIterable, List, Optional
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from urllib.parse import urlencode
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from fastapi import Body, Request
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from fastapi.concurrency import run_in_threadpool
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from langchain.callbacks import AsyncIteratorCallbackHandler
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from langchain.chains import LLMChain
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from langchain.prompts import PromptTemplate
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from langchain.prompts.chat import ChatPromptTemplate
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from sse_starlette.sse import EventSourceResponse
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from configs import (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, LLM_MODELS)
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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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from server.reranker.reranker import LangchainReranker
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from server.utils import BaseResponse, get_prompt_template
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from server.utils import embedding_device
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from server.utils import wrap_done, get_ChatOpenAI
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from collections import defaultdict
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from server.custom.custom_fun import chapter_overview_summary
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async def task(param):
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contextk = param["contextk"]
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i = param["i"]
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model_name = param["model_name"]
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temperature = param["temperature"]
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max_tokens = param["max_tokens"]
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chat_prompt = param["chat_prompt"]
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print(f"i:{i}，len_context:{len(contextk)}\n")
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callback_temp = AsyncIteratorCallbackHandler()
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model_temp = 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_temp],
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)
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chain_temp = LLMChain(prompt=chat_prompt, llm=model_temp)
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task_temp = wrap_done(chain_temp.acall({"context": contextk,
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"question": "对该部分内容进行总结"}),
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callback_temp.done)
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await task_temp
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async for token in callback_temp.aiter():
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yield token
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# 使用多线程执行任务
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async def run_tasks_concurrently(params):
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result = []
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async for data in asyncio.as_completed([task(param) async for param in params]):
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result.append(''.join([token async for token in data]))
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return result
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async def chapter_overview(query: str = Body("为我总结这些内容", description="用户输入", examples=["你好"]),
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knowledge_base_name: str = Body(..., description="知识库名称", examples=["samples"]),
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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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"Chapter Overview",
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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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source_name_list: List[str] = Body([], description="资源列表"),
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request: Request = None,
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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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async def chapter_overview_iterator(
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model_name: str = model_name,
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) -> AsyncIterable[str]:
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nonlocal max_tokens
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if isinstance(max_tokens, int) and max_tokens <= 0:
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max_tokens = None
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docs = await run_in_threadpool(kb.get_doc_by_sources_name,source_name_list=source_name_list)
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chapter_summaries, global_summary = await chapter_overview_summary(docs, model_name, temperature, max_tokens)
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if stream:
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for h1, summaries in chapter_summaries.items():
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for summary in summaries:
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yield json.dumps({"chapter_title": h1, "summary": summary}, ensure_ascii=False)
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yield json.dumps({"global_summary": global_summary}, ensure_ascii=False)
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# yield json.dumps({"docs": source_documents}, ensure_ascii=False)
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else:
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result = {
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"chapter_summaries": chapter_summaries,
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"global_summary": global_summary,
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}
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yield json.dumps(result, ensure_ascii=False)
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return EventSourceResponse(chapter_overview_iterator(model_name=model_name))
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