mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-26 00:33:35 +08:00
d316efe8d3
## 🛠 新增功能 - 支持百川在线模型 (@hzg0601 @liunux4odoo in #1623) - 支持 Azure OpenAI 与 claude 等 Langchain 自带模型 (@zRzRzRzRzRzRzR in #1808) - Agent 功能大量更新，支持更多的工具、更换提示词、检索知识库 (@zRzRzRzRzRzRzR in #1626 #1666 #1785) - 加长 32k 模型的历史记录 (@zRzRzRzRzRzRzR in #1629 #1630) - *_chat 接口支持 max_tokens 参数 (@liunux4odoo in #1744) - 实现 API 和 WebUI 的前后端分离 (@liunux4odoo in #1772) - 支持 zlilliz 向量库 (@zRzRzRzRzRzRzR in #1785) - 支持 metaphor 搜索引擎 (@liunux4odoo in #1792) - 支持 p-tuning 模型 (@hzg0601 in #1810) - 更新完善文档和 Wiki (@imClumsyPanda @zRzRzRzRzRzRzR @glide-the in #1680 #1811) ## 🐞 问题修复 - 修复 bge-* 模型匹配超过 1 的问题 (@zRzRzRzRzRzRzR in #1652) - 修复系统代理为空的问题 (@glide-the in #1654) - 修复重建知识库时 `d == self.d assert error` (@liunux4odoo in #1766) - 修复对话历史消息错误 (@liunux4odoo in #1801) - 修复 OpenAI 无法调用的 bug (@zRzRzRzRzRzRzR in #1808) - 修复 windows下 BIND_HOST=0.0.0.0 时对话出错的问题 (@hzg0601 in #1810)
59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
from fastapi.responses import StreamingResponse
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from typing import List
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import openai
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from configs import LLM_MODEL, logger, log_verbose
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from server.utils import get_model_worker_config, fschat_openai_api_address
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from pydantic import BaseModel
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class OpenAiMessage(BaseModel):
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role: str = "user"
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content: str = "hello"
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class OpenAiChatMsgIn(BaseModel):
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model: str = LLM_MODEL
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messages: List[OpenAiMessage]
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temperature: float = 0.7
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n: int = 1
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max_tokens: int = None
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stop: List[str] = []
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stream: bool = False
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presence_penalty: int = 0
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frequency_penalty: int = 0
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async def openai_chat(msg: OpenAiChatMsgIn):
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config = get_model_worker_config(msg.model)
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openai.api_key = config.get("api_key", "EMPTY")
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print(f"{openai.api_key=}")
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openai.api_base = config.get("api_base_url", fschat_openai_api_address())
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print(f"{openai.api_base=}")
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print(msg)
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async def get_response(msg):
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data = msg.dict()
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try:
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response = await openai.ChatCompletion.acreate(**data)
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if msg.stream:
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async for data in response:
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if choices := data.choices:
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if chunk := choices[0].get("delta", {}).get("content"):
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print(chunk, end="", flush=True)
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yield chunk
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else:
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if response.choices:
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answer = response.choices[0].message.content
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print(answer)
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yield(answer)
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except Exception as e:
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msg = f"获取ChatCompletion时出错：{e}"
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logger.error(f'{e.__class__.__name__}: {msg}',
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exc_info=e if log_verbose else None)
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return StreamingResponse(
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get_response(msg),
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media_type='text/event-stream',
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)
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