diff --git a/backend/app/services/zep_tools.py b/backend/app/services/zep_tools.py index 832ce468..3bc8a57a 100644 --- a/backend/app/services/zep_tools.py +++ b/backend/app/services/zep_tools.py @@ -18,7 +18,7 @@ from zep_cloud.client import Zep from ..config import Config from ..utils.logger import get_logger from ..utils.llm_client import LLMClient -from ..utils.locale import get_locale +from ..utils.locale import get_locale, t from ..utils.zep_paging import fetch_all_nodes, fetch_all_edges logger = get_logger('mirofish.zep_tools') @@ -430,7 +430,7 @@ class ZepToolsService: self.client = Zep(api_key=self.api_key) # LLM客户端用于InsightForge生成子问题 self._llm_client = llm_client - logger.info("ZepToolsService 初始化完成") + logger.info(t("console.zepToolsInitialized")) @property def llm(self) -> LLMClient: @@ -452,13 +452,12 @@ class ZepToolsService: last_exception = e if attempt < max_retries - 1: logger.warning( - f"Zep {operation_name} 第 {attempt + 1} 次尝试失败: {str(e)[:100]}, " - f"{delay:.1f}秒后重试..." + t("console.zepRetryAttempt", operation=operation_name, attempt=attempt + 1, error=str(e)[:100], delay=f"{delay:.1f}") ) time.sleep(delay) delay *= 2 else: - logger.error(f"Zep {operation_name} 在 {max_retries} 次尝试后仍失败: {str(e)}") + logger.error(t("console.zepAllRetriesFailed", operation=operation_name, retries=max_retries, error=str(e))) raise last_exception @@ -484,7 +483,7 @@ class ZepToolsService: Returns: SearchResult: 搜索结果 """ - logger.info(f"图谱搜索: graph_id={graph_id}, query={query[:50]}...") + logger.info(t("console.graphSearch", graphId=graph_id, query=query[:50])) # 尝试使用Zep Cloud Search API try: @@ -496,7 +495,7 @@ class ZepToolsService: scope=scope, reranker="cross_encoder" ), - operation_name=f"图谱搜索(graph={graph_id})" + operation_name=t("console.graphSearchOp", graphId=graph_id) ) facts = [] @@ -529,7 +528,7 @@ class ZepToolsService: if hasattr(node, 'summary') and node.summary: facts.append(f"[{node.name}]: {node.summary}") - logger.info(f"搜索完成: 找到 {len(facts)} 条相关事实") + logger.info(t("console.searchComplete", count=len(facts))) return SearchResult( facts=facts, @@ -540,7 +539,7 @@ class ZepToolsService: ) except Exception as e: - logger.warning(f"Zep Search API失败，降级为本地搜索: {str(e)}") + logger.warning(t("console.zepSearchApiFallback", error=str(e))) # 降级：使用本地关键词匹配搜索 return self._local_search(graph_id, query, limit, scope) @@ -565,7 +564,7 @@ class ZepToolsService: Returns: SearchResult: 搜索结果 """ - logger.info(f"使用本地搜索: query={query[:30]}...") + logger.info(t("console.usingLocalSearch", query=query[:30])) facts = [] edges_result = [] @@ -635,10 +634,10 @@ class ZepToolsService: if node.summary: facts.append(f"[{node.name}]: {node.summary}") - logger.info(f"本地搜索完成: 找到 {len(facts)} 条相关事实") + logger.info(t("console.localSearchComplete", count=len(facts))) except Exception as e: - logger.error(f"本地搜索失败: {str(e)}") + logger.error(t("console.localSearchFailed", error=str(e))) return SearchResult( facts=facts, @@ -658,7 +657,7 @@ class ZepToolsService: Returns: 节点列表 """ - logger.info(f"获取图谱 {graph_id} 的所有节点...") + logger.info(t("console.fetchingAllNodes", graphId=graph_id)) nodes = fetch_all_nodes(self.client, graph_id) @@ -673,7 +672,7 @@ class ZepToolsService: attributes=node.attributes or {} )) - logger.info(f"获取到 {len(result)} 个节点") + logger.info(t("console.fetchedNodes", count=len(result))) return result def get_all_edges(self, graph_id: str, include_temporal: bool = True) -> List[EdgeInfo]: @@ -687,7 +686,7 @@ class ZepToolsService: Returns: 边列表（包含created_at, valid_at, invalid_at, expired_at） """ - logger.info(f"获取图谱 {graph_id} 的所有边...") + logger.info(t("console.fetchingAllEdges", graphId=graph_id)) edges = fetch_all_edges(self.client, graph_id) @@ -711,7 +710,7 @@ class ZepToolsService: result.append(edge_info) - logger.info(f"获取到 {len(result)} 条边") + logger.info(t("console.fetchedEdges", count=len(result))) return result def get_node_detail(self, node_uuid: str) -> Optional[NodeInfo]: @@ -724,12 +723,12 @@ class ZepToolsService: Returns: 节点信息或None """ - logger.info(f"获取节点详情: {node_uuid[:8]}...") + logger.info(t("console.fetchingNodeDetail", uuid=node_uuid[:8])) try: node = self._call_with_retry( func=lambda: self.client.graph.node.get(uuid_=node_uuid), - operation_name=f"获取节点详情(uuid={node_uuid[:8]}...)" + operation_name=t("console.fetchNodeDetailOp", uuid=node_uuid[:8]) ) if not node: @@ -743,7 +742,7 @@ class ZepToolsService: attributes=node.attributes or {} ) except Exception as e: - logger.error(f"获取节点详情失败: {str(e)}") + logger.error(t("console.fetchNodeDetailFailed", error=str(e))) return None def get_node_edges(self, graph_id: str, node_uuid: str) -> List[EdgeInfo]: @@ -759,7 +758,7 @@ class ZepToolsService: Returns: 边列表 """ - logger.info(f"获取节点 {node_uuid[:8]}... 的相关边") + logger.info(t("console.fetchingNodeEdges", uuid=node_uuid[:8])) try: # 获取图谱所有边，然后过滤 @@ -771,11 +770,11 @@ class ZepToolsService: if edge.source_node_uuid == node_uuid or edge.target_node_uuid == node_uuid: result.append(edge) - logger.info(f"找到 {len(result)} 条与节点相关的边") + logger.info(t("console.foundNodeEdges", count=len(result))) return result except Exception as e: - logger.warning(f"获取节点边失败: {str(e)}") + logger.warning(t("console.fetchNodeEdgesFailed", error=str(e))) return [] def get_entities_by_type( @@ -793,7 +792,7 @@ class ZepToolsService: Returns: 符合类型的实体列表 """ - logger.info(f"获取类型为 {entity_type} 的实体...") + logger.info(t("console.fetchingEntitiesByType", type=entity_type)) all_nodes = self.get_all_nodes(graph_id) @@ -803,7 +802,7 @@ class ZepToolsService: if entity_type in node.labels: filtered.append(node) - logger.info(f"找到 {len(filtered)} 个 {entity_type} 类型的实体") + logger.info(t("console.foundEntitiesByType", count=len(filtered), type=entity_type)) return filtered def get_entity_summary( @@ -823,7 +822,7 @@ class ZepToolsService: Returns: 实体摘要信息 """ - logger.info(f"获取实体 {entity_name} 的关系摘要...") + logger.info(t("console.fetchingEntitySummary", name=entity_name)) # 先搜索该实体相关的信息 search_result = self.search_graph( @@ -863,7 +862,7 @@ class ZepToolsService: Returns: 统计信息 """ - logger.info(f"获取图谱 {graph_id} 的统计信息...") + logger.info(t("console.fetchingGraphStats", graphId=graph_id)) nodes = self.get_all_nodes(graph_id) edges = self.get_all_edges(graph_id) @@ -907,7 +906,7 @@ class ZepToolsService: Returns: 模拟上下文信息 """ - logger.info(f"获取模拟上下文: {simulation_requirement[:50]}...") + logger.info(t("console.fetchingSimContext", requirement=simulation_requirement[:50])) # 搜索与模拟需求相关的信息 search_result = self.search_graph( @@ -971,7 +970,7 @@ class ZepToolsService: Returns: InsightForgeResult: 深度洞察检索结果 """ - logger.info(f"InsightForge 深度洞察检索: {query[:50]}...") + logger.info(t("console.insightForgeStart", query=query[:50])) result = InsightForgeResult( query=query, @@ -987,7 +986,7 @@ class ZepToolsService: max_queries=max_sub_queries ) result.sub_queries = sub_queries - logger.info(f"生成 {len(sub_queries)} 个子问题") + logger.info(t("console.generatedSubQueries", count=len(sub_queries))) # Step 2: 对每个子问题进行语义搜索 all_facts = [] @@ -1087,7 +1086,7 @@ class ZepToolsService: result.relationship_chains = relationship_chains result.total_relationships = len(relationship_chains) - logger.info(f"InsightForge完成: {result.total_facts}条事实, {result.total_entities}个实体, {result.total_relationships}条关系") + logger.info(t("console.insightForgeComplete", facts=result.total_facts, entities=result.total_entities, relationships=result.total_relationships)) return result def _generate_sub_queries( @@ -1134,7 +1133,7 @@ class ZepToolsService: return [str(sq) for sq in sub_queries[:max_queries]] except Exception as e: - logger.warning(f"生成子问题失败: {str(e)}，使用默认子问题") + logger.warning(t("console.generateSubQueriesFailed", error=str(e))) # 降级：返回基于原问题的变体 return [ query, @@ -1169,7 +1168,7 @@ class ZepToolsService: Returns: PanoramaResult: 广度搜索结果 """ - logger.info(f"PanoramaSearch 广度搜索: {query[:50]}...") + logger.info(t("console.panoramaSearchStart", query=query[:50])) result = PanoramaResult(query=query) @@ -1232,7 +1231,7 @@ class ZepToolsService: result.active_count = len(active_facts) result.historical_count = len(historical_facts) - logger.info(f"PanoramaSearch完成: {result.active_count}条有效, {result.historical_count}条历史") + logger.info(t("console.panoramaSearchComplete", active=result.active_count, historical=result.historical_count)) return result def quick_search( @@ -1257,7 +1256,7 @@ class ZepToolsService: Returns: SearchResult: 搜索结果 """ - logger.info(f"QuickSearch 简单搜索: {query[:50]}...") + logger.info(t("console.quickSearchStart", query=query[:50])) # 直接调用现有的search_graph方法 result = self.search_graph( @@ -1267,7 +1266,7 @@ class ZepToolsService: scope="edges" ) - logger.info(f"QuickSearch完成: {result.total_count}条结果") + logger.info(t("console.quickSearchComplete", count=result.total_count)) return result def interview_agents( @@ -1307,7 +1306,7 @@ class ZepToolsService: """ from .simulation_runner import SimulationRunner - logger.info(f"InterviewAgents 深度采访（真实API）: {interview_requirement[:50]}...") + logger.info(t("console.interviewAgentsStart", requirement=interview_requirement[:50])) result = InterviewResult( interview_topic=interview_requirement, @@ -1318,12 +1317,12 @@ class ZepToolsService: profiles = self._load_agent_profiles(simulation_id) if not profiles: - logger.warning(f"未找到模拟 {simulation_id} 的人设文件") + logger.warning(t("console.profilesNotFound", simId=simulation_id)) result.summary = "未找到可采访的Agent人设文件" return result result.total_agents = len(profiles) - logger.info(f"加载到 {len(profiles)} 个Agent人设") + logger.info(t("console.loadedProfiles", count=len(profiles))) # Step 2: 使用LLM选择要采访的Agent（返回agent_id列表） selected_agents, selected_indices, selection_reasoning = self._select_agents_for_interview( @@ -1335,7 +1334,7 @@ class ZepToolsService: result.selected_agents = selected_agents result.selection_reasoning = selection_reasoning - logger.info(f"选择了 {len(selected_agents)} 个Agent进行采访: {selected_indices}") + logger.info(t("console.selectedAgentsForInterview", count=len(selected_agents), indices=selected_indices)) # Step 3: 生成采访问题（如果没有提供） if not result.interview_questions: @@ -1344,7 +1343,7 @@ class ZepToolsService: simulation_requirement=simulation_requirement, selected_agents=selected_agents ) - logger.info(f"生成了 {len(result.interview_questions)} 个采访问题") + logger.info(t("console.generatedInterviewQuestions", count=len(result.interview_questions))) # 将问题合并为一个采访prompt combined_prompt = "\n".join([f"{i+1}. {q}" for i, q in enumerate(result.interview_questions)]) @@ -1374,7 +1373,7 @@ class ZepToolsService: # 不指定platform，API会在twitter和reddit两个平台都采访 }) - logger.info(f"调用批量采访API（双平台）: {len(interviews_request)} 个Agent") + logger.info(t("console.callingBatchInterviewApi", count=len(interviews_request))) # 调用 SimulationRunner 的批量采访方法（不传platform，双平台采访） api_result = SimulationRunner.interview_agents_batch( @@ -1384,12 +1383,12 @@ class ZepToolsService: timeout=180.0 # 双平台需要更长超时 ) - logger.info(f"采访API返回: {api_result.get('interviews_count', 0)} 个结果, success={api_result.get('success')}") + logger.info(t("console.interviewApiReturned", count=api_result.get('interviews_count', 0), success=api_result.get('success'))) # 检查API调用是否成功 if not api_result.get("success", False): error_msg = api_result.get("error", "未知错误") - logger.warning(f"采访API返回失败: {error_msg}") + logger.warning(t("console.interviewApiReturnedFailure", error=error_msg)) result.summary = f"采访API调用失败：{error_msg}。请检查OASIS模拟环境状态。" return result @@ -1462,11 +1461,11 @@ class ZepToolsService: except ValueError as e: # 模拟环境未运行 - logger.warning(f"采访API调用失败（环境未运行？）: {e}") + logger.warning(t("console.interviewApiCallFailed", error=e)) result.summary = f"采访失败：{str(e)}。模拟环境可能已关闭，请确保OASIS环境正在运行。" return result except Exception as e: - logger.error(f"采访API调用异常: {e}") + logger.error(t("console.interviewApiCallException", error=e)) import traceback logger.error(traceback.format_exc()) result.summary = f"采访过程发生错误：{str(e)}" @@ -1479,7 +1478,7 @@ class ZepToolsService: interview_requirement=interview_requirement ) - logger.info(f"InterviewAgents完成: 采访了 {result.interviewed_count} 个Agent（双平台）") + logger.info(t("console.interviewAgentsComplete", count=result.interviewed_count)) return result @staticmethod @@ -1522,10 +1521,10 @@ class ZepToolsService: try: with open(reddit_profile_path, 'r', encoding='utf-8') as f: profiles = json.load(f) - logger.info(f"从 reddit_profiles.json 加载了 {len(profiles)} 个人设") + logger.info(t("console.loadedRedditProfiles", count=len(profiles))) return profiles except Exception as e: - logger.warning(f"读取 reddit_profiles.json 失败: {e}") + logger.warning(t("console.readRedditProfilesFailed", error=e)) # 尝试读取Twitter CSV格式 twitter_profile_path = os.path.join(sim_dir, "twitter_profiles.csv") @@ -1542,10 +1541,10 @@ class ZepToolsService: "persona": row.get("user_char", ""), "profession": "未知" }) - logger.info(f"从 twitter_profiles.csv 加载了 {len(profiles)} 个人设") + logger.info(t("console.loadedTwitterProfiles", count=len(profiles))) return profiles except Exception as e: - logger.warning(f"读取 twitter_profiles.csv 失败: {e}") + logger.warning(t("console.readTwitterProfilesFailed", error=e)) return profiles @@ -1626,7 +1625,7 @@ class ZepToolsService: return selected_agents, valid_indices, reasoning except Exception as e: - logger.warning(f"LLM选择Agent失败，使用默认选择: {e}") + logger.warning(t("console.llmSelectAgentFailed", error=e)) # 降级：选择前N个 selected = profiles[:max_agents] indices = list(range(min(max_agents, len(profiles)))) @@ -1674,7 +1673,7 @@ class ZepToolsService: return response.get("questions", [f"关于{interview_requirement}，您有什么看法？"]) except Exception as e: - logger.warning(f"生成采访问题失败: {e}") + logger.warning(t("console.generateInterviewQuestionsFailed", error=e)) return [ f"关于{interview_requirement}，您的观点是什么？", "这件事对您或您所代表的群体有什么影响？", @@ -1732,6 +1731,6 @@ class ZepToolsService: return summary except Exception as e: - logger.warning(f"生成采访摘要失败: {e}") + logger.warning(t("console.generateInterviewSummaryFailed", error=e)) # 降级：简单拼接 return f"共采访了{len(interviews)}位受访者，包括：" + "、".join([i.agent_name for i in interviews])