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@@ -41,9 +41,72 @@ class FunctionKnowledge:
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self.prompt_dir = os.path.join(current_dir, "prompt")
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self.use_cache = use_cache
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self.cache = CacheManager() if use_cache else None
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+
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+ # 执行详情收集
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+ self.execution_detail = {
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+ "generate_query": {},
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+ "select_tool": {},
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+ "extract_params": {},
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+ "execution_time": 0,
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+ "cache_hits": []
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+ }
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+
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logger.info(f"缓存状态: {'启用' if use_cache else '禁用'}")
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logger.info("=" * 80)
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+ def _save_execution_detail(self, cache_key: str):
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+ """保存执行详情到缓存（支持合并旧记录）"""
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+ if not self.use_cache or not self.cache:
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+ return
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+
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+ try:
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+ import hashlib
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+ question_hash = hashlib.md5(cache_key.encode('utf-8')).hexdigest()[:12]
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+ detail_dir = os.path.join(
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+ self.cache.base_cache_dir,
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+ question_hash,
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+ 'function_knowledge'
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+ )
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+ os.makedirs(detail_dir, exist_ok=True)
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+
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+ detail_file = os.path.join(detail_dir, 'execution_detail.json')
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+
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+ # 准备最终要保存的数据，默认为当前内存中的数据
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+ final_detail = self.execution_detail.copy()
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+
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+ # 尝试读取旧文件进行合并
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+ if os.path.exists(detail_file):
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+ try:
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+ with open(detail_file, 'r', encoding='utf-8') as f:
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+ old_detail = json.load(f)
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+
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+ # 智能合并逻辑：保留更有价值的历史信息
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+ for key, new_val in self.execution_detail.items():
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+ # 跳过非字典字段或旧文件中不存在的字段
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+ if not isinstance(new_val, dict) or key not in old_detail:
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+ continue
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+
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+ old_val = old_detail[key]
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+ if not isinstance(old_val, dict):
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+ continue
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+
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+ # 核心逻辑：如果新记录是缓存命中(cached=True)，而旧记录包含prompt(说明是当初生成的)
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+ # 则保留旧记录，防止被简略信息覆盖
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+ if new_val.get("cached", False) is True and "prompt" in old_val:
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+ # logger.debug(f" 保留 {key} 的历史详细记录")
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+ final_detail[key] = old_val
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+
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+ except Exception as e:
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+ logger.warning(f" ⚠ 读取旧详情失败，将使用新记录: {e}")
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+
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+ with open(detail_file, 'w', encoding='utf-8') as f:
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+ json.dump(final_detail, f, ensure_ascii=False, indent=2)
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+
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+ logger.info(f"✓ 执行详情已保存: {detail_file}")
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+
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+ except Exception as e:
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+ logger.error(f"✗ 保存执行详情失败: {e}")
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+
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def _load_prompt(self, filename: str) -> str:
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"""加载prompt文件内容"""
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prompt_path = os.path.join(self.prompt_dir, filename)
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@@ -52,29 +115,26 @@ class FunctionKnowledge:
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with open(prompt_path, 'r', encoding='utf-8') as f:
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return f.read().strip()
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- def generate_query(self, question: str, post_info: str, persona_info: str) -> tuple:
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+ def generate_query(self, question: str, post_info: str, persona_info: str) -> str:
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"""
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生成查询语句
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Returns:
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- tuple: (query, detail_info)
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- - query: 生成的查询语句
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- - detail_info: 详细信息dict，包含prompt和response
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+ str: 生成的查询语句
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"""
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logger.info(f"[步骤1] 生成Query...")
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# 组合问题的唯一标识
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combined_question = f"{question}||{post_info}||{persona_info}"
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- detail_info = {"cached": False, "prompt": None, "response": None}
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-
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# 尝试从缓存读取
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if self.use_cache:
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cached_query = self.cache.get(combined_question, 'function_knowledge', 'generated_query.txt')
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if cached_query:
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logger.info(f"✓ 使用缓存的Query: {cached_query}")
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- detail_info["cached"] = True
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- return cached_query, detail_info
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+ # 记录缓存命中
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+ self.execution_detail["generate_query"].update({"cached": True, "query": cached_query})
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+ return cached_query
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try:
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prompt_template = self._load_prompt("function_generate_query_prompt.md")
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@@ -84,53 +144,57 @@ class FunctionKnowledge:
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persona_info=persona_info
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)
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- detail_info["prompt"] = prompt
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-
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logger.info("→ 调用Gemini生成Query...")
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query = generate_text(prompt=prompt)
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query = query.strip()
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- detail_info["response"] = query
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-
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logger.info(f"✓ 生成Query: {query}")
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# 写入缓存
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if self.use_cache:
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self.cache.set(combined_question, 'function_knowledge', 'generated_query.txt', query)
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- return query, detail_info
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+ # 记录详情
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+ self.execution_detail["generate_query"] = {
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+ "cached": False,
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+ "prompt": prompt,
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+ "response": query,
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+ "query": query
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+ }
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+
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+ return query
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except Exception as e:
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logger.error(f"✗ 生成Query失败: {e}")
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- detail_info["error"] = str(e)
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- return question, detail_info # 降级使用原问题
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+ return question # 降级使用原问题
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- def select_tool(self, combined_question: str, query: str) -> tuple:
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+ def select_tool(self, combined_question: str, query: str) -> str:
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"""
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选择合适的工具
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Returns:
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- tuple: (tool_name, detail_info)
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+ str: 工具名称，如果没有合适的工具则返回"None"
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"""
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logger.info(f"[步骤2] 选择工具...")
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- detail_info = {"cached": False, "prompt": None, "response": None, "available_tools_count": 0}
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-
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# 尝试从缓存读取
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if self.use_cache:
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cached_tool = self.cache.get(combined_question, 'function_knowledge', 'selected_tool.txt')
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if cached_tool:
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logger.info(f"✓ 使用缓存的工具: {cached_tool}")
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- detail_info["cached"] = True
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- return cached_tool, detail_info
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+ # 记录缓存命中
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+ self.execution_detail["select_tool"].update({
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+ "cached": True,
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+ "tool_name": cached_tool
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+ })
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+ return cached_tool
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try:
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all_tool_infos = get_all_tool_infos()
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if not all_tool_infos:
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logger.info(" 工具库为空，无可用工具")
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- return "None", detail_info
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+ return "None"
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tool_count = len(all_tool_infos.split('--- Tool:')) - 1
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- detail_info["available_tools_count"] = tool_count
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logger.info(f" 当前可用工具数: {tool_count}")
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prompt_template = self._load_prompt("function_knowledge_select_tools_prompt.md")
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@@ -139,28 +203,31 @@ class FunctionKnowledge:
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tool_infos=all_tool_infos
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)
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- detail_info["prompt"] = prompt
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- detail_info["tool_infos"] = all_tool_infos
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-
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logger.info("→ 调用Gemini选择工具...")
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tool_name = generate_text(prompt=prompt)
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tool_name = tool_name.strip()
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- detail_info["response"] = tool_name
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-
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logger.info(f"✓ 选择结果: {tool_name}")
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# 写入缓存
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if self.use_cache:
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self.cache.set(combined_question, 'function_knowledge', 'selected_tool.txt', tool_name)
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- return tool_name, detail_info
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+ # 记录详情
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+ self.execution_detail["select_tool"] = {
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+ "cached": False,
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+ "prompt": prompt,
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+ "response": tool_name,
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+ "tool_name": tool_name,
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+ "available_tools_count": tool_count
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+ }
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+
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+ return tool_name
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except Exception as e:
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logger.error(f"✗ 选择工具失败: {e}")
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- detail_info["error"] = str(e)
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- return "None", detail_info
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+ return "None"
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- def extract_tool_params(self, combined_question: str, tool_name: str, query: str) -> tuple:
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+ def extract_tool_params(self, combined_question: str, tool_name: str, query: str) -> dict:
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"""
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根据工具信息和查询提取调用参数
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@@ -170,32 +237,29 @@ class FunctionKnowledge:
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query: 查询内容
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Returns:
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- tuple: (params, detail_info)
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+ dict: 提取的参数字典
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"""
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logger.info(f"[步骤3] 提取工具参数...")
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- # 初始化detail_info
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- detail_info = {"cached": False, "prompt": None, "response": None, "tool_info": None}
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-
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# 尝试从缓存读取
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if self.use_cache:
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cached_params = self.cache.get(combined_question, 'function_knowledge', 'tool_params.json')
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if cached_params:
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logger.info(f"✓ 使用缓存的参数: {cached_params}")
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- detail_info["cached"] = True
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- return cached_params, detail_info
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+ # 记录缓存命中
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+ self.execution_detail["extract_params"].update({
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+ "cached": True,
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+ "params": cached_params
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+ })
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+ return cached_params
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try:
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# 获取工具信息
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tool_info = get_tool_info(tool_name)
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if not tool_info:
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logger.warning(f" ⚠ 未找到工具 {tool_name} 的信息，使用默认参数")
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- # 降级：使用query作为keyword
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- default_params = {"keyword": query}
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- detail_info["fallback"] = "tool_info_not_found"
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- return default_params, detail_info
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+ return {"keyword": query}
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- detail_info["tool_info"] = tool_info
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logger.info(f" 工具 {tool_name} 信息长度: {len(tool_info)}")
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# 加载prompt
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@@ -205,12 +269,9 @@ class FunctionKnowledge:
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tool_info=tool_info
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)
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- detail_info["prompt"] = prompt
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-
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# 调用LLM提取参数
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logger.info(" → 调用Gemini提取参数...")
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response_text = generate_text(prompt=prompt)
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- detail_info["response"] = response_text
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# 解析JSON
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logger.info(" → 解析参数JSON...")
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@@ -233,7 +294,15 @@ class FunctionKnowledge:
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if self.use_cache:
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self.cache.set(combined_question, 'function_knowledge', 'tool_params.json', params)
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- return params, detail_info
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+ # 记录详情
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+ self.execution_detail["extract_params"].update({
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+ "cached": False,
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+ "prompt": prompt,
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+ "response": response_text,
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+ "params": params
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+ })
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+
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+ return params
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except json.JSONDecodeError as e:
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logger.error(f" ✗ 解析JSON失败: {e}")
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@@ -241,16 +310,12 @@ class FunctionKnowledge:
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# 降级：使用query作为keyword
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default_params = {"keyword": query}
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logger.warning(f" 使用默认参数: {default_params}")
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- detail_info["fallback"] = "json_decode_error"
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- return default_params, detail_info
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+ return default_params
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except Exception as e:
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logger.error(f"✗ 提取工具参数失败: {e}")
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# 降级：使用query作为keyword
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- default_params = {"keyword": query}
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- detail_info["error"] = str(e)
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- detail_info["fallback"] = "exception"
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- return default_params, detail_info
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+ return {"keyword": query}
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def save_knowledge_to_file(self, knowledge: str, combined_question: str):
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"""保存获取到的知识到文件"""
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@@ -282,20 +347,14 @@ class FunctionKnowledge:
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def get_knowledge(self, question: str, post_info: str, persona_info: str) -> dict:
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"""
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- 获取方法知识的主流程
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+ 获取方法知识的主流程（重构后）
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Returns:
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- dict: 包含完整执行信息的字典
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- {
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- "input": {...}, # 原始输入
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- "execution": {...}, # 执行过程信息
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- "result": {...}, # 最终结果
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- "metadata": {...} # 元数据
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- }
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+ dict: 完整的执行记录
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"""
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import time
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- start_time = time.time()
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timestamp = time.strftime("%Y-%m-%d %H:%M:%S")
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+ start_time = time.time()
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logger.info("=" * 80)
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logger.info(f"Function Knowledge - 开始处理")
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@@ -307,86 +366,17 @@ class FunctionKnowledge:
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# 组合问题的唯一标识
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combined_question = f"{question}||{post_info}||{persona_info}"
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- # 初始化执行记录
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- execution_record = {
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- "input": {
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- "question": question,
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- "post_info": post_info,
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- "persona_info": persona_info,
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- "timestamp": timestamp
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- },
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- "execution": {
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- "steps": [],
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- "tool_info": None,
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- "knowledge_search_info": None
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- },
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- "result": {
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- "type": None, # "tool" 或 "knowledge"
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- "content": None,
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- "raw_data": None
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- },
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- "metadata": {
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- "execution_time": None,
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- "cache_hits": [],
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- "errors": []
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- }
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- }
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-
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- # 检查最终结果缓存
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- if self.use_cache:
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- cached_final = self.cache.get(combined_question, 'function_knowledge', 'final_result.json')
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- if cached_final:
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- logger.info(f"✓ 使用缓存的最终结果")
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- logger.info("=" * 80 + "\n")
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- # 如果是完整的执行记录，直接返回
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- if isinstance(cached_final, dict) and "execution" in cached_final:
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- return cached_final
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- # 否则构造一个简单的返回
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- return {
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- "input": execution_record["input"],
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- "execution": {"cached": True},
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- "result": {"type": "cached", "content": cached_final},
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- "metadata": {"cache_hit": True}
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- }
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-
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try:
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# 步骤1: 生成Query
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- step1_start = time.time()
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- query, query_detail = self.generate_query(question, post_info, persona_info)
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- execution_record["execution"]["steps"].append({
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- "step": 1,
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- "name": "generate_query",
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- "duration": time.time() - step1_start,
|
|
|
- "output": query,
|
|
|
- "detail": query_detail # 包含prompt和response
|
|
|
- })
|
|
|
+ query = self.generate_query(question, post_info, persona_info)
|
|
|
|
|
|
# 步骤2: 选择工具
|
|
|
- step2_start = time.time()
|
|
|
- tool_name, tool_select_detail = self.select_tool(combined_question, query)
|
|
|
- execution_record["execution"]["steps"].append({
|
|
|
- "step": 2,
|
|
|
- "name": "select_tool",
|
|
|
- "duration": time.time() - step2_start,
|
|
|
- "output": tool_name,
|
|
|
- "detail": tool_select_detail # 包含prompt、response和可用工具列表
|
|
|
- })
|
|
|
+ tool_name = self.select_tool(combined_question, query)
|
|
|
|
|
|
- result_content = None
|
|
|
if tool_name and tool_name != "None":
|
|
|
# 路径A: 使用工具
|
|
|
- execution_record["result"]["type"] = "tool"
|
|
|
-
|
|
|
# 步骤3: 提取参数
|
|
|
- step3_start = time.time()
|
|
|
- arguments, params_detail = self.extract_tool_params(combined_question, tool_name, query)
|
|
|
- execution_record["execution"]["steps"].append({
|
|
|
- "step": 3,
|
|
|
- "name": "extract_tool_params",
|
|
|
- "duration": time.time() - step3_start,
|
|
|
- "output": arguments,
|
|
|
- "detail": params_detail # 包含prompt、response和工具信息
|
|
|
- })
|
|
|
+ arguments = self.extract_tool_params(combined_question, tool_name, query)
|
|
|
|
|
|
# 步骤4: 调用工具
|
|
|
logger.info(f"[步骤4] 调用工具: {tool_name}")
|
|
|
@@ -396,102 +386,52 @@ class FunctionKnowledge:
|
|
|
cached_tool_result = self.cache.get(combined_question, 'function_knowledge', 'tool_result.json')
|
|
|
if cached_tool_result:
|
|
|
logger.info(f"✓ 使用缓存的工具调用结果")
|
|
|
- execution_record["metadata"]["cache_hits"].append("tool_result")
|
|
|
tool_result = cached_tool_result
|
|
|
else:
|
|
|
- step4_start = time.time()
|
|
|
logger.info(f" → 调用工具，参数: {arguments}")
|
|
|
tool_result = call_tool(tool_name, arguments)
|
|
|
-
|
|
|
# 缓存工具调用结果
|
|
|
self.cache.set(combined_question, 'function_knowledge', 'tool_result.json', tool_result)
|
|
|
-
|
|
|
- execution_record["execution"]["steps"].append({
|
|
|
- "step": 4,
|
|
|
- "name": "call_tool",
|
|
|
- "duration": time.time() - step4_start,
|
|
|
- "output": "success"
|
|
|
- })
|
|
|
else:
|
|
|
- step4_start = time.time()
|
|
|
logger.info(f" → 调用工具，参数: {arguments}")
|
|
|
tool_result = call_tool(tool_name, arguments)
|
|
|
- execution_record["execution"]["steps"].append({
|
|
|
- "step": 4,
|
|
|
- "name": "call_tool",
|
|
|
- "duration": time.time() - step4_start,
|
|
|
- "output": "success"
|
|
|
- })
|
|
|
-
|
|
|
- # 记录工具调用信息
|
|
|
- execution_record["execution"]["tool_info"] = {
|
|
|
- "tool_name": tool_name,
|
|
|
- "parameters": arguments,
|
|
|
- "result": tool_result
|
|
|
- }
|
|
|
-
|
|
|
- result_content = f"工具 {tool_name} 执行结果: {json.dumps(tool_result, ensure_ascii=False)}"
|
|
|
- execution_record["result"]["content"] = result_content
|
|
|
- execution_record["result"]["raw_data"] = tool_result
|
|
|
|
|
|
logger.info(f"✓ 工具调用完成")
|
|
|
|
|
|
else:
|
|
|
# 路径B: 知识搜索
|
|
|
- execution_record["result"]["type"] = "knowledge_search"
|
|
|
-
|
|
|
logger.info("[步骤4] 未找到合适工具，调用 MultiSearch...")
|
|
|
|
|
|
- step4_start = time.time()
|
|
|
knowledge = get_multi_search_knowledge(query, cache_key=combined_question)
|
|
|
|
|
|
- execution_record["execution"]["steps"].append({
|
|
|
- "step": 4,
|
|
|
- "name": "multi_search_knowledge",
|
|
|
- "duration": time.time() - step4_start,
|
|
|
- "output": f"knowledge_length: {len(knowledge)}"
|
|
|
- })
|
|
|
-
|
|
|
- # 记录知识搜索信息
|
|
|
- execution_record["execution"]["knowledge_search_info"] = {
|
|
|
- "query": query,
|
|
|
- "knowledge_length": len(knowledge),
|
|
|
- "source": "multi_search"
|
|
|
- }
|
|
|
-
|
|
|
- result_content = knowledge
|
|
|
- execution_record["result"]["content"] = knowledge
|
|
|
- execution_record["result"]["raw_data"] = {"knowledge": knowledge, "query": query}
|
|
|
-
|
|
|
- # 异步生成新工具
|
|
|
- logger.info("[后台任务] 启动新工具生成线程...")
|
|
|
+ # 异步保存知识到文件
|
|
|
+ logger.info("[后台任务] 保存知识到文件...")
|
|
|
threading.Thread(target=self.save_knowledge_to_file, args=(knowledge, combined_question)).start()
|
|
|
- # 计算总执行时间
|
|
|
- execution_record["metadata"]["execution_time"] = time.time() - start_time
|
|
|
|
|
|
- # 保存完整的执行记录到JSON文件
|
|
|
- if self.use_cache:
|
|
|
- self.cache.set(combined_question, 'function_knowledge', 'final_result.json', execution_record)
|
|
|
-
|
|
|
- # 同时保存一个格式化的JSON文件供人类阅读
|
|
|
- from knowledge_v2.cache_manager import CacheManager
|
|
|
- cache = CacheManager()
|
|
|
- import hashlib
|
|
|
- question_hash = hashlib.md5(combined_question.encode('utf-8')).hexdigest()[:12]
|
|
|
- output_file = os.path.join(cache.base_cache_dir, question_hash, '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"✓ 完整执行记录已保存: {output_file}")
|
|
|
- except Exception as e:
|
|
|
- logger.error(f"保存执行记录失败: {e}")
|
|
|
+ # 计算执行时间并保存详情
|
|
|
+ self.execution_detail["execution_time"] = time.time() - start_time
|
|
|
+ self._save_execution_detail(combined_question)
|
|
|
+
|
|
|
+ # 收集所有执行记录
|
|
|
+ logger.info("=" * 80)
|
|
|
+ logger.info("收集执行记录...")
|
|
|
+ logger.info("=" * 80)
|
|
|
+
|
|
|
+ from knowledge_v2.execution_collector import collect_and_save_execution_record
|
|
|
+
|
|
|
+ execution_record = collect_and_save_execution_record(
|
|
|
+ combined_question,
|
|
|
+ {
|
|
|
+ "question": question,
|
|
|
+ "post_info": post_info,
|
|
|
+ "persona_info": persona_info,
|
|
|
+ "timestamp": timestamp
|
|
|
+ }
|
|
|
+ )
|
|
|
|
|
|
logger.info("=" * 80)
|
|
|
logger.info(f"✓ Function Knowledge 完成")
|
|
|
- logger.info(f" 类型: {execution_record['result']['type']}")
|
|
|
- logger.info(f" 结果长度: {len(result_content) if result_content else 0}")
|
|
|
- logger.info(f" 执行时间: {execution_record['metadata']['execution_time']:.2f}秒")
|
|
|
+ logger.info(f" 执行时间: {execution_record.get('metadata', {}).get('execution_time', 0):.2f}秒")
|
|
|
logger.info("=" * 80 + "\n")
|
|
|
|
|
|
return execution_record
|
|
|
@@ -499,27 +439,42 @@ class FunctionKnowledge:
|
|
|
except Exception as e:
|
|
|
logger.error(f"✗ 执行失败: {e}")
|
|
|
import traceback
|
|
|
- error_trace = traceback.format_exc()
|
|
|
-
|
|
|
- execution_record["metadata"]["errors"].append({
|
|
|
- "error": str(e),
|
|
|
- "traceback": error_trace
|
|
|
- })
|
|
|
- execution_record["result"]["type"] = "error"
|
|
|
- execution_record["result"]["content"] = f"执行失败: {str(e)}"
|
|
|
- execution_record["metadata"]["execution_time"] = time.time() - start_time
|
|
|
+ logger.error(traceback.format_exc())
|
|
|
|
|
|
- return execution_record
|
|
|
-
|
|
|
-def get_knowledge(question: str, post_info: str, persona_info: str) -> dict:
|
|
|
- """
|
|
|
- 便捷调用函数
|
|
|
-
|
|
|
- Returns:
|
|
|
- dict: 完整的执行记录，包含输入、执行过程、结果和元数据
|
|
|
- """
|
|
|
- agent = FunctionKnowledge()
|
|
|
- return agent.get_knowledge(question, post_info, persona_info)
|
|
|
+ # 即使失败也尝试保存详情和收集记录
|
|
|
+ try:
|
|
|
+ self.execution_detail["execution_time"] = time.time() - start_time
|
|
|
+ self._save_execution_detail(combined_question)
|
|
|
+
|
|
|
+ from knowledge_v2.execution_collector import collect_and_save_execution_record
|
|
|
+ execution_record = collect_and_save_execution_record(
|
|
|
+ combined_question,
|
|
|
+ {
|
|
|
+ "question": question,
|
|
|
+ "post_info": post_info,
|
|
|
+ "persona_info": persona_info,
|
|
|
+ "timestamp": timestamp
|
|
|
+ }
|
|
|
+ )
|
|
|
+ return execution_record
|
|
|
+ except Exception as collect_error:
|
|
|
+ logger.error(f"收集执行记录也失败: {collect_error}")
|
|
|
+ # 返回基本错误信息
|
|
|
+ return {
|
|
|
+ "input": {
|
|
|
+ "question": question,
|
|
|
+ "post_info": post_info,
|
|
|
+ "persona_info": persona_info,
|
|
|
+ "timestamp": timestamp
|
|
|
+ },
|
|
|
+ "result": {
|
|
|
+ "type": "error",
|
|
|
+ "content": f"执行失败: {str(e)}"
|
|
|
+ },
|
|
|
+ "metadata": {
|
|
|
+ "errors": [str(e)]
|
|
|
+ }
|
|
|
+ }
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
# 测试代码
|
|
|
@@ -533,9 +488,7 @@ if __name__ == "__main__":
|
|
|
print("=" * 50)
|
|
|
print("执行结果:")
|
|
|
print("=" * 50)
|
|
|
- print(f"类型: {execution_result['result']['type']}")
|
|
|
- print(f"内容预览: {execution_result['result']['content'][:200]}...")
|
|
|
- print(f"执行时间: {execution_result['metadata']['execution_time']:.2f}秒")
|
|
|
+ print(json.dumps(execution_result, ensure_ascii=False, indent=2))
|
|
|
print(f"\n完整JSON已保存到缓存目录")
|
|
|
except Exception as e:
|
|
|
logger.error(f"测试失败: {e}")
|
TanJingyu