From f84c17d1c4e5a634817b746fb98fdd74c11e79a3 Mon Sep 17 00:00:00 2001 From: "majiahui@haimaqingfan.com" Date: Thu, 10 Apr 2025 16:26:50 +0800 Subject: [PATCH] =?UTF-8?q?=E4=BF=AE=E5=A4=8Dbug?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- main.py | 341 ++++++++++++++++++++++++++++++++++++++++++++++------------------ 1 file changed, 248 insertions(+), 93 deletions(-) diff --git a/main.py b/main.py index 306b1bf..f65eb35 100644 --- a/main.py +++ b/main.py @@ -2,21 +2,37 @@ # 按 Shift+F10 执行或将其替换为您的代码。 # 按 双击 Shift 在所有地方搜索类、文件、工具窗口、操作和设置。 +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 -from flask import request +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 app = Flask(__name__) +CORS(app) app.config["JSON_AS_ASCII"] = False -model = SentenceTransformer('/home/majiahui/project/models-llm/bge-large-zh-v1.5') +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，{} @@ -26,7 +42,7 @@ propmt_connect_ziliao = '''在“{}”资料中，有如下相关内容： {}''' -def dialog_line_parse(url, text): +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 @@ -34,8 +50,9 @@ def dialog_line_parse(url, text): :return: 模型返回结果 """ + url_predict = "http://118.178.228.101:12004/predict" response = requests.post( - url, + url_predict, json=text, timeout=100000 ) @@ -49,46 +66,167 @@ def dialog_line_parse(url, text): # ) print("【{}】 Failed to get a proper response from remote " "server. Status Code: {}. Response: {}" - "".format(url, response.status_code, response.text)) + "".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(data, normalize_embeddings=True) + embs = model_encode.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 Building_vector_database(title, df): + # 加载需要处理的数据（有效且未向量化） + to_process = df[(df["有效"] == True) & (df["已向量化"] == False)] + if len(to_process) == 0: + print("无新增数据需要向量化") + return -def ulit_request_file(file, title): - file_name = file.filename - file_name_save = "data_file/{}.csv".format(title) - file.save(file_name_save) + # 生成向量数组 + new_vectors = shengcehng_array(to_process["总结"].tolist()) # 假设这是你的向量生成函数 - # 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) + # 加载现有向量库和索引 + vector_path = f"data_np/{title}.npy" + index_path = f"data_np/{title}_index.json" - # content_list = [i for i in content.split("\n")] - df = pd.read_csv(file_name_save, sep="\t", encoding="utf-8").values.tolist() + vectors = np.load(vector_path) if os.path.exists(vector_path) else np.empty((0, 1024)) + index_data = {} + if os.path.exists(index_path): + with open(index_path, "r") as f: + index_data = json.load(f) - return df + # 更新索引和向量库 + start_idx = len(vectors) + vectors = np.vstack([vectors, new_vectors]) + + for i, (_, row) in enumerate(to_process.iterrows()): + index_data[row["ID"]] = { + "row": start_idx + i, + "valid": True + } + + # 保存数据 + np.save(vector_path, vectors) + with open(index_path, "w") as f: + json.dump(index_data, f) + # 标记已向量化 + df.loc[to_process.index, "已向量化"] = True + df.to_csv(f"data_file_res/{title}.csv", sep="\t", index=False) + + +def delete_data(title, data_id): + # 更新CSV标记 + csv_path = f"data_file_res/{title}.csv" + df = pd.read_csv(csv_path, sep="\t", dtype={"ID": str}) + df.loc[df["ID"] == data_id, "有效"] = False + df.to_csv(csv_path, sep="\t", index=False) + + # 更新索引标记 + index_path = f"data_np/{title}_index.json" + if os.path.exists(index_path): + with open(index_path, "r+") as f: + index_data = json.load(f) + if data_id in index_data: + index_data[data_id]["valid"] = False + f.seek(0) + json.dump(index_data, f) + f.truncate() + + +def check_file_exists(file_path): + """ + 检查文件是否存在 -def main(question, db_type, top): + 参数: + file_path (str): 要检查的文件路径 + + 返回: + bool: 文件存在返回True，否则返回False + """ + return os.path.isfile(file_path) + + +def ulit_request_file(new_id, sentence, title): + file_name_res_save = f"data_file_res/{title}.csv" + + # 初始化或读取CSV文件 + if os.path.exists(file_name_res_save): + df = pd.read_csv(file_name_res_save, sep="\t") + # 检查是否已存在相同正文 + if sentence in df["正文"].values: + print("正文已存在，跳过处理") + return df + else: + df = pd.DataFrame(columns=["ID", "正文", "总结", "有效", "已向量化"]) + + # 添加新数据（生成唯一ID） + new_row = { + "ID": str(new_id), + "正文": sentence, + "总结": None, + "有效": True, + "已向量化": False + } + df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True) + + # 筛选需要处理的记录 + to_process = df[(df["总结"].isna()) & (df["有效"] == True)] + + # 调用API生成总结（示例保留原有逻辑） + data_list = [] + for _, row in to_process.iterrows(): + data_list.append({ + "model": "gpt-4-turbo", + "messages": [{ + "role": "user", + "content": f"{row['正文']}\n以上这条中可能包含了一些病情或者症状，请帮我归纳这条中所对应的病情或者症状是哪些，总结出来，不需要很长，简单归纳即可，直接输出症状或者病情，可以包含一些形容词来辅助描述，不需要有辅助词汇" + }], + "top_p": 0.9, + "temperature": 0.6 + }) + + # 并发处理请求 + with concurrent.futures.ThreadPoolExecutor(200) as executor: + results = list(executor.map(dialog_line_parse, data_list)) + + # 更新总结字段 + for idx, result in zip(to_process.index, results): + summary = result['choices'][0]['message']['content'] + df.at[idx, "总结"] = summary + + # 保存更新后的CSV + df.to_csv(file_name_res_save, sep="\t", index=False) + return df + +def main(question, title, top): db_dict = { "1": "yetianshi" } @@ -114,30 +252,43 @@ def main(question, db_type, top): 根据提问匹配上下文 ''' d = 1024 - db_type_list = db_type.split(",") + db_type_list = title.split(",") paper_list_str = "" - for i in db_type_list: + for title_dan 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) + + # 查找向量 + vector_path = f"data_np/{title_dan}.npy" + index_path = f"data_np/{title_dan}_index.json" + + if not os.path.exists(vector_path) or not os.path.exists(index_path): + return np.empty((0, 1024)) + + vectors = np.load(vector_path) + with open(index_path, "r") as f: + index_data = json.load(f) + + data_str = pd.read_csv(f"data_file_res/{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]) + if data_str[i][3] == True: + 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]) + 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) + 9/0 propmt_connect_ziliao_input = [] for i in db_type_list: propmt_connect_ziliao_input.append(propmt_connect_ziliao.format(i, paper_list_str)) @@ -147,61 +298,70 @@ def main(question, db_type, top): ''' 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, 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 + 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 @app.route("/upload_file_check", methods=["POST"]) def upload_file_check(): print(request.remote_addr) - file = request.files.get('file') + sentence = request.form.get('sentence') title = request.form.get("title") - df = ulit_request_file(file, title) - Building_vector_database("1", title, df) + new_id = request.form.get("id") + state = request.form.get("state") + ''' + { + "1": "csv", + "2": "xlsx", + "3": "txt", + "4": "pdf" + } + ''' + state_res = "" + if state == "1": + df = ulit_request_file(new_id, sentence, title) + Building_vector_database(title, df) + state_res = "上传完成" + elif state == "2": + delete_data(title, new_id) + state_res = "删除完成" return_json = { "code": 200, - "info": "上传完成" + "info": state_res } return jsonify(return_json) # 返回结果 @@ -210,15 +370,10 @@ def upload_file_check(): def search(): print(request.remote_addr) texts = request.json["texts"] - text_type = request.json["text_type"] + title = request.json["title"] 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 + response = main(texts, title, top) + return Response(response, mimetype='text/plain; charset=utf-8') # 返回结果 if __name__ == "__main__":