majiahui@haimaqingfan.com
1 month ago
1 changed files with 241 additions and 0 deletions
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# 这是一个示例 Python 脚本。 |
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# 按 Shift+F10 执行或将其替换为您的代码。 |
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# 按 双击 Shift 在所有地方搜索类、文件、工具窗口、操作和设置。 |
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import os |
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import faiss |
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import numpy as np |
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from tqdm import tqdm |
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from sentence_transformers import SentenceTransformer |
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import requests |
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import time |
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from flask import Flask, jsonify, Response, request |
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from openai import OpenAI |
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from flask_cors import CORS |
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import pandas as pd |
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import concurrent.futures |
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import json |
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from threading import Thread |
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import redis |
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import asyncio |
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import websockets |
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app = Flask(__name__) |
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CORS(app) |
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app.config["JSON_AS_ASCII"] = False |
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pool = redis.ConnectionPool(host='localhost', port=63179, max_connections=100, db=1, password="zhicheng123*") |
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redis_ = redis.Redis(connection_pool=pool, decode_responses=True) |
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db_key_query = 'query' |
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db_key_querying = 'querying' |
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batch_size = 32 |
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openai_api_key = "token-abc123" |
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openai_api_base = "http://127.0.0.1:12011/v1" |
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client = OpenAI( |
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api_key=openai_api_key, |
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base_url=openai_api_base, |
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) |
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models = client.models.list() |
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model = models.data[0].id |
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# model = "1" |
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model_encode = SentenceTransformer('/home/majiahui/project/models-llm/bge-large-zh-v1.5') |
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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: |
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{} |
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根据这些症状，我通过查找资料，{} |
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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''' |
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propmt_connect_ziliao = '''在“{}”资料中，有如下相关内容： |
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{}''' |
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def dialog_line_parse(text): |
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""" |
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将数据输入模型进行分析并输出结果 |
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:param url: 模型url |
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:param text: 进入模型的数据 |
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:return: 模型返回结果 |
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""" |
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url_predict = "http://118.178.228.101:12004/predict" |
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response = requests.post( |
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url_predict, |
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json=text, |
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timeout=100000 |
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) |
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if response.status_code == 200: |
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return response.json() |
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else: |
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# logger.error( |
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# "【{}】 Failed to get a proper response from remote " |
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# "server. Status Code: {}. Response: {}" |
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# "".format(url, response.status_code, response.text) |
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# ) |
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print("【{}】 Failed to get a proper response from remote " |
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"server. Status Code: {}. Response: {}" |
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"".format(url_predict, response.status_code, response.text)) |
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return {} |
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# ['choices'][0]['message']['content'] |
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# |
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# text = text['messages'][0]['content'] |
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# return_text = { |
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# 'code': 200, |
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# 'id': "1", |
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# 'object': 0, |
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# 'created': 0, |
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# 'model': 0, |
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# 'choices': [ |
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# { |
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# 'index': 0, |
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# 'message': { |
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# 'role': 'assistant', |
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# 'content': text |
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# }, |
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# 'logprobs': None, |
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# 'finish_reason': 'stop' |
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# } |
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# ], |
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# 'usage': 0, |
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# 'system_fingerprint': 0 |
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# } |
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# return return_text |
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def shengcehng_array(data): |
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embs = model_encode.encode(data, normalize_embeddings=True) |
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return embs |
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def main(question, title, top): |
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db_dict = { |
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"1": "yetianshi" |
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} |
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''' |
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定义文件路径 |
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''' |
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''' |
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加载文件 |
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''' |
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''' |
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文本分割 |
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''' |
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''' |
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构建向量数据库 |
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1. 正常匹配 |
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2. 把文本使用大模型生成一个问题之后再进行匹配 |
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''' |
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''' |
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根据提问匹配上下文 |
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''' |
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d = 1024 |
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db_type_list = title.split(",") |
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paper_list_str = "" |
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for title_dan in db_type_list: |
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embs = shengcehng_array([question]) |
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index = faiss.IndexFlatIP(d) # buid the index |
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# 查找向量 |
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vector_path = f"data_np/{title_dan}.npy" |
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vectors = np.load(vector_path) |
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data_str = pd.read_csv(f"data_file/{title_dan}.csv", sep="\t", encoding="utf-8").values.tolist() |
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index.add(vectors) |
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D, I = index.search(embs, int(top)) |
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print(I) |
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reference_list = [] |
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for i, j in zip(I[0], D[0]): |
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reference_list.append([data_str[i], j]) |
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for i, j in enumerate(reference_list): |
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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]) |
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''' |
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构造prompt |
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''' |
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print("paper_list_str", paper_list_str) |
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propmt_connect_ziliao_input = [] |
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for i in db_type_list: |
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propmt_connect_ziliao_input.append(propmt_connect_ziliao.format(i, paper_list_str)) |
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propmt_connect_ziliao_input_str = "，".join(propmt_connect_ziliao_input) |
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propmt_connect_input = propmt_connect.format(question, propmt_connect_ziliao_input_str) |
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''' |
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生成回答 |
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''' |
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return model_generate_stream(propmt_connect_input) |
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def model_generate_stream(prompt): |
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messages = [ |
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{"role": "user", "content": prompt} |
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] |
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stream = client.chat.completions.create(model=model, |
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messages=messages, |
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stream=True) |
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printed_reasoning_content = False |
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printed_content = False |
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for chunk in stream: |
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reasoning_content = None |
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content = None |
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# Check the content is reasoning_content or content |
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if hasattr(chunk.choices[0].delta, "reasoning_content"): |
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reasoning_content = chunk.choices[0].delta.reasoning_content |
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elif hasattr(chunk.choices[0].delta, "content"): |
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content = chunk.choices[0].delta.content |
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if reasoning_content is not None: |
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if not printed_reasoning_content: |
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printed_reasoning_content = True |
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print("reasoning_content:", end="", flush=True) |
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print(reasoning_content, end="", flush=True) |
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elif content is not None: |
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if not printed_content: |
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printed_content = True |
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print("\ncontent:", end="", flush=True) |
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# Extract and print the content |
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# print(content, end="", flush=True) |
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print(content) |
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yield content |
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async def handle_websocket(websocket): |
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print("客户端已连接") |
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try: |
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while True: |
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message = await websocket.recv() |
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data = json.loads(message) |
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texts = data.get("texts") |
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title = data.get("title") |
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top = data.get("top") |
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print("收到消息:", message) |
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response = main(texts, title, top) |
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# response = message + "111" |
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for char in response: |
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await websocket.send(char) |
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# await asyncio.sleep(0.3) |
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await websocket.send("[DONE]") |
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except websockets.exceptions.ConnectionClosed: |
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print("客户端断开连接") |
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async def main_api(): |
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async with websockets.serve(handle_websocket, "0.0.0.0", 27001): |
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print("WebSocket 服务器已启动，监听端口 27001") |
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await asyncio.Future() # 永久运行 |
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if __name__ == "__main__": |
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asyncio.run(main_api()) # 正确启动事件循环 |
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