""" Zep检索工具服务 封装图谱搜索、节点读取、边查询等工具，供Report Agent使用 核心检索工具（优化后）： 1. InsightForge（深度洞察检索）- 最强大的混合检索，自动生成子问题并多维度检索 2. PanoramaSearch（广度搜索）- 获取全貌，包括过期内容 3. QuickSearch（简单搜索）- 快速检索 """ import time import json from typing import Dict, Any, List, Optional from dataclasses import dataclass, field 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, t from ..utils.zep_paging import fetch_all_nodes, fetch_all_edges logger = get_logger('mirofish.zep_tools') @dataclass class SearchResult: """搜索结果""" facts: List[str] edges: List[Dict[str, Any]] nodes: List[Dict[str, Any]] query: str total_count: int def to_dict(self) -> Dict[str, Any]: return { "facts": self.facts, "edges": self.edges, "nodes": self.nodes, "query": self.query, "total_count": self.total_count } def to_text(self) -> str: """转换为文本格式，供LLM理解""" text_parts = [f"Search query: {self.query}", f"Found {self.total_count} relevant items"] if self.facts: text_parts.append("\n### Relevant facts:") for i, fact in enumerate(self.facts, 1): text_parts.append(f"{i}. {fact}") return "\n".join(text_parts) @dataclass class NodeInfo: """节点信息""" uuid: str name: str labels: List[str] summary: str attributes: Dict[str, Any] def to_dict(self) -> Dict[str, Any]: return { "uuid": self.uuid, "name": self.name, "labels": self.labels, "summary": self.summary, "attributes": self.attributes } def to_text(self) -> str: """转换为文本格式""" entity_type = next((l for l in self.labels if l not in ["Entity", "Node"]), "Unknown type") return f"Entity: {self.name} (Type: {entity_type})\nSummary: {self.summary}" @dataclass class EdgeInfo: """边信息""" uuid: str name: str fact: str source_node_uuid: str target_node_uuid: str source_node_name: Optional[str] = None target_node_name: Optional[str] = None # 时间信息 created_at: Optional[str] = None valid_at: Optional[str] = None invalid_at: Optional[str] = None expired_at: Optional[str] = None def to_dict(self) -> Dict[str, Any]: return { "uuid": self.uuid, "name": self.name, "fact": self.fact, "source_node_uuid": self.source_node_uuid, "target_node_uuid": self.target_node_uuid, "source_node_name": self.source_node_name, "target_node_name": self.target_node_name, "created_at": self.created_at, "valid_at": self.valid_at, "invalid_at": self.invalid_at, "expired_at": self.expired_at } def to_text(self, include_temporal: bool = False) -> str: """转换为文本格式""" source = self.source_node_name or self.source_node_uuid[:8] target = self.target_node_name or self.target_node_uuid[:8] base_text = f"Relation: {source} --[{self.name}]--> {target}\nFact: {self.fact}" if include_temporal: valid_at = self.valid_at or "Unknown" invalid_at = self.invalid_at or "present" base_text += f"\nValidity: {valid_at} - {invalid_at}" if self.expired_at: base_text += f" (Expired: {self.expired_at})" return base_text @property def is_expired(self) -> bool: """是否已过期""" return self.expired_at is not None @property def is_invalid(self) -> bool: """是否已失效""" return self.invalid_at is not None @dataclass class InsightForgeResult: """ 深度洞察检索结果 (InsightForge) 包含多个子问题的检索结果，以及综合分析 """ query: str simulation_requirement: str sub_queries: List[str] # 各维度检索结果 semantic_facts: List[str] = field(default_factory=list) # 语义搜索结果 entity_insights: List[Dict[str, Any]] = field(default_factory=list) # Entity洞察 relationship_chains: List[str] = field(default_factory=list) # 关系链 # 统计信息 total_facts: int = 0 total_entities: int = 0 total_relationships: int = 0 def to_dict(self) -> Dict[str, Any]: return { "query": self.query, "simulation_requirement": self.simulation_requirement, "sub_queries": self.sub_queries, "semantic_facts": self.semantic_facts, "entity_insights": self.entity_insights, "relationship_chains": self.relationship_chains, "total_facts": self.total_facts, "total_entities": self.total_entities, "total_relationships": self.total_relationships } def to_text(self) -> str: """转换为详细的文本格式，供LLM理解""" text_parts = [ f"## Future Prediction Deep Analysis", f"Analysis question: {self.query}", f"Prediction scenario: {self.simulation_requirement}", f"\n### Prediction Statistics", f"- Related prediction facts: {self.total_facts} items", f"- Involved entities: {self.total_entities}", f"- Relationship chains: {self.total_relationships} items" ] # 子问题 if self.sub_queries: text_parts.append(f"\n### Analyzed Sub-questions") for i, sq in enumerate(self.sub_queries, 1): text_parts.append(f"{i}. {sq}") # 语义搜索结果 if self.semantic_facts: text_parts.append(f"\n### [Key Facts] (Please quote these in the report)") for i, fact in enumerate(self.semantic_facts, 1): text_parts.append(f"{i}. \"{fact}\"") # Entity洞察 if self.entity_insights: text_parts.append(f"\n### [Core Entities]") for entity in self.entity_insights: text_parts.append(f"- **{entity.get('name', 'Unknown')}** ({entity.get('type', 'Entity')})") if entity.get('summary'): text_parts.append(f" Summary: \"{entity.get('summary')}\"") if entity.get('related_facts'): text_parts.append(f" Related facts: {len(entity.get('related_facts', []))} items") # 关系链 if self.relationship_chains: text_parts.append(f"\n### [Relationship Chains]") for chain in self.relationship_chains: text_parts.append(f"- {chain}") return "\n".join(text_parts) @dataclass class PanoramaResult: """ 广度搜索结果 (Panorama) 包含所有相关信息，包括过期内容 """ query: str # 全部节点 all_nodes: List[NodeInfo] = field(default_factory=list) # 全部边（包括过期的） all_edges: List[EdgeInfo] = field(default_factory=list) # 当前有效的事实 active_facts: List[str] = field(default_factory=list) # 已过期/失效的事实（历史记录） historical_facts: List[str] = field(default_factory=list) # 统计 total_nodes: int = 0 total_edges: int = 0 active_count: int = 0 historical_count: int = 0 def to_dict(self) -> Dict[str, Any]: return { "query": self.query, "all_nodes": [n.to_dict() for n in self.all_nodes], "all_edges": [e.to_dict() for e in self.all_edges], "active_facts": self.active_facts, "historical_facts": self.historical_facts, "total_nodes": self.total_nodes, "total_edges": self.total_edges, "active_count": self.active_count, "historical_count": self.historical_count } def to_text(self) -> str: """转换为文本格式（完整版本，不截断）""" text_parts = [ f"## Panorama Search Results (Future Panoramic View)", f"Query: {self.query}", f"\n### Statistics", f"- Total nodes: {self.total_nodes}", f"- Total edges: {self.total_edges}", f"- Currently active facts: {self.active_count} items", f"- Historical/expired facts: {self.historical_count} items" ] # 当前有效的事实（完整输出，不截断） if self.active_facts: text_parts.append(f"\n### [Currently Active Facts] (Simulation results)") for i, fact in enumerate(self.active_facts, 1): text_parts.append(f"{i}. \"{fact}\"") # 历史/过期事实（完整输出，不截断） if self.historical_facts: text_parts.append(f"\n### [Historical/Expired Facts] (Evolution records)") for i, fact in enumerate(self.historical_facts, 1): text_parts.append(f"{i}. \"{fact}\"") # 关键Entity（完整输出，不截断） if self.all_nodes: text_parts.append(f"\n### [Involved Entities]") for node in self.all_nodes: entity_type = next((l for l in node.labels if l not in ["Entity", "Node"]), "Entity") text_parts.append(f"- **{node.name}** ({entity_type})") return "\n".join(text_parts) @dataclass class AgentInterview: """单个Agent的采访结果""" agent_name: str agent_role: str # 角色类型（如：学生、教师、媒体等） agent_bio: str # 简介 question: str # 采访问题 response: str # 采访回答 key_quotes: List[str] = field(default_factory=list) # 关键引言 def to_dict(self) -> Dict[str, Any]: return { "agent_name": self.agent_name, "agent_role": self.agent_role, "agent_bio": self.agent_bio, "question": self.question, "response": self.response, "key_quotes": self.key_quotes } def to_text(self) -> str: text = f"**{self.agent_name}** ({self.agent_role})\n" # 显示完整的agent_bio，不截断 text += f"_Bio: {self.agent_bio}_\n\n" text += f"**Q:** {self.question}\n\n" text += f"**A:** {self.response}\n" if self.key_quotes: text += "\n**Key quotes:**\n" for quote in self.key_quotes: # 清理各种引号 clean_quote = quote.replace('\u201c', '').replace('\u201d', '').replace('"', '') clean_quote = clean_quote.replace('\u300c', '').replace('\u300d', '') clean_quote = clean_quote.strip() # 去掉开头的标点 while clean_quote and clean_quote[0] in '，,；;：:、。！？\n\r\t ': clean_quote = clean_quote[1:] # 过滤包含问题编号的垃圾内容（问题1-9） skip = False for d in '123456789': if f'\u95ee\u9898{d}' in clean_quote: skip = True break if skip: continue # 截断过长内容（按句号截断，而非硬截断） if len(clean_quote) > 150: dot_pos = clean_quote.find('\u3002', 80) if dot_pos > 0: clean_quote = clean_quote[:dot_pos + 1] else: clean_quote = clean_quote[:147] + "..." if clean_quote and len(clean_quote) >= 10: text += f'> "{clean_quote}"\n' return text @dataclass class InterviewResult: """ 采访结果 (Interview) 包含多个模拟Agent的采访回答 """ interview_topic: str # 采访主题 interview_questions: List[str] # 采访问题列表 # 采访选择的Agent selected_agents: List[Dict[str, Any]] = field(default_factory=list) # 各Agent的采访回答 interviews: List[AgentInterview] = field(default_factory=list) # 选择Agent的理由 selection_reasoning: str = "" # 整合后的采访摘要 summary: str = "" # 统计 total_agents: int = 0 interviewed_count: int = 0 def to_dict(self) -> Dict[str, Any]: return { "interview_topic": self.interview_topic, "interview_questions": self.interview_questions, "selected_agents": self.selected_agents, "interviews": [i.to_dict() for i in self.interviews], "selection_reasoning": self.selection_reasoning, "summary": self.summary, "total_agents": self.total_agents, "interviewed_count": self.interviewed_count } def to_text(self) -> str: """转换为详细的文本格式，供LLM理解和报告引用""" text_parts = [ "## Deep Interview Report", f"**Interview topic:** {self.interview_topic}", f"**Interviewed:** {self.interviewed_count} / {self.total_agents} simulation Agents", "\n### Interviewee Selection Reasoning", self.selection_reasoning or "(Auto-selected)", "\n---", "\n### Interview Transcripts", ] if self.interviews: for i, interview in enumerate(self.interviews, 1): text_parts.append(f"\n#### Interview #{i}: {interview.agent_name}") text_parts.append(interview.to_text()) text_parts.append("\n---") else: text_parts.append("(No interview records)\n\n---") text_parts.append("\n### Interview Summary & Key Perspectives") text_parts.append(self.summary or "(No summary)") return "\n".join(text_parts) class ZepToolsService: """ Zep检索工具服务 【核心检索工具 - 优化后】 1. insight_forge - 深度洞察检索（最强大，自动生成子问题，多维度检索） 2. panorama_search - 广度搜索（获取全貌，包括过期内容） 3. quick_search - 简单搜索（快速检索） 4. interview_agents - 深度采访（采访模拟Agent，获取多视角观点） 【基础工具】 - search_graph - 图谱语义搜索 - get_all_nodes - 获取图谱所有节点 - get_all_edges - 获取图谱所有边（含时间信息） - get_node_detail - 获取节点详细信息 - get_node_edges - 获取节点相关的边 - get_entities_by_type - 按类型获取Entity - get_entity_summary - 获取Entity的关系摘要 """ # 重试配置 MAX_RETRIES = 3 RETRY_DELAY = 2.0 def __init__(self, api_key: Optional[str] = None, llm_client: Optional[LLMClient] = None): self.api_key = api_key or Config.ZEP_API_KEY if not self.api_key: raise ValueError("ZEP_API_KEY 未配置") 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(t("console.zepToolsInitialized")) @property def llm(self) -> LLMClient: """延迟初始化LLM客户端""" if self._llm_client is None: self._llm_client = LLMClient() return self._llm_client def _call_with_retry(self, func, operation_name: str, max_retries: int = None): """带重试机制的API调用""" max_retries = max_retries or self.MAX_RETRIES last_exception = None delay = self.RETRY_DELAY for attempt in range(max_retries): try: return func() except Exception as e: last_exception = e if attempt < max_retries - 1: logger.warning( 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(t("console.zepAllRetriesFailed", operation=operation_name, retries=max_retries, error=str(e))) raise last_exception def search_graph( self, graph_id: str, query: str, limit: int = 10, scope: str = "edges" ) -> SearchResult: """ 图谱语义搜索 使用混合搜索（语义+BM25）在图谱中搜索相关信息。 如果Zep Cloud的search API不可用，则降级为本地关键词匹配。 Args: graph_id: 图谱ID (Standalone Graph) query: 搜索查询 limit: 返回结果数量 scope: 搜索范围，"edges" 或 "nodes" Returns: SearchResult: 搜索结果 """ logger.info(t("console.graphSearch", graphId=graph_id, query=query[:50])) # 尝试使用Zep Cloud Search API try: search_results = self._call_with_retry( func=lambda: self.client.graph.search( graph_id=graph_id, query=query, limit=limit, scope=scope, reranker="cross_encoder" ), operation_name=t("console.graphSearchOp", graphId=graph_id) ) facts = [] edges = [] nodes = [] # 解析边搜索结果 if hasattr(search_results, 'edges') and search_results.edges: for edge in search_results.edges: if hasattr(edge, 'fact') and edge.fact: facts.append(edge.fact) edges.append({ "uuid": getattr(edge, 'uuid_', None) or getattr(edge, 'uuid', ''), "name": getattr(edge, 'name', ''), "fact": getattr(edge, 'fact', ''), "source_node_uuid": getattr(edge, 'source_node_uuid', ''), "target_node_uuid": getattr(edge, 'target_node_uuid', ''), }) # 解析节点搜索结果 if hasattr(search_results, 'nodes') and search_results.nodes: for node in search_results.nodes: nodes.append({ "uuid": getattr(node, 'uuid_', None) or getattr(node, 'uuid', ''), "name": getattr(node, 'name', ''), "labels": getattr(node, 'labels', []), "summary": getattr(node, 'summary', ''), }) # 节点摘要也算作事实 if hasattr(node, 'summary') and node.summary: facts.append(f"[{node.name}]: {node.summary}") logger.info(t("console.searchComplete", count=len(facts))) return SearchResult( facts=facts, edges=edges, nodes=nodes, query=query, total_count=len(facts) ) except Exception as 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) def _local_search( self, graph_id: str, query: str, limit: int = 10, scope: str = "edges" ) -> SearchResult: """ 本地关键词匹配搜索（作为Zep Search API的降级方案） 获取所有边/节点，然后在本地进行关键词匹配 Args: graph_id: 图谱ID query: 搜索查询 limit: 返回结果数量 scope: 搜索范围 Returns: SearchResult: 搜索结果 """ logger.info(t("console.usingLocalSearch", query=query[:30])) facts = [] edges_result = [] nodes_result = [] # 提取查询关键词（简单分词） query_lower = query.lower() keywords = [w.strip() for w in query_lower.replace(',', ' ').replace('，', ' ').split() if len(w.strip()) > 1] def match_score(text: str) -> int: """计算文本与查询的匹配分数""" if not text: return 0 text_lower = text.lower() # 完全匹配查询 if query_lower in text_lower: return 100 # 关键词匹配 score = 0 for keyword in keywords: if keyword in text_lower: score += 10 return score try: if scope in ["edges", "both"]: # 获取所有边并匹配 all_edges = self.get_all_edges(graph_id) scored_edges = [] for edge in all_edges: score = match_score(edge.fact) + match_score(edge.name) if score > 0: scored_edges.append((score, edge)) # 按分数排序 scored_edges.sort(key=lambda x: x[0], reverse=True) for score, edge in scored_edges[:limit]: if edge.fact: facts.append(edge.fact) edges_result.append({ "uuid": edge.uuid, "name": edge.name, "fact": edge.fact, "source_node_uuid": edge.source_node_uuid, "target_node_uuid": edge.target_node_uuid, }) if scope in ["nodes", "both"]: # 获取所有节点并匹配 all_nodes = self.get_all_nodes(graph_id) scored_nodes = [] for node in all_nodes: score = match_score(node.name) + match_score(node.summary) if score > 0: scored_nodes.append((score, node)) scored_nodes.sort(key=lambda x: x[0], reverse=True) for score, node in scored_nodes[:limit]: nodes_result.append({ "uuid": node.uuid, "name": node.name, "labels": node.labels, "summary": node.summary, }) if node.summary: facts.append(f"[{node.name}]: {node.summary}") logger.info(t("console.localSearchComplete", count=len(facts))) except Exception as e: logger.error(t("console.localSearchFailed", error=str(e))) return SearchResult( facts=facts, edges=edges_result, nodes=nodes_result, query=query, total_count=len(facts) ) def get_all_nodes(self, graph_id: str) -> List[NodeInfo]: """ 获取图谱的所有节点（分页获取） Args: graph_id: 图谱ID Returns: 节点列表 """ logger.info(t("console.fetchingAllNodes", graphId=graph_id)) nodes = fetch_all_nodes(self.client, graph_id) result = [] for node in nodes: node_uuid = getattr(node, 'uuid_', None) or getattr(node, 'uuid', None) or "" result.append(NodeInfo( uuid=str(node_uuid) if node_uuid else "", name=node.name or "", labels=node.labels or [], summary=node.summary or "", attributes=node.attributes or {} )) logger.info(t("console.fetchedNodes", count=len(result))) return result def get_all_edges(self, graph_id: str, include_temporal: bool = True) -> List[EdgeInfo]: """ 获取图谱的所有边（分页获取，包含时间信息） Args: graph_id: 图谱ID include_temporal: 是否包含时间信息（默认True） Returns: 边列表（包含created_at, valid_at, invalid_at, expired_at） """ logger.info(t("console.fetchingAllEdges", graphId=graph_id)) edges = fetch_all_edges(self.client, graph_id) result = [] for edge in edges: edge_uuid = getattr(edge, 'uuid_', None) or getattr(edge, 'uuid', None) or "" edge_info = EdgeInfo( uuid=str(edge_uuid) if edge_uuid else "", name=edge.name or "", fact=edge.fact or "", source_node_uuid=edge.source_node_uuid or "", target_node_uuid=edge.target_node_uuid or "" ) # 添加时间信息 if include_temporal: edge_info.created_at = getattr(edge, 'created_at', None) edge_info.valid_at = getattr(edge, 'valid_at', None) edge_info.invalid_at = getattr(edge, 'invalid_at', None) edge_info.expired_at = getattr(edge, 'expired_at', None) result.append(edge_info) logger.info(t("console.fetchedEdges", count=len(result))) return result def get_node_detail(self, node_uuid: str) -> Optional[NodeInfo]: """ 获取单个节点的详细信息 Args: node_uuid: 节点UUID Returns: 节点信息或None """ 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=t("console.fetchNodeDetailOp", uuid=node_uuid[:8]) ) if not node: return None return NodeInfo( uuid=getattr(node, 'uuid_', None) or getattr(node, 'uuid', ''), name=node.name or "", labels=node.labels or [], summary=node.summary or "", attributes=node.attributes or {} ) except Exception as e: logger.error(t("console.fetchNodeDetailFailed", error=str(e))) return None def get_node_edges(self, graph_id: str, node_uuid: str) -> List[EdgeInfo]: """ 获取节点相关的所有边 通过获取图谱所有边，然后过滤出与指定节点相关的边 Args: graph_id: 图谱ID node_uuid: 节点UUID Returns: 边列表 """ logger.info(t("console.fetchingNodeEdges", uuid=node_uuid[:8])) try: # 获取图谱所有边，然后过滤 all_edges = self.get_all_edges(graph_id) result = [] for edge in all_edges: # 检查边是否与指定节点相关（作为源或目标） if edge.source_node_uuid == node_uuid or edge.target_node_uuid == node_uuid: result.append(edge) logger.info(t("console.foundNodeEdges", count=len(result))) return result except Exception as e: logger.warning(t("console.fetchNodeEdgesFailed", error=str(e))) return [] def get_entities_by_type( self, graph_id: str, entity_type: str ) -> List[NodeInfo]: """ 按类型获取Entity Args: graph_id: 图谱ID entity_type: Entity类型（如 Student, PublicFigure 等） Returns: 符合类型的Entity列表 """ logger.info(t("console.fetchingEntitiesByType", type=entity_type)) all_nodes = self.get_all_nodes(graph_id) filtered = [] for node in all_nodes: # 检查labels是否包含指定类型 if entity_type in node.labels: filtered.append(node) logger.info(t("console.foundEntitiesByType", count=len(filtered), type=entity_type)) return filtered def get_entity_summary( self, graph_id: str, entity_name: str ) -> Dict[str, Any]: """ 获取指定Entity的关系摘要 搜索与该Entity相关的所有信息，并生成摘要 Args: graph_id: 图谱ID entity_name: Entity名称 Returns: Entity摘要信息 """ logger.info(t("console.fetchingEntitySummary", name=entity_name)) # 先搜索该Entity相关的信息 search_result = self.search_graph( graph_id=graph_id, query=entity_name, limit=20 ) # 尝试在所有节点中找到该Entity all_nodes = self.get_all_nodes(graph_id) entity_node = None for node in all_nodes: if node.name.lower() == entity_name.lower(): entity_node = node break related_edges = [] if entity_node: # 传入graph_id参数 related_edges = self.get_node_edges(graph_id, entity_node.uuid) return { "entity_name": entity_name, "entity_info": entity_node.to_dict() if entity_node else None, "related_facts": search_result.facts, "related_edges": [e.to_dict() for e in related_edges], "total_relations": len(related_edges) } def get_graph_statistics(self, graph_id: str) -> Dict[str, Any]: """ 获取图谱的统计信息 Args: graph_id: 图谱ID Returns: 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) # 统计Entity类型分布 entity_types = {} for node in nodes: for label in node.labels: if label not in ["Entity", "Node"]: entity_types[label] = entity_types.get(label, 0) + 1 # 统计关系类型分布 relation_types = {} for edge in edges: relation_types[edge.name] = relation_types.get(edge.name, 0) + 1 return { "graph_id": graph_id, "total_nodes": len(nodes), "total_edges": len(edges), "entity_types": entity_types, "relation_types": relation_types } def get_simulation_context( self, graph_id: str, simulation_requirement: str, limit: int = 30 ) -> Dict[str, Any]: """ 获取模拟相关的上下文信息 综合搜索与模拟需求相关的所有信息 Args: graph_id: 图谱ID simulation_requirement: 模拟需求描述 limit: 每类信息的数量限制 Returns: 模拟上下文信息 """ 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( graph_id=graph_id, query=simulation_requirement, limit=limit ) # 获取图谱统计 stats = self.get_graph_statistics(graph_id) # 获取所有Entity节点 all_nodes = self.get_all_nodes(graph_id) # 筛选有实际类型的Entity（非纯Entity节点） entities = [] for node in all_nodes: custom_labels = [l for l in node.labels if l not in ["Entity", "Node"]] if custom_labels: entities.append({ "name": node.name, "type": custom_labels[0], "summary": node.summary }) return { "simulation_requirement": simulation_requirement, "related_facts": search_result.facts, "graph_statistics": stats, "entities": entities[:limit], # 限制数量 "total_entities": len(entities) } # ========== 核心检索工具（优化后） ========== def insight_forge( self, graph_id: str, query: str, simulation_requirement: str, report_context: str = "", max_sub_queries: int = 5 ) -> InsightForgeResult: """ 【InsightForge - 深度洞察检索】 最强大的混合检索函数，自动分解问题并多维度检索： 1. 使用LLM将问题分解为多个子问题 2. 对每个子问题进行语义搜索 3. 提取相关Entity并获取其详细信息 4. 追踪关系链 5. 整合所有结果，生成深度洞察 Args: graph_id: 图谱ID query: 用户问题 simulation_requirement: 模拟需求描述 report_context: 报告上下文（可选，用于更精准的子问题生成） max_sub_queries: 最大子问题数量 Returns: InsightForgeResult: 深度洞察检索结果 """ logger.info(t("console.insightForgeStart", query=query[:50])) result = InsightForgeResult( query=query, simulation_requirement=simulation_requirement, sub_queries=[] ) # Step 1: 使用LLM生成子问题 sub_queries = self._generate_sub_queries( query=query, simulation_requirement=simulation_requirement, report_context=report_context, max_queries=max_sub_queries ) result.sub_queries = sub_queries 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 = [] all_edges = [] seen_facts = set() for sub_query in sub_queries: search_result = self.search_graph( graph_id=graph_id, query=sub_query, limit=15, scope="edges" ) for fact in search_result.facts: if fact not in seen_facts: all_facts.append(fact) seen_facts.add(fact) all_edges.extend(search_result.edges) # 对原始问题也进行搜索 main_search = self.search_graph( graph_id=graph_id, query=query, limit=20, scope="edges" ) for fact in main_search.facts: if fact not in seen_facts: all_facts.append(fact) seen_facts.add(fact) result.semantic_facts = all_facts result.total_facts = len(all_facts) # Step 3: 从边中提取相关EntityUUID，只获取这些Entity的信息（不获取全部节点） entity_uuids = set() for edge_data in all_edges: if isinstance(edge_data, dict): source_uuid = edge_data.get('source_node_uuid', '') target_uuid = edge_data.get('target_node_uuid', '') if source_uuid: entity_uuids.add(source_uuid) if target_uuid: entity_uuids.add(target_uuid) # 获取所有相关Entity的详情（不限制数量，完整输出） entity_insights = [] node_map = {} # 用于后续关系链构建 for uuid in list(entity_uuids): # 处理所有Entity，不截断 if not uuid: continue try: # 单独获取每个相关节点的信息 node = self.get_node_detail(uuid) if node: node_map[uuid] = node entity_type = next((l for l in node.labels if l not in ["Entity", "Node"]), "Entity") # 获取该Entity相关的所有事实（不截断） related_facts = [ f for f in all_facts if node.name.lower() in f.lower() ] entity_insights.append({ "uuid": node.uuid, "name": node.name, "type": entity_type, "summary": node.summary, "related_facts": related_facts # 完整输出，不截断 }) except Exception as e: logger.debug(f"获取节点 {uuid} 失败: {e}") continue result.entity_insights = entity_insights result.total_entities = len(entity_insights) # Step 4: 构建所有关系链（不限制数量） relationship_chains = [] for edge_data in all_edges: # 处理所有边，不截断 if isinstance(edge_data, dict): source_uuid = edge_data.get('source_node_uuid', '') target_uuid = edge_data.get('target_node_uuid', '') relation_name = edge_data.get('name', '') source_name = node_map.get(source_uuid, NodeInfo('', '', [], '', {})).name or source_uuid[:8] target_name = node_map.get(target_uuid, NodeInfo('', '', [], '', {})).name or target_uuid[:8] chain = f"{source_name} --[{relation_name}]--> {target_name}" if chain not in relationship_chains: relationship_chains.append(chain) result.relationship_chains = relationship_chains result.total_relationships = len(relationship_chains) logger.info(t("console.insightForgeComplete", facts=result.total_facts, entities=result.total_entities, relationships=result.total_relationships)) return result def _generate_sub_queries( self, query: str, simulation_requirement: str, report_context: str = "", max_queries: int = 5 ) -> List[str]: """ 使用LLM生成子问题 将复杂问题分解为多个可以独立检索的子问题 """ system_prompt = """You are a professional question analysis expert. Your task is to decompose a complex question into multiple sub-questions that can be independently observed in the simulation world. Requirements: 1. Each sub-question should be specific enough to find relevant Agent behavior or events in the simulation world 2. Sub-questions should cover different dimensions of the original question (who, what, why, how, when, where) 3. Sub-questions should be relevant to the simulation scenario 4. Return in JSON format: {"sub_queries": ["sub-question 1", "sub-question 2", ...]}""" user_prompt = f"""Simulation requirement background: {simulation_requirement} {f"Report context: {report_context[:500]}" if report_context else ""} Please decompose the following question into {max_queries} sub-questions: {query} Return the sub-questions in JSON format.""" try: response = self.llm.chat_json( messages=[ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt} ], temperature=0.3 ) sub_queries = response.get("sub_queries", []) # 确保是字符串列表 return [str(sq) for sq in sub_queries[:max_queries]] except Exception as e: logger.warning(t("console.generateSubQueriesFailed", error=str(e))) # 降级：返回基于原问题的变体 return [ query, f"{query} 的主要参与者", f"{query} 的原因和影响", f"{query} 的发展过程" ][:max_queries] def panorama_search( self, graph_id: str, query: str, include_expired: bool = True, limit: int = 50 ) -> PanoramaResult: """ 【PanoramaSearch - 广度搜索】 获取全貌视图，包括所有相关内容和历史/过期信息： 1. 获取所有相关节点 2. 获取所有边（包括已过期/失效的） 3. 分类整理当前有效和历史信息 这个工具适用于需要了解事件全貌、追踪演变过程的场景。 Args: graph_id: 图谱ID query: 搜索查询（用于相关性排序） include_expired: 是否包含过期内容（默认True） limit: 返回结果数量限制 Returns: PanoramaResult: 广度搜索结果 """ logger.info(t("console.panoramaSearchStart", query=query[:50])) result = PanoramaResult(query=query) # 获取所有节点 all_nodes = self.get_all_nodes(graph_id) node_map = {n.uuid: n for n in all_nodes} result.all_nodes = all_nodes result.total_nodes = len(all_nodes) # 获取所有边（包含时间信息） all_edges = self.get_all_edges(graph_id, include_temporal=True) result.all_edges = all_edges result.total_edges = len(all_edges) # 分类事实 active_facts = [] historical_facts = [] for edge in all_edges: if not edge.fact: continue # 为事实添加Entity名称 source_name = node_map.get(edge.source_node_uuid, NodeInfo('', '', [], '', {})).name or edge.source_node_uuid[:8] target_name = node_map.get(edge.target_node_uuid, NodeInfo('', '', [], '', {})).name or edge.target_node_uuid[:8] # 判断是否过期/失效 is_historical = edge.is_expired or edge.is_invalid if is_historical: # 历史/过期事实，添加时间标记 valid_at = edge.valid_at or "Unknown" invalid_at = edge.invalid_at or edge.expired_at or "Unknown" fact_with_time = f"[{valid_at} - {invalid_at}] {edge.fact}" historical_facts.append(fact_with_time) else: # 当前有效事实 active_facts.append(edge.fact) # 基于查询进行相关性排序 query_lower = query.lower() keywords = [w.strip() for w in query_lower.replace(',', ' ').replace('，', ' ').split() if len(w.strip()) > 1] def relevance_score(fact: str) -> int: fact_lower = fact.lower() score = 0 if query_lower in fact_lower: score += 100 for kw in keywords: if kw in fact_lower: score += 10 return score # 排序并限制数量 active_facts.sort(key=relevance_score, reverse=True) historical_facts.sort(key=relevance_score, reverse=True) result.active_facts = active_facts[:limit] result.historical_facts = historical_facts[:limit] if include_expired else [] result.active_count = len(active_facts) result.historical_count = len(historical_facts) logger.info(t("console.panoramaSearchComplete", active=result.active_count, historical=result.historical_count)) return result def quick_search( self, graph_id: str, query: str, limit: int = 10 ) -> SearchResult: """ 【QuickSearch - 简单搜索】 快速、轻量级的检索工具： 1. 直接调用Zep语义搜索 2. 返回最相关的结果 3. 适用于简单、直接的检索需求 Args: graph_id: 图谱ID query: 搜索查询 limit: 返回结果数量 Returns: SearchResult: 搜索结果 """ logger.info(t("console.quickSearchStart", query=query[:50])) # 直接调用现有的search_graph方法 result = self.search_graph( graph_id=graph_id, query=query, limit=limit, scope="edges" ) logger.info(t("console.quickSearchComplete", count=result.total_count)) return result def interview_agents( self, simulation_id: str, interview_requirement: str, simulation_requirement: str = "", max_agents: int = 5, custom_questions: List[str] = None ) -> InterviewResult: """ 【InterviewAgents - 深度采访】 调用真实的OASIS采访API，采访模拟中正在运行的Agent： 1. 自动读取人设文件，了解所有模拟Agent 2. 使用LLM分析采访需求，智能选择最相关的Agent 3. 使用LLM生成采访问题 4. 调用 /api/simulation/interview/batch 接口进行真实采访（双平台同时采访） 5. 整合所有采访结果，生成采访报告 【重要】此功能需要模拟环境处于运行状态（OASIS环境未关闭） 【使用场景】 - 需要从不同角色视角了解事件看法 - 需要收集多方意见和观点 - 需要获取模拟Agent的真实回答（非LLM模拟） Args: simulation_id: 模拟ID（用于定位人设文件和调用采访API） interview_requirement: 采访需求描述（非结构化，如"了解学生对事件的看法"） simulation_requirement: 模拟需求背景（可选） max_agents: 最多采访的Agent数量 custom_questions: 自定义采访问题（可选，若不提供则自动生成） Returns: InterviewResult: 采访结果 """ from .simulation_runner import SimulationRunner logger.info(t("console.interviewAgentsStart", requirement=interview_requirement[:50])) result = InterviewResult( interview_topic=interview_requirement, interview_questions=custom_questions or [] ) # Step 1: 读取人设文件 profiles = self._load_agent_profiles(simulation_id) if not profiles: 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(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( profiles=profiles, interview_requirement=interview_requirement, simulation_requirement=simulation_requirement, max_agents=max_agents ) result.selected_agents = selected_agents result.selection_reasoning = selection_reasoning 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: result.interview_questions = self._generate_interview_questions( interview_requirement=interview_requirement, simulation_requirement=simulation_requirement, selected_agents=selected_agents ) 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)]) # 添加优化前缀，约束Agent回复格式 INTERVIEW_PROMPT_PREFIX = ( "You are being interviewed. Please draw on your persona, all past memories and actions, " "and answer the following questions directly in plain text.\n" "Response requirements:\n" "1. Answer directly in natural language, do not call any tools\n" "2. Do not return JSON format or tool call format\n" "3. Do not use Markdown headings (such as #, ##, ###)\n" "4. Answer each question by number, starting each answer with 'Question X:' (X is the question number)\n" "5. Separate each answer with a blank line\n" "6. Provide substantive answers, at least 2-3 sentences per question\n\n" ) optimized_prompt = f"{INTERVIEW_PROMPT_PREFIX}{combined_prompt}" # Step 4: 调用真实的采访API（不指定platform，默认双平台同时采访） try: # 构建批量采访列表（不指定platform，双平台采访） interviews_request = [] for agent_idx in selected_indices: interviews_request.append({ "agent_id": agent_idx, "prompt": optimized_prompt # 使用优化后的prompt # 不指定platform，API会在twitter和reddit两个平台都采访 }) 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( simulation_id=simulation_id, interviews=interviews_request, platform=None, # 不指定platform，双平台采访 timeout=180.0 # 双平台需要更长超时 ) 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", "Unknown error") logger.warning(t("console.interviewApiReturnedFailure", error=error_msg)) result.summary = f"Interview API call failed: {error_msg}. Please check OASIS simulation environment status." return result # Step 5: 解析API返回结果，构建AgentInterview对象 # 双平台模式返回格式: {"twitter_0": {...}, "reddit_0": {...}, "twitter_1": {...}, ...} api_data = api_result.get("result", {}) results_dict = api_data.get("results", {}) if isinstance(api_data, dict) else {} for i, agent_idx in enumerate(selected_indices): agent = selected_agents[i] agent_name = agent.get("realname", agent.get("username", f"Agent_{agent_idx}")) agent_role = agent.get("profession", "Unknown") agent_bio = agent.get("bio", "") # 获取该Agent在两个平台的采访结果 twitter_result = results_dict.get(f"twitter_{agent_idx}", {}) reddit_result = results_dict.get(f"reddit_{agent_idx}", {}) twitter_response = twitter_result.get("response", "") reddit_response = reddit_result.get("response", "") # 清理可能的工具调用 JSON 包裹 twitter_response = self._clean_tool_call_response(twitter_response) reddit_response = self._clean_tool_call_response(reddit_response) # 始终输出双平台标记 twitter_text = twitter_response if twitter_response else "（该平台未获得回复）" reddit_text = reddit_response if reddit_response else "（该平台未获得回复）" response_text = f"【Twitter平台回答】\n{twitter_text}\n\n【Reddit平台回答】\n{reddit_text}" # 提取关键引言（从两个平台的回答中） import re combined_responses = f"{twitter_response} {reddit_response}" # 清理响应文本：去掉标记、编号、Markdown 等干扰 clean_text = re.sub(r'#{1,6}\s+', '', combined_responses) clean_text = re.sub(r'\{[^}]*tool_name[^}]*\}', '', clean_text) clean_text = re.sub(r'[*_`|>~\-]{2,}', '', clean_text) clean_text = re.sub(r'问题\d+[：:]\s*', '', clean_text) clean_text = re.sub(r'【[^】]+】', '', clean_text) # 策略1（主）: 提取完整的有实质内容的句子 sentences = re.split(r'[。！？]', clean_text) meaningful = [ s.strip() for s in sentences if 20 <= len(s.strip()) <= 150 and not re.match(r'^[\s\W，,；;：:、]+', s.strip()) and not s.strip().startswith(('{', '问题')) ] meaningful.sort(key=len, reverse=True) key_quotes = [s + "。" for s in meaningful[:3]] # 策略2（补充）: 正确配对的中文引号「」内长文本 if not key_quotes: paired = re.findall(r'\u201c([^\u201c\u201d]{15,100})\u201d', clean_text) paired += re.findall(r'\u300c([^\u300c\u300d]{15,100})\u300d', clean_text) key_quotes = [q for q in paired if not re.match(r'^[，,；;：:、]', q)][:3] interview = AgentInterview( agent_name=agent_name, agent_role=agent_role, agent_bio=agent_bio[:1000], # 扩大bio长度限制 question=combined_prompt, response=response_text, key_quotes=key_quotes[:5] ) result.interviews.append(interview) result.interviewed_count = len(result.interviews) except ValueError as e: # 模拟环境未运行 logger.warning(t("console.interviewApiCallFailed", error=e)) result.summary = f"Interview failed: {str(e)}. Simulation environment may have closed. Please ensure OASIS environment is running." return result except Exception as 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)}" return result # Step 6: 生成采访摘要 if result.interviews: result.summary = self._generate_interview_summary( interviews=result.interviews, interview_requirement=interview_requirement ) logger.info(t("console.interviewAgentsComplete", count=result.interviewed_count)) return result @staticmethod def _clean_tool_call_response(response: str) -> str: """清理 Agent 回复中的 JSON 工具调用包裹，提取实际内容""" if not response or not response.strip().startswith('{'): return response text = response.strip() if 'tool_name' not in text[:80]: return response import re as _re try: data = json.loads(text) if isinstance(data, dict) and 'arguments' in data: for key in ('content', 'text', 'body', 'message', 'reply'): if key in data['arguments']: return str(data['arguments'][key]) except (json.JSONDecodeError, KeyError, TypeError): match = _re.search(r'"content"\s*:\s*"((?:[^"\\]|\\.)*)"', text) if match: return match.group(1).replace('\\n', '\n').replace('\\"', '"') return response def _load_agent_profiles(self, simulation_id: str) -> List[Dict[str, Any]]: """加载模拟的Agent人设文件""" import os import csv # 构建人设文件路径 sim_dir = os.path.join( os.path.dirname(__file__), f'../../uploads/simulations/{simulation_id}' ) profiles = [] # 优先尝试读取Reddit JSON格式 reddit_profile_path = os.path.join(sim_dir, "reddit_profiles.json") if os.path.exists(reddit_profile_path): try: with open(reddit_profile_path, 'r', encoding='utf-8') as f: profiles = json.load(f) logger.info(t("console.loadedRedditProfiles", count=len(profiles))) return profiles except Exception as 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") if os.path.exists(twitter_profile_path): try: with open(twitter_profile_path, 'r', encoding='utf-8') as f: reader = csv.DictReader(f) for row in reader: # CSV格式转换为统一格式 profiles.append({ "realname": row.get("name", ""), "username": row.get("username", ""), "bio": row.get("description", ""), "persona": row.get("user_char", ""), "profession": "Unknown" }) logger.info(t("console.loadedTwitterProfiles", count=len(profiles))) return profiles except Exception as e: logger.warning(t("console.readTwitterProfilesFailed", error=e)) return profiles def _select_agents_for_interview( self, profiles: List[Dict[str, Any]], interview_requirement: str, simulation_requirement: str, max_agents: int ) -> tuple: """ 使用LLM选择要采访的Agent Returns: tuple: (selected_agents, selected_indices, reasoning) - selected_agents: 选中Agent的完整信息列表 - selected_indices: 选中Agent的索引列表（用于API调用） - reasoning: 选择理由 """ # 构建Agent摘要列表 agent_summaries = [] for i, profile in enumerate(profiles): summary = { "index": i, "name": profile.get("realname", profile.get("username", f"Agent_{i}")), "profession": profile.get("profession", "Unknown"), "bio": profile.get("bio", "")[:200], "interested_topics": profile.get("interested_topics", []) } agent_summaries.append(summary) system_prompt = """You are a professional interview planning expert. Your task is to select the most suitable agents to interview from the simulation agent list based on the interview requirements. Selection criteria: 1. Agent's identity/profession is relevant to the interview topic 2. Agent may hold unique or valuable perspectives 3. Select diverse viewpoints (e.g., supporters, opponents, neutral parties, professionals) 4. Prioritize roles directly related to the event Return in JSON format: { "selected_indices": [list of selected agent indices], "reasoning": "Explanation of selection reasoning" }""" user_prompt = f"""Interview requirements: {interview_requirement} Simulation background: {simulation_requirement if simulation_requirement else "Not provided"} Available agent list ({len(agent_summaries)} total): {json.dumps(agent_summaries, ensure_ascii=False, indent=2)} Please select up to {max_agents} agents most suitable for interview and explain your reasoning.""" try: response = self.llm.chat_json( messages=[ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt} ], temperature=0.3 ) selected_indices = response.get("selected_indices", [])[:max_agents] reasoning = response.get("reasoning", "Auto-selected based on relevance") # 获取选中的Agent完整信息 selected_agents = [] valid_indices = [] for idx in selected_indices: if 0 <= idx < len(profiles): selected_agents.append(profiles[idx]) valid_indices.append(idx) return selected_agents, valid_indices, reasoning except Exception as 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)))) return selected, indices, "使用默认选择策略" def _generate_interview_questions( self, interview_requirement: str, simulation_requirement: str, selected_agents: List[Dict[str, Any]] ) -> List[str]: """使用LLM生成采访问题""" agent_roles = [a.get("profession", "Unknown") for a in selected_agents] system_prompt = """You are a professional journalist/interviewer. Based on the interview requirements, generate 3-5 in-depth interview questions. Question requirements: 1. Open-ended questions that encourage detailed answers 2. Different roles may have different answers 3. Cover multiple dimensions: facts, opinions, feelings 4. Natural language, like a real interview 5. Keep each question under 50 words, concise and clear 6. Ask directly, do not include background explanations or prefixes Return in JSON format: {"questions": ["question 1", "question 2", ...]}""" user_prompt = f"""Interview requirements: {interview_requirement} Simulation background: {simulation_requirement if simulation_requirement else "Not provided"} Interviewee roles: {', '.join(agent_roles)} Please generate 3-5 interview questions.""" try: response = self.llm.chat_json( messages=[ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt} ], temperature=0.5 ) return response.get("questions", [f"What is your view on {interview_requirement}?"]) except Exception as e: logger.warning(t("console.generateInterviewQuestionsFailed", error=e)) return [ f"What is your perspective on {interview_requirement}?", "How does this affect you or the group you represent?", "What do you think should be done to address or improve this issue?" ] def _generate_interview_summary( self, interviews: List[AgentInterview], interview_requirement: str ) -> str: """生成采访摘要""" if not interviews: return "No interviews completed" # 收集所有采访内容 interview_texts = [] for interview in interviews: interview_texts.append(f"【{interview.agent_name}（{interview.agent_role}）】\n{interview.response[:500]}") quote_instruction = 'Use quotation marks "" when quoting interviewees' system_prompt = f"""You are a professional news editor. Based on multiple interviewees' responses, generate an interview summary. Summary requirements: 1. Extract key viewpoints from each party 2. Identify consensus and disagreements 3. Highlight valuable quotes 4. Stay objective and neutral, do not favor any party 5. Keep within 1000 words Format constraints (must follow): - Use plain text paragraphs, separate sections with blank lines - Do not use Markdown headings (such as #, ##, ###) - Do not use dividers (such as ---, ***) - {quote_instruction} - You may use **bold** to mark key words, but do not use other Markdown syntax""" user_prompt = f"""Interview topic: {interview_requirement} Interview content: {"".join(interview_texts)} Please generate an interview summary.""" try: summary = self.llm.chat( messages=[ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt} ], temperature=0.3, max_tokens=800 ) return summary except Exception as 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])