166 lines
6.6 KiB
Python
166 lines
6.6 KiB
Python
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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#
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# Copyright @2024 AI. Inspur Inc.
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#
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# @author: suojiayi <suojiayi@inspur.com>
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# @date: 2025/05/13
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import json
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import requests
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import pandas as pd
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from concurrent.futures import ThreadPoolExecutor, as_completed
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def read_jsonl_lines_in_batches(file_path, batch_size=10000):
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"""按批次读取 JSONL 文件"""
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batch = []
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with open(file_path, mode="r", encoding="utf-8") as f:
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for line in f:
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try:
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batch.append(json.loads(line.strip()))
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if len(batch) == batch_size:
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yield batch
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batch = []
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except json.JSONDecodeError as e:
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print(f"Error decoding JSON: {e}")
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if batch:
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yield batch
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def process_data_concurrently(data_list, api_url, headers, max_workers=10):
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"""并发处理数据并调用 API"""
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result_data = []
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def process_single_data(data):
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try:
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query = data.get('data')
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if query:
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input_content = f'''你是一个多轮对话分析专家。请根据以下维度对给定的多轮对话（2-3轮）进行细粒度分类，并按统一格式返回结果。
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# 输入规范
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1. 输入为完整的多轮对话（2-3组问答对）
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2. 每组问答包含明确的用户提问和系统/客服响应
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# 核心分析维度
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## 1. 指代消解模式
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- 显式指代：直接使用完整名词（订单号123）
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- 代词依赖：需解析"它/这个/那个"等代词
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- 省略恢复：需补全省略成分（"还有呢？"→"其他问题"）
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- 零指代：无主语句（"还没处理"）
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## 2. 信息流结构
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- 自足单元：当前轮次信息完整
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- 跨轮依赖：需前文关键信息
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- 隐式跳转：无过渡的话题切换
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- 意图延续：延续前轮任务目标
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- 推理关联：需逻辑推算建立联系
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## 3. 上下文管理
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- 短期记忆：依赖1-2轮内信息
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- 长期记忆：引用3轮前历史记录
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- 推理记忆：需计算/推理关联信息
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## 4. 状态演化
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- 静态查询：纯信息检索
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- 动态演进：逐步完善任务参数
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- 混合操作：查询+修改组合
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- 信息修正：更正前轮错误数据
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# 输出规范'''+'''```json
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{
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"labels": ["指代消解::显式指代", "信息流::跨轮依赖", "上下文::推理记忆", "状态演化::动态演进"],
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"analysis": {
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"指代消解": {
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"类型": "显式指代",
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"证据": "例句：'订单A123'直接使用完整标识符"
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},
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"信息流": {
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"类型": "跨轮依赖",
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"证据": "客服要求提供订单号是对用户首轮请求的响应"
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},
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"上下文": {
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"类型": "推理记忆",
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"证据": "需要根据首轮日期推算当前状态"
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},
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"状态演化": {
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"类型": "动态演进",
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"证据": "从查询请求逐步收集必要参数"
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}
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}
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}```'''+f'''让我们一步一步思考，给出最后的返回结果，输入的多轮对话：{query}'''
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response = requests.post(
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api_url,
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headers=headers,
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json={
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"model": "Qwen2.5-72B-Instruct",
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"stream": False,
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"temperature": 0.01,
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"messages": [{"role": "user", "content": input_content}]
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}
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)
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if response.status_code == 200:
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try:
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content = response.json()["choices"][0]["message"]["content"]
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except (KeyError, IndexError, json.JSONDecodeError):
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content = "无法解析返回内容"
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else:
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content = f"API请求失败，状态码：{response.status_code}"
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return {
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"uid": data.get('uid'),
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"data": query,
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"answer": content
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}
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except Exception as e:
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print(e)
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return None
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with ThreadPoolExecutor(max_workers=max_workers) as executor:
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futures = [executor.submit(process_single_data, data) for data in data_list]
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for future in as_completed(futures):
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result = future.result()
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if result:
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result_data.append(result)
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return result_data
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def save_to_excel_in_batches(data_list, output_file, batch_size=10000):
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"""按批次保存数据到 Excel 文件"""
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df = pd.DataFrame(data_list)
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writer = pd.ExcelWriter(output_file, engine='openpyxl')
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for i in range(0, len(df), batch_size):
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batch_df = df.iloc[i:i + batch_size]
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batch_df.to_excel(writer, index=False, startrow=i)
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writer.close()
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print(f"数据已成功保存到 {output_file}")
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def save_to_jsonl_in_batches(data_list, output_file, batch_size=10000):
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"""按批次保存数据到 JSONL 文件"""
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with open(output_file, 'w', encoding='utf-8') as f:
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for i in range(0, len(data_list), batch_size):
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# 获取当前批次的数据
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batch_data = data_list[i:i + batch_size]
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# 将每个数据对象写入文件，每行一个 JSON 对象
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for item in batch_data:
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f.write(json.dumps(item, ensure_ascii=False) + '\n')
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print(f"数据已成功保存到 {output_file}")
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if __name__ == "__main__":
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#output_excel_file = 'result-taoli-5.xlsx'
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# api_url = "http://100.105.149.39:8000/v1/chat/completions"
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api_url = "http://100.105.230.95:8000/v1/chat/completions"
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headers = {
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"Content-Type": "application/json",
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"Authorization": "7c3eafb5-2d6e-100d-ab0f-7b2c1cdafb3c"
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}
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#file_path = '/dataset-pvc/suojiayi/new/train_prepare/20250423_020157/tmp_data/instruct_data_BELLE_Multiturn_Chat_filtered_2504232014.jsonl'
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file_path = '/data/suojiayi/buquan/split_dhbq/part_2.jsonl'
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output_file = './dhbq/duihuabuquan_prompt_2.jsonl'
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#file_path = '/dataset-pvc/suojiayi/new/train_prepare/20250423_020157/tmp_data/instruct_data_COIG_filtered_2504212014.jsonl'
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all_results = []
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for batch in read_jsonl_lines_in_batches(file_path, batch_size=10000):
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processed_batch = process_data_concurrently(batch, api_url, headers, max_workers=20)
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all_results.extend(processed_batch)
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# save_to_excel_in_batches(all_results, output_excel_file, batch_size=23000)
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save_to_jsonl_in_batches(all_results, output_file, batch_size=10000)
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