iomgaa
4d7a7b1ba4
主要功能： - 完整的argparse参数系统，支持线程数、模型类型、数据范围等配置 - 集成AgentOps追踪功能，支持会话管理和性能监控 - 线程安全的BatchProcessor管理器，支持并发执行和进度统计 - 数据集加载和验证功能，支持范围选择和格式检查 - 多线程执行框架，使用ThreadPoolExecutor管理线程池 - 单样本处理函数，调用MedicalWorkflow并集成追踪 - 实时进度监控，后台线程定期报告处理状态 - 完整的错误处理和异常恢复机制 - 结果汇总和报告生成，支持JSON和文本格式 - 统一配置管理，AgentOps API密钥集成到config.py 技术特性： - 支持20个并发线程处理1677个医疗病例样本 - 线程安全的进度追踪和状态管理 - 详细的日志记录和调试信息输出 - 试运行模式支持配置验证 - 优雅的中断处理和资源清理 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
93 lines
2.7 KiB
Python
93 lines
2.7 KiB
Python
import os
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API_KEY = "sk-263038d4bf4e46a0bed16532587cff40"
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# AgentOps API密钥
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AGENTOPS_API_KEY = "8c30718a-0485-4adb-a852-05d02e50e3cb"
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# {project_root}/medsynthai
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BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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# LLM model configuration based on agno
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LLM_CONFIG = {
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"deepseek": {
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"class": "DeepSeek",
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"params": {
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"id": "deepseek-chat",
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"api_key": API_KEY,
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"base_url": "https://api.deepseek.com"
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}
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},
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"gpt-oss:latest": {
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"class": "OpenAILike",
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"params": {
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"id": "gpt-oss:latest",
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"base_url": "http://192.168.31.228:11434/v1", # Ollama OpenAI兼容端点
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"api_key": "ollama" # Ollama不需要真实API密钥，任意字符串即可
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}
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},
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"deepseek-v3": {
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"class": "OpenAILike",
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"params": {
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"id": "deepseek-v3",
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"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
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"api_key": API_KEY
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}
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},
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"deepseek-r1": {
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"class": "OpenAILike",
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"params": {
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"id": "deepseek-r1",
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"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
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"api_key": API_KEY
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}
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},
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"qwen-max": {
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"class": "OpenAILike",
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"params": {
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"id": "qwen-max",
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"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
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"api_key": API_KEY
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}
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},
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"qwen-vl-max": {
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"class": "OpenAILike", # 使用OpenAI兼容类
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"params": {
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"id": "qwen-vl-max",
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"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1", # OpenAI兼容端点 # 降低随机性，提高结果一致性
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"api_key": API_KEY
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}
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},
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"aliyun": {
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"class": "OpenAILike",
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"params": {
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"id": "qwen-max", # 默认使用qwen-max模型
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"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
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"api_key": API_KEY
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}
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}
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}
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AGENT_CONFIG = {
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"medical_image_analysis_agent": "qwen-vl-max"
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}
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RAG_CONFIG = {
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"lightrag": {
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"working_dir": "./Vector_DB_Med",
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"tokenizer_name": "trueto/medbert-base-chinese",
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"model_name": "trueto/medbert-base-chinese",
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"embedding_dim": 768,
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"max_token_size": 512
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},
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"chroma_db": {
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"api_key": API_KEY,
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"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
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"collection_name": "doctor",
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"batch_size": 100,
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"chroma_db_path": os.path.join(BASE_DIR, "static/rag/chroma_db"),
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"csv_path": os.path.join(BASE_DIR, "static/files/zhongkang_doctor_list.csv")
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}
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}
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