580 lines
26 KiB
Python
580 lines
26 KiB
Python
"""
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集成LangSmith监控的迭代检索器
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Token监控通过OneAPILLM自动处理
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"""
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import os
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import time
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import json
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from datetime import datetime
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from typing import Dict, Any, Optional
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# LangSmith追踪支持
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from langsmith import traceable
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from retriver.langgraph.iterative_retriever import IterativeRetriever
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from retriver.langgraph.graph_state import create_initial_state
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class LangSmithIterativeRetriever(IterativeRetriever):
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"""
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集成LangSmith监控的迭代检索器
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Token消耗通过OneAPILLM的_generate方法自动记录
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"""
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def __init__(
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self,
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keyword: str,
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top_k: int = 2,
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max_iterations: int = 2,
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max_parallel_retrievals: int = 2,
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oneapi_key: Optional[str] = None,
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oneapi_base_url: Optional[str] = None,
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model_name: Optional[str] = None,
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embed_model_name: Optional[str] = None,
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complexity_model_name: Optional[str] = None,
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sufficiency_model_name: Optional[str] = None,
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langsmith_project: Optional[str] = None,
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skip_llm_generation: bool = False
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):
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# 配置LangSmith
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self._setup_langsmith(langsmith_project)
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# 调用父类初始化
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super().__init__(
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keyword=keyword,
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top_k=top_k,
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max_iterations=max_iterations,
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max_parallel_retrievals=max_parallel_retrievals,
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oneapi_key=oneapi_key,
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oneapi_base_url=oneapi_base_url,
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model_name=model_name,
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embed_model_name=embed_model_name,
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complexity_model_name=complexity_model_name,
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sufficiency_model_name=sufficiency_model_name,
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skip_llm_generation=skip_llm_generation
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)
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print("[SEARCH] LangSmith监控已启用")
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def _setup_langsmith(self, project_name: Optional[str] = None):
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"""配置LangSmith环境"""
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# 设置环境变量
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if not os.getenv("LANGCHAIN_TRACING_V2"):
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os.environ["LANGCHAIN_TRACING_V2"] = "true"
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if not os.getenv("LANGCHAIN_ENDPOINT"):
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os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.langchain.com"
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# 设置项目名称
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if project_name:
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os.environ["LANGCHAIN_PROJECT"] = project_name
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elif not os.getenv("LANGCHAIN_PROJECT"):
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os.environ["LANGCHAIN_PROJECT"] = "hipporag-retriever"
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# 检查API密钥
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if not os.getenv("LANGCHAIN_API_KEY") or os.getenv("LANGCHAIN_API_KEY") == "your_langsmith_api_key_here":
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print("[WARNING] 请设置正确的LANGCHAIN_API_KEY环境变量")
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print("访问 https://smith.langchain.com 获取API密钥")
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@traceable(name="Complete_Retrieval_Process")
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def retrieve(self, query: str, mode: str = "0") -> Dict[str, Any]:
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"""
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带LangSmith追踪的完整检索过程
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"""
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print(f"[STARTING] 开始检索过程 (LangSmith追踪)")
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print(f"[POINT] 项目: {os.getenv('LANGCHAIN_PROJECT', 'hipporag-retriever')}")
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print(f"[SEARCH] 查询: {query}")
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print(f"[BUG] 调试模式: {mode}")
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start_time = time.time()
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try:
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# 创建初始状态
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initial_state = create_initial_state(
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original_query=query,
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max_iterations=self.max_iterations,
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debug_mode=mode
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)
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# 执行工作流 - LangChain会自动追踪所有LLM调用
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# 配置LangSmith避免追踪大型数据结构
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config = {
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"recursion_limit": 50,
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"metadata": {
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"note": "大型PageRank数据仅保存本地，不上传LangSmith"
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},
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"callbacks": [], # 禁用额外的回调避免数据泄漏
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"run_name": f"Retrieval-{query[:20]}..."
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}
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result_state = self.workflow.invoke(initial_state, config=config)
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# 构建结果
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end_time = time.time()
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total_time = end_time - start_time
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# 构建完整结果（本地使用）
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full_result = self._build_final_result(result_state, total_time)
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# 清理结果状态中的大型数据结构以避免LangSmith传输
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cleaned_state = self._clean_state_for_langsmith(result_state)
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# 为LangSmith创建轻量级版本，移除大型数据结构
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langsmith_result = self._build_langsmith_safe_result(cleaned_state, total_time)
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print(f"[OK] 检索完成 (耗时: {total_time:.2f}秒)")
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print(f"[LINK] 在LangSmith中查看详细追踪信息")
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# 返回轻量级版本给LangSmith，但将完整结果保存到文件
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self._save_full_result_to_file(full_result)
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# 为了兼容langsmith_example.py，返回包含真实文档数据的结果
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# 但移除PageRank等大型数据以避免LangSmith传输问题
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result_with_docs = {
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"query": langsmith_result.get("query", ""),
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"answer": langsmith_result.get("answer", ""),
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"total_passages": langsmith_result.get("total_passages", 0),
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"retrieval_path": langsmith_result.get("retrieval_path", "unknown"),
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"iterations": langsmith_result.get("iterations", 0),
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"is_sufficient": langsmith_result.get("is_sufficient", False),
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"sub_queries": langsmith_result.get("sub_queries", [])[:3],
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# 包含真实的文档数据（为langsmith_example.py使用）
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"all_documents": result_state.get('all_documents', []),
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"all_passages": result_state.get('all_passages', []),
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"passage_sources": result_state.get('passage_sources', []),
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# 其他完整信息
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"query_complexity": result_state.get('query_complexity', {}),
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"decomposed_sub_queries": result_state.get('decomposed_sub_queries', []),
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"sufficiency_check": result_state.get('sufficiency_check', {}),
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"debug_info": {
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"total_time": total_time,
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"langsmith_project": langsmith_result.get("debug_info", {}).get("langsmith_project", ""),
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"note": "包含真实文档数据的完整版本"
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}
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}
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return result_with_docs
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except KeyboardInterrupt:
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print(f"\n[WARNING] 检索被用户中断")
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end_time = time.time()
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return {
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"query": query,
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"answer": "检索被用户中断",
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"error": "KeyboardInterrupt",
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"total_time": end_time - start_time,
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"iterations": 0,
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"total_passages": 0,
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"sub_queries": [],
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"debug_info": {"error": "KeyboardInterrupt", "total_time": end_time - start_time}
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}
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except Exception as e:
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print(f"[ERROR] 检索过程出错: {e}")
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end_time = time.time()
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return {
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"query": query,
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"answer": f"抱歉，检索过程中遇到错误: {str(e)}",
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"error": str(e),
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"total_time": end_time - start_time,
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"iterations": 0,
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"total_passages": 0,
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"sub_queries": [],
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"debug_info": {"error": str(e), "total_time": end_time - start_time}
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}
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def _build_final_result(self, final_state, total_time: float) -> Dict[str, Any]:
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"""构建最终结果"""
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# 获取最终的Token使用统计
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final_token_info = self._get_token_usage_info()
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return {
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"query": final_state.get('original_query', ''),
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"answer": final_state.get('final_answer', '') or "未能生成答案",
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# 查询复杂度信息
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"query_complexity": final_state.get('query_complexity', {}),
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"is_complex_query": final_state.get('is_complex_query', False),
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"retrieval_path": "complex_hipporag" if final_state.get('is_complex_query', False) else "simple_vector",
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"iterations": final_state.get('current_iteration', 0),
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"total_passages": len(final_state.get('all_passages', [])),
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"sub_queries": final_state.get('sub_queries', []),
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"decomposed_sub_queries": final_state.get('decomposed_sub_queries', []),
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"initial_retrieval_details": final_state.get('initial_retrieval_details', {}),
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"sufficiency_check": final_state.get('sufficiency_check', {}),
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"current_sub_queries": final_state.get('current_sub_queries', []),
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"is_sufficient": final_state.get('is_sufficient', False),
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# 完整的文档和段落数据（本地文件专用）
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"all_documents": final_state.get('all_documents', []),
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"all_passages": final_state.get('all_passages', []),
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"passage_sources": final_state.get('passage_sources', []),
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# PageRank数据已移除状态存储，改为本地文件存储（避免LangSmith传输）
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"pagerank_data_available": final_state.get('pagerank_data_available', False),
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"pagerank_summary": final_state.get('pagerank_summary', {}),
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"concept_exploration_results": final_state.get('concept_exploration_results', {}),
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"exploration_round": final_state.get('exploration_round', 0),
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"debug_info": {
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"total_time": total_time,
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"retrieval_calls": final_state.get('debug_info', {}).get('retrieval_calls', 0),
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"llm_calls": final_state.get('debug_info', {}).get('llm_calls', 0),
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"langsmith_project": os.getenv('LANGCHAIN_PROJECT', 'hipporag-retriever'),
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# Token使用统计
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"token_usage_summary": final_token_info,
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# 路径统计
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"complexity_analysis": {
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"is_complex": final_state.get('is_complex_query', False),
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"complexity_level": final_state.get('query_complexity', {}).get('complexity_level', 'unknown'),
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"confidence": final_state.get('query_complexity', {}).get('confidence', 0),
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"reason": final_state.get('query_complexity', {}).get('reason', '')
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},
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# 调试模式信息
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"debug_mode_analysis": {
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"debug_mode": final_state.get('debug_mode', '0'),
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"debug_override": final_state.get('query_complexity', {}).get('debug_override', {}),
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"path_override_applied": bool(final_state.get('query_complexity', {}).get('debug_override', {}))
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},
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# 充分性检查历史
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"sufficiency_analysis": {
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"final_sufficiency": final_state.get('is_sufficient', False),
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"sufficiency_check_details": final_state.get('sufficiency_check', {}),
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"iteration_sufficiency_history": [
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{
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"iteration": item.get('iteration', 0),
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"is_sufficient": item.get('is_sufficient', False),
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"confidence": item.get('sufficiency_check', {}).get('confidence', 0),
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"reason": item.get('sufficiency_check', {}).get('reason', '')
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}
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for item in final_state.get('iteration_history', [])
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if 'sufficiency_check' in item
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],
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"sufficiency_progression": self._analyze_sufficiency_progression(final_state)
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},
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# 路由决策历史
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"routing_analysis": {
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"total_routing_decisions": len([
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item for item in final_state.get('iteration_history', [])
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if item.get('action') in ['sufficiency_check', 'sub_query_generation', 'parallel_retrieval', 'collect_pagerank_scores']
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]),
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"sub_query_generation_count": len([
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item for item in final_state.get('iteration_history', [])
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if item.get('action') == 'sub_query_generation'
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]),
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"parallel_retrieval_count": len([
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item for item in final_state.get('iteration_history', [])
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if item.get('action') == 'parallel_retrieval'
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]),
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"pagerank_collection_count": len([
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item for item in final_state.get('iteration_history', [])
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if item.get('action') == 'collect_pagerank_scores'
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])
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},
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# 概念探索分析（新增）
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"concept_exploration_analysis": {
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"exploration_enabled": final_state.get('exploration_round', 0) > 0,
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"exploration_rounds": final_state.get('exploration_round', 0),
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"pagerank_nodes_analyzed": len(final_state.get('pagerank_summary', {}).get('all_nodes_sorted', [])),
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"successful_branches_total": sum([
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round_data.get('successful_branches', 0)
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for round_key, round_data in final_state.get('concept_exploration_results', {}).items()
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if round_key.startswith('round_')
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]),
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"total_branches_attempted": sum([
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round_data.get('total_branches', 0)
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for round_key, round_data in final_state.get('concept_exploration_results', {}).items()
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if round_key.startswith('round_')
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])
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}
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},
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"all_passages": final_state.get('all_passages', []),
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"all_documents": final_state.get('all_documents', []), # 添加文档列表
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"iteration_history": final_state.get('iteration_history', [])
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}
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def _clean_state_for_langsmith(self, state: Dict[str, Any]) -> Dict[str, Any]:
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"""清理状态中的大型数据结构，避免发送给LangSmith"""
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cleaned_state = state.copy()
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# PageRank数据已从状态中移除，不需要清理
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if cleaned_state.get('pagerank_data_available', False):
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print(f"[?] PageRank数据已存储在本地，未包含在状态中")
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# 清理概念探索结果中的大型数据
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if 'concept_exploration_results' in cleaned_state:
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cleaned_exploration = {}
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for key, value in cleaned_state['concept_exploration_results'].items():
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if isinstance(value, dict):
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# 只保留统计信息，移除具体的节点数据
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cleaned_exploration[key] = {
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'total_branches': value.get('total_branches', 0),
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'successful_branches': value.get('successful_branches', 0),
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'exploration_type': value.get('exploration_type', 'unknown')
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}
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else:
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cleaned_exploration[key] = value
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cleaned_state['concept_exploration_results'] = cleaned_exploration
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print(f"[?] 已清理概念探索详细结果，只保留统计信息")
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# 清理PageRank汇总中的详细节点数据
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if 'pagerank_summary' in cleaned_state:
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summary = cleaned_state['pagerank_summary']
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if isinstance(summary, dict) and 'all_nodes_sorted' in summary:
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nodes_count = len(summary.get('all_nodes_sorted', []))
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cleaned_summary = {k: v for k, v in summary.items() if k != 'all_nodes_sorted'}
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cleaned_summary['nodes_count'] = nodes_count
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cleaned_state['pagerank_summary'] = cleaned_summary
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print(f"[?] 已清理 {nodes_count} 个PageRank节点详情，只保留统计")
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return cleaned_state
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def _analyze_sufficiency_progression(self, final_state) -> Dict[str, Any]:
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"""分析充分性检查的进展"""
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iteration_history = final_state.get('iteration_history', [])
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sufficiency_checks = [
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item for item in iteration_history
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if 'sufficiency_check' in item
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]
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if not sufficiency_checks:
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return {"status": "no_sufficiency_checks"}
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# 分析进展模式
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confidences = [sc.get('sufficiency_check', {}).get('confidence', 0) for sc in sufficiency_checks]
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sufficiencies = [sc.get('is_sufficient', False) for sc in sufficiency_checks]
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progression_pattern = "unknown"
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if len(sufficiencies) >= 2:
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if not sufficiencies[0] and sufficiencies[-1]:
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progression_pattern = "improved_to_sufficient"
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elif all(sufficiencies):
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progression_pattern = "consistently_sufficient"
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elif not any(sufficiencies):
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progression_pattern = "consistently_insufficient"
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else:
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progression_pattern = "mixed"
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return {
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"total_checks": len(sufficiency_checks),
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"confidence_progression": confidences,
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"sufficiency_progression": sufficiencies,
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"pattern": progression_pattern,
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"final_confidence": confidences[-1] if confidences else 0,
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"confidence_improvement": confidences[-1] - confidences[0] if len(confidences) >= 2 else 0
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}
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def _get_token_usage_info(self) -> Dict[str, Any]:
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"""
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获取当前的Token使用信息
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"""
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try:
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# 尝试从不同的属性路径获取Token信息
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debug_info = {}
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# 检查self.llm是否存在
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if hasattr(self, 'llm'):
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debug_info["has_llm"] = True
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# 检查oneapi_generator是否存在
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if hasattr(self.llm, 'oneapi_generator'):
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debug_info["has_generator"] = True
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generator = self.llm.oneapi_generator
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# 获取Token统计
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last_usage = getattr(generator, 'last_token_usage', {})
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total_usage = getattr(generator, 'total_token_usage', {})
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model_name = getattr(generator, 'model_name', 'unknown')
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debug_info.update({
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"last_call": last_usage,
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"total_usage": total_usage,
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"model_name": model_name,
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"has_last_usage": bool(last_usage),
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"has_total_usage": bool(total_usage)
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})
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return debug_info
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else:
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debug_info["has_generator"] = False
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else:
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debug_info["has_llm"] = False
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debug_info["error"] = "无法找到Token信息"
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return debug_info
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except Exception as e:
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return {
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"error": f"获取Token信息失败: {str(e)}",
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"exception_type": type(e).__name__
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}
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@traceable(name="Simple_Retrieve")
|
||
def retrieve_simple(self, query: str, mode: str = "0") -> str:
|
||
"""简单检索接口"""
|
||
result = self.retrieve(query, mode)
|
||
return result.get('answer', '')
|
||
|
||
def _build_langsmith_safe_result(self, final_state, total_time: float) -> Dict[str, Any]:
|
||
"""构建LangSmith安全的结果，移除大型数据结构以避免传输限制"""
|
||
final_token_info = self._get_token_usage_info()
|
||
|
||
return {
|
||
"query": final_state.get('original_query', ''),
|
||
"answer": final_state.get('final_answer', '') or "未能生成答案",
|
||
|
||
# 查询复杂度信息
|
||
"query_complexity": final_state.get('query_complexity', {}),
|
||
"is_complex_query": final_state.get('is_complex_query', False),
|
||
"retrieval_path": "complex_hipporag" if final_state.get('is_complex_query', False) else "simple_vector",
|
||
|
||
"iterations": final_state.get('current_iteration', 0),
|
||
"total_passages": len(final_state.get('all_passages', [])),
|
||
"sub_queries": final_state.get('sub_queries', []),
|
||
"decomposed_sub_queries": final_state.get('decomposed_sub_queries', []),
|
||
"sufficiency_check": final_state.get('sufficiency_check', {}),
|
||
"is_sufficient": final_state.get('is_sufficient', False),
|
||
|
||
# 添加文档和段落数据（langsmith_example.py需要这些数据）
|
||
# 注意：这些数据仅供本地脚本使用，不会发送到LangSmith web端
|
||
"all_documents": final_state.get('all_documents', []),
|
||
"all_passages": final_state.get('all_passages', []),
|
||
|
||
# 简化的统计信息，不包含大型数据结构
|
||
"pagerank_summary_stats": {
|
||
"data_available": final_state.get('pagerank_data_available', False),
|
||
"exploration_rounds": final_state.get('exploration_round', 0),
|
||
"has_concept_exploration": bool(final_state.get('concept_exploration_results', {}))
|
||
},
|
||
|
||
"debug_info": {
|
||
"total_time": total_time,
|
||
"retrieval_calls": final_state.get('debug_info', {}).get('retrieval_calls', 0),
|
||
"llm_calls": final_state.get('debug_info', {}).get('llm_calls', 0),
|
||
"langsmith_project": os.getenv('LANGCHAIN_PROJECT', 'hipporag-retriever'),
|
||
"token_usage_summary": final_token_info,
|
||
"complexity_analysis": {
|
||
"is_complex": final_state.get('is_complex_query', False),
|
||
"complexity_level": final_state.get('query_complexity', {}).get('complexity_level', 'unknown'),
|
||
"confidence": final_state.get('query_complexity', {}).get('confidence', 0),
|
||
"reason": final_state.get('query_complexity', {}).get('reason', '')
|
||
},
|
||
"final_sufficiency": final_state.get('is_sufficient', False),
|
||
"note": "完整结果已保存到本地文件，此为LangSmith优化版本"
|
||
}
|
||
}
|
||
|
||
def _save_full_result_to_file(self, full_result: Dict[str, Any]):
|
||
"""将完整结果保存到本地文件"""
|
||
try:
|
||
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
|
||
filename = f"langsmith_full_{timestamp}.json"
|
||
|
||
# 创建json_langsmith目录（如果不存在）
|
||
json_dir = os.path.join(os.path.dirname(__file__), "json_langsmith")
|
||
os.makedirs(json_dir, exist_ok=True)
|
||
|
||
filepath = os.path.join(json_dir, filename)
|
||
|
||
# 序列化时需要特殊处理numpy数组和Document对象等
|
||
def json_serializer(obj):
|
||
# 处理langchain Document对象
|
||
if hasattr(obj, 'page_content') and hasattr(obj, 'metadata'):
|
||
return {
|
||
'page_content': obj.page_content,
|
||
'metadata': obj.metadata
|
||
}
|
||
# 处理numpy数组
|
||
elif hasattr(obj, 'tolist'):
|
||
return obj.tolist()
|
||
# 处理其他自定义对象
|
||
elif hasattr(obj, '__dict__'):
|
||
try:
|
||
return obj.__dict__
|
||
except:
|
||
return str(obj)
|
||
else:
|
||
return str(obj)
|
||
|
||
with open(filepath, 'w', encoding='utf-8') as f:
|
||
json.dump(full_result, f, ensure_ascii=False, indent=2, default=json_serializer)
|
||
|
||
print(f"[FOLDER] 完整结果已保存: {filename}")
|
||
|
||
except Exception as e:
|
||
print(f"[WARNING] 保存完整结果失败: {e}")
|
||
|
||
|
||
def create_langsmith_retriever(
|
||
keyword: str,
|
||
top_k: int = 2,
|
||
max_iterations: int = 2,
|
||
max_parallel_retrievals: int = 2,
|
||
langsmith_project: Optional[str] = None,
|
||
**kwargs
|
||
) -> LangSmithIterativeRetriever:
|
||
"""创建带LangSmith监控的迭代检索器"""
|
||
return LangSmithIterativeRetriever(
|
||
keyword=keyword,
|
||
top_k=top_k,
|
||
max_iterations=max_iterations,
|
||
max_parallel_retrievals=max_parallel_retrievals,
|
||
langsmith_project=langsmith_project,
|
||
**kwargs
|
||
)
|
||
|
||
|
||
# 用于检查LangSmith连接状态的工具函数
|
||
def check_langsmith_connection() -> bool:
|
||
"""检查LangSmith连接状态，带重试机制"""
|
||
import time
|
||
max_retries = 3
|
||
retry_delay = 2 # 秒
|
||
|
||
for attempt in range(max_retries):
|
||
try:
|
||
from langsmith import Client
|
||
|
||
api_key = os.getenv("LANGCHAIN_API_KEY")
|
||
if not api_key or api_key == "your_langsmith_api_key_here":
|
||
print("[ERROR] LangSmith API密钥未设置或无效")
|
||
return False
|
||
|
||
# 设置较短的超时时间
|
||
client = Client()
|
||
# 尝试获取项目列表来验证连接
|
||
projects = list(client.list_projects(limit=1))
|
||
print("[OK] LangSmith连接正常")
|
||
return True
|
||
|
||
except Exception as e:
|
||
error_msg = str(e)
|
||
|
||
# 检查是否为已知的服务不可用错误
|
||
if any(keyword in error_msg.lower() for keyword in ['503', 'service unavailable', 'server error', 's3', 'timeout', 'deadline exceeded']):
|
||
print(f"[WARNING] LangSmith服务暂时不可用 (尝试 {attempt + 1}/{max_retries}): {error_msg[:100]}...")
|
||
|
||
if attempt < max_retries - 1: # 还有重试次数
|
||
print(f"⏰ {retry_delay}秒后重试...")
|
||
time.sleep(retry_delay)
|
||
retry_delay *= 1.5 # 递增重试间隔
|
||
continue
|
||
else:
|
||
print("[ERROR] LangSmith服务持续不可用，将在本地模式下运行")
|
||
return False
|
||
else:
|
||
print(f"[ERROR] LangSmith连接失败: {e}")
|
||
return False
|
||
|
||
return False |