kg_agent/knowledge_v2/execution_collector.py

"""
执行记录收集器

从各个模块的缓存目录收集执行详情，汇总成完整的execution_record.json
"""

import os
import json
import hashlib
from typing import Dict, Any, List
from loguru import logger


class ExecutionCollector:
    """执行记录收集器"""
    
    def __init__(self, base_cache_dir: str = None):
        """
        初始化
        
        Args:
            base_cache_dir: 缓存基础目录，默认为当前目录下的.cache
        """
        if base_cache_dir is None:
            current_dir = os.path.dirname(os.path.abspath(__file__))
            base_cache_dir = os.path.join(current_dir, '.cache')
        
        self.base_cache_dir = base_cache_dir
    
    def collect_execution_record(self, cache_key: str, input_info: Dict[str, Any]) -> Dict[str, Any]:
        """
        收集完整的执行记录
        
        Args:
            cache_key: 缓存键（通常是combined_question）
            input_info: 输入信息 {"question": ..., "post_info": ..., "persona_info": ...}
            
        Returns:
            dict: 完整的执行记录
        """
        logger.info("=" * 60)
        logger.info("开始收集执行记录...")
        
        # 计算hash
        question_hash = hashlib.md5(cache_key.encode('utf-8')).hexdigest()[:12]
        cache_dir = os.path.join(self.base_cache_dir, question_hash)
        
        if not os.path.exists(cache_dir):
            logger.warning(f"缓存目录不存在: {cache_dir}")
            return self._create_empty_record(input_info)
        
        # 初始化执行记录
        execution_record = {
            "input": input_info,
            "execution": {},
            "output": {
                "result": None,
            },
            "metadata": {
                "execution_time": input_info.get("execution_time", 0),
                "cache_hits": [],
                "errors": []
            }
        }
        
        try:
            # 1. Generate Query
            query_detail = self._read_json(cache_dir, 'function_knowledge', 'generated_query.json')
            if query_detail:
                execution_record["execution"]["generate_query"] = query_detail
                if query_detail.get("cached"):
                    execution_record["metadata"]["cache_hits"].append("generate_query")

            # 2. Select Tool
            tool_detail = self._read_json(cache_dir, 'function_knowledge', 'selected_tool.json')
            if tool_detail:
                execution_record["execution"]["select_tool"] = tool_detail
                if tool_detail.get("cached"):
                    execution_record["metadata"]["cache_hits"].append("select_tool")

            # 3. Check for Search or Tool Call
            tool_call_detail = self._read_json(cache_dir, 'function_knowledge', 'tool_call.json')
            
            if tool_call_detail:
                # Flow A: Tool Call
                
                # Extract Params
                params_detail = self._read_json(cache_dir, 'function_knowledge', 'extracted_params.json')
                if params_detail:
                    execution_record["execution"]["extract_params"] = params_detail
                    if params_detail.get("cached"):
                        execution_record["metadata"]["cache_hits"].append("extract_params")
                
                # Tool Call
                execution_record["execution"]["tool_call"] = tool_call_detail
                if tool_call_detail.get("cached"):
                    execution_record["metadata"]["cache_hits"].append("tool_call")
                
                # Result
                execution_record["output"]["result"] = tool_call_detail.get("result", "")
                execution_record["output"]["tool"] = tool_call_detail.get("tool_name", "")

            else:
                # Flow B: Search (Multi/LLM)
                search_detail = self._collect_search_detail(cache_dir)
                if search_detail:
                    execution_record["execution"]["knowledge_search"] = search_detail
                    
                    # Result
                    # merged_knowledge_detail from multi_search usually contains final response
                    merged_detail = search_detail.get("multi_search_merge")
                    if merged_detail:
                        execution_record["result"]["type"] = "knowledge_search"
                        response = merged_detail.get("response", "")
                        execution_record["result"]["content"] = response
                        execution_record["result"]["success"] = True
                        if merged_detail.get("cached"):
                            execution_record["metadata"]["cache_hits"].append("multi_search_merge")
            
            # Clean up metadata
            # execution_time is retrieved from input_info if provided
            
            logger.info("✓ 执行记录收集完成")
            logger.info("=" * 60)
            
        except Exception as e:
            logger.error(f"✗ 收集执行记录失败: {e}")
            execution_record["metadata"]["errors"].append(str(e))
        
        return execution_record

    def _read_json(self, base_dir: str, *paths) -> Dict[str, Any]:
        """读取JSON文件"""
        file_path = os.path.join(base_dir, *paths)
        if os.path.exists(file_path):
            try:
                with open(file_path, 'r', encoding='utf-8') as f:
                    return json.load(f)
            except Exception as e:
                logger.warning(f"读取JSON文件失败 {file_path}: {e}")
        return None

    def _collect_search_detail(self, cache_dir: str) -> Dict[str, Any]:
        """收集搜索流程详情"""
        search_detail = {}
        
        # 1. LLM Search
        llm_detail = {}
        
        # Generated Queries
        queries_detail = self._read_json(cache_dir, 'llm_search', 'generated_queries.json')
        if queries_detail:
            llm_detail["generated_queries"] = queries_detail
            
        # Search Results
        # Search for search_result_XXX.json
        search_results = []
        llm_search_dir = os.path.join(cache_dir, 'llm_search', 'search_results')
        if os.path.exists(llm_search_dir):
            for filename in sorted(os.listdir(llm_search_dir)):
                if filename.endswith('.json') and filename.startswith('search_result_'):
                    res = self._read_json(llm_search_dir, filename)
                    if res:
                        search_results.append(res)
        if search_results:
            llm_detail["search_results"] = search_results
            
        # Merge
        merge_detail_llm = self._read_json(cache_dir, 'llm_search', 'merged_knowledge_detail.json')
        if merge_detail_llm:
            llm_detail["merge"] = merge_detail_llm
            
        if llm_detail:
            search_detail["llm_search"] = llm_detail
            
        # 2. Multi Search Merge
        merge_detail_multi = self._read_json(cache_dir, 'multi_search', 'merged_knowledge_detail.json')
        if merge_detail_multi:
            search_detail["multi_search_merge"] = merge_detail_multi
            
        return search_detail if search_detail else None

    def _create_empty_record(self, input_info: Dict[str, Any]) -> Dict[str, Any]:
        """创建空的执行记录"""
        return {
            "input": input_info,
            "execution": {},
            "result": {
                "type": "error",
                "content": "缓存目录不存在",
                "raw_data": None
            },
            "metadata": {
                "execution_time": 0,
                "cache_hits": [],
                "errors": ["缓存目录不存在"]
            }
        }
    
    def save_execution_record(self, cache_key: str, execution_record: Dict[str, Any]) -> str:
        """
        保存执行记录到文件
        
        Args:
            cache_key: 缓存键
            execution_record: 执行记录
            
        Returns:
            str: 保存的文件路径
        """
        question_hash = hashlib.md5(cache_key.encode('utf-8')).hexdigest()[:12]
        cache_dir = os.path.join(self.base_cache_dir, question_hash)
        os.makedirs(cache_dir, exist_ok=True)
        
        output_file = os.path.join(cache_dir, 'execution_record.json')
        
        try:
            with open(output_file, 'w', encoding='utf-8') as f:
                json.dump(execution_record, f, ensure_ascii=False, indent=2)
            
            logger.info(f"✓ 执行记录已保存: {output_file}")
            return output_file
            
        except Exception as e:
            logger.error(f"✗ 保存执行记录失败: {e}")
            raise


def collect_and_save_execution_record(cache_key: str, input_info: Dict[str, Any]) -> Dict[str, Any]:
    """
    便捷函数：收集并保存执行记录
    
    Args:
        cache_key: 缓存键
        input_info: 输入信息
        
    Returns:
        dict: 完整的执行记录
    """
    collector = ExecutionCollector()
    execution_record = collector.collect_execution_record(cache_key, input_info)
    collector.save_execution_record(cache_key, execution_record)
    return execution_record


if __name__ == "__main__":
    # 测试
    import time
    
    cache_key = "测试问题||无||测试人设"
    input_info = {
        "question": "测试问题",
        "post_info": "无",
        "persona_info": "测试人设",
        "timestamp": time.strftime("%Y-%m-%d %H:%M:%S")
    }
    
    # 注意：测试时需要确保缓存目录有数据，否则会返回空记录
    try:
        record = collect_and_save_execution_record(cache_key, input_info)
        print(json.dumps(record, ensure_ascii=False, indent=2))
    except Exception as e:
        print(f"Test failed: {e}")