langchain_self/main_scokt.py

# 这是一个示例 Python 脚本。

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import os
import faiss
import numpy as np
from tqdm import tqdm
from sentence_transformers import SentenceTransformer
import requests
import time
from flask import Flask, jsonify, Response, request
from openai import OpenAI
from flask_cors import CORS
import pandas as pd
import concurrent.futures
import json
from threading import Thread
import redis
import asyncio
import websockets

app = Flask(__name__)
CORS(app)
app.config["JSON_AS_ASCII"] = False

pool = redis.ConnectionPool(host='localhost', port=63179, max_connections=100, db=1, password="zhicheng123*")
redis_ = redis.Redis(connection_pool=pool, decode_responses=True)

db_key_query = 'query'
db_key_querying = 'querying'
batch_size = 32

openai_api_key = "token-abc123"
openai_api_base = "http://127.0.0.1:12011/v1"

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id
# model = "1"
model_encode = SentenceTransformer('/home/majiahui/project/models-llm/bge-large-zh-v1.5')
propmt_connect = '''我是一名中医，你是一个中医的医生的助理，我的患者有一个症状，症状如下:
{}
根据这些症状，我通过查找资料，{}
请根据上面的这些资料和方子，并根据每篇文章的转发数确定文章的重要程度，转发数越高的文章，最终答案的参考度越高，反之越低。根据患者的症状和上面的文章的资料的重要程度以及文章和症状的匹配程度，帮我开出正确的药方和治疗方案'''

propmt_connect_ziliao = '''在“{}”资料中，有如下相关内容：
{}'''


def dialog_line_parse(text):
    """
    将数据输入模型进行分析并输出结果
    :param url: 模型url
    :param text: 进入模型的数据
    :return: 模型返回结果
    """

    url_predict = "http://118.178.228.101:12004/predict"
    response = requests.post(
        url_predict,
        json=text,
        timeout=100000
    )
    if response.status_code == 200:
        return response.json()
    else:
        # logger.error(
        #     "【{}】 Failed to get a proper response from remote "
        #     "server. Status Code: {}. Response: {}"
        #     "".format(url, response.status_code, response.text)
        # )
        print("【{}】 Failed to get a proper response from remote "
              "server. Status Code: {}. Response: {}"
              "".format(url_predict, response.status_code, response.text))
        return {}

    # ['choices'][0]['message']['content']
    #
    # text = text['messages'][0]['content']
    # return_text = {
    #     'code': 200,
    #     'id': "1",
    #     'object': 0,
    #     'created': 0,
    #     'model': 0,
    #     'choices': [
    #         {
    #             'index': 0,
    #             'message': {
    #                 'role': 'assistant',
    #                 'content': text
    #             },
    #             'logprobs': None,
    #             'finish_reason': 'stop'
    #         }
    #     ],
    #     'usage': 0,
    #     'system_fingerprint': 0
    # }
    # return return_text


def shengcehng_array(data):
    embs = model_encode.encode(data, normalize_embeddings=True)
    return embs


def main(question, title, top):
    db_dict = {
        "1": "yetianshi"
    }
    '''
    定义文件路径
    '''

    '''
    加载文件
    '''

    '''
    文本分割
    '''

    '''
    构建向量数据库
        1. 正常匹配
        2. 把文本使用大模型生成一个问题之后再进行匹配      
    '''

    '''
    根据提问匹配上下文
    '''
    d = 1024
    db_type_list = title.split(",")

    paper_list_str = ""
    for title_dan in db_type_list:
        embs = shengcehng_array([question])
        index = faiss.IndexFlatIP(d)  # buid the index

        # 查找向量
        vector_path = f"data_np/{title_dan}.npy"
        vectors = np.load(vector_path)

        data_str = pd.read_csv(f"data_file/{title_dan}.csv", sep="\t", encoding="utf-8").values.tolist()
        index.add(vectors)
        D, I = index.search(embs, int(top))
        print(I)

        reference_list = []
        for i, j in zip(I[0], D[0]):
            reference_list.append([data_str[i], j])

        for i, j in enumerate(reference_list):
            paper_list_str += "第{}篇\n{}，此篇文章跟问题的相关度为{}%\n".format(str(i + 1), j[0][1], j[1])


    '''
    构造prompt
    '''
    print("paper_list_str", paper_list_str)
    propmt_connect_ziliao_input = []
    for i in db_type_list:
        propmt_connect_ziliao_input.append(propmt_connect_ziliao.format(i, paper_list_str))

    propmt_connect_ziliao_input_str = "，".join(propmt_connect_ziliao_input)
    propmt_connect_input = propmt_connect.format(question, propmt_connect_ziliao_input_str)
    '''
    生成回答
    '''
    return model_generate_stream(propmt_connect_input)


def model_generate_stream(prompt):
    messages = [
        {"role": "user", "content": prompt}
    ]

    stream = client.chat.completions.create(model=model,
                                            messages=messages,
                                            stream=True)
    printed_reasoning_content = False
    printed_content = False

    for chunk in stream:
        reasoning_content = None
        content = None
        # Check the content is reasoning_content or content
        if hasattr(chunk.choices[0].delta, "reasoning_content"):
            reasoning_content = chunk.choices[0].delta.reasoning_content
        elif hasattr(chunk.choices[0].delta, "content"):
            content = chunk.choices[0].delta.content

        if reasoning_content is not None:
            if not printed_reasoning_content:
                printed_reasoning_content = True
                print("reasoning_content:", end="", flush=True)
            print(reasoning_content, end="", flush=True)
        elif content is not None:
            if not printed_content:
                printed_content = True
                print("\ncontent:", end="", flush=True)
            # Extract and print the content
            # print(content, end="", flush=True)
            print(content)
            yield content


async def handle_websocket(websocket):
    print("客户端已连接")
    try:
        while True:
            message = await websocket.recv()
            data = json.loads(message)
            texts = data.get("texts")
            title = data.get("title")
            top = data.get("top")
            print("收到消息:", message)

            response = main(texts, title, top)
            # response = message + "111"
            for char in response:
                await websocket.send(char)
                # await asyncio.sleep(0.3)
            await websocket.send("[DONE]")
    except websockets.exceptions.ConnectionClosed:
        print("客户端断开连接")


async def main_api():
    async with websockets.serve(handle_websocket, "0.0.0.0", 27001):
        print("WebSocket 服务器已启动，监听端口 27001")
        await asyncio.Future()  # 永久运行

if __name__ == "__main__":
    asyncio.run(main_api())  # 正确启动事件循环