mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-23 23:40:03 +08:00
6cb1bdf623
* 添加LLM模型切换功能，需要在server_config中设置可切换的模型 * add tests for api.py/llm_model/* * - 支持模型切换 - 支持智普AI线上模型 - startup.py增加参数`--api-worker`，自动运行所有的线上API模型。使用`-a (--all-webui), --all-api`时默认开启该选项 * 修复被fastchat覆盖的标准输出 * 对fastchat日志进行更细致的控制，startup.py中增加-q(--quiet)开关，可以减少无用的fastchat日志输出 * 修正chatglm api的对话模板 Co-authored-by: liunux4odoo <liunu@qq.com>
68 lines
2.7 KiB
Python
68 lines
2.7 KiB
Python
from fastapi import Body
|
|
from fastapi.responses import StreamingResponse
|
|
from configs.model_config import llm_model_dict, LLM_MODEL
|
|
from server.chat.utils import wrap_done
|
|
from langchain.chat_models import ChatOpenAI
|
|
from langchain import LLMChain
|
|
from langchain.callbacks import AsyncIteratorCallbackHandler
|
|
from typing import AsyncIterable
|
|
import asyncio
|
|
from langchain.prompts.chat import ChatPromptTemplate
|
|
from typing import List
|
|
from server.chat.utils import History
|
|
|
|
|
|
def chat(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
|
|
history: List[History] = Body([],
|
|
description="历史对话",
|
|
examples=[[
|
|
{"role": "user", "content": "我们来玩成语接龙，我先来，生龙活虎"},
|
|
{"role": "assistant", "content": "虎头虎脑"}]]
|
|
),
|
|
stream: bool = Body(False, description="流式输出"),
|
|
model_name: str = Body(LLM_MODEL, description="LLM 模型名称。"),
|
|
):
|
|
history = [History.from_data(h) for h in history]
|
|
|
|
async def chat_iterator(query: str,
|
|
history: List[History] = [],
|
|
model_name: str = LLM_MODEL,
|
|
) -> AsyncIterable[str]:
|
|
callback = AsyncIteratorCallbackHandler()
|
|
|
|
model = ChatOpenAI(
|
|
streaming=True,
|
|
verbose=True,
|
|
callbacks=[callback],
|
|
openai_api_key=llm_model_dict[model_name]["api_key"],
|
|
openai_api_base=llm_model_dict[model_name]["api_base_url"],
|
|
model_name=model_name,
|
|
openai_proxy=llm_model_dict[model_name].get("openai_proxy")
|
|
)
|
|
|
|
input_msg = History(role="user", content="{{ input }}").to_msg_template(False)
|
|
chat_prompt = ChatPromptTemplate.from_messages(
|
|
[i.to_msg_template() for i in history] + [input_msg])
|
|
chain = LLMChain(prompt=chat_prompt, llm=model)
|
|
|
|
# Begin a task that runs in the background.
|
|
task = asyncio.create_task(wrap_done(
|
|
chain.acall({"input": query}),
|
|
callback.done),
|
|
)
|
|
|
|
if stream:
|
|
async for token in callback.aiter():
|
|
# Use server-sent-events to stream the response
|
|
yield token
|
|
else:
|
|
answer = ""
|
|
async for token in callback.aiter():
|
|
answer += token
|
|
yield answer
|
|
|
|
await task
|
|
|
|
return StreamingResponse(chat_iterator(query, history, model_name),
|
|
media_type="text/event-stream")
|