78 lines
3.8 KiB
Python
78 lines
3.8 KiB
Python
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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 chi_translation,eng_translation
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async def paper_translation(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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"eng_chi",
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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 translation_iterator(
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query: str,
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model_name: str = model_name,
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prompt_name: str = prompt_name,
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) -> AsyncIterable[str]:
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nonlocal max_tokens
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callback = AsyncIteratorCallbackHandler()
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if isinstance(max_tokens, int) and max_tokens <= 0:
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max_tokens = None
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docs = []
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docs = await run_in_threadpool(kb.get_doc_by_sources_name,
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source_name_list=source_name_list)
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if len(docs) == 0: # 如果没有找到相关文档，使用empty模板
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prompt_template = get_prompt_template("knowledge_base_chat", "empty")
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else:
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prompt_template = get_prompt_template("knowledge_base_chat", prompt_name)
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# input_msg = History(role="user", content=prompt_template).to_msg_template(False)
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# chat_prompt = ChatPromptTemplate.from_messages([input_msg])
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# chain = LLMChain(prompt=chat_prompt, llm=model)
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if prompt_name == "chi_eng":
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async for chunk in chi_translation(docs, prompt_template, model_name, temperature, max_tokens):
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yield chunk
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else:
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async for chunk in eng_translation(docs, prompt_template, model_name, temperature, max_tokens):
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yield chunk
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return EventSourceResponse(translation_iterator(query, model_name=model_name, prompt_name=prompt_name))
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