langchain_self/main.py

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import faiss
import numpy as np
from tqdm import tqdm
from sentence_transformers import SentenceTransformer
import requests
import time
from flask import Flask, jsonify
from flask import request
import pandas as pd


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

model = 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(url, 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
    """

    response = requests.post(
        url,
        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, response.status_code, response.text))
        return {}


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

def Building_vector_database(type, name, df):
    data_ndarray = np.empty((0, 1024))
    for sen in df:
        data_ndarray = np.concatenate((data_ndarray, shengcehng_array([sen[0]])))
        print("data_ndarray.shape", data_ndarray.shape)

    print("data_ndarray.shape", data_ndarray.shape)
    np.save(f'data_np/{name}.npy', data_ndarray)



def ulit_request_file(file, title):
    file_name = file.filename
    file_name_save = "data_file/{}.csv".format(title)
    file.save(file_name_save)

    # try:
    #     with open(file_name_save, encoding="gbk") as f:
    #         content = f.read()
    # except:
    #     with open(file_name_save, encoding="utf-8") as f:
    #         content = f.read()
    # elif file_name.split(".")[-1] == "docx":
    #     content = docx2txt.process(file_name_save)

    # content_list = [i for i in content.split("\n")]
    df = pd.read_csv(file_name_save, sep="\t", encoding="utf-8").values.tolist()

    return df


def main(question, db_type, 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 = db_type.split(",")

    paper_list_str = ""
    for i in db_type_list:
        embs = shengcehng_array([question])
        index = faiss.IndexFlatIP(d)  # buid the index
        data_np = np.load(f"data_np/{i}.npy")
        # data_str = open(f"data_file/{i}.txt").read().split("\n")
        data_str = pd.read_csv(f"data_file/{i}.csv", sep="\t", encoding="utf-8").values.tolist()
        index.add(data_np)
        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{}，此篇文章的转发数为{}，评论数为{}，点赞数为{}\n，此篇文章跟问题的相关度为{}%\n".format(str(i+1), j[0][0], j[0][1], j[0][2], j[0][3], 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
    '''
    url_predict = "http://192.168.31.74:26000/predict"
    url_search = "http://192.168.31.74:26000/search"

    # data = {
    #     "content": propmt_connect_input
    # }
    data = {
        "content": propmt_connect_input,
        "model": "qwq-32",
        "top_p": 0.9,
        "temperature": 0.6
    }

    res = dialog_line_parse(url_predict, data)
    id_ = res["texts"]["id"]

    data = {
        "id": id_
    }

    while True:
        res = dialog_line_parse(url_search, data)
        if res["code"] == 200:
            break
        else:
            time.sleep(1)
    spilt_str = "</think>"
    think, response = str(res["text"]).split(spilt_str)
    return think, response


@app.route("/upload_file", methods=["POST"])
def upload_file():
    print(request.remote_addr)
    file = request.files.get('file')
    title = request.form.get("title")
    df = ulit_request_file(file, title)
    Building_vector_database("1", title, df)
    return_json = {
        "code": 200,
        "info": "上传完成"
    }
    return jsonify(return_json)  # 返回结果


@app.route("/upload_file_check", methods=["POST"])
def upload_file_check():
    print(request.remote_addr)
    file = request.files.get('file')
    title = request.form.get("title")
    df = ulit_request_file(file, title)
    Building_vector_database("1", title, df)
    return_json = {
        "code": 200,
        "info": "上传完成"
    }
    return jsonify(return_json)  # 返回结果


@app.route("/search", methods=["POST"])
def search():
    print(request.remote_addr)
    texts = request.json["texts"]
    text_type = request.json["text_type"]
    top = request.json["top"]
    think, response = main(texts, text_type, top)
    return_json = {
        "code": 200,
        "think": think,
        "response": response
    }
    return jsonify(return_json)  # 返回结果


if __name__ == "__main__":
    app.run(host="0.0.0.0", port=27000, threaded=True, debug=False)