mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-19 21:37:20 +08:00
b4c68ddd05
* 优化在线 API ，支持 completion 和 embedding，简化在线 API 开发方式 新功能 - 智谱AI、Minimax、千帆、千问 4 个在线模型支持 embeddings（不通过Fastchat，后续会单独提供相关api接口） - 在线模型自动检测传入参数，在传入非 messages 格式的 prompt 时，自动转换为 completion 形式，以支持 completion 接口 开发者： - 重构ApiModelWorker: - 所有在线 API 请求封装到 do_chat 方法：自动传入参数 ApiChatParams，简化参数与配置项的获取；自动处理与fastchat的接口 - 加强 API 请求错误处理，返回更有意义的信息 - 改用 qianfan sdk 重写 qianfan-api - 将所有在线模型的测试用例统一在一起，简化测试用例编写 * Delete requirements_langflow.txt
73 lines
3.3 KiB
Python
73 lines
3.3 KiB
Python
from fastapi import Body
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from fastapi.responses import StreamingResponse
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from configs import LLM_MODEL, TEMPERATURE
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from server.utils import wrap_done, get_ChatOpenAI
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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
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import asyncio
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from langchain.prompts.chat import ChatPromptTemplate
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from typing import List, Optional
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from server.chat.utils import History
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from server.utils import get_prompt_template
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async def chat(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
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history: List[History] = Body([],
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description="历史对话",
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examples=[[
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{"role": "user", "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"}]]
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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_MODEL, 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(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("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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history = [History.from_data(h) for h in history]
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async def chat_iterator(query: str,
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history: List[History] = [],
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model_name: str = LLM_MODEL,
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prompt_name: str = prompt_name,
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) -> AsyncIterable[str]:
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callback = AsyncIteratorCallbackHandler()
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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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prompt_template = get_prompt_template("llm_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(
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[i.to_msg_template() for i in history] + [input_msg])
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chain = LLMChain(prompt=chat_prompt, llm=model)
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# Begin a task that runs in the background.
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task = asyncio.create_task(wrap_done(
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chain.acall({"input": query}),
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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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yield token
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else:
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answer = ""
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async for token in callback.aiter():
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answer += token
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yield answer
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await task
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return StreamingResponse(chat_iterator(query=query,
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history=history,
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model_name=model_name,
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prompt_name=prompt_name),
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media_type="text/event-stream")
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