欧美成人在线网站,国内视频一区二区三区,国产成人精品三级在线

# @place: Pudong, Shanghai
# @file: sanic_langchain_stream.py
# @time: 2023/9/19 18:18
# sanic==23.6.0
import asyncio
from sanic import Sanic
from sanic.response import text, json, ResponseStream

from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage
from langchain.callbacks.streaming_aiter import AsyncIteratorCallbackHandler

app = Sanic("benchmark")

@app.route("/")
async def index(request):
return text("hello")

@app.route("/test", methods=["POST"])
async def answer(request):
content = request.json["content"]
return json({"text": content})

@app.route("/csv")
async def test(request):
async def sample_streaming_fn(response):
await response.write("foo,")
await response.write("bar")

return ResponseStream(sample_streaming_fn, content_type="text/csv")

@app.route("/answer/async", methods=["POST"])
async def answer_async(request):
content = request.json["content"]

async def predict(response):
handler = AsyncIteratorCallbackHandler()
model_message = [HumanMessage(content=content)]
chat = ChatOpenAI(streaming=True,
callbacks=[handler],
temperature=0,
openai_api_key="")
asyncio.create_task(chat.apredict_messages(model_message))
async for token in handler.aiter():
await response.write(f"data: {token}\n\n")

return ResponseStream(predict, content_type="text/event-stream")

if __name__ == "__main__":
app.run(host="0.0.0.0", port=3000, debug=False, access_log=True)

FastAPI框架

# -*- coding: utf-8 -*-

# @place: Pudong, Shanghai 

# @file: fastapi_langchain_stream.py

# @time: 2023/9/20 17:36

# fastapi==0.101.1

import uvicorn

import asyncio

from fastapi import FastAPI

from pydantic import BaseModel

from fastapi.responses import StreamingResponse, JSONResponse



from langchain.chat_models import ChatOpenAI

from langchain.schema import HumanMessage

from langchain.callbacks.streaming_aiter import AsyncIteratorCallbackHandler



app = FastAPI(description="langchain_streaming")



class Item(BaseModel):

    text: str



class Question(BaseModel):

    text: str



async def fake_video_streamer():

    for i in range(10):

        yield b"some fake video bytes\n"



@app.get("/")

async def main():

    return StreamingResponse(fake_video_streamer())



@app.post("/test")

async def test(item: Item):

    return JSONResponse({"content": item.text})



@app.post("/answer/async")

async def answer_async(q: Question):

    content = q.text



    async def predict():

        handler = AsyncIteratorCallbackHandler()

        model_message = [HumanMessage(content=content)]

        chat = ChatOpenAI(streaming=True,

                          callbacks=[handler],

                          temperature=0,

                          openai_api_key="sk-xxx")

        asyncio.create_task(chat.apredict_messages(model_message))

        async for token in handler.aiter():

            yield f"data: {token}\n\n"



    return StreamingResponse(predict())



if __name__ == "__main__":

    uvicorn.run(app, host="0.0.0.0", port=8000, log_level="info")

可視化

可視化功能采用HTML，頁面較為簡陋，因筆者的HTML知識有限（甚至可憐）。

HTML文件（form.html）:

<body>

<form action="/qa/" method="post">

    <p>question: <br><br><textarea rows="3" cols="50" name="question"></textarea></p>

    <input type="submit">

</form>



<p>{{ answer }}</p>



</body>

Python代碼：

# -*- coding: utf-8 -*-

# @place: Pudong, Shanghai

# @file: qa_with_form.py

# @time: 2023/9/20 21:36

from starlette.requests import Request

from fastapi import FastAPI, Form

from starlette.templating import Jinja2Templates

import uvicorn

import asyncio

from fastapi.responses import StreamingResponse



from langchain.chat_models import ChatOpenAI

from langchain.schema import HumanMessage

from langchain.callbacks.streaming_aiter import AsyncIteratorCallbackHandler



app = FastAPI()

template = Jinja2Templates(directory='template')



@app.get('/')  # 接受get請求

async def get_user(request: Request):

    return template.TemplateResponse('form.html', {'request': request})



@app.post('/qa/')  # 接受post請求

async def get_user(request: Request,

                   question: str = Form(...)

                   ):



    async def predict():

        handler = AsyncIteratorCallbackHandler()

        model_message = [HumanMessage(content=question)]

        chat = ChatOpenAI(streaming=True,

                          callbacks=[handler],

                          temperature=0,

                          openai_api_key="sk-xxx")

        asyncio.create_task(chat.apredict_messages(model_message))

        async for token in handler.aiter():

            answer = token.replace("\n", "<br>")

            yield answer



        yield '<br><a href="/" color="red">返回</a>'



    return StreamingResponse(predict(), media_type='text/html')



if __name__ == '__main__':

    uvicorn.run(app, host='0.0.0.0', port=8080)