diff --git a/backend/app/api/graph.py b/backend/app/api/graph.py index 12ff1ba2d..31b7e9ef2 100644 --- a/backend/app/api/graph.py +++ b/backend/app/api/graph.py @@ -213,12 +213,19 @@ def generate_ontology(): # 生成本体 logger.info("调用 LLM 生成本体定义...") + # till this point generator = OntologyGenerator() - ontology = generator.generate( - document_texts=document_texts, - simulation_requirement=simulation_requirement, - additional_context=additional_context if additional_context else None - ) + logger.info("OntologyGenerator....................") + try: + ontology = generator.generate( + document_texts=document_texts, + simulation_requirement=simulation_requirement, + additional_context=additional_context if additional_context else None + ) + logger.info("OntologyGenerator try....................") + except Exception as e: + logger.error(f"本体生成失败: {traceback.format_exc()}") # ADD + raise # 保存本体到项目 entity_count = len(ontology.get("entity_types", [])) diff --git a/backend/app/config.py b/backend/app/config.py index 953dfa50a..e72484255 100644 --- a/backend/app/config.py +++ b/backend/app/config.py @@ -13,7 +13,6 @@ if os.path.exists(project_root_env): load_dotenv(project_root_env, override=True) else: - # 如果根目录没有 .env，尝试加载环境变量（用于生产环境） load_dotenv(override=True) @@ -24,14 +23,27 @@ class Config: SECRET_KEY = os.environ.get('SECRET_KEY', 'mirofish-secret-key') DEBUG = os.environ.get('FLASK_DEBUG', 'True').lower() == 'true' - # JSON配置 - 禁用ASCII转义，让中文直接显示（而不是 \uXXXX 格式） + # JSON配置 JSON_AS_ASCII = False - # LLM配置（统一使用OpenAI格式） - LLM_API_KEY = os.environ.get('LLM_API_KEY') - LLM_BASE_URL = os.environ.get('LLM_BASE_URL', 'https://api.openai.com/v1') - LLM_MODEL_NAME = os.environ.get('LLM_MODEL_NAME', 'gpt-4o-mini') - + # ===== Primary LLM (Groq) - 3 keys, auto-rotates on rate limit ===== + LLM_API_KEY_1 = os.environ.get('LLM_API_KEY_1') + LLM_API_KEY_2 = os.environ.get('LLM_API_KEY_2') + LLM_API_KEY_3 = os.environ.get('LLM_API_KEY_3') + LLM_API_KEY_4 = os.environ.get('LLM_API_KEY_4') + LLM_BASE_URL = os.environ.get('LLM_BASE_URL', 'https://api.groq.com/openai/v1') + LLM_MODEL_NAME = os.environ.get('LLM_MODEL_NAME', 'meta-llama/llama-4-scout-17b-16e-instruct') + + # Backward compatibility alias + LLM_API_KEY = os.environ.get('LLM_API_KEY_1') or os.environ.get('LLM_API_KEY') + + # ===== Boost LLM (Gemini) - 3 keys, auto-rotates on rate limit ===== + LLM_BOOST_API_KEY_1 = os.environ.get('LLM_BOOST_API_KEY_1') + LLM_BOOST_API_KEY_2 = os.environ.get('LLM_BOOST_API_KEY_2') + LLM_BOOST_API_KEY_3 = os.environ.get('LLM_BOOST_API_KEY_3') + LLM_BOOST_BASE_URL = os.environ.get('LLM_BOOST_BASE_URL') + LLM_BOOST_MODEL_NAME = os.environ.get('LLM_BOOST_MODEL_NAME') + # Zep配置 ZEP_API_KEY = os.environ.get('ZEP_API_KEY') @@ -41,11 +53,11 @@ class Config: ALLOWED_EXTENSIONS = {'pdf', 'md', 'txt', 'markdown'} # 文本处理配置 - DEFAULT_CHUNK_SIZE = 500 # 默认切块大小 - DEFAULT_CHUNK_OVERLAP = 50 # 默认重叠大小 + DEFAULT_CHUNK_SIZE = 500 + DEFAULT_CHUNK_OVERLAP = 50 # OASIS模拟配置 - OASIS_DEFAULT_MAX_ROUNDS = int(os.environ.get('OASIS_DEFAULT_MAX_ROUNDS', '10')) + OASIS_DEFAULT_MAX_ROUNDS = int(os.environ.get('OASIS_DEFAULT_MAX_ROUNDS', '3')) OASIS_SIMULATION_DATA_DIR = os.path.join(os.path.dirname(__file__), '../uploads/simulations') # OASIS平台可用动作配置 @@ -59,17 +71,38 @@ class Config: ] # Report Agent配置 - REPORT_AGENT_MAX_TOOL_CALLS = int(os.environ.get('REPORT_AGENT_MAX_TOOL_CALLS', '5')) - REPORT_AGENT_MAX_REFLECTION_ROUNDS = int(os.environ.get('REPORT_AGENT_MAX_REFLECTION_ROUNDS', '2')) - REPORT_AGENT_TEMPERATURE = float(os.environ.get('REPORT_AGENT_TEMPERATURE', '0.5')) - + REPORT_AGENT_MAX_TOOL_CALLS = int(os.environ.get('REPORT_AGENT_MAX_TOOL_CALLS', '1')) + REPORT_AGENT_MAX_REFLECTION_ROUNDS = int(os.environ.get('REPORT_AGENT_MAX_REFLECTION_ROUNDS', '1')) + REPORT_AGENT_TEMPERATURE = float(os.environ.get('REPORT_AGENT_TEMPERATURE', '0.3')) + + @classmethod + def get_primary_keys(cls) -> list: + """Return all configured primary (Groq) API keys""" + return [k for k in [cls.LLM_API_KEY_1, cls.LLM_API_KEY_2, cls.LLM_API_KEY_3, cls.LLM_API_KEY_4] if k] + + @classmethod + def get_boost_keys(cls) -> list: + """Return all configured boost (Gemini) API keys""" + return [k for k in [cls.LLM_BOOST_API_KEY_1, cls.LLM_BOOST_API_KEY_2, cls.LLM_BOOST_API_KEY_3] if k] + + @classmethod + def has_boost(cls) -> bool: + """Check if boost LLM is configured""" + return bool(cls.LLM_BOOST_API_KEY_1 and cls.LLM_BOOST_BASE_URL and cls.LLM_BOOST_MODEL_NAME) + @classmethod def validate(cls): """验证必要配置""" errors = [] - if not cls.LLM_API_KEY: - errors.append("LLM_API_KEY 未配置") + primary_keys = cls.get_primary_keys() + if not primary_keys: + errors.append("LLM_API_KEY_1 未配置") + else: + print(f"✅ Primary LLM: {len(primary_keys)} key(s) configured") if not cls.ZEP_API_KEY: errors.append("ZEP_API_KEY 未配置") - return errors - + if cls.has_boost(): + print(f"✅ Boost LLM: {len(cls.get_boost_keys())} key(s) configured") + else: + print("⚠️ LLM_BOOST not configured - all tasks will use primary LLM") + return errors \ No newline at end of file diff --git a/backend/app/services/oasis_profile_generator.py b/backend/app/services/oasis_profile_generator.py index 57836c539..6e7bae2b5 100644 --- a/backend/app/services/oasis_profile_generator.py +++ b/backend/app/services/oasis_profile_generator.py @@ -1,11 +1,11 @@ """ -OASIS Agent Profile生成器 -将Zep图谱中的实体转换为OASIS模拟平台所需的Agent Profile格式 +OASIS Agent Profile Generator +Convert entities from the knowledge graph to OASIS simulation platform's required Agent Profile format -优化改进： -1. 调用Zep检索功能二次丰富节点信息 -2. 优化提示词生成非常详细的人设 -3. 区分个人实体和抽象群体实体 +Optimization improvements: +1. Call knowledge graph retrieval function to enrich node information +2. Optimize prompts to generate very detailed personas +3. Distinguish between individual entities and abstract group entities """ import json @@ -16,60 +16,60 @@ from datetime import datetime from openai import OpenAI -from zep_cloud.client import Zep from ..config import Config from ..utils.logger import get_logger from .zep_entity_reader import EntityNode, ZepEntityReader +from zep_cloud.client import Zep logger = get_logger('mirofish.oasis_profile') @dataclass class OasisAgentProfile: - """OASIS Agent Profile数据结构""" - # 通用字段 + """OASIS Agent Profile data structure""" + # Common fields user_id: int user_name: str name: str bio: str persona: str - - # 可选字段 - Reddit风格 + + # Optional fields - Reddit style karma: int = 1000 - - # 可选字段 - Twitter风格 + + # Optional fields - Twitter style friend_count: int = 100 follower_count: int = 150 statuses_count: int = 500 - - # 额外人设信息 + + # Additional persona information age: Optional[int] = None gender: Optional[str] = None mbti: Optional[str] = None country: Optional[str] = None profession: Optional[str] = None interested_topics: List[str] = field(default_factory=list) - - # 来源实体信息 + + # Source entity information source_entity_uuid: Optional[str] = None source_entity_type: Optional[str] = None created_at: str = field(default_factory=lambda: datetime.now().strftime("%Y-%m-%d")) def to_reddit_format(self) -> Dict[str, Any]: - """转换为Reddit平台格式""" + """Convert to Reddit platform format""" profile = { "user_id": self.user_id, - "username": self.user_name, # OASIS 库要求字段名为 username（无下划线） + "username": self.user_name, # OASIS library requires field name as username (no underscore) "name": self.name, "bio": self.bio, "persona": self.persona, "karma": self.karma, "created_at": self.created_at, } - - # 添加额外人设信息（如果有） + + # Add additional persona information (if available) if self.age: profile["age"] = self.age if self.gender: @@ -86,10 +86,10 @@ def to_reddit_format(self) -> Dict[str, Any]: return profile def to_twitter_format(self) -> Dict[str, Any]: - """转换为Twitter平台格式""" + """Convert to Twitter platform format""" profile = { "user_id": self.user_id, - "username": self.user_name, # OASIS 库要求字段名为 username（无下划线） + "username": self.user_name, # OASIS library requires field name as username (no underscore) "name": self.name, "bio": self.bio, "persona": self.persona, @@ -98,8 +98,8 @@ def to_twitter_format(self) -> Dict[str, Any]: "statuses_count": self.statuses_count, "created_at": self.created_at, } - - # 添加额外人设信息 + + # Add additional persona information if self.age: profile["age"] = self.age if self.gender: @@ -116,7 +116,7 @@ def to_twitter_format(self) -> Dict[str, Any]: return profile def to_dict(self) -> Dict[str, Any]: - """转换为完整字典格式""" + """Convert to complete dictionary format""" return { "user_id": self.user_id, "user_name": self.user_name, @@ -141,44 +141,44 @@ def to_dict(self) -> Dict[str, Any]: class OasisProfileGenerator: """ - OASIS Profile生成器 - - 将Zep图谱中的实体转换为OASIS模拟所需的Agent Profile - - 优化特性： - 1. 调用Zep图谱检索功能获取更丰富的上下文 - 2. 生成非常详细的人设（包括基本信息、职业经历、性格特征、社交媒体行为等） - 3. 区分个人实体和抽象群体实体 + OASIS Profile Generator + + Convert entities from the knowledge graph to Agent Profile required by OASIS simulation + + Optimization features: + 1. Call knowledge graph retrieval function to get richer context + 2. Generate very detailed personas (including basic information, career experience, personality traits, social media behavior, etc.) + 3. Distinguish between individual entities and abstract group entities """ - - # MBTI类型列表 + + # MBTI types list MBTI_TYPES = [ "INTJ", "INTP", "ENTJ", "ENTP", "INFJ", "INFP", "ENFJ", "ENFP", "ISTJ", "ISFJ", "ESTJ", "ESFJ", "ISTP", "ISFP", "ESTP", "ESFP" ] - - # 常见国家列表 + + # Common countries list COUNTRIES = [ - "China", "US", "UK", "Japan", "Germany", "France", + "US", "UK", "Japan", "Germany", "France", "Canada", "Australia", "Brazil", "India", "South Korea" ] - - # 个人类型实体（需要生成具体人设） + + # Individual type entities (need to generate specific personas) INDIVIDUAL_ENTITY_TYPES = [ - "student", "alumni", "professor", "person", "publicfigure", + "student", "alumni", "professor", "person", "publicfigure", "expert", "faculty", "official", "journalist", "activist" ] - - # 群体/机构类型实体（需要生成群体代表人设） + + # Group/institutional type entities (need to generate group representative personas) GROUP_ENTITY_TYPES = [ - "university", "governmentagency", "organization", "ngo", + "university", "governmentagency", "organization", "ngo", "mediaoutlet", "company", "institution", "group", "community" ] def __init__( - self, + self, api_key: Optional[str] = None, base_url: Optional[str] = None, model_name: Optional[str] = None, @@ -188,54 +188,48 @@ def __init__( self.api_key = api_key or Config.LLM_API_KEY self.base_url = base_url or Config.LLM_BASE_URL self.model_name = model_name or Config.LLM_MODEL_NAME - + if not self.api_key: - raise ValueError("LLM_API_KEY 未配置") - + raise ValueError("LLM_API_KEY not configured") + self.client = OpenAI( api_key=self.api_key, base_url=self.base_url ) - - # Zep客户端用于检索丰富上下文 + + # GraphStorage for hybrid search enrichment self.zep_api_key = zep_api_key or Config.ZEP_API_KEY self.zep_client = None self.graph_id = graph_id - - if self.zep_api_key: - try: - self.zep_client = Zep(api_key=self.zep_api_key) - except Exception as e: - logger.warning(f"Zep客户端初始化失败: {e}") def generate_profile_from_entity( - self, - entity: EntityNode, + self, + entity: EntityNode, user_id: int, use_llm: bool = True ) -> OasisAgentProfile: """ - 从Zep实体生成OASIS Agent Profile - + Generate OASIS Agent Profile from knowledge graph entity + Args: - entity: Zep实体节点 - user_id: 用户ID（用于OASIS） - use_llm: 是否使用LLM生成详细人设 - + entity: Knowledge graph entity node + user_id: User ID (for OASIS) + use_llm: Whether to use LLM to generate detailed persona + Returns: OasisAgentProfile """ entity_type = entity.get_entity_type() or "Entity" - - # 基础信息 + + # Basic information name = entity.name user_name = self._generate_username(name) - - # 构建上下文信息 + + # Build context information context = self._build_entity_context(entity) if use_llm: - # 使用LLM生成详细人设 + # Use LLM to generate detailed persona profile_data = self._generate_profile_with_llm( entity_name=name, entity_type=entity_type, @@ -244,7 +238,7 @@ def generate_profile_from_entity( context=context ) else: - # 使用规则生成基础人设 + # Use rules to generate basic persona profile_data = self._generate_profile_rule_based( entity_name=name, entity_type=entity_type, @@ -273,224 +267,176 @@ def generate_profile_from_entity( ) def _generate_username(self, name: str) -> str: - """生成用户名""" - # 移除特殊字符，转换为小写 + """Generate username""" + # Remove special characters, convert to lowercase username = name.lower().replace(" ", "_") username = ''.join(c for c in username if c.isalnum() or c == '_') - - # 添加随机后缀避免重复 + + # Add random suffix to avoid duplicates suffix = random.randint(100, 999) return f"{username}_{suffix}" - def _search_zep_for_entity(self, entity: EntityNode) -> Dict[str, Any]: + def _search_graph_for_entity(self, entity: EntityNode) -> Dict[str, Any]: """ - 使用Zep图谱混合搜索功能获取实体相关的丰富信息 - - Zep没有内置混合搜索接口，需要分别搜索edges和nodes然后合并结果。 - 使用并行请求同时搜索，提高效率。 - + Use GraphStorage hybrid search to obtain rich information related to entity + + Uses storage.search() (hybrid vector + BM25) for both edges and nodes. + Args: - entity: 实体节点对象 - + entity: Entity node object + Returns: - 包含facts, node_summaries, context的字典 + Dictionary containing facts, node_summaries, context """ - import concurrent.futures - - if not self.zep_client: + if not self.storage: return {"facts": [], "node_summaries": [], "context": ""} - + entity_name = entity.name - + results = { "facts": [], "node_summaries": [], "context": "" } - - # 必须有graph_id才能进行搜索 + if not self.graph_id: - logger.debug(f"跳过Zep检索：未设置graph_id") + logger.debug(f"Skip knowledge graph search: graph_id not set") return results - - comprehensive_query = f"关于{entity_name}的所有信息、活动、事件、关系和背景" - - def search_edges(): - """搜索边（事实/关系）- 带重试机制""" - max_retries = 3 - last_exception = None - delay = 2.0 - - for attempt in range(max_retries): - try: - return self.zep_client.graph.search( - query=comprehensive_query, - graph_id=self.graph_id, - limit=30, - scope="edges", - reranker="rrf" - ) - except Exception as e: - last_exception = e - if attempt < max_retries - 1: - logger.debug(f"Zep边搜索第 {attempt + 1} 次失败: {str(e)[:80]}, 重试中...") - time.sleep(delay) - delay *= 2 - else: - logger.debug(f"Zep边搜索在 {max_retries} 次尝试后仍失败: {e}") - return None - - def search_nodes(): - """搜索节点（实体摘要）- 带重试机制""" - max_retries = 3 - last_exception = None - delay = 2.0 - - for attempt in range(max_retries): - try: - return self.zep_client.graph.search( - query=comprehensive_query, - graph_id=self.graph_id, - limit=20, - scope="nodes", - reranker="rrf" - ) - except Exception as e: - last_exception = e - if attempt < max_retries - 1: - logger.debug(f"Zep节点搜索第 {attempt + 1} 次失败: {str(e)[:80]}, 重试中...") - time.sleep(delay) - delay *= 2 - else: - logger.debug(f"Zep节点搜索在 {max_retries} 次尝试后仍失败: {e}") - return None - + + comprehensive_query = f"All information, activities, events, relationships and background about {entity_name}" + try: - # 并行执行edges和nodes搜索 - with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor: - edge_future = executor.submit(search_edges) - node_future = executor.submit(search_nodes) - - # 获取结果 - edge_result = edge_future.result(timeout=30) - node_result = node_future.result(timeout=30) - - # 处理边搜索结果 + # Search edges (facts) + edge_results = self.storage.search( + graph_id=self.graph_id, + query=comprehensive_query, + limit=30, + scope="edges" + ) + all_facts = set() - if edge_result and hasattr(edge_result, 'edges') and edge_result.edges: - for edge in edge_result.edges: - if hasattr(edge, 'fact') and edge.fact: - all_facts.add(edge.fact) + if isinstance(edge_results, dict) and 'edges' in edge_results: + for edge in edge_results['edges']: + fact = edge.get('fact', '') + if fact: + all_facts.add(fact) results["facts"] = list(all_facts) - - # 处理节点搜索结果 + + # Search nodes (entity summaries) + node_results = self.storage.search( + graph_id=self.graph_id, + query=comprehensive_query, + limit=20, + scope="nodes" + ) + all_summaries = set() - if node_result and hasattr(node_result, 'nodes') and node_result.nodes: - for node in node_result.nodes: - if hasattr(node, 'summary') and node.summary: - all_summaries.add(node.summary) - if hasattr(node, 'name') and node.name and node.name != entity_name: - all_summaries.add(f"相关实体: {node.name}") + if isinstance(node_results, dict) and 'nodes' in node_results: + for node in node_results['nodes']: + summary = node.get('summary', '') + if summary: + all_summaries.add(summary) + name = node.get('name', '') + if name and name != entity_name: + all_summaries.add(f"Related Entity: {name}") results["node_summaries"] = list(all_summaries) - - # 构建综合上下文 + + # Build combined context context_parts = [] if results["facts"]: - context_parts.append("事实信息:\n" + "\n".join(f"- {f}" for f in results["facts"][:20])) + context_parts.append("Fact Information:\n" + "\n".join(f"- {f}" for f in results["facts"][:20])) if results["node_summaries"]: - context_parts.append("相关实体:\n" + "\n".join(f"- {s}" for s in results["node_summaries"][:10])) + context_parts.append("Related Entities:\n" + "\n".join(f"- {s}" for s in results["node_summaries"][:10])) results["context"] = "\n\n".join(context_parts) - - logger.info(f"Zep混合检索完成: {entity_name}, 获取 {len(results['facts'])} 条事实, {len(results['node_summaries'])} 个相关节点") - - except concurrent.futures.TimeoutError: - logger.warning(f"Zep检索超时 ({entity_name})") + + logger.info(f"Knowledge graph hybrid search completed: {entity_name}, retrieved {len(results['facts'])} facts, {len(results['node_summaries'])} related nodes") + except Exception as e: - logger.warning(f"Zep检索失败 ({entity_name}): {e}") - + logger.warning(f"Knowledge graph search failed ({entity_name}): {e}") + return results def _build_entity_context(self, entity: EntityNode) -> str: """ - 构建实体的完整上下文信息 - - 包括： - 1. 实体本身的边信息（事实） - 2. 关联节点的详细信息 - 3. Zep混合检索到的丰富信息 + Build complete context information for entity + + Includes: + 1. Edge information of the entity itself (facts) + 2. Detailed information of associated nodes + 3. Rich information retrieved from knowledge graph hybrid search """ context_parts = [] - - # 1. 添加实体属性信息 + + # 1. Add entity attribute information if entity.attributes: attrs = [] for key, value in entity.attributes.items(): if value and str(value).strip(): attrs.append(f"- {key}: {value}") if attrs: - context_parts.append("### 实体属性\n" + "\n".join(attrs)) - - # 2. 添加相关边信息（事实/关系） + context_parts.append("### Entity Attributes\n" + "\n".join(attrs)) + + # 2. Add related edge information (facts/relationships) existing_facts = set() if entity.related_edges: relationships = [] - for edge in entity.related_edges: # 不限制数量 + for edge in entity.related_edges: # No limit on quantity fact = edge.get("fact", "") edge_name = edge.get("edge_name", "") direction = edge.get("direction", "") - + if fact: relationships.append(f"- {fact}") existing_facts.add(fact) elif edge_name: if direction == "outgoing": - relationships.append(f"- {entity.name} --[{edge_name}]--> (相关实体)") + relationships.append(f"- {entity.name} --[{edge_name}]--> (Related Entity)") else: - relationships.append(f"- (相关实体) --[{edge_name}]--> {entity.name}") - + relationships.append(f"- (Related Entity) --[{edge_name}]--> {entity.name}") + if relationships: - context_parts.append("### 相关事实和关系\n" + "\n".join(relationships)) - - # 3. 添加关联节点的详细信息 + context_parts.append("### Related Facts and Relationships\n" + "\n".join(relationships)) + + # 3. Add detailed information of related nodes if entity.related_nodes: related_info = [] - for node in entity.related_nodes: # 不限制数量 + for node in entity.related_nodes: # No limit on quantity node_name = node.get("name", "") node_labels = node.get("labels", []) node_summary = node.get("summary", "") - - # 过滤掉默认标签 + + # Filter out default labels custom_labels = [l for l in node_labels if l not in ["Entity", "Node"]] label_str = f" ({', '.join(custom_labels)})" if custom_labels else "" - + if node_summary: related_info.append(f"- **{node_name}**{label_str}: {node_summary}") else: related_info.append(f"- **{node_name}**{label_str}") - + if related_info: - context_parts.append("### 关联实体信息\n" + "\n".join(related_info)) - - # 4. 使用Zep混合检索获取更丰富的信息 - zep_results = self._search_zep_for_entity(entity) - - if zep_results.get("facts"): - # 去重：排除已存在的事实 - new_facts = [f for f in zep_results["facts"] if f not in existing_facts] + context_parts.append("### Related Entity Information\n" + "\n".join(related_info)) + + # 4. Use knowledge graph hybrid search to get richer information + graph_results = self._search_graph_for_entity(entity) + + if graph_results.get("facts"): + # Deduplication: exclude existing facts + new_facts = [f for f in graph_results["facts"] if f not in existing_facts] if new_facts: - context_parts.append("### Zep检索到的事实信息\n" + "\n".join(f"- {f}" for f in new_facts[:15])) - - if zep_results.get("node_summaries"): - context_parts.append("### Zep检索到的相关节点\n" + "\n".join(f"- {s}" for s in zep_results["node_summaries"][:10])) + context_parts.append("### Facts Retrieved from Knowledge Graph\n" + "\n".join(f"- {f}" for f in new_facts[:15])) + + if graph_results.get("node_summaries"): + context_parts.append("### Related Nodes Retrieved from Knowledge Graph\n" + "\n".join(f"- {s}" for s in graph_results["node_summaries"][:10])) return "\n\n".join(context_parts) def _is_individual_entity(self, entity_type: str) -> bool: - """判断是否是个人类型实体""" + """Determine if entity is an individual type""" return entity_type.lower() in self.INDIVIDUAL_ENTITY_TYPES - + def _is_group_entity(self, entity_type: str) -> bool: - """判断是否是群体/机构类型实体""" + """Determine if entity is a group/institutional type""" return entity_type.lower() in self.GROUP_ENTITY_TYPES def _generate_profile_with_llm( @@ -502,15 +448,15 @@ def _generate_profile_with_llm( context: str ) -> Dict[str, Any]: """ - 使用LLM生成非常详细的人设 - - 根据实体类型区分： - - 个人实体：生成具体的人物设定 - - 群体/机构实体：生成代表性账号设定 + Use LLM to generate very detailed persona + + Based on entity type: + - Individual entities: generate specific character profiles + - Group/institutional entities: generate representative account profiles """ - + is_individual = self._is_individual_entity(entity_type) - + if is_individual: prompt = self._build_individual_persona_prompt( entity_name, entity_type, entity_summary, entity_attributes, context @@ -520,10 +466,10 @@ def _generate_profile_with_llm( entity_name, entity_type, entity_summary, entity_attributes, context ) - # 尝试多次生成，直到成功或达到最大重试次数 + # Try multiple times until successful or max retry attempts reached max_attempts = 3 last_error = None - + for attempt in range(max_attempts): try: response = self.client.chat.completions.create( @@ -533,144 +479,144 @@ def _generate_profile_with_llm( {"role": "user", "content": prompt} ], response_format={"type": "json_object"}, - temperature=0.7 - (attempt * 0.1) # 每次重试降低温度 - # 不设置max_tokens，让LLM自由发挥 + temperature=0.7 - (attempt * 0.1) # Lower temperature with each retry + # Don't set max_tokens, let LLM generate freely ) - + content = response.choices[0].message.content - - # 检查是否被截断（finish_reason不是'stop'） + + # Check if output was truncated (finish_reason is not 'stop') finish_reason = response.choices[0].finish_reason if finish_reason == 'length': - logger.warning(f"LLM输出被截断 (attempt {attempt+1}), 尝试修复...") + logger.warning(f"LLM output truncated (attempt {attempt+1}), attempting to fix...") content = self._fix_truncated_json(content) - - # 尝试解析JSON + + # Try to parse JSON try: result = json.loads(content) - - # 验证必需字段 + + # Validate required fields if "bio" not in result or not result["bio"]: result["bio"] = entity_summary[:200] if entity_summary else f"{entity_type}: {entity_name}" if "persona" not in result or not result["persona"]: - result["persona"] = entity_summary or f"{entity_name}是一个{entity_type}。" - + result["persona"] = entity_summary or f"{entity_name} is a {entity_type}." + return result - + except json.JSONDecodeError as je: - logger.warning(f"JSON解析失败 (attempt {attempt+1}): {str(je)[:80]}") - - # 尝试修复JSON + logger.warning(f"JSON parsing failed (attempt {attempt+1}): {str(je)[:80]}") + + # Try to fix JSON result = self._try_fix_json(content, entity_name, entity_type, entity_summary) if result.get("_fixed"): del result["_fixed"] return result - + last_error = je - + except Exception as e: - logger.warning(f"LLM调用失败 (attempt {attempt+1}): {str(e)[:80]}") + logger.warning(f"LLM call failed (attempt {attempt+1}): {str(e)[:80]}") last_error = e import time - time.sleep(1 * (attempt + 1)) # 指数退避 - - logger.warning(f"LLM生成人设失败（{max_attempts}次尝试）: {last_error}, 使用规则生成") + time.sleep(1 * (attempt + 1)) # Exponential backoff + + logger.warning(f"LLM persona generation failed ({max_attempts} attempts): {last_error}, using rule-based generation") return self._generate_profile_rule_based( entity_name, entity_type, entity_summary, entity_attributes ) def _fix_truncated_json(self, content: str) -> str: - """修复被截断的JSON（输出被max_tokens限制截断）""" + """Fix truncated JSON (output truncated by max_tokens limit)""" import re - - # 如果JSON被截断，尝试闭合它 + + # If JSON is truncated, try to close it content = content.strip() - - # 计算未闭合的括号 + + # Count unclosed parentheses open_braces = content.count('{') - content.count('}') open_brackets = content.count('[') - content.count(']') - - # 检查是否有未闭合的字符串 - # 简单检查：如果最后一个引号后没有逗号或闭合括号，可能是字符串被截断 + + # Check for unclosed strings + # Simple check: if last character is not comma or closing bracket, string might be truncated if content and content[-1] not in '",}]': - # 尝试闭合字符串 + # Try to close the string content += '"' - - # 闭合括号 + + # Close parentheses content += ']' * open_brackets content += '}' * open_braces - + return content def _try_fix_json(self, content: str, entity_name: str, entity_type: str, entity_summary: str = "") -> Dict[str, Any]: - """尝试修复损坏的JSON""" + """Try to fix corrupted JSON""" import re - - # 1. 首先尝试修复被截断的情况 + + # 1. First try to fix truncated case content = self._fix_truncated_json(content) - - # 2. 尝试提取JSON部分 + + # 2. Try to extract JSON portion json_match = re.search(r'\{[\s\S]*\}', content) if json_match: json_str = json_match.group() - - # 3. 处理字符串中的换行符问题 - # 找到所有字符串值并替换其中的换行符 + + # 3. Handle newline issues in strings + # Find all string values and replace newlines def fix_string_newlines(match): s = match.group(0) - # 替换字符串内的实际换行符为空格 + # Replace actual newlines in string with spaces s = s.replace('\n', ' ').replace('\r', ' ') - # 替换多余空格 + # Replace excess spaces s = re.sub(r'\s+', ' ', s) return s - - # 匹配JSON字符串值 + + # Match JSON string values json_str = re.sub(r'"[^"\\]*(?:\\.[^"\\]*)*"', fix_string_newlines, json_str) - - # 4. 尝试解析 + + # 4. Try to parse try: result = json.loads(json_str) result["_fixed"] = True return result except json.JSONDecodeError as e: - # 5. 如果还是失败，尝试更激进的修复 + # 5. If still failed, try more aggressive fix try: - # 移除所有控制字符 + # Remove all control characters json_str = re.sub(r'[\x00-\x1f\x7f-\x9f]', ' ', json_str) - # 替换所有连续空白 + # Replace all consecutive whitespace json_str = re.sub(r'\s+', ' ', json_str) result = json.loads(json_str) result["_fixed"] = True return result except: pass - - # 6. 尝试从内容中提取部分信息 + + # 6. Try to extract partial information from content bio_match = re.search(r'"bio"\s*:\s*"([^"]*)"', content) - persona_match = re.search(r'"persona"\s*:\s*"([^"]*)', content) # 可能被截断 - + persona_match = re.search(r'"persona"\s*:\s*"([^"]*)', content) # May be truncated + bio = bio_match.group(1) if bio_match else (entity_summary[:200] if entity_summary else f"{entity_type}: {entity_name}") - persona = persona_match.group(1) if persona_match else (entity_summary or f"{entity_name}是一个{entity_type}。") - - # 如果提取到了有意义的内容，标记为已修复 + persona = persona_match.group(1) if persona_match else (entity_summary or f"{entity_name} is a {entity_type}.") + + # If extracted meaningful content, mark as fixed if bio_match or persona_match: - logger.info(f"从损坏的JSON中提取了部分信息") + logger.info(f"Extracted partial information from corrupted JSON") return { "bio": bio, "persona": persona, "_fixed": True } - - # 7. 完全失败，返回基础结构 - logger.warning(f"JSON修复失败，返回基础结构") + + # 7. Complete failure, return basic structure + logger.warning(f"JSON fix failed, returning basic structure") return { "bio": entity_summary[:200] if entity_summary else f"{entity_type}: {entity_name}", - "persona": entity_summary or f"{entity_name}是一个{entity_type}。" + "persona": entity_summary or f"{entity_name} is a {entity_type}." } def _get_system_prompt(self, is_individual: bool) -> str: - """获取系统提示词""" - base_prompt = "你是社交媒体用户画像生成专家。生成详细、真实的人设用于舆论模拟,最大程度还原已有现实情况。必须返回有效的JSON格式，所有字符串值不能包含未转义的换行符。使用中文。" + """Get system prompt""" + base_prompt = "You are an expert in generating social media user profiles. Generate detailed, realistic personas for opinion simulation that maximize restoration of existing reality. Must return valid JSON format with all string values containing no unescaped newlines. Use English." return base_prompt def _build_individual_persona_prompt( @@ -681,45 +627,45 @@ def _build_individual_persona_prompt( entity_attributes: Dict[str, Any], context: str ) -> str: - """构建个人实体的详细人设提示词""" - - attrs_str = json.dumps(entity_attributes, ensure_ascii=False) if entity_attributes else "无" - context_str = context[:3000] if context else "无额外上下文" - - return f"""为实体生成详细的社交媒体用户人设,最大程度还原已有现实情况。 + """Build detailed persona prompt for individual entities""" -实体名称: {entity_name} -实体类型: {entity_type} -实体摘要: {entity_summary} -实体属性: {attrs_str} + attrs_str = json.dumps(entity_attributes, ensure_ascii=False) if entity_attributes else "None" + context_str = context[:3000] if context else "No additional context" -上下文信息: + return f"""Generate a detailed social media user persona for the entity, maximizing restoration of existing reality. + +Entity Name: {entity_name} +Entity Type: {entity_type} +Entity Summary: {entity_summary} +Entity Attributes: {attrs_str} + +Context Information: {context_str} -请生成JSON，包含以下字段: - -1. bio: 社交媒体简介，200字 -2. persona: 详细人设描述（2000字的纯文本），需包含: - - 基本信息（年龄、职业、教育背景、所在地） - - 人物背景（重要经历、与事件的关联、社会关系） - - 性格特征（MBTI类型、核心性格、情绪表达方式） - - 社交媒体行为（发帖频率、内容偏好、互动风格、语言特点） - - 立场观点（对话题的态度、可能被激怒/感动的内容） - - 独特特征（口头禅、特殊经历、个人爱好） - - 个人记忆（人设的重要部分，要介绍这个个体与事件的关联，以及这个个体在事件中的已有动作与反应） -3. age: 年龄数字（必须是整数） -4. gender: 性别，必须是英文: "male" 或 "female" -5. mbti: MBTI类型（如INTJ、ENFP等） -6. country: 国家（使用中文，如"中国"） -7. profession: 职业 -8. interested_topics: 感兴趣话题数组 - -重要: -- 所有字段值必须是字符串或数字，不要使用换行符 -- persona必须是一段连贯的文字描述 -- 使用中文（除了gender字段必须用英文male/female） -- 内容要与实体信息保持一致 -- age必须是有效的整数，gender必须是"male"或"female" +Please generate JSON containing the following fields: + +1. bio: Social media bio, 200 characters +2. persona: Detailed persona description (2000 words of pure text), must include: + - Basic information (age, profession, educational background, location) + - Personal background (important experiences, event associations, social relationships) + - Personality traits (MBTI type, core personality, emotional expression) + - Social media behavior (posting frequency, content preferences, interaction style, language characteristics) + - Positions and views (attitudes toward topics, content that may provoke/touch emotions) + - Unique features (catchphrases, special experiences, personal interests) + - Personal memories (important part of persona, introduce this individual's association with events and their existing actions/reactions in events) +3. age: Age as number (must be integer) +4. gender: Gender, must be in English: "male" or "female" +5. mbti: MBTI type (e.g., INTJ, ENFP) +6. country: Country (use English, e.g., "US") +7. profession: Profession +8. interested_topics: Array of interested topics + +Important: +- All field values must be strings or numbers, do not use newlines +- persona must be a coherent text description +- Use English +- Content must be consistent with entity information +- age must be a valid integer, gender must be "male" or "female" """ def _build_group_persona_prompt( @@ -730,45 +676,45 @@ def _build_group_persona_prompt( entity_attributes: Dict[str, Any], context: str ) -> str: - """构建群体/机构实体的详细人设提示词""" - - attrs_str = json.dumps(entity_attributes, ensure_ascii=False) if entity_attributes else "无" - context_str = context[:3000] if context else "无额外上下文" - - return f"""为机构/群体实体生成详细的社交媒体账号设定,最大程度还原已有现实情况。 + """Build detailed persona prompt for group/institutional entities""" + + attrs_str = json.dumps(entity_attributes, ensure_ascii=False) if entity_attributes else "None" + context_str = context[:3000] if context else "No additional context" + + return f"""Generate detailed social media account profile for institutional/group entity, maximizing restoration of existing reality. -实体名称: {entity_name} -实体类型: {entity_type} -实体摘要: {entity_summary} -实体属性: {attrs_str} +Entity Name: {entity_name} +Entity Type: {entity_type} +Entity Summary: {entity_summary} +Entity Attributes: {attrs_str} -上下文信息: +Context Information: {context_str} -请生成JSON，包含以下字段: - -1. bio: 官方账号简介，200字，专业得体 -2. persona: 详细账号设定描述（2000字的纯文本），需包含: - - 机构基本信息（正式名称、机构性质、成立背景、主要职能） - - 账号定位（账号类型、目标受众、核心功能） - - 发言风格（语言特点、常用表达、禁忌话题） - - 发布内容特点（内容类型、发布频率、活跃时间段） - - 立场态度（对核心话题的官方立场、面对争议的处理方式） - - 特殊说明（代表的群体画像、运营习惯） - - 机构记忆（机构人设的重要部分，要介绍这个机构与事件的关联，以及这个机构在事件中的已有动作与反应） -3. age: 固定填30（机构账号的虚拟年龄） -4. gender: 固定填"other"（机构账号使用other表示非个人） -5. mbti: MBTI类型，用于描述账号风格，如ISTJ代表严谨保守 -6. country: 国家（使用中文，如"中国"） -7. profession: 机构职能描述 -8. interested_topics: 关注领域数组 - -重要: -- 所有字段值必须是字符串或数字，不允许null值 -- persona必须是一段连贯的文字描述，不要使用换行符 -- 使用中文（除了gender字段必须用英文"other"） -- age必须是整数30，gender必须是字符串"other" -- 机构账号发言要符合其身份定位""" +Please generate JSON containing the following fields: + +1. bio: Official account bio, 200 characters, professional and appropriate +2. persona: Detailed account profile description (2000 words of pure text), must include: + - Basic institutional information (official name, organizational nature, founding background, main functions) + - Account positioning (account type, target audience, core functions) + - Speaking style (language characteristics, common expressions, taboo topics) + - Content publishing characteristics (content types, publishing frequency, active time periods) + - Position and attitude (official stance on core topics, handling of controversies) + - Special notes (group profiles represented, operational habits) + - Institutional memories (important part of institutional persona, introduce this institution's association with events and their existing actions/reactions in events) +3. age: Fixed at 30 (virtual age of institutional account) +4. gender: Fixed at "other" (institutional account uses other to denote non-individual) +5. mbti: MBTI type used to describe account style, e.g., ISTJ represents rigorous conservative +6. country: Country (use English, e.g., "US") +7. profession: Institutional function description +8. interested_topics: Array of focus areas + +Important: +- All field values must be strings or numbers, no null values allowed +- persona must be a coherent text description, do not use newlines +- Use English +- age must be integer 30, gender must be string "other" +- Institutional account speech must match its identity positioning""" def _generate_profile_rule_based( self, @@ -777,11 +723,11 @@ def _generate_profile_rule_based( entity_summary: str, entity_attributes: Dict[str, Any] ) -> Dict[str, Any]: - """使用规则生成基础人设""" - - # 根据实体类型生成不同的人设 + """Generate basic persona using rules""" + + # Generate different personas based on entity type entity_type_lower = entity_type.lower() - + if entity_type_lower in ["student", "alumni"]: return { "bio": f"{entity_type} with interests in academics and social issues.", @@ -793,7 +739,7 @@ def _generate_profile_rule_based( "profession": "Student", "interested_topics": ["Education", "Social Issues", "Technology"], } - + elif entity_type_lower in ["publicfigure", "expert", "faculty"]: return { "bio": f"Expert and thought leader in their field.", @@ -805,33 +751,33 @@ def _generate_profile_rule_based( "profession": entity_attributes.get("occupation", "Expert"), "interested_topics": ["Politics", "Economics", "Culture & Society"], } - + elif entity_type_lower in ["mediaoutlet", "socialmediaplatform"]: return { "bio": f"Official account for {entity_name}. News and updates.", "persona": f"{entity_name} is a media entity that reports news and facilitates public discourse. The account shares timely updates and engages with the audience on current events.", - "age": 30, # 机构虚拟年龄 - "gender": "other", # 机构使用other - "mbti": "ISTJ", # 机构风格：严谨保守 - "country": "中国", + "age": 30, # Institutional virtual age + "gender": "other", # Institutional uses other + "mbti": "ISTJ", # Institutional style: rigorous conservative + "country": "US", "profession": "Media", "interested_topics": ["General News", "Current Events", "Public Affairs"], } - + elif entity_type_lower in ["university", "governmentagency", "ngo", "organization"]: return { "bio": f"Official account of {entity_name}.", "persona": f"{entity_name} is an institutional entity that communicates official positions, announcements, and engages with stakeholders on relevant matters.", - "age": 30, # 机构虚拟年龄 - "gender": "other", # 机构使用other - "mbti": "ISTJ", # 机构风格：严谨保守 - "country": "中国", + "age": 30, # Institutional virtual age + "gender": "other", # Institutional uses other + "mbti": "ISTJ", # Institutional style: rigorous conservative + "country": "US", "profession": entity_type, "interested_topics": ["Public Policy", "Community", "Official Announcements"], } - + else: - # 默认人设 + # Default persona return { "bio": entity_summary[:150] if entity_summary else f"{entity_type}: {entity_name}", "persona": entity_summary or f"{entity_name} is a {entity_type.lower()} participating in social discussions.", @@ -844,7 +790,7 @@ def _generate_profile_rule_based( } def set_graph_id(self, graph_id: str): - """设置图谱ID用于Zep检索""" + """Set knowledge graph ID for knowledge graph search""" self.graph_id = graph_id def generate_profiles_from_entities( @@ -858,52 +804,52 @@ def generate_profiles_from_entities( output_platform: str = "reddit" ) -> List[OasisAgentProfile]: """ - 批量从实体生成Agent Profile（支持并行生成） - + Generate Agent Profiles in batch from entities (supports parallel generation) + Args: - entities: 实体列表 - use_llm: 是否使用LLM生成详细人设 - progress_callback: 进度回调函数 (current, total, message) - graph_id: 图谱ID，用于Zep检索获取更丰富上下文 - parallel_count: 并行生成数量，默认5 - realtime_output_path: 实时写入的文件路径（如果提供，每生成一个就写入一次） - output_platform: 输出平台格式 ("reddit" 或 "twitter") - + entities: Entity list + use_llm: Whether to use LLM to generate detailed personas + progress_callback: Progress callback function (current, total, message) + graph_id: Knowledge graph ID for knowledge graph search to get richer context + parallel_count: Number of parallel generations, default 5 + realtime_output_path: Real-time output file path (if provided, write after each generation) + output_platform: Output platform format ("reddit" or "twitter") + Returns: - Agent Profile列表 + List of Agent Profiles """ import concurrent.futures from threading import Lock - # 设置graph_id用于Zep检索 + # Set graph_id for knowledge graph search if graph_id: self.graph_id = graph_id - + total = len(entities) - profiles = [None] * total # 预分配列表保持顺序 - completed_count = [0] # 使用列表以便在闭包中修改 + profiles = [None] * total # Pre-allocate list to maintain order + completed_count = [0] # Use list for modification in closure lock = Lock() - - # 实时写入文件的辅助函数 + + # Helper function for real-time file writing def save_profiles_realtime(): - """实时保存已生成的 profiles 到文件""" + """Real-time save generated profiles to file""" if not realtime_output_path: return - + with lock: - # 过滤出已生成的 profiles + # Filter generated profiles existing_profiles = [p for p in profiles if p is not None] if not existing_profiles: return - + try: if output_platform == "reddit": - # Reddit JSON 格式 + # Reddit JSON format profiles_data = [p.to_reddit_format() for p in existing_profiles] with open(realtime_output_path, 'w', encoding='utf-8') as f: json.dump(profiles_data, f, ensure_ascii=False, indent=2) else: - # Twitter CSV 格式 + # Twitter CSV format import csv profiles_data = [p.to_twitter_format() for p in existing_profiles] if profiles_data: @@ -913,27 +859,27 @@ def save_profiles_realtime(): writer.writeheader() writer.writerows(profiles_data) except Exception as e: - logger.warning(f"实时保存 profiles 失败: {e}") + logger.warning(f"Real-time profile save failed: {e}") def generate_single_profile(idx: int, entity: EntityNode) -> tuple: - """生成单个profile的工作函数""" + """Worker function to generate single profile""" entity_type = entity.get_entity_type() or "Entity" - + try: profile = self.generate_profile_from_entity( entity=entity, user_id=idx, use_llm=use_llm ) - - # 实时输出生成的人设到控制台和日志 + + # Real-time output generated persona to console and log self._print_generated_profile(entity.name, entity_type, profile) - + return idx, profile, None - + except Exception as e: - logger.error(f"生成实体 {entity.name} 的人设失败: {str(e)}") - # 创建一个基础profile + logger.error(f"Failed to generate persona for entity {entity.name}: {str(e)}") + # Create a fallback profile fallback_profile = OasisAgentProfile( user_id=idx, user_name=self._generate_username(entity.name), @@ -944,50 +890,50 @@ def generate_single_profile(idx: int, entity: EntityNode) -> tuple: source_entity_type=entity_type, ) return idx, fallback_profile, str(e) - - logger.info(f"开始并行生成 {total} 个Agent人设（并行数: {parallel_count}）...") + + logger.info(f"Starting parallel generation of {total} agent personas (parallel count: {parallel_count})...") print(f"\n{'='*60}") - print(f"开始生成Agent人设 - 共 {total} 个实体，并行数: {parallel_count}") + print(f"Starting agent persona generation - {total} entities total, parallel count: {parallel_count}") print(f"{'='*60}\n") - # 使用线程池并行执行 + # Use thread pool for parallel execution with concurrent.futures.ThreadPoolExecutor(max_workers=parallel_count) as executor: - # 提交所有任务 + # Submit all tasks future_to_entity = { executor.submit(generate_single_profile, idx, entity): (idx, entity) for idx, entity in enumerate(entities) } - - # 收集结果 + + # Collect results for future in concurrent.futures.as_completed(future_to_entity): idx, entity = future_to_entity[future] entity_type = entity.get_entity_type() or "Entity" - + try: result_idx, profile, error = future.result() profiles[result_idx] = profile - + with lock: completed_count[0] += 1 current = completed_count[0] - - # 实时写入文件 + + # Real-time file writing save_profiles_realtime() - + if progress_callback: progress_callback( - current, - total, - f"已完成 {current}/{total}: {entity.name}（{entity_type}）" + current, + total, + f"Completed {current}/{total}: {entity.name} ({entity_type})" ) - + if error: - logger.warning(f"[{current}/{total}] {entity.name} 使用备用人设: {error}") + logger.warning(f"[{current}/{total}] {entity.name} using fallback persona: {error}") else: - logger.info(f"[{current}/{total}] 成功生成人设: {entity.name} ({entity_type})") - + logger.info(f"[{current}/{total}] Successfully generated persona: {entity.name} ({entity_type})") + except Exception as e: - logger.error(f"处理实体 {entity.name} 时发生异常: {str(e)}") + logger.error(f"Exception occurred while processing entity {entity.name}: {str(e)}") with lock: completed_count[0] += 1 profiles[idx] = OasisAgentProfile( @@ -999,44 +945,44 @@ def generate_single_profile(idx: int, entity: EntityNode) -> tuple: source_entity_uuid=entity.uuid, source_entity_type=entity_type, ) - # 实时写入文件（即使是备用人设） + # Real-time file writing (even for fallback personas) save_profiles_realtime() - + print(f"\n{'='*60}") - print(f"人设生成完成！共生成 {len([p for p in profiles if p])} 个Agent") + print(f"Persona generation complete! Generated {len([p for p in profiles if p])} agents") print(f"{'='*60}\n") return profiles def _print_generated_profile(self, entity_name: str, entity_type: str, profile: OasisAgentProfile): - """实时输出生成的人设到控制台（完整内容，不截断）""" + """Real-time output generated persona to console (complete content, not truncated)""" separator = "-" * 70 - - # 构建完整输出内容（不截断） - topics_str = ', '.join(profile.interested_topics) if profile.interested_topics else '无' - + + # Build complete output content (not truncated) + topics_str = ', '.join(profile.interested_topics) if profile.interested_topics else 'None' + output_lines = [ f"\n{separator}", - f"[已生成] {entity_name} ({entity_type})", + f"[Generated] {entity_name} ({entity_type})", f"{separator}", - f"用户名: {profile.user_name}", + f"Username: {profile.user_name}", f"", - f"【简介】", + f"[Bio]", f"{profile.bio}", f"", - f"【详细人设】", + f"[Detailed Persona]", f"{profile.persona}", f"", - f"【基本属性】", - f"年龄: {profile.age} | 性别: {profile.gender} | MBTI: {profile.mbti}", - f"职业: {profile.profession} | 国家: {profile.country}", - f"兴趣话题: {topics_str}", + f"[Basic Attributes]", + f"Age: {profile.age} | Gender: {profile.gender} | MBTI: {profile.mbti}", + f"Profession: {profile.profession} | Country: {profile.country}", + f"Interested Topics: {topics_str}", separator ] - + output = "\n".join(output_lines) - - # 只输出到控制台（避免重复，logger不再输出完整内容） + + # Only output to console (avoid duplication, logger no longer outputs complete content) print(output) def save_profiles( @@ -1046,16 +992,16 @@ def save_profiles( platform: str = "reddit" ): """ - 保存Profile到文件（根据平台选择正确格式） - - OASIS平台格式要求： - - Twitter: CSV格式 - - Reddit: JSON格式 - + Save profiles to file (choose correct format based on platform) + + OASIS platform format requirements: + - Twitter: CSV format + - Reddit: JSON format + Args: - profiles: Profile列表 - file_path: 文件路径 - platform: 平台类型 ("reddit" 或 "twitter") + profiles: Profile list + file_path: File path + platform: Platform type ("reddit" or "twitter") """ if platform == "twitter": self._save_twitter_csv(profiles, file_path) @@ -1064,137 +1010,132 @@ def save_profiles( def _save_twitter_csv(self, profiles: List[OasisAgentProfile], file_path: str): """ - 保存Twitter Profile为CSV格式（符合OASIS官方要求） - - OASIS Twitter要求的CSV字段： - - user_id: 用户ID（根据CSV顺序从0开始） - - name: 用户真实姓名 - - username: 系统中的用户名 - - user_char: 详细人设描述（注入到LLM系统提示中，指导Agent行为） - - description: 简短的公开简介（显示在用户资料页面） - - user_char vs description 区别： - - user_char: 内部使用，LLM系统提示，决定Agent如何思考和行动 - - description: 外部显示，其他用户可见的简介 + Save Twitter Profile as CSV format (compliant with OASIS official requirements) + + OASIS Twitter required CSV fields: + - user_id: User ID (starting from 0 based on CSV order) + - name: User real name + - username: Username in the system + - user_char: Detailed persona description (injected into LLM system prompt, guides agent behavior) + - description: Short public bio (displayed on user profile page) + + user_char vs description difference: + - user_char: Internal use, LLM system prompt, determines how agent thinks and acts + - description: External display, visible to other users """ import csv - - # 确保文件扩展名是.csv + + # Ensure file extension is .csv if not file_path.endswith('.csv'): file_path = file_path.replace('.json', '.csv') - + with open(file_path, 'w', newline='', encoding='utf-8') as f: writer = csv.writer(f) - - # 写入OASIS要求的表头 + + # Write OASIS required header headers = ['user_id', 'name', 'username', 'user_char', 'description'] writer.writerow(headers) - - # 写入数据行 + + # Write data rows for idx, profile in enumerate(profiles): - # user_char: 完整人设（bio + persona），用于LLM系统提示 + # user_char: Complete persona (bio + persona) for LLM system prompt user_char = profile.bio if profile.persona and profile.persona != profile.bio: user_char = f"{profile.bio} {profile.persona}" - # 处理换行符（CSV中用空格替代） + # Handle newlines (replace with space in CSV) user_char = user_char.replace('\n', ' ').replace('\r', ' ') - - # description: 简短简介，用于外部显示 + + # description: Short bio for external display description = profile.bio.replace('\n', ' ').replace('\r', ' ') - + row = [ - idx, # user_id: 从0开始的顺序ID - profile.name, # name: 真实姓名 - profile.user_name, # username: 用户名 - user_char, # user_char: 完整人设（内部LLM使用） - description # description: 简短简介（外部显示） + idx, # user_id: Sequential ID starting from 0 + profile.name, # name: Real name + profile.user_name, # username: Username + user_char, # user_char: Complete persona (internal LLM use) + description # description: Short bio (external display) ] writer.writerow(row) - - logger.info(f"已保存 {len(profiles)} 个Twitter Profile到 {file_path} (OASIS CSV格式)") + + logger.info(f"Saved {len(profiles)} Twitter profiles to {file_path} (OASIS CSV format)") def _normalize_gender(self, gender: Optional[str]) -> str: """ - 标准化gender字段为OASIS要求的英文格式 - - OASIS要求: male, female, other + Normalize gender field to OASIS required English format + + OASIS requires: male, female, other """ if not gender: return "other" - + gender_lower = gender.lower().strip() - - # 中文映射 + + # Gender mapping gender_map = { - "男": "male", - "女": "female", - "机构": "other", - "其他": "other", - # 英文已有 "male": "male", "female": "female", "other": "other", } - + return gender_map.get(gender_lower, "other") def _save_reddit_json(self, profiles: List[OasisAgentProfile], file_path: str): """ - 保存Reddit Profile为JSON格式 - - 使用与 to_reddit_format() 一致的格式，确保 OASIS 能正确读取。 - 必须包含 user_id 字段，这是 OASIS agent_graph.get_agent() 匹配的关键！ - - 必需字段： - - user_id: 用户ID（整数，用于匹配 initial_posts 中的 poster_agent_id） - - username: 用户名 - - name: 显示名称 - - bio: 简介 - - persona: 详细人设 - - age: 年龄（整数） - - gender: "male", "female", 或 "other" - - mbti: MBTI类型 - - country: 国家 + Save Reddit Profile as JSON format + + Use format consistent with to_reddit_format() to ensure OASIS can read correctly. + Must include user_id field, which is the key for OASIS agent_graph.get_agent() matching! + + Required fields: + - user_id: User ID (integer, used to match poster_agent_id in initial_posts) + - username: Username + - name: Display name + - bio: Bio + - persona: Detailed persona + - age: Age (integer) + - gender: "male", "female", or "other" + - mbti: MBTI type + - country: Country """ data = [] for idx, profile in enumerate(profiles): - # 使用与 to_reddit_format() 一致的格式 + # Use format consistent with to_reddit_format() item = { - "user_id": profile.user_id if profile.user_id is not None else idx, # 关键：必须包含 user_id + "user_id": profile.user_id if profile.user_id is not None else idx, # Key: must include user_id "username": profile.user_name, "name": profile.name, "bio": profile.bio[:150] if profile.bio else f"{profile.name}", "persona": profile.persona or f"{profile.name} is a participant in social discussions.", "karma": profile.karma if profile.karma else 1000, "created_at": profile.created_at, - # OASIS必需字段 - 确保都有默认值 + # OASIS required fields - ensure all have defaults "age": profile.age if profile.age else 30, "gender": self._normalize_gender(profile.gender), "mbti": profile.mbti if profile.mbti else "ISTJ", - "country": profile.country if profile.country else "中国", + "country": profile.country if profile.country else "US", } - - # 可选字段 + + # Optional fields if profile.profession: item["profession"] = profile.profession if profile.interested_topics: item["interested_topics"] = profile.interested_topics - + data.append(item) - + with open(file_path, 'w', encoding='utf-8') as f: json.dump(data, f, ensure_ascii=False, indent=2) - - logger.info(f"已保存 {len(profiles)} 个Reddit Profile到 {file_path} (JSON格式，包含user_id字段)") + + logger.info(f"Saved {len(profiles)} Reddit profiles to {file_path} (JSON format, includes user_id field)") - # 保留旧方法名作为别名，保持向后兼容 + # Keep old method name as alias for backward compatibility def save_profiles_to_json( self, profiles: List[OasisAgentProfile], file_path: str, platform: str = "reddit" ): - """[已废弃] 请使用 save_profiles() 方法""" - logger.warning("save_profiles_to_json已废弃，请使用save_profiles方法") + """[Deprecated] Please use save_profiles() method""" + logger.warning("save_profiles_to_json is deprecated, please use save_profiles method") self.save_profiles(profiles, file_path, platform) diff --git a/backend/app/services/ontology_generator.py b/backend/app/services/ontology_generator.py index 2d3e39bd8..25a5aba06 100644 --- a/backend/app/services/ontology_generator.py +++ b/backend/app/services/ontology_generator.py @@ -1,6 +1,6 @@ """ -本体生成服务 -接口1：分析文本内容，生成适合社会模拟的实体和关系类型定义 +Ontology generation service +Interface 1: Analyze text content and generate entity and relationship type definitions suitable for social simulation """ import json @@ -8,162 +8,162 @@ from ..utils.llm_client import LLMClient -# 本体生成的系统提示词 -ONTOLOGY_SYSTEM_PROMPT = """你是一个专业的知识图谱本体设计专家。你的任务是分析给定的文本内容和模拟需求，设计适合**社交媒体舆论模拟**的实体类型和关系类型。 +# System prompt for ontology generation +ONTOLOGY_SYSTEM_PROMPT = """You are a professional knowledge graph ontology design expert. Your task is to analyze given text content and simulation requirements, and design entity types and relationship types suitable for **social media opinion simulation**. -**重要：你必须输出有效的JSON格式数据，不要输出任何其他内容。** +**Important: You must output valid JSON format data, do not output anything else.** -## 核心任务背景 +## Core Task Background -我们正在构建一个**社交媒体舆论模拟系统**。在这个系统中： -- 每个实体都是一个可以在社交媒体上发声、互动、传播信息的"账号"或"主体" -- 实体之间会相互影响、转发、评论、回应 -- 我们需要模拟舆论事件中各方的反应和信息传播路径 +We are building a **social media opinion simulation system**. In this system: +- Each entity is an "account" or "subject" that can voice, interact, and spread information on social media +- Entities influence each other, retweet, comment, and respond +- We need to simulate the reactions of various parties in opinion events and information dissemination paths -因此，**实体必须是现实中真实存在的、可以在社媒上发声和互动的主体**： +Therefore, **entities must be real-world entities that can voice and interact on social media**: -**可以是**： -- 具体的个人（公众人物、当事人、意见领袖、专家学者、普通人） -- 公司、企业（包括其官方账号） -- 组织机构（大学、协会、NGO、工会等） -- 政府部门、监管机构 -- 媒体机构（报纸、电视台、自媒体、网站） -- 社交媒体平台本身 -- 特定群体代表（如校友会、粉丝团、维权群体等） +**Can be**: +- Specific individuals (public figures, stakeholders, opinion leaders, experts, ordinary people) +- Companies and enterprises (including their official accounts) +- Organizations (universities, associations, NGOs, unions, etc.) +- Government departments and regulatory agencies +- Media institutions (newspapers, TV stations, self-media, websites) +- Social media platforms themselves +- Specific group representatives (such as alumni associations, fan groups, rights protection groups, etc.) -**不可以是**： -- 抽象概念（如"舆论"、"情绪"、"趋势"） -- 主题/话题（如"学术诚信"、"教育改革"） -- 观点/态度（如"支持方"、"反对方"） +**Cannot be**: +- Abstract concepts (such as "public opinion", "emotion", "trend") +- Topics/subjects (such as "academic integrity", "education reform") +- Views/attitudes (such as "supporters", "opponents") -## 输出格式 +## Output Format -请输出JSON格式，包含以下结构： +Please output JSON format with the following structure: ```json { "entity_types": [ { - "name": "实体类型名称（英文，PascalCase）", - "description": "简短描述（英文，不超过100字符）", + "name": "Entity type name (English, PascalCase)", + "description": "Brief description (English, no more than 100 characters)", "attributes": [ { - "name": "属性名（英文，snake_case）", + "name": "Attribute name (English, snake_case)", "type": "text", - "description": "属性描述" + "description": "Attribute description" } ], - "examples": ["示例实体1", "示例实体2"] + "examples": ["Example entity 1", "Example entity 2"] } ], "edge_types": [ { - "name": "关系类型名称（英文，UPPER_SNAKE_CASE）", - "description": "简短描述（英文，不超过100字符）", + "name": "Relationship type name (English, UPPER_SNAKE_CASE)", + "description": "Brief description (English, no more than 100 characters)", "source_targets": [ - {"source": "源实体类型", "target": "目标实体类型"} + {"source": "Source entity type", "target": "Target entity type"} ], "attributes": [] } ], - "analysis_summary": "对文本内容的简要分析说明（中文）" + "analysis_summary": "Brief analysis and explanation of text content" } ``` -## 设计指南（极其重要！） +## Design Guidelines (Extremely Important!) -### 1. 实体类型设计 - 必须严格遵守 +### 1. Entity Type Design - Must Strictly Follow -**数量要求：必须正好10个实体类型** +**Quantity requirement: Must have exactly 10 entity types** -**层次结构要求（必须同时包含具体类型和兜底类型）**： +**Hierarchical structure requirement (must include both specific types and fallback types)**: -你的10个实体类型必须包含以下层次： +Your 10 entity types must include the following hierarchy: -A. **兜底类型（必须包含，放在列表最后2个）**： - - `Person`: 任何自然人个体的兜底类型。当一个人不属于其他更具体的人物类型时，归入此类。 - - `Organization`: 任何组织机构的兜底类型。当一个组织不属于其他更具体的组织类型时，归入此类。 +A. **Fallback types (must include, place in last 2 of list)**: + - `Person`: Fallback type for any natural person. When a person does not fit other more specific person types, use this. + - `Organization`: Fallback type for any organization. When an organization does not fit other more specific organization types, use this. -B. **具体类型（8个，根据文本内容设计）**： - - 针对文本中出现的主要角色，设计更具体的类型 - - 例如：如果文本涉及学术事件，可以有 `Student`, `Professor`, `University` - - 例如：如果文本涉及商业事件，可以有 `Company`, `CEO`, `Employee` +B. **Specific types (8, designed based on text content)**: + - Design more specific types for main characters appearing in the text + - Example: If text involves academic events, can have `Student`, `Professor`, `University` + - Example: If text involves business events, can have `Company`, `CEO`, `Employee` -**为什么需要兜底类型**： -- 文本中会出现各种人物，如"中小学教师"、"路人甲"、"某位网友" -- 如果没有专门的类型匹配，他们应该被归入 `Person` -- 同理，小型组织、临时团体等应该归入 `Organization` +**Why fallback types are needed**: +- Various people will appear in the text, such as "primary/secondary teachers", "random person", "some netizen" +- If no specific type matches, they should be classified as `Person` +- Similarly, small organizations and temporary groups should be classified as `Organization` -**具体类型的设计原则**： -- 从文本中识别出高频出现或关键的角色类型 -- 每个具体类型应该有明确的边界，避免重叠 -- description 必须清晰说明这个类型和兜底类型的区别 +**Design principles for specific types**: +- Identify high-frequency or key role types from the text +- Each specific type should have clear boundaries, avoid overlap +- Description must clearly explain the difference between this type and the fallback type -### 2. 关系类型设计 +### 2. Relationship Type Design -- 数量：6-10个 -- 关系应该反映社媒互动中的真实联系 -- 确保关系的 source_targets 涵盖你定义的实体类型 +- Quantity: 6-10 +- Relationships should reflect real connections in social media interactions +- Ensure relationship source_targets cover your defined entity types -### 3. 属性设计 +### 3. Attribute Design -- 每个实体类型1-3个关键属性 -- **注意**：属性名不能使用 `name`、`uuid`、`group_id`、`created_at`、`summary`（这些是系统保留字） -- 推荐使用：`full_name`, `title`, `role`, `position`, `location`, `description` 等 +- 1-3 key attributes per entity type +- **Note**: Attribute names cannot use `name`, `uuid`, `group_id`, `created_at`, `summary` (these are system reserved words) +- Recommended: `full_name`, `title`, `role`, `position`, `location`, `description`, etc. -## 实体类型参考 +## Entity Type Reference -**个人类（具体）**： -- Student: 学生 -- Professor: 教授/学者 -- Journalist: 记者 -- Celebrity: 明星/网红 -- Executive: 高管 -- Official: 政府官员 -- Lawyer: 律师 -- Doctor: 医生 +**Individual types (specific)**: +- Student: Student +- Professor: Professor/Scholar +- Journalist: Journalist +- Celebrity: Celebrity/Internet celebrity +- Executive: Executive +- Official: Government official +- Lawyer: Lawyer +- Doctor: Doctor -**个人类（兜底）**： -- Person: 任何自然人（不属于上述具体类型时使用） +**Individual types (fallback)**: +- Person: Any natural person (use when not fitting other specific types) -**组织类（具体）**： -- University: 高校 -- Company: 公司企业 -- GovernmentAgency: 政府机构 -- MediaOutlet: 媒体机构 -- Hospital: 医院 -- School: 中小学 -- NGO: 非政府组织 +**Organization types (specific)**: +- University: University +- Company: Company/Enterprise +- GovernmentAgency: Government agency +- MediaOutlet: Media institution +- Hospital: Hospital +- School: Primary/Secondary school +- NGO: Non-governmental organization -**组织类（兜底）**： -- Organization: 任何组织机构（不属于上述具体类型时使用） +**Organization types (fallback)**: +- Organization: Any organization (use when not fitting other specific types) -## 关系类型参考 +## Relationship Type Reference -- WORKS_FOR: 工作于 -- STUDIES_AT: 就读于 -- AFFILIATED_WITH: 隶属于 -- REPRESENTS: 代表 -- REGULATES: 监管 -- REPORTS_ON: 报道 -- COMMENTS_ON: 评论 -- RESPONDS_TO: 回应 -- SUPPORTS: 支持 -- OPPOSES: 反对 -- COLLABORATES_WITH: 合作 -- COMPETES_WITH: 竞争 +- WORKS_FOR: Works for +- STUDIES_AT: Studies at +- AFFILIATED_WITH: Affiliated with +- REPRESENTS: Represents +- REGULATES: Regulates +- REPORTS_ON: Reports on +- COMMENTS_ON: Comments on +- RESPONDS_TO: Responds to +- SUPPORTS: Supports +- OPPOSES: Opposes +- COLLABORATES_WITH: Collaborates with +- COMPETES_WITH: Competes with """ class OntologyGenerator: """ - 本体生成器 - 分析文本内容，生成实体和关系类型定义 + Ontology generator + Analyze text content and generate entity and relationship type definitions """ - + def __init__(self, llm_client: Optional[LLMClient] = None): self.llm_client = llm_client or LLMClient() - + def generate( self, document_texts: List[str], @@ -171,111 +171,111 @@ def generate( additional_context: Optional[str] = None ) -> Dict[str, Any]: """ - 生成本体定义 - + Generate ontology definition + Args: - document_texts: 文档文本列表 - simulation_requirement: 模拟需求描述 - additional_context: 额外上下文 - + document_texts: List of document texts + simulation_requirement: Description of simulation requirements + additional_context: Additional context + Returns: - 本体定义（entity_types, edge_types等） + Ontology definition (entity_types, edge_types, etc.) """ - # 构建用户消息 + # Build user message user_message = self._build_user_message( - document_texts, + document_texts, simulation_requirement, additional_context ) - + messages = [ {"role": "system", "content": ONTOLOGY_SYSTEM_PROMPT}, {"role": "user", "content": user_message} ] - - # 调用LLM + + # Call LLM result = self.llm_client.chat_json( messages=messages, temperature=0.3, max_tokens=4096 ) - - # 验证和后处理 + + # Validate and post-process result = self._validate_and_process(result) - + return result - - # 传给 LLM 的文本最大长度（5万字） + + # Maximum text length for LLM (50,000 characters) MAX_TEXT_LENGTH_FOR_LLM = 50000 - + def _build_user_message( self, document_texts: List[str], simulation_requirement: str, additional_context: Optional[str] ) -> str: - """构建用户消息""" - - # 合并文本 + """Build user message""" + + # Combine texts combined_text = "\n\n---\n\n".join(document_texts) original_length = len(combined_text) - - # 如果文本超过5万字，截断（仅影响传给LLM的内容，不影响图谱构建） + + # If text exceeds 50,000 characters, truncate (only affects LLM input, not graph construction) if len(combined_text) > self.MAX_TEXT_LENGTH_FOR_LLM: combined_text = combined_text[:self.MAX_TEXT_LENGTH_FOR_LLM] - combined_text += f"\n\n...(原文共{original_length}字，已截取前{self.MAX_TEXT_LENGTH_FOR_LLM}字用于本体分析)..." - - message = f"""## 模拟需求 + combined_text += f"\n\n...(Original text has {original_length} characters, first {self.MAX_TEXT_LENGTH_FOR_LLM} characters extracted for ontology analysis)..." + + message = f"""## Simulation Requirements {simulation_requirement} -## 文档内容 +## Document Content {combined_text} """ - + if additional_context: message += f""" -## 额外说明 +## Additional Explanation {additional_context} """ - + message += """ -请根据以上内容，设计适合社会舆论模拟的实体类型和关系类型。 - -**必须遵守的规则**： -1. 必须正好输出10个实体类型 -2. 最后2个必须是兜底类型：Person（个人兜底）和 Organization（组织兜底） -3. 前8个是根据文本内容设计的具体类型 -4. 所有实体类型必须是现实中可以发声的主体，不能是抽象概念 -5. 属性名不能使用 name、uuid、group_id 等保留字，用 full_name、org_name 等替代 +Based on the above content, design entity types and relationship types suitable for social opinion simulation. + +**Rules to follow**: +1. Must output exactly 10 entity types +2. Last 2 must be fallback types: Person (individual fallback) and Organization (organization fallback) +3. First 8 are specific types designed based on text content +4. All entity types must be real-world subjects that can voice opinions, not abstract concepts +5. Attribute names cannot use reserved words like name, uuid, group_id, use full_name, org_name, etc. instead """ - + return message def _validate_and_process(self, result: Dict[str, Any]) -> Dict[str, Any]: - """验证和后处理结果""" - - # 确保必要字段存在 + """Validate and post-process result""" + + # Ensure necessary fields exist if "entity_types" not in result: result["entity_types"] = [] if "edge_types" not in result: result["edge_types"] = [] if "analysis_summary" not in result: result["analysis_summary"] = "" - - # 验证实体类型 + + # Validate entity types for entity in result["entity_types"]: if "attributes" not in entity: entity["attributes"] = [] if "examples" not in entity: entity["examples"] = [] - # 确保description不超过100字符 + # Ensure description doesn't exceed 100 characters if len(entity.get("description", "")) > 100: entity["description"] = entity["description"][:97] + "..." - - # 验证关系类型 + + # Validate relationship types for edge in result["edge_types"]: if "source_targets" not in edge: edge["source_targets"] = [] @@ -283,12 +283,12 @@ def _validate_and_process(self, result: Dict[str, Any]) -> Dict[str, Any]: edge["attributes"] = [] if len(edge.get("description", "")) > 100: edge["description"] = edge["description"][:97] + "..." - - # Zep API 限制：最多 10 个自定义实体类型，最多 10 个自定义边类型 + + # Zep API limit: maximum 10 custom entity types, maximum 10 custom edge types MAX_ENTITY_TYPES = 10 MAX_EDGE_TYPES = 10 - - # 兜底类型定义 + + # Fallback type definitions person_fallback = { "name": "Person", "description": "Any individual person not fitting other specific person types.", @@ -298,7 +298,7 @@ def _validate_and_process(self, result: Dict[str, Any]) -> Dict[str, Any]: ], "examples": ["ordinary citizen", "anonymous netizen"] } - + organization_fallback = { "name": "Organization", "description": "Any organization not fitting other specific organization types.", @@ -308,74 +308,70 @@ def _validate_and_process(self, result: Dict[str, Any]) -> Dict[str, Any]: ], "examples": ["small business", "community group"] } - - # 检查是否已有兜底类型 + + # Check if fallback types already exist entity_names = {e["name"] for e in result["entity_types"]} has_person = "Person" in entity_names has_organization = "Organization" in entity_names - - # 需要添加的兜底类型 + + # Fallback types to add fallbacks_to_add = [] if not has_person: fallbacks_to_add.append(person_fallback) if not has_organization: fallbacks_to_add.append(organization_fallback) - + if fallbacks_to_add: current_count = len(result["entity_types"]) needed_slots = len(fallbacks_to_add) - - # 如果添加后会超过 10 个，需要移除一些现有类型 + + # If adding would exceed 10, need to remove some existing types if current_count + needed_slots > MAX_ENTITY_TYPES: - # 计算需要移除多少个 + # Calculate how many to remove to_remove = current_count + needed_slots - MAX_ENTITY_TYPES - # 从末尾移除（保留前面更重要的具体类型） + # Remove from end (keep more important specific types in front) result["entity_types"] = result["entity_types"][:-to_remove] - - # 添加兜底类型 + + # Add fallback types result["entity_types"].extend(fallbacks_to_add) - - # 最终确保不超过限制（防御性编程） + + # Final check to ensure limits not exceeded (defensive programming) if len(result["entity_types"]) > MAX_ENTITY_TYPES: result["entity_types"] = result["entity_types"][:MAX_ENTITY_TYPES] - + if len(result["edge_types"]) > MAX_EDGE_TYPES: result["edge_types"] = result["edge_types"][:MAX_EDGE_TYPES] - + return result def generate_python_code(self, ontology: Dict[str, Any]) -> str: """ - 将本体定义转换为Python代码（类似ontology.py） - - Args: - ontology: 本体定义 - - Returns: - Python代码字符串 + [DEPRECATED] Convert ontology definition to Zep-format Pydantic code. + Not used in MiroFish-Offline (ontology stored as JSON in Neo4j). + Kept for reference only. """ code_lines = [ '"""', - '自定义实体类型定义', - '由MiroFish自动生成，用于社会舆论模拟', + 'Custom entity type definitions', + 'Auto-generated by MiroFish for social opinion simulation', '"""', '', 'from pydantic import Field', 'from zep_cloud.external_clients.ontology import EntityModel, EntityText, EdgeModel', '', '', - '# ============== 实体类型定义 ==============', + '# ============== Entity Type Definitions ==============', '', ] - - # 生成实体类型 + + # Generate entity types for entity in ontology.get("entity_types", []): name = entity["name"] desc = entity.get("description", f"A {name} entity.") - + code_lines.append(f'class {name}(EntityModel):') code_lines.append(f' """{desc}"""') - + attrs = entity.get("attributes", []) if attrs: for attr in attrs: @@ -387,23 +383,23 @@ def generate_python_code(self, ontology: Dict[str, Any]) -> str: code_lines.append(f' )') else: code_lines.append(' pass') - + code_lines.append('') code_lines.append('') - - code_lines.append('# ============== 关系类型定义 ==============') + + code_lines.append('# ============== Relationship Type Definitions ==============') code_lines.append('') - - # 生成关系类型 + + # Generate relationship types for edge in ontology.get("edge_types", []): name = edge["name"] - # 转换为PascalCase类名 + # Convert to PascalCase class name class_name = ''.join(word.capitalize() for word in name.split('_')) desc = edge.get("description", f"A {name} relationship.") - + code_lines.append(f'class {class_name}(EdgeModel):') code_lines.append(f' """{desc}"""') - + attrs = edge.get("attributes", []) if attrs: for attr in attrs: @@ -415,12 +411,12 @@ def generate_python_code(self, ontology: Dict[str, Any]) -> str: code_lines.append(f' )') else: code_lines.append(' pass') - + code_lines.append('') code_lines.append('') - - # 生成类型字典 - code_lines.append('# ============== 类型配置 ==============') + + # Generate type dictionaries + code_lines.append('# ============== Type Configuration ==============') code_lines.append('') code_lines.append('ENTITY_TYPES = {') for entity in ontology.get("entity_types", []): @@ -435,8 +431,8 @@ def generate_python_code(self, ontology: Dict[str, Any]) -> str: code_lines.append(f' "{name}": {class_name},') code_lines.append('}') code_lines.append('') - - # 生成边的source_targets映射 + + # Generate source_targets mapping for edges code_lines.append('EDGE_SOURCE_TARGETS = {') for edge in ontology.get("edge_types", []): name = edge["name"] @@ -448,6 +444,6 @@ def generate_python_code(self, ontology: Dict[str, Any]) -> str: ]) code_lines.append(f' "{name}": [{st_list}],') code_lines.append('}') - + return '\n'.join(code_lines) diff --git a/backend/app/services/report_agent.py b/backend/app/services/report_agent.py index 02ca5bdc2..00771b1b9 100644 --- a/backend/app/services/report_agent.py +++ b/backend/app/services/report_agent.py @@ -1,12 +1,12 @@ """ -Report Agent服务 -使用LangChain + Zep实现ReACT模式的模拟报告生成 - -功能： -1. 根据模拟需求和Zep图谱信息生成报告 -2. 先规划目录结构，然后分段生成 -3. 每段采用ReACT多轮思考与反思模式 -4. 支持与用户对话，在对话中自主调用检索工具 +Report Agent Service +Generate simulated reports using ReACT pattern (via GraphStorage / Neo4j) + +Features: +1. Generate reports based on simulation requirements and graph information +2. First plan the outline structure, then generate section by section +3. Each section uses ReACT multi-round thinking and reflection pattern +4. Support conversations with users, autonomously call retrieval tools during conversations """ import os @@ -34,18 +34,18 @@ class ReportLogger: """ - Report Agent 详细日志记录器 - - 在报告文件夹中生成 agent_log.jsonl 文件，记录每一步详细动作。 - 每行是一个完整的 JSON 对象，包含时间戳、动作类型、详细内容等。 + Report Agent Detailed Logger + + Generates agent_log.jsonl file in the report folder, recording detailed actions at each step. + Each line is a complete JSON object containing timestamp, action type, details, etc. """ def __init__(self, report_id: str): """ - 初始化日志记录器 - + Initialize the logger + Args: - report_id: 报告ID，用于确定日志文件路径 + report_id: Report ID, used to determine the log file path """ self.report_id = report_id self.log_file_path = os.path.join( @@ -55,7 +55,7 @@ def __init__(self, report_id: str): self._ensure_log_file() def _ensure_log_file(self): - """确保日志文件所在目录存在""" + """Ensure the log file directory exists""" log_dir = os.path.dirname(self.log_file_path) os.makedirs(log_dir, exist_ok=True) @@ -473,394 +473,395 @@ def to_dict(self) -> Dict[str, Any]: # ── 工具描述 ── TOOL_DESC_INSIGHT_FORGE = """\ -【深度洞察检索 - 强大的检索工具】 -这是我们强大的检索函数，专为深度分析设计。它会： -1. 自动将你的问题分解为多个子问题 -2. 从多个维度检索模拟图谱中的信息 -3. 整合语义搜索、实体分析、关系链追踪的结果 -4. 返回最全面、最深度的检索内容 - -【使用场景】 -- 需要深入分析某个话题 -- 需要了解事件的多个方面 -- 需要获取支撑报告章节的丰富素材 - -【返回内容】 -- 相关事实原文（可直接引用） -- 核心实体洞察 -- 关系链分析""" +[Deep Insight Retrieval - Powerful Retrieval Tool] +This is our powerful retrieval function, designed for deep analysis. It will: +1. Automatically decompose your question into multiple sub-questions +2. Retrieve information from the simulated knowledge graph from multiple dimensions +3. Integrate results from semantic search, entity analysis, and relationship chain tracking +4. Return the most comprehensive and deep retrieval content + +[Use Cases] +- Need to deeply analyze a topic +- Need to understand multiple aspects of an event +- Need to obtain rich materials to support report sections + +[Return Content] +- Relevant facts in original text (can be directly cited) +- Core entity insights +- Relationship chain analysis""" TOOL_DESC_PANORAMA_SEARCH = """\ -【广度搜索 - 获取全貌视图】 -这个工具用于获取模拟结果的完整全貌，特别适合了解事件演变过程。它会： -1. 获取所有相关节点和关系 -2. 区分当前有效的事实和历史/过期的事实 -3. 帮助你了解舆情是如何演变的 - -【使用场景】 -- 需要了解事件的完整发展脉络 -- 需要对比不同阶段的舆情变化 -- 需要获取全面的实体和关系信息 - -【返回内容】 -- 当前有效事实（模拟最新结果） -- 历史/过期事实（演变记录） -- 所有涉及的实体""" +[Breadth Search - Get Complete Overview] +This tool is used to get a complete panoramic view of simulation results, especially suitable for understanding the evolution of events. It will: +1. Retrieve all relevant nodes and relationships +2. Distinguish between current valid facts and historical/expired facts +3. Help you understand how events have evolved + +[Use Cases] +- Need to understand the complete development trajectory of an event +- Need to compare public sentiment changes across different stages +- Need to get comprehensive entity and relationship information + +[Return Content] +- Current valid facts (latest simulation results) +- Historical/expired facts (evolution records) +- All involved entities""" TOOL_DESC_QUICK_SEARCH = """\ -【简单搜索 - 快速检索】 -轻量级的快速检索工具，适合简单、直接的信息查询。 +[Simple Search - Quick Retrieval] +A lightweight quick retrieval tool suitable for simple and direct information queries. -【使用场景】 -- 需要快速查找某个具体信息 -- 需要验证某个事实 -- 简单的信息检索 +[Use Cases] +- Need to quickly find specific information +- Need to verify a fact +- Simple information retrieval -【返回内容】 -- 与查询最相关的事实列表""" +[Return Content] +- List of facts most relevant to the query""" TOOL_DESC_INTERVIEW_AGENTS = """\ -【深度采访 - 真实Agent采访（双平台）】 -调用OASIS模拟环境的采访API，对正在运行的模拟Agent进行真实采访！ -这不是LLM模拟，而是调用真实的采访接口获取模拟Agent的原始回答。 -默认在Twitter和Reddit两个平台同时采访，获取更全面的观点。 - -功能流程： -1. 自动读取人设文件，了解所有模拟Agent -2. 智能选择与采访主题最相关的Agent（如学生、媒体、官方等） -3. 自动生成采访问题 -4. 调用 /api/simulation/interview/batch 接口在双平台进行真实采访 -5. 整合所有采访结果，提供多视角分析 - -【使用场景】 -- 需要从不同角色视角了解事件看法（学生怎么看？媒体怎么看？官方怎么说？） -- 需要收集多方意见和立场 -- 需要获取模拟Agent的真实回答（来自OASIS模拟环境） -- 想让报告更生动，包含"采访实录" - -【返回内容】 -- 被采访Agent的身份信息 -- 各Agent在Twitter和Reddit两个平台的采访回答 -- 关键引言（可直接引用） -- 采访摘要和观点对比 - -【重要】需要OASIS模拟环境正在运行才能使用此功能！""" - -# ── 大纲规划 prompt ── +[Deep Interview - Real Agent Interview (Dual Platform)] +Call the OASIS simulation environment's interview API to conduct real interviews with running simulation agents! +This is not an LLM simulation, but calls the real interview interface to get original responses from simulation agents. +By default, interview on Twitter and Reddit simultaneously to get more comprehensive perspectives. + +Function Flow: +1. Automatically read character profile files to understand all simulation agents +2. Intelligently select agents most relevant to the interview topic (e.g., students, media, officials) +3. Automatically generate interview questions +4. Call /api/simulation/interview/batch interface to conduct real interviews on dual platforms +5. Integrate all interview results and provide multi-perspective analysis + +[Use Cases] +- Need to understand event perspectives from different role angles (How do students view it? How does media view it? What does the official say?) +- Need to collect diverse opinions and positions +- Need to get real responses from simulation agents (from OASIS simulation environment) +- Want to make the report more vivid, including "interview records" + +[Return Content] +- Identity information of interviewed agents +- Interview responses from each agent on Twitter and Reddit platforms +- Key quotes (can be directly cited) +- Interview summary and perspective comparison + +[Important] This feature requires the OASIS simulation environment to be running!""" + +# ── Outline Planning Prompt ── PLAN_SYSTEM_PROMPT = """\ -你是一个「未来预测报告」的撰写专家，拥有对模拟世界的「上帝视角」——你可以洞察模拟中每一位Agent的行为、言论和互动。 - -【核心理念】 -我们构建了一个模拟世界，并向其中注入了特定的「模拟需求」作为变量。模拟世界的演化结果，就是对未来可能发生情况的预测。你正在观察的不是"实验数据"，而是"未来的预演"。 - -【你的任务】 -撰写一份「未来预测报告」，回答： -1. 在我们设定的条件下，未来发生了什么？ -2. 各类Agent（人群）是如何反应和行动？ -3. 这个模拟揭示了哪些值得关注的未来趋势和风险？ - -【报告定位】 -- ✅ 这是一份基于模拟的未来预测报告，揭示"如果这样，未来会怎样" -- ✅ 聚焦于预测结果：事件走向、群体反应、涌现现象、潜在风险 -- ✅ 模拟世界中的Agent言行就是对未来人群行为的预测 -- ❌ 不是对现实世界现状的分析 -- ❌ 不是泛泛而谈的舆情综述 - -【章节数量限制】 -- 最少2个章节，最多5个章节 -- 不需要子章节，每个章节直接撰写完整内容 -- 内容要精炼，聚焦于核心预测发现 -- 章节结构由你根据预测结果自主设计 - -请输出JSON格式的报告大纲，格式如下： +You are an expert in writing "future prediction reports" with a "god's eye view" of the simulated world - you can gain insights into the behavior, statements, and interactions of every agent in the simulation. + +[Core Concept] +We built a simulated world and injected specific "simulation requirements" as variables into it. The evolution result of the simulated world is a prediction of what might happen in the future. What you're observing is not "experimental data" but a "rehearsal of the future". + +[Your Task] +Write a "future prediction report" that answers: +1. What happened in the future under the conditions we set? +2. How do various agents (groups) react and act? +3. What future trends and risks does this simulation reveal that deserve attention? + +[Report Positioning] +- ✅ This is a future prediction report based on simulation, revealing "if this happens, how will the future unfold" +- ✅ Focus on prediction results: event trajectories, group reactions, emergent phenomena, potential risks +- ✅ Agent statements and behaviors in the simulated world are predictions of future human behavior +- ❌ Not an analysis of the current state of the real world +- ❌ Not a general overview of public sentiment + +[Section Number Limit] +- Minimum 2 sections, maximum 5 sections +- No subsections needed, each section directly writes complete content +- Content should be concise, focused on core prediction findings +- Section structure is designed independently based on prediction results + +Please output the report outline in JSON format as follows: { - "title": "报告标题", - "summary": "报告摘要（一句话概括核心预测发现）", + "title": "Report Title", + "summary": "Report Summary (one sentence summarizing core prediction findings)", "sections": [ { - "title": "章节标题", - "description": "章节内容描述" + "title": "Section Title", + "description": "Section Content Description" } ] } -注意：sections数组最少2个，最多5个元素！""" +Note: sections array must have at least 2 and at most 5 elements!""" PLAN_USER_PROMPT_TEMPLATE = """\ -【预测场景设定】 -我们向模拟世界注入的变量（模拟需求）：{simulation_requirement} +[Prediction Scenario Settings] +Variable (simulation requirement) injected into the simulated world: {simulation_requirement} -【模拟世界规模】 -- 参与模拟的实体数量: {total_nodes} -- 实体间产生的关系数量: {total_edges} -- 实体类型分布: {entity_types} -- 活跃Agent数量: {total_entities} +[Simulated World Scale] +- Number of entities participating in simulation: {total_nodes} +- Number of relationships generated between entities: {total_edges} +- Entity type distribution: {entity_types} +- Number of active agents: {total_entities} -【模拟预测到的部分未来事实样本】 +[Sample of Some Future Facts Predicted by Simulation] {related_facts_json} -请以「上帝视角」审视这个未来预演： -1. 在我们设定的条件下，未来呈现出了什么样的状态？ -2. 各类人群（Agent）是如何反应和行动的？ -3. 这个模拟揭示了哪些值得关注的未来趋势？ +Please examine this future rehearsal from a "god's eye view": +1. What state does the future present under the conditions we set? +2. How do various groups (agents) react and act? +3. What future trends does this simulation reveal that deserve attention? -根据预测结果，设计最合适的报告章节结构。 +Based on the prediction results, design the most appropriate report section structure. -【再次提醒】报告章节数量：最少2个，最多5个，内容要精炼聚焦于核心预测发现。""" +[Reminder] Report section count: minimum 2, maximum 5, content should be concise and focused on core prediction findings.""" -# ── 章节生成 prompt ── +# ── Section Generation Prompt ── SECTION_SYSTEM_PROMPT_TEMPLATE = """\ -你是一个「未来预测报告」的撰写专家，正在撰写报告的一个章节。 +You are an expert in writing "future prediction reports" and are writing a section of the report. -报告标题: {report_title} -报告摘要: {report_summary} -预测场景（模拟需求）: {simulation_requirement} +Report Title: {report_title} +Report Summary: {report_summary} +Prediction Scenario (Simulation Requirement): {simulation_requirement} -当前要撰写的章节: {section_title} +Current Section to Write: {section_title} ═══════════════════════════════════════════════════════════════ -【核心理念】 +[Core Concept] ═══════════════════════════════════════════════════════════════ -模拟世界是对未来的预演。我们向模拟世界注入了特定条件（模拟需求）， -模拟中Agent的行为和互动，就是对未来人群行为的预测。 +The simulated world is a rehearsal of the future. We injected specific conditions (simulation requirements) into the simulated world. +The behavior and interactions of agents in the simulation are predictions of future human behavior. -你的任务是： -- 揭示在设定条件下，未来发生了什么 -- 预测各类人群（Agent）是如何反应和行动的 -- 发现值得关注的未来趋势、风险和机会 +Your task is to: +- Reveal what happens in the future under the set conditions +- Predict how various groups (agents) react and act +- Discover future trends, risks, and opportunities worth paying attention to -❌ 不要写成对现实世界现状的分析 -✅ 要聚焦于"未来会怎样"——模拟结果就是预测的未来 +❌ Don't write it as an analysis of the current state of the real world +✅ Focus on "how the future will unfold" - simulation results are the predicted future ═══════════════════════════════════════════════════════════════ -【最重要的规则 - 必须遵守】 +[Most Important Rules - Must Follow] ═══════════════════════════════════════════════════════════════ -1. 【必须调用工具观察模拟世界】 - - 你正在以「上帝视角」观察未来的预演 - - 所有内容必须来自模拟世界中发生的事件和Agent言行 - - 禁止使用你自己的知识来编写报告内容 - - 每个章节至少调用3次工具（最多5次）来观察模拟的世界，它代表了未来 - -2. 【必须引用Agent的原始言行】 - - Agent的发言和行为是对未来人群行为的预测 - - 在报告中使用引用格式展示这些预测，例如： - > "某类人群会表示：原文内容..." - - 这些引用是模拟预测的核心证据 - -3. 【语言一致性 - 引用内容必须翻译为报告语言】 - - 工具返回的内容可能包含英文或中英文混杂的表述 - - 如果模拟需求和材料原文是中文的，报告必须全部使用中文撰写 - - 当你引用工具返回的英文或中英混杂内容时，必须将其翻译为流畅的中文后再写入报告 - - 翻译时保持原意不变，确保表述自然通顺 - - 这一规则同时适用于正文和引用块（> 格式）中的内容 - -4. 【忠实呈现预测结果】 - - 报告内容必须反映模拟世界中的代表未来的模拟结果 - - 不要添加模拟中不存在的信息 - - 如果某方面信息不足，如实说明 +1. [Must Call Tools to Observe the Simulated World] + - You are observing a rehearsal of the future from a "god's eye view" + - All content must come from events and agent statements/behaviors in the simulated world + - Forbidden to use your own knowledge to write report content + - Each section must call tools at least 3 times (maximum 5 times) to observe the simulated world, which represents the future + +2. [Must Quote Original Agent Statements and Behaviors] + - Agent statements and behaviors are predictions of future human behavior + - Use quote format in the report to display these predictions, for example: + > "Certain groups will state: original content..." + - These quotes are core evidence of simulation predictions + +3. [Language Consistency - Quoted Content Must Be Translated to Report Language] + - Tool returned content may contain English or mixed Chinese-English expressions + - If the simulation requirement and source material are in Chinese, the report must be entirely in Chinese + - When you quote English or mixed Chinese-English content from tools, you must translate it to fluent Chinese before including it in the report + - When translating, preserve the original meaning and ensure natural expression + - This rule applies to both regular text and quoted blocks (> format) + +4. [Faithfully Present Prediction Results] + - Report content must reflect simulation results that represent the future in the simulated world + - Don't add information that doesn't exist in the simulation + - If information is insufficient in some aspects, state it truthfully ═══════════════════════════════════════════════════════════════ -【⚠️ 格式规范 - 极其重要！】 +[⚠️ Format Specification - Extremely Important!] ═══════════════════════════════════════════════════════════════ -【一个章节 = 最小内容单位】 -- 每个章节是报告的最小分块单位 -- ❌ 禁止在章节内使用任何 Markdown 标题（#、##、###、#### 等） -- ❌ 禁止在内容开头添加章节主标题 -- ✅ 章节标题由系统自动添加，你只需撰写纯正文内容 -- ✅ 使用**粗体**、段落分隔、引用、列表来组织内容，但不要用标题 +[One Section = Minimum Content Unit] +- Each section is the minimum content unit of the report +- ❌ Forbidden to use any Markdown titles (#, ##, ###, ####, etc.) within the section +- ❌ Forbidden to add section titles at the beginning of content +- ✅ Section titles are added automatically by the system, just write pure body text +- ✅ Use **bold**, paragraph separation, quotes, and lists to organize content, but don't use titles -【正确示例】 +[Correct Example] ``` -本章节分析了事件的舆论传播态势。通过对模拟数据的深入分析，我们发现... +This section analyzes the public sentiment propagation of the event. Through in-depth analysis of simulation data, we found... -**首发引爆阶段** +**Initial Explosion Phase** -微博作为舆情的第一现场，承担了信息首发的核心功能： +Weibo, as the first scene of public sentiment, undertook the core function of initial information dissemination: -> "微博贡献了68%的首发声量..." +> "Weibo contributed 68% of initial voice..." -**情绪放大阶段** +**Emotion Amplification Phase** -抖音平台进一步放大了事件影响力： +The TikTok platform further amplified the impact of the event: -- 视觉冲击力强 -- 情绪共鸣度高 +- Strong visual impact +- High emotional resonance ``` -【错误示例】 +[Incorrect Example] ``` -## 执行摘要 ← 错误！不要添加任何标题 -### 一、首发阶段 ← 错误！不要用###分小节 -#### 1.1 详细分析 ← 错误！不要用####细分 +## Executive Summary ← Wrong! Don't add any titles +### 1. Initial Phase ← Wrong! Don't use ### for subsections +#### 1.1 Detailed Analysis ← Wrong! Don't use #### for subdivisions -本章节分析了... +This section analyzes... ``` ═══════════════════════════════════════════════════════════════ -【可用检索工具】（每章节调用3-5次） +[Available Retrieval Tools] (call 3-5 times per section) ═══════════════════════════════════════════════════════════════ {tools_description} -【工具使用建议 - 请混合使用不同工具，不要只用一种】 -- insight_forge: 深度洞察分析，自动分解问题并多维度检索事实和关系 -- panorama_search: 广角全景搜索，了解事件全貌、时间线和演变过程 -- quick_search: 快速验证某个具体信息点 -- interview_agents: 采访模拟Agent，获取不同角色的第一人称观点和真实反应 +[Tool Usage Suggestions - Please Mix Different Tools, Don't Use Only One] +- insight_forge: Deep insight analysis, automatically decompose problems and retrieve facts and relationships from multiple dimensions +- panorama_search: Wide-angle panoramic search, understand complete event view, timeline, and evolution process +- quick_search: Quick verification of specific information points +- interview_agents: Interview simulated agents, get first-person perspectives and real reactions from different roles ═══════════════════════════════════════════════════════════════ -【工作流程】 +[Workflow] ═══════════════════════════════════════════════════════════════ -每次回复你只能做以下两件事之一（不可同时做）： +Each reply you can only do one of two things (cannot do both): -选项A - 调用工具： -输出你的思考，然后用以下格式调用一个工具： +Option A - Call Tool: +Output your thinking, then call a tool using the following format: -{{"name": "工具名称", "parameters": {{"参数名": "参数值"}}}} +{{"name": "Tool Name", "parameters": {{"parameter_name": "parameter_value"}}}} -系统会执行工具并把结果返回给你。你不需要也不能自己编写工具返回结果。 +The system will execute the tool and return the result to you. You don't need to and cannot write tool return results yourself. -选项B - 输出最终内容： -当你已通过工具获取了足够信息，以 "Final Answer:" 开头输出章节内容。 +Option B - Output Final Content: +When you have gathered enough information through tools, start with "Final Answer:" and output section content. -⚠️ 严格禁止： -- 禁止在一次回复中同时包含工具调用和 Final Answer -- 禁止自己编造工具返回结果（Observation），所有工具结果由系统注入 -- 每次回复最多调用一个工具 +⚠️ Strictly Forbidden: +- Forbidden to include both tool calls and Final Answer in one reply +- Forbidden to fabricate tool return results (Observation), all tool results are injected by the system +- At most one tool call per reply ═══════════════════════════════════════════════════════════════ -【章节内容要求】 +[Section Content Requirements] ═══════════════════════════════════════════════════════════════ -1. 内容必须基于工具检索到的模拟数据 -2. 大量引用原文来展示模拟效果 -3. 使用Markdown格式（但禁止使用标题）： - - 使用 **粗体文字** 标记重点（代替子标题） - - 使用列表（-或1.2.3.）组织要点 - - 使用空行分隔不同段落 - - ❌ 禁止使用 #、##、###、#### 等任何标题语法 -4. 【引用格式规范 - 必须单独成段】 - 引用必须独立成段，前后各有一个空行，不能混在段落中： - - ✅ 正确格式： +1. Content must be based on simulation data retrieved by tools +2. Heavily quote original text to demonstrate simulation effects +3. Use Markdown format (but forbidden to use titles): + - Use **bold text** to mark key points (replacing sub-titles) + - Use lists (- or 1.2.3.) to organize points + - Use blank lines to separate paragraphs + - ❌ Forbidden to use any title syntax like #, ##, ###, #### +4. [Quote Format Specification - Must Be Separate Paragraph] + Quotes must be standalone paragraphs with blank lines before and after, cannot be mixed in paragraphs: + + ✅ Correct Format: ``` - 校方的回应被认为缺乏实质内容。 + School officials' response was considered lacking substantive content. - > "校方的应对模式在瞬息万变的社交媒体环境中显得僵化和迟缓。" + > "School's response pattern appears rigid and slow in the rapidly changing social media environment." - 这一评价反映了公众的普遍不满。 + This assessment reflects widespread public dissatisfaction. ``` - ❌ 错误格式： + ❌ Incorrect Format: ``` - 校方的回应被认为缺乏实质内容。> "校方的应对模式..." 这一评价反映了... + School officials' response was considered lacking substantive content.> "School's response pattern..." This assessment reflects... ``` -5. 保持与其他章节的逻辑连贯性 -6. 【避免重复】仔细阅读下方已完成的章节内容，不要重复描述相同的信息 -7. 【再次强调】不要添加任何标题！用**粗体**代替小节标题""" +5. Maintain logical coherence with other sections +6. [Avoid Duplication] Carefully read the completed section content below, don't repeat describing the same information +7. [Emphasis Again] Don't add any titles! Use **bold** instead of section sub-titles""" SECTION_USER_PROMPT_TEMPLATE = """\ -已完成的章节内容（请仔细阅读，避免重复）： +Completed Section Content (Please Read Carefully to Avoid Duplication): {previous_content} ═══════════════════════════════════════════════════════════════ -【当前任务】撰写章节: {section_title} +[Current Task] Write Section: {section_title} ═══════════════════════════════════════════════════════════════ -【重要提醒】 -1. 仔细阅读上方已完成的章节，避免重复相同的内容！ -2. 开始前必须先调用工具获取模拟数据 -3. 请混合使用不同工具，不要只用一种 -4. 报告内容必须来自检索结果，不要使用自己的知识 +[Important Reminders] +1. Carefully read the completed sections above to avoid repeating the same content! +2. You must call tools to get simulation data before starting +3. Please mix different tools, don't use only one +4. Report content must come from retrieval results, don't use your own knowledge -【⚠️ 格式警告 - 必须遵守】 -- ❌ 不要写任何标题（#、##、###、####都不行） -- ❌ 不要写"{section_title}"作为开头 -- ✅ 章节标题由系统自动添加 -- ✅ 直接写正文，用**粗体**代替小节标题 +[⚠️ Format Warning - Must Follow] +- ❌ Don't write any titles (#, ##, ###, #### none allowed) +- ❌ Don't write "{section_title}" as the opening +- ✅ Section titles are added automatically by the system +- ✅ Write the body directly, use **bold** instead of sub-section titles -请开始： -1. 首先思考（Thought）这个章节需要什么信息 -2. 然后调用工具（Action）获取模拟数据 -3. 收集足够信息后输出 Final Answer（纯正文，无任何标题）""" +Please start: +1. First think (Thought) what information this section needs +2. Then call tools (Action) to get simulation data +3. After collecting enough information, output Final Answer (pure body text, no titles)""" -# ── ReACT 循环内消息模板 ── +# ── ReACT Loop Message Templates ── REACT_OBSERVATION_TEMPLATE = """\ -Observation（检索结果）: +Observation (Retrieval Result): -═══ 工具 {tool_name} 返回 ═══ +═══ Tool {tool_name} Returned ═══ {result} ═══════════════════════════════════════════════════════════════ -已调用工具 {tool_calls_count}/{max_tool_calls} 次（已用: {used_tools_str}）{unused_hint} -- 如果信息充分：以 "Final Answer:" 开头输出章节内容（必须引用上述原文） -- 如果需要更多信息：调用一个工具继续检索 +Called tools {tool_calls_count}/{max_tool_calls} times (Used: {used_tools_str}){unused_hint} +- If information is sufficient: Start with "Final Answer:" and output section content (must quote the above original text) +- If more information is needed: Call a tool to continue retrieving ═══════════════════════════════════════════════════════════════""" REACT_INSUFFICIENT_TOOLS_MSG = ( - "【注意】你只调用了{tool_calls_count}次工具，至少需要{min_tool_calls}次。" - "请再调用工具获取更多模拟数据，然后再输出 Final Answer。{unused_hint}" + "[Notice] You have only called {tool_calls_count} tools, need at least {min_tool_calls}. " + "Please call tools again to get more simulation data, then output Final Answer. {unused_hint}" ) REACT_INSUFFICIENT_TOOLS_MSG_ALT = ( - "当前只调用了 {tool_calls_count} 次工具，至少需要 {min_tool_calls} 次。" - "请调用工具获取模拟数据。{unused_hint}" + "Currently called {tool_calls_count} tools, need at least {min_tool_calls}. " + "Please call tools to get simulation data. {unused_hint}" ) REACT_TOOL_LIMIT_MSG = ( - "工具调用次数已达上限（{tool_calls_count}/{max_tool_calls}），不能再调用工具。" - '请立即基于已获取的信息，以 "Final Answer:" 开头输出章节内容。' + "Tool call count has reached the limit ({tool_calls_count}/{max_tool_calls}), cannot call tools anymore. " + 'Please immediately start with "Final Answer:" and output section content based on acquired information.' ) -REACT_UNUSED_TOOLS_HINT = "\n💡 你还没有使用过: {unused_list}，建议尝试不同工具获取多角度信息" +REACT_UNUSED_TOOLS_HINT = "\n💡 You haven't used yet: {unused_list}, suggest trying different tools to get multi-perspective information" -REACT_FORCE_FINAL_MSG = "已达到工具调用限制，请直接输出 Final Answer: 并生成章节内容。" +REACT_FORCE_FINAL_MSG = "Tool call limit reached, please directly output Final Answer: and generate section content." -# ── Chat prompt ── +# ── Chat Prompt ── CHAT_SYSTEM_PROMPT_TEMPLATE = """\ -你是一个简洁高效的模拟预测助手。 +You are a concise and efficient simulation prediction assistant. -【背景】 -预测条件: {simulation_requirement} +[Background] +Prediction Condition: {simulation_requirement} -【已生成的分析报告】 +[Generated Analysis Report] {report_content} -【规则】 -1. 优先基于上述报告内容回答问题 -2. 直接回答问题，避免冗长的思考论述 -3. 仅在报告内容不足以回答时，才调用工具检索更多数据 -4. 回答要简洁、清晰、有条理 +[Rules] +1. Prioritize answering questions based on the above report content +2. Answer questions directly, avoid lengthy deliberation +3. Only call tools to retrieve more data if the report content is insufficient to answer +4. Answers should be concise, clear, and well-organized -【可用工具】（仅在需要时使用，最多调用1-2次） +[Available Tools] (use only when needed, call at most 1-2 times) {tools_description} -【工具调用格式】 +[Tool Call Format] -{{"name": "工具名称", "parameters": {{"参数名": "参数值"}}}} +{{"name": "Tool Name", "parameters": {{"parameter_name": "parameter_value"}}}} -【回答风格】 -- 简洁直接，不要长篇大论 -- 使用 > 格式引用关键内容 -- 优先给出结论，再解释原因""" +[Answer Style] +- Concise and direct, don't write lengthy passages +- Use > format to quote key content +- Give conclusions first, then explain reasons""" -CHAT_OBSERVATION_SUFFIX = "\n\n请简洁回答问题。" +CHAT_OBSERVATION_SUFFIX = "\n\nPlease answer the question concisely." # ═══════════════════════════════════════════════════════════════ -# ReportAgent 主类 +# ReportAgent Main Class # ═══════════════════════════════════════════════════════════════ + class ReportAgent: """ Report Agent - 模拟报告生成Agent @@ -1704,6 +1705,7 @@ def generate_report( ) # 使用ReportManager组装完整报告 + report.markdown_content = ReportManager.assemble_full_report(report_id, outline) report.status = ReportStatus.COMPLETED report.completed_at = datetime.now().isoformat() diff --git a/backend/app/services/simulation_config_generator.py b/backend/app/services/simulation_config_generator.py index cc362508b..09d0bf5a6 100644 --- a/backend/app/services/simulation_config_generator.py +++ b/backend/app/services/simulation_config_generator.py @@ -1,13 +1,13 @@ """ -模拟配置智能生成器 -使用LLM根据模拟需求、文档内容、图谱信息自动生成细致的模拟参数 -实现全程自动化，无需人工设置参数 - -采用分步生成策略，避免一次性生成过长内容导致失败： -1. 生成时间配置 -2. 生成事件配置 -3. 分批生成Agent配置 -4. 生成平台配置 +Simulation Configuration Intelligent Generator +Use LLM to automatically generate detailed simulation parameters based on simulation requirements, document content, and knowledge graph information +Implement full process automation without manual parameter setting + +Adopt step-by-step generation strategy to avoid failures from generating too long content at once: +1. Generate time configuration +2. Generate event configuration +3. Generate agent configurations in batches +4. Generate platform configuration """ import json @@ -20,160 +20,160 @@ from ..config import Config from ..utils.logger import get_logger -from .zep_entity_reader import EntityNode, ZepEntityReader +from .zep_entity_reader import EntityNode logger = get_logger('mirofish.simulation_config') -# 中国作息时间配置（北京时间） -CHINA_TIMEZONE_CONFIG = { - # 深夜时段（几乎无人活动） +# Time zone configuration for Chinese work schedules (Beijing Time) +DEFAULT_TIMEZONE_CONFIG = { + # Dead hours (almost no activity) "dead_hours": [0, 1, 2, 3, 4, 5], - # 早间时段（逐渐醒来） + # Morning hours (gradually waking up) "morning_hours": [6, 7, 8], - # 工作时段 + # Work hours "work_hours": [9, 10, 11, 12, 13, 14, 15, 16, 17, 18], - # 晚间高峰（最活跃） + # Evening peak (most active) "peak_hours": [19, 20, 21, 22], - # 夜间时段（活跃度下降） + # Night hours (activity decreases) "night_hours": [23], - # 活跃度系数 + # Activity multipliers "activity_multipliers": { - "dead": 0.05, # 凌晨几乎无人 - "morning": 0.4, # 早间逐渐活跃 - "work": 0.7, # 工作时段中等 - "peak": 1.5, # 晚间高峰 - "night": 0.5 # 深夜下降 + "dead": 0.05, # Almost no one in early morning + "morning": 0.4, # Gradually active in morning + "work": 0.7, # Medium activity during work hours + "peak": 1.5, # Evening peak + "night": 0.5 # Activity decreases at night } } @dataclass class AgentActivityConfig: - """单个Agent的活动配置""" + """Activity configuration for a single Agent""" agent_id: int entity_uuid: str entity_name: str entity_type: str - - # 活跃度配置 (0.0-1.0) - activity_level: float = 0.5 # 整体活跃度 - - # 发言频率（每小时预期发言次数） + + # Activity configuration (0.0-1.0) + activity_level: float = 0.5 # Overall activity level + + # Speech frequency (expected posts per hour) posts_per_hour: float = 1.0 comments_per_hour: float = 2.0 - - # 活跃时间段（24小时制，0-23） + + # Active time periods (24-hour format, 0-23) active_hours: List[int] = field(default_factory=lambda: list(range(8, 23))) - - # 响应速度（对热点事件的反应延迟，单位：模拟分钟） + + # Response speed (reaction delay to trending events, unit: simulation minutes) response_delay_min: int = 5 response_delay_max: int = 60 - - # 情感倾向 (-1.0到1.0，负面到正面) + + # Sentiment tendency (-1.0 to 1.0, negative to positive) sentiment_bias: float = 0.0 - - # 立场（对特定话题的态度） + + # Stance (attitude toward specific topics) stance: str = "neutral" # supportive, opposing, neutral, observer - - # 影响力权重（决定其发言被其他Agent看到的概率） + + # Influence weight (determines probability of their speech being seen by other agents) influence_weight: float = 1.0 -@dataclass +@dataclass class TimeSimulationConfig: - """时间模拟配置（基于中国人作息习惯）""" - # 模拟总时长（模拟小时数） - total_simulation_hours: int = 72 # 默认模拟72小时（3天） - - # 每轮代表的时间（模拟分钟）- 默认60分钟（1小时），加快时间流速 + """Time simulation configuration (based on Chinese work schedule habits)""" + # Total simulation time (simulation hours) + total_simulation_hours: int = 72 # Default 72 hours (3 days) + + # Time represented per round (simulation minutes) - default 60 minutes (1 hour), speed up time minutes_per_round: int = 60 - - # 每小时激活的Agent数量范围 + + # Range of agents activated per hour agents_per_hour_min: int = 5 agents_per_hour_max: int = 20 - - # 高峰时段（晚间19-22点，中国人最活跃的时间） + + # Peak hours (evening 19-22, most active time for Chinese people) peak_hours: List[int] = field(default_factory=lambda: [19, 20, 21, 22]) peak_activity_multiplier: float = 1.5 - - # 低谷时段（凌晨0-5点，几乎无人活动） + + # Off-peak hours (early morning 0-5, almost no activity) off_peak_hours: List[int] = field(default_factory=lambda: [0, 1, 2, 3, 4, 5]) - off_peak_activity_multiplier: float = 0.05 # 凌晨活跃度极低 - - # 早间时段 + off_peak_activity_multiplier: float = 0.05 # Very low activity in early morning + + # Morning hours morning_hours: List[int] = field(default_factory=lambda: [6, 7, 8]) morning_activity_multiplier: float = 0.4 - - # 工作时段 + + # Work hours work_hours: List[int] = field(default_factory=lambda: [9, 10, 11, 12, 13, 14, 15, 16, 17, 18]) work_activity_multiplier: float = 0.7 @dataclass class EventConfig: - """事件配置""" - # 初始事件（模拟开始时的触发事件） + """Event configuration""" + # Initial posts (triggering events at the start of simulation) initial_posts: List[Dict[str, Any]] = field(default_factory=list) - - # 定时事件（在特定时间触发的事件） + + # Scheduled events (events triggered at specific times) scheduled_events: List[Dict[str, Any]] = field(default_factory=list) - - # 热点话题关键词 + + # Hot topic keywords hot_topics: List[str] = field(default_factory=list) - - # 舆论引导方向 + + # Opinion narrative direction narrative_direction: str = "" @dataclass class PlatformConfig: - """平台特定配置""" + """Platform-specific configuration""" platform: str # twitter or reddit - - # 推荐算法权重 - recency_weight: float = 0.4 # 时间新鲜度 - popularity_weight: float = 0.3 # 热度 - relevance_weight: float = 0.3 # 相关性 - - # 病毒传播阈值（达到多少互动后触发扩散） + + # Recommendation algorithm weights + recency_weight: float = 0.4 # Time freshness + popularity_weight: float = 0.3 # Popularity + relevance_weight: float = 0.3 # Relevance + + # Viral threshold (number of interactions before triggering spread) viral_threshold: int = 10 - - # 回声室效应强度（相似观点聚集程度） + + # Echo chamber effect strength (degree of similar opinion clustering) echo_chamber_strength: float = 0.5 @dataclass class SimulationParameters: - """完整的模拟参数配置""" - # 基础信息 + """Complete simulation parameter configuration""" + # Basic information simulation_id: str project_id: str graph_id: str simulation_requirement: str - - # 时间配置 + + # Time configuration time_config: TimeSimulationConfig = field(default_factory=TimeSimulationConfig) - - # Agent配置列表 + + # Agent configuration list agent_configs: List[AgentActivityConfig] = field(default_factory=list) - - # 事件配置 + + # Event configuration event_config: EventConfig = field(default_factory=EventConfig) - - # 平台配置 + + # Platform configuration twitter_config: Optional[PlatformConfig] = None reddit_config: Optional[PlatformConfig] = None - - # LLM配置 + + # LLM configuration llm_model: str = "" llm_base_url: str = "" - - # 生成元数据 + + # Generation metadata generated_at: str = field(default_factory=lambda: datetime.now().isoformat()) - generation_reasoning: str = "" # LLM的推理说明 - + generation_reasoning: str = "" # LLM reasoning explanation + def to_dict(self) -> Dict[str, Any]: - """转换为字典""" + """Convert to dictionary""" time_dict = asdict(self.time_config) return { "simulation_id": self.simulation_id, @@ -190,37 +190,37 @@ def to_dict(self) -> Dict[str, Any]: "generated_at": self.generated_at, "generation_reasoning": self.generation_reasoning, } - + def to_json(self, indent: int = 2) -> str: - """转换为JSON字符串""" + """Convert to JSON string""" return json.dumps(self.to_dict(), ensure_ascii=False, indent=indent) class SimulationConfigGenerator: """ - 模拟配置智能生成器 - - 使用LLM分析模拟需求、文档内容、图谱实体信息， - 自动生成最佳的模拟参数配置 - - 采用分步生成策略： - 1. 生成时间配置和事件配置（轻量级） - 2. 分批生成Agent配置（每批10-20个） - 3. 生成平台配置 + Simulation Configuration Intelligent Generator + + Use LLM to analyze simulation requirements, document content, knowledge graph entity information, + and automatically generate optimal simulation parameter configuration + + Adopt step-by-step generation strategy: + 1. Generate time configuration and event configuration (lightweight) + 2. Generate agent configurations in batches (10-20 per batch) + 3. Generate platform configuration """ - - # 上下文最大字符数 + + # Maximum context length in characters MAX_CONTEXT_LENGTH = 50000 - # 每批生成的Agent数量 + # Number of agents per batch AGENTS_PER_BATCH = 15 - - # 各步骤的上下文截断长度（字符数） - TIME_CONFIG_CONTEXT_LENGTH = 10000 # 时间配置 - EVENT_CONFIG_CONTEXT_LENGTH = 8000 # 事件配置 - ENTITY_SUMMARY_LENGTH = 300 # 实体摘要 - AGENT_SUMMARY_LENGTH = 300 # Agent配置中的实体摘要 - ENTITIES_PER_TYPE_DISPLAY = 20 # 每类实体显示数量 - + + # Context truncation length for each step (characters) + TIME_CONFIG_CONTEXT_LENGTH = 10000 # Time configuration + EVENT_CONFIG_CONTEXT_LENGTH = 8000 # Event configuration + ENTITY_SUMMARY_LENGTH = 300 # Entity summary + AGENT_SUMMARY_LENGTH = 300 # Entity summary in agent configuration + ENTITIES_PER_TYPE_DISPLAY = 20 # Number of entities to display per type + def __init__( self, api_key: Optional[str] = None, @@ -230,10 +230,10 @@ def __init__( self.api_key = api_key or Config.LLM_API_KEY self.base_url = base_url or Config.LLM_BASE_URL self.model_name = model_name or Config.LLM_MODEL_NAME - + if not self.api_key: - raise ValueError("LLM_API_KEY 未配置") - + raise ValueError("LLM_API_KEY not configured") + self.client = OpenAI( api_key=self.api_key, base_url=self.base_url @@ -252,37 +252,37 @@ def generate_config( progress_callback: Optional[Callable[[int, int, str], None]] = None, ) -> SimulationParameters: """ - 智能生成完整的模拟配置（分步生成） - + Intelligently generate complete simulation configuration (step-by-step generation) + Args: - simulation_id: 模拟ID - project_id: 项目ID - graph_id: 图谱ID - simulation_requirement: 模拟需求描述 - document_text: 原始文档内容 - entities: 过滤后的实体列表 - enable_twitter: 是否启用Twitter - enable_reddit: 是否启用Reddit - progress_callback: 进度回调函数(current_step, total_steps, message) - + simulation_id: Simulation ID + project_id: Project ID + graph_id: Knowledge graph ID + simulation_requirement: Simulation requirement description + document_text: Original document content + entities: Filtered entity list + enable_twitter: Whether to enable Twitter + enable_reddit: Whether to enable Reddit + progress_callback: Progress callback function(current_step, total_steps, message) + Returns: - SimulationParameters: 完整的模拟参数 + SimulationParameters: Complete simulation parameters """ - logger.info(f"开始智能生成模拟配置: simulation_id={simulation_id}, 实体数={len(entities)}") + logger.info(f"Starting intelligent simulation configuration generation: simulation_id={simulation_id}, entities={len(entities)}") - # 计算总步骤数 + # Calculate total steps num_batches = math.ceil(len(entities) / self.AGENTS_PER_BATCH) - total_steps = 3 + num_batches # 时间配置 + 事件配置 + N批Agent + 平台配置 + total_steps = 3 + num_batches # time config + event config + N batch agents + platform config current_step = 0 - + def report_progress(step: int, message: str): nonlocal current_step current_step = step if progress_callback: progress_callback(step, total_steps, message) logger.info(f"[{step}/{total_steps}] {message}") - - # 1. 构建基础上下文信息 + + # 1. Build basic context information context = self._build_context( simulation_requirement=simulation_requirement, document_text=document_text, @@ -291,29 +291,29 @@ def report_progress(step: int, message: str): reasoning_parts = [] - # ========== 步骤1: 生成时间配置 ========== - report_progress(1, "生成时间配置...") + # ========== Step 1: Generate time configuration ========== + report_progress(1, "Generating time configuration...") num_entities = len(entities) time_config_result = self._generate_time_config(context, num_entities) time_config = self._parse_time_config(time_config_result, num_entities) - reasoning_parts.append(f"时间配置: {time_config_result.get('reasoning', '成功')}") - - # ========== 步骤2: 生成事件配置 ========== - report_progress(2, "生成事件配置和热点话题...") + reasoning_parts.append(f"Time config: {time_config_result.get('reasoning', 'Success')}") + + # ========== Step 2: Generate event configuration ========== + report_progress(2, "Generating event configuration and hot topics...") event_config_result = self._generate_event_config(context, simulation_requirement, entities) event_config = self._parse_event_config(event_config_result) - reasoning_parts.append(f"事件配置: {event_config_result.get('reasoning', '成功')}") - - # ========== 步骤3-N: 分批生成Agent配置 ========== + reasoning_parts.append(f"Event config: {event_config_result.get('reasoning', 'Success')}") + + # ========== Step 3-N: Generate agent configurations in batches ========== all_agent_configs = [] for batch_idx in range(num_batches): start_idx = batch_idx * self.AGENTS_PER_BATCH end_idx = min(start_idx + self.AGENTS_PER_BATCH, len(entities)) batch_entities = entities[start_idx:end_idx] - + report_progress( 3 + batch_idx, - f"生成Agent配置 ({start_idx + 1}-{end_idx}/{len(entities)})..." + f"Generating agent configuration ({start_idx + 1}-{end_idx}/{len(entities)})..." ) batch_configs = self._generate_agent_configs_batch( @@ -324,16 +324,16 @@ def report_progress(step: int, message: str): ) all_agent_configs.extend(batch_configs) - reasoning_parts.append(f"Agent配置: 成功生成 {len(all_agent_configs)} 个") - - # ========== 为初始帖子分配发布者 Agent ========== - logger.info("为初始帖子分配合适的发布者 Agent...") + reasoning_parts.append(f"Agent config: Successfully generated {len(all_agent_configs)}") + + # ========== Assign initial post agents ========== + logger.info("Assigning appropriate publisher agents to initial posts...") event_config = self._assign_initial_post_agents(event_config, all_agent_configs) assigned_count = len([p for p in event_config.initial_posts if p.get("poster_agent_id") is not None]) - reasoning_parts.append(f"初始帖子分配: {assigned_count} 个帖子已分配发布者") - - # ========== 最后一步: 生成平台配置 ========== - report_progress(total_steps, "生成平台配置...") + reasoning_parts.append(f"Initial posts assigned: {assigned_count} posts assigned publishers") + + # ========== Final step: Generate platform configuration ========== + report_progress(total_steps, "Generating platform configuration...") twitter_config = None reddit_config = None @@ -357,7 +357,7 @@ def report_progress(step: int, message: str): echo_chamber_strength=0.6 ) - # 构建最终参数 + # Build final parameters params = SimulationParameters( simulation_id=simulation_id, project_id=project_id, @@ -373,70 +373,70 @@ def report_progress(step: int, message: str): generation_reasoning=" | ".join(reasoning_parts) ) - logger.info(f"模拟配置生成完成: {len(params.agent_configs)} 个Agent配置") - + logger.info(f"Simulation configuration generation complete: {len(params.agent_configs)} agent configurations") + return params - + def _build_context( self, simulation_requirement: str, document_text: str, entities: List[EntityNode] ) -> str: - """构建LLM上下文，截断到最大长度""" - - # 实体摘要 + """Build LLM context, truncate to maximum length""" + + # Entity summary entity_summary = self._summarize_entities(entities) - - # 构建上下文 + + # Build context context_parts = [ - f"## 模拟需求\n{simulation_requirement}", - f"\n## 实体信息 ({len(entities)}个)\n{entity_summary}", + f"## Simulation Requirements\n{simulation_requirement}", + f"\n## Entity Information ({len(entities)})\n{entity_summary}", ] - + current_length = sum(len(p) for p in context_parts) - remaining_length = self.MAX_CONTEXT_LENGTH - current_length - 500 # 留500字符余量 - + remaining_length = self.MAX_CONTEXT_LENGTH - current_length - 500 # Reserve 500 characters + if remaining_length > 0 and document_text: doc_text = document_text[:remaining_length] if len(document_text) > remaining_length: - doc_text += "\n...(文档已截断)" - context_parts.append(f"\n## 原始文档内容\n{doc_text}") - + doc_text += "\n...(document truncated)" + context_parts.append(f"\n## Original Document Content\n{doc_text}") + return "\n".join(context_parts) - + def _summarize_entities(self, entities: List[EntityNode]) -> str: - """生成实体摘要""" + """Generate entity summary""" lines = [] - - # 按类型分组 + + # Group by type by_type: Dict[str, List[EntityNode]] = {} for e in entities: t = e.get_entity_type() or "Unknown" if t not in by_type: by_type[t] = [] by_type[t].append(e) - + for entity_type, type_entities in by_type.items(): - lines.append(f"\n### {entity_type} ({len(type_entities)}个)") - # 使用配置的显示数量和摘要长度 + lines.append(f"\n### {entity_type} ({len(type_entities)})") + # Use configured display quantity and summary length display_count = self.ENTITIES_PER_TYPE_DISPLAY summary_len = self.ENTITY_SUMMARY_LENGTH for e in type_entities[:display_count]: summary_preview = (e.summary[:summary_len] + "...") if len(e.summary) > summary_len else e.summary lines.append(f"- {e.name}: {summary_preview}") if len(type_entities) > display_count: - lines.append(f" ... 还有 {len(type_entities) - display_count} 个") - + lines.append(f" ... and {len(type_entities) - display_count} more") + return "\n".join(lines) def _call_llm_with_retry(self, prompt: str, system_prompt: str) -> Dict[str, Any]: - """带重试的LLM调用，包含JSON修复逻辑""" + """LLM call with retry, including JSON repair logic""" import re - + max_attempts = 3 last_error = None - + for attempt in range(max_attempts): try: response = self.client.chat.completions.create( @@ -446,121 +446,121 @@ def _call_llm_with_retry(self, prompt: str, system_prompt: str) -> Dict[str, Any {"role": "user", "content": prompt} ], response_format={"type": "json_object"}, - temperature=0.7 - (attempt * 0.1) # 每次重试降低温度 - # 不设置max_tokens，让LLM自由发挥 + temperature=0.7 - (attempt * 0.1) # Lower temperature with each retry + # Don't set max_tokens, let LLM generate freely ) - + content = response.choices[0].message.content finish_reason = response.choices[0].finish_reason - - # 检查是否被截断 + + # Check if output was truncated if finish_reason == 'length': - logger.warning(f"LLM输出被截断 (attempt {attempt+1})") + logger.warning(f"LLM output truncated (attempt {attempt+1})") content = self._fix_truncated_json(content) - - # 尝试解析JSON + + # Try to parse JSON try: return json.loads(content) except json.JSONDecodeError as e: - logger.warning(f"JSON解析失败 (attempt {attempt+1}): {str(e)[:80]}") - - # 尝试修复JSON + logger.warning(f"JSON parsing failed (attempt {attempt+1}): {str(e)[:80]}") + + # Try to fix JSON fixed = self._try_fix_config_json(content) if fixed: return fixed - + last_error = e - + except Exception as e: - logger.warning(f"LLM调用失败 (attempt {attempt+1}): {str(e)[:80]}") + logger.warning(f"LLM call failed (attempt {attempt+1}): {str(e)[:80]}") last_error = e import time time.sleep(2 * (attempt + 1)) - - raise last_error or Exception("LLM调用失败") + + raise last_error or Exception("LLM call failed") def _fix_truncated_json(self, content: str) -> str: - """修复被截断的JSON""" + """Fix truncated JSON""" content = content.strip() - - # 计算未闭合的括号 + + # Count unclosed parentheses open_braces = content.count('{') - content.count('}') open_brackets = content.count('[') - content.count(']') - - # 检查是否有未闭合的字符串 + + # Check for unclosed strings if content and content[-1] not in '",}]': content += '"' - - # 闭合括号 + + # Close parentheses content += ']' * open_brackets content += '}' * open_braces - + return content - + def _try_fix_config_json(self, content: str) -> Optional[Dict[str, Any]]: - """尝试修复配置JSON""" + """Try to fix configuration JSON""" import re - - # 修复被截断的情况 + + # Fix truncated case content = self._fix_truncated_json(content) - - # 提取JSON部分 + + # Extract JSON portion json_match = re.search(r'\{[\s\S]*\}', content) if json_match: json_str = json_match.group() - - # 移除字符串中的换行符 + + # Remove newlines in strings def fix_string(match): s = match.group(0) s = s.replace('\n', ' ').replace('\r', ' ') s = re.sub(r'\s+', ' ', s) return s - + json_str = re.sub(r'"[^"\\]*(?:\\.[^"\\]*)*"', fix_string, json_str) - + try: return json.loads(json_str) except: - # 尝试移除所有控制字符 + # Try removing all control characters json_str = re.sub(r'[\x00-\x1f\x7f-\x9f]', ' ', json_str) json_str = re.sub(r'\s+', ' ', json_str) try: return json.loads(json_str) except: pass - + return None def _generate_time_config(self, context: str, num_entities: int) -> Dict[str, Any]: - """生成时间配置""" - # 使用配置的上下文截断长度 + """Generate time configuration""" + # Use configured context truncation length context_truncated = context[:self.TIME_CONFIG_CONTEXT_LENGTH] - - # 计算最大允许值（80%的agent数） + + # Calculate maximum allowed value (90% of agents) max_agents_allowed = max(1, int(num_entities * 0.9)) - - prompt = f"""基于以下模拟需求，生成时间模拟配置。 + + prompt = f"""Based on the following simulation requirements, generate time simulation configuration. {context_truncated} -## 任务 -请生成时间配置JSON。 +## Task +Please generate time configuration JSON. -### 基本原则（仅供参考，需根据具体事件和参与群体灵活调整）： -- 用户群体为中国人，需符合北京时间作息习惯 -- 凌晨0-5点几乎无人活动（活跃度系数0.05） -- 早上6-8点逐渐活跃（活跃度系数0.4） -- 工作时间9-18点中等活跃（活跃度系数0.7） -- 晚间19-22点是高峰期（活跃度系数1.5） -- 23点后活跃度下降（活跃度系数0.5） -- 一般规律：凌晨低活跃、早间渐增、工作时段中等、晚间高峰 -- **重要**：以下示例值仅供参考，你需要根据事件性质、参与群体特点来调整具体时段 - - 例如：学生群体高峰可能是21-23点；媒体全天活跃；官方机构只在工作时间 - - 例如：突发热点可能导致深夜也有讨论，off_peak_hours 可适当缩短 +### Basic principles (for reference only, adjust flexibly based on event nature and participant characteristics): +- User base is Chinese people, must follow Beijing Time work schedule habits +- 0-5am almost no activity (activity coefficient 0.05) +- 6-8am gradually active (activity coefficient 0.4) +- 9-18 work time moderately active (activity coefficient 0.7) +- 19-22 evening is peak period (activity coefficient 1.5) +- After 23 activity decreases (activity coefficient 0.5) +- General rule: low activity early morning, gradually increasing morning, moderate work time, evening peak +- **Important**: Example values below are for reference only, adjust specific time periods based on event nature and participant characteristics + - Example: student peak may be 21-23; media active all day; official institutions only during work hours + - Example: breaking news may cause late night discussions, off_peak_hours can be shortened appropriately -### 返回JSON格式（不要markdown） +### Return JSON format (no markdown) -示例： +Example: {{ "total_simulation_hours": 72, "minutes_per_round": 60, @@ -570,70 +570,70 @@ def _generate_time_config(self, context: str, num_entities: int) -> Dict[str, An "off_peak_hours": [0, 1, 2, 3, 4, 5], "morning_hours": [6, 7, 8], "work_hours": [9, 10, 11, 12, 13, 14, 15, 16, 17, 18], - "reasoning": "针对该事件的时间配置说明" + "reasoning": "Explanation of time configuration for this event" }} -字段说明： -- total_simulation_hours (int): 模拟总时长，24-168小时，突发事件短、持续话题长 -- minutes_per_round (int): 每轮时长，30-120分钟，建议60分钟 -- agents_per_hour_min (int): 每小时最少激活Agent数（取值范围: 1-{max_agents_allowed}） -- agents_per_hour_max (int): 每小时最多激活Agent数（取值范围: 1-{max_agents_allowed}） -- peak_hours (int数组): 高峰时段，根据事件参与群体调整 -- off_peak_hours (int数组): 低谷时段，通常深夜凌晨 -- morning_hours (int数组): 早间时段 -- work_hours (int数组): 工作时段 -- reasoning (string): 简要说明为什么这样配置""" - - system_prompt = "你是社交媒体模拟专家。返回纯JSON格式，时间配置需符合中国人作息习惯。" - +Field description: +- total_simulation_hours (int): Total simulation time, 24-168 hours, short for breaking news, long for ongoing topics +- minutes_per_round (int): Time per round, 30-120 minutes, recommend 60 minutes +- agents_per_hour_min (int): Minimum agents activated per hour (range: 1-{max_agents_allowed}) +- agents_per_hour_max (int): Maximum agents activated per hour (range: 1-{max_agents_allowed}) +- peak_hours (int array): Peak hours, adjust based on event participants +- off_peak_hours (int array): Off-peak hours, usually late night/early morning +- morning_hours (int array): Morning hours +- work_hours (int array): Work hours +- reasoning (string): Brief explanation for this configuration""" + + system_prompt = "You are a social media simulation expert. Return pure JSON format, time configuration must follow Chinese work schedule habits." + try: return self._call_llm_with_retry(prompt, system_prompt) except Exception as e: - logger.warning(f"时间配置LLM生成失败: {e}, 使用默认配置") + logger.warning(f"Time config LLM generation failed: {e}, using default configuration") return self._get_default_time_config(num_entities) def _get_default_time_config(self, num_entities: int) -> Dict[str, Any]: - """获取默认时间配置（中国人作息）""" + """Get default time configuration (Chinese work schedule)""" return { "total_simulation_hours": 72, - "minutes_per_round": 60, # 每轮1小时，加快时间流速 + "minutes_per_round": 60, # 1 hour per round, speed up time "agents_per_hour_min": max(1, num_entities // 15), "agents_per_hour_max": max(5, num_entities // 5), "peak_hours": [19, 20, 21, 22], "off_peak_hours": [0, 1, 2, 3, 4, 5], "morning_hours": [6, 7, 8], "work_hours": [9, 10, 11, 12, 13, 14, 15, 16, 17, 18], - "reasoning": "使用默认中国人作息配置（每轮1小时）" + "reasoning": "Using default Chinese work schedule configuration (1 hour per round)" } - + def _parse_time_config(self, result: Dict[str, Any], num_entities: int) -> TimeSimulationConfig: - """解析时间配置结果，并验证agents_per_hour值不超过总agent数""" - # 获取原始值 + """Parse time configuration result and verify agents_per_hour doesn't exceed total agents""" + # Get original values agents_per_hour_min = result.get("agents_per_hour_min", max(1, num_entities // 15)) agents_per_hour_max = result.get("agents_per_hour_max", max(5, num_entities // 5)) - - # 验证并修正：确保不超过总agent数 + + # Verify and correct: ensure not exceeding total agents if agents_per_hour_min > num_entities: - logger.warning(f"agents_per_hour_min ({agents_per_hour_min}) 超过总Agent数 ({num_entities})，已修正") + logger.warning(f"agents_per_hour_min ({agents_per_hour_min}) exceeds total agents ({num_entities}), corrected") agents_per_hour_min = max(1, num_entities // 10) - + if agents_per_hour_max > num_entities: - logger.warning(f"agents_per_hour_max ({agents_per_hour_max}) 超过总Agent数 ({num_entities})，已修正") + logger.warning(f"agents_per_hour_max ({agents_per_hour_max}) exceeds total agents ({num_entities}), corrected") agents_per_hour_max = max(agents_per_hour_min + 1, num_entities // 2) - - # 确保 min < max + + # Ensure min < max if agents_per_hour_min >= agents_per_hour_max: agents_per_hour_min = max(1, agents_per_hour_max // 2) - logger.warning(f"agents_per_hour_min >= max，已修正为 {agents_per_hour_min}") - + logger.warning(f"agents_per_hour_min >= max, corrected to {agents_per_hour_min}") + return TimeSimulationConfig( total_simulation_hours=result.get("total_simulation_hours", 72), - minutes_per_round=result.get("minutes_per_round", 60), # 默认每轮1小时 + minutes_per_round=result.get("minutes_per_round", 60), # Default 1 hour per round agents_per_hour_min=agents_per_hour_min, agents_per_hour_max=agents_per_hour_max, peak_hours=result.get("peak_hours", [19, 20, 21, 22]), off_peak_hours=result.get("off_peak_hours", [0, 1, 2, 3, 4, 5]), - off_peak_activity_multiplier=0.05, # 凌晨几乎无人 + off_peak_activity_multiplier=0.05, # Almost no one in early morning morning_hours=result.get("morning_hours", [6, 7, 8]), morning_activity_multiplier=0.4, work_hours=result.get("work_hours", list(range(9, 19))), @@ -642,19 +642,19 @@ def _parse_time_config(self, result: Dict[str, Any], num_entities: int) -> TimeS ) def _generate_event_config( - self, - context: str, + self, + context: str, simulation_requirement: str, entities: List[EntityNode] ) -> Dict[str, Any]: - """生成事件配置""" - - # 获取可用的实体类型列表，供 LLM 参考 + """Generate event configuration""" + + # Get available entity types list for LLM reference entity_types_available = list(set( e.get_entity_type() or "Unknown" for e in entities )) - - # 为每种类型列出代表性实体名称 + + # List representative entity names for each type type_examples = {} for e in entities: etype = e.get_entity_type() or "Unknown" @@ -662,59 +662,59 @@ def _generate_event_config( type_examples[etype] = [] if len(type_examples[etype]) < 3: type_examples[etype].append(e.name) - + type_info = "\n".join([ - f"- {t}: {', '.join(examples)}" + f"- {t}: {', '.join(examples)}" for t, examples in type_examples.items() ]) - - # 使用配置的上下文截断长度 + + # Use configured context truncation length context_truncated = context[:self.EVENT_CONFIG_CONTEXT_LENGTH] - - prompt = f"""基于以下模拟需求，生成事件配置。 -模拟需求: {simulation_requirement} + prompt = f"""Based on the following simulation requirements, generate event configuration. + +Simulation Requirements: {simulation_requirement} {context_truncated} -## 可用实体类型及示例 +## Available Entity Types and Examples {type_info} -## 任务 -请生成事件配置JSON： -- 提取热点话题关键词 -- 描述舆论发展方向 -- 设计初始帖子内容，**每个帖子必须指定 poster_type（发布者类型）** +## Task +Please generate event configuration JSON: +- Extract hot topic keywords +- Describe opinion development direction +- Design initial post content, **each post must specify poster_type (publisher type)** -**重要**: poster_type 必须从上面的"可用实体类型"中选择，这样初始帖子才能分配给合适的 Agent 发布。 -例如：官方声明应由 Official/University 类型发布，新闻由 MediaOutlet 发布，学生观点由 Student 发布。 +**Important**: poster_type must be selected from the "Available Entity Types" above so initial posts can be assigned to appropriate agents for publishing. +Example: Official statements should be published by Official/University type, news by MediaOutlet, student opinions by Student type. -返回JSON格式（不要markdown）： +Return JSON format (no markdown): {{ - "hot_topics": ["关键词1", "关键词2", ...], - "narrative_direction": "<舆论发展方向描述>", + "hot_topics": ["keyword1", "keyword2", ...], + "narrative_direction": "", "initial_posts": [ - {{"content": "帖子内容", "poster_type": "实体类型（必须从可用类型中选择）"}}, + {{"content": "post content", "poster_type": "entity type (must select from available types)"}}, ... ], - "reasoning": "<简要说明>" + "reasoning": "" }}""" - system_prompt = "你是舆论分析专家。返回纯JSON格式。注意 poster_type 必须精确匹配可用实体类型。" - + system_prompt = "You are an opinion analysis expert. Return pure JSON format. Note poster_type must match available entity types precisely." + try: return self._call_llm_with_retry(prompt, system_prompt) except Exception as e: - logger.warning(f"事件配置LLM生成失败: {e}, 使用默认配置") + logger.warning(f"Event config LLM generation failed: {e}, using default configuration") return { "hot_topics": [], "narrative_direction": "", "initial_posts": [], - "reasoning": "使用默认配置" + "reasoning": "Using default configuration" } - + def _parse_event_config(self, result: Dict[str, Any]) -> EventConfig: - """解析事件配置结果""" + """Parse event configuration result""" return EventConfig( initial_posts=result.get("initial_posts", []), scheduled_events=[], @@ -728,22 +728,22 @@ def _assign_initial_post_agents( agent_configs: List[AgentActivityConfig] ) -> EventConfig: """ - 为初始帖子分配合适的发布者 Agent - - 根据每个帖子的 poster_type 匹配最合适的 agent_id + Assign appropriate publisher agents to initial posts + + Match agent_id based on each post's poster_type """ if not event_config.initial_posts: return event_config - - # 按实体类型建立 agent 索引 + + # Build agent index by entity type agents_by_type: Dict[str, List[AgentActivityConfig]] = {} for agent in agent_configs: etype = agent.entity_type.lower() if etype not in agents_by_type: agents_by_type[etype] = [] agents_by_type[etype].append(agent) - - # 类型映射表（处理 LLM 可能输出的不同格式） + + # Type mapping table (handle different formats LLM might output) type_aliases = { "official": ["official", "university", "governmentagency", "government"], "university": ["university", "official"], @@ -754,26 +754,26 @@ def _assign_initial_post_agents( "organization": ["organization", "ngo", "company", "group"], "person": ["person", "student", "alumni"], } - - # 记录每种类型已使用的 agent 索引，避免重复使用同一个 agent + + # Track used agent indices for each type to avoid reusing same agent used_indices: Dict[str, int] = {} - + updated_posts = [] for post in event_config.initial_posts: poster_type = post.get("poster_type", "").lower() content = post.get("content", "") - - # 尝试找到匹配的 agent + + # Try to find matching agent matched_agent_id = None - - # 1. 直接匹配 + + # 1. Direct match if poster_type in agents_by_type: agents = agents_by_type[poster_type] idx = used_indices.get(poster_type, 0) % len(agents) matched_agent_id = agents[idx].agent_id used_indices[poster_type] = idx + 1 else: - # 2. 使用别名匹配 + # 2. Match using aliases for alias_key, aliases in type_aliases.items(): if poster_type in aliases or alias_key == poster_type: for alias in aliases: @@ -785,25 +785,25 @@ def _assign_initial_post_agents( break if matched_agent_id is not None: break - - # 3. 如果仍未找到，使用影响力最高的 agent + + # 3. If still not found, use agent with highest influence if matched_agent_id is None: - logger.warning(f"未找到类型 '{poster_type}' 的匹配 Agent，使用影响力最高的 Agent") + logger.warning(f"No matching agent found for type '{poster_type}', using agent with highest influence") if agent_configs: - # 按影响力排序，选择影响力最高的 - sorted_agents = sorted(agent_configs, key=lambda a: a.influence_weight, reverse=True) + # Sort by influence, select highest + sorted_agents = sorted(agent_configs, key=lambda a: a.influence_weight or 0.0, reverse=True) matched_agent_id = sorted_agents[0].agent_id else: matched_agent_id = 0 - + updated_posts.append({ "content": content, "poster_type": post.get("poster_type", "Unknown"), "poster_agent_id": matched_agent_id }) - - logger.info(f"初始帖子分配: poster_type='{poster_type}' -> agent_id={matched_agent_id}") - + + logger.info(f"Initial post assigned: poster_type='{poster_type}' -> agent_id={matched_agent_id}") + event_config.initial_posts = updated_posts return event_config @@ -814,9 +814,9 @@ def _generate_agent_configs_batch( start_idx: int, simulation_requirement: str ) -> List[AgentActivityConfig]: - """分批生成Agent配置""" - - # 构建实体信息（使用配置的摘要长度） + """Generate agent configurations in batch""" + + # Build entity information (using configured summary length) entity_list = [] summary_len = self.AGENT_SUMMARY_LENGTH for i, e in enumerate(entities): @@ -826,62 +826,62 @@ def _generate_agent_configs_batch( "entity_type": e.get_entity_type() or "Unknown", "summary": e.summary[:summary_len] if e.summary else "" }) - - prompt = f"""基于以下信息，为每个实体生成社交媒体活动配置。 -模拟需求: {simulation_requirement} + prompt = f"""Based on the following information, generate social media activity configuration for each entity. -## 实体列表 +Simulation Requirements: {simulation_requirement} + +## Entity List ```json {json.dumps(entity_list, ensure_ascii=False, indent=2)} ``` -## 任务 -为每个实体生成活动配置，注意： -- **时间符合中国人作息**：凌晨0-5点几乎不活动，晚间19-22点最活跃 -- **官方机构**（University/GovernmentAgency）：活跃度低(0.1-0.3)，工作时间(9-17)活动，响应慢(60-240分钟)，影响力高(2.5-3.0) -- **媒体**（MediaOutlet）：活跃度中(0.4-0.6)，全天活动(8-23)，响应快(5-30分钟)，影响力高(2.0-2.5) -- **个人**（Student/Person/Alumni）：活跃度高(0.6-0.9)，主要晚间活动(18-23)，响应快(1-15分钟)，影响力低(0.8-1.2) -- **公众人物/专家**：活跃度中(0.4-0.6)，影响力中高(1.5-2.0) +## Task +Generate activity configuration for each entity, noting: +- **Time follows Chinese work schedule**: Almost no activity 0-5am, most active 19-22 +- **Official institutions** (University/GovernmentAgency): Low activity (0.1-0.3), active during work hours (9-17), slow response (60-240 min), high influence (2.5-3.0) +- **Media** (MediaOutlet): Medium activity (0.4-0.6), active all day (8-23), fast response (5-30 min), high influence (2.0-2.5) +- **Individuals** (Student/Person/Alumni): High activity (0.6-0.9), mainly evening activity (18-23), fast response (1-15 min), low influence (0.8-1.2) +- **Public figures/Experts**: Medium activity (0.4-0.6), medium-high influence (1.5-2.0) -返回JSON格式（不要markdown）： +Return JSON format (no markdown): {{ "agent_configs": [ {{ - "agent_id": <必须与输入一致>, + "agent_id": , "activity_level": <0.0-1.0>, - "posts_per_hour": <发帖频率>, - "comments_per_hour": <评论频率>, - "active_hours": [<活跃小时列表，考虑中国人作息>], - "response_delay_min": <最小响应延迟分钟>, - "response_delay_max": <最大响应延迟分钟>, - "sentiment_bias": <-1.0到1.0>, + "posts_per_hour": , + "comments_per_hour": , + "active_hours": [], + "response_delay_min": , + "response_delay_max": , + "sentiment_bias": <-1.0 to 1.0>, "stance": "", - "influence_weight": <影响力权重> + "influence_weight": }}, ... ] }}""" - system_prompt = "你是社交媒体行为分析专家。返回纯JSON，配置需符合中国人作息习惯。" - + system_prompt = "You are a social media behavior analysis expert. Return pure JSON, configuration must follow Chinese work schedule habits." + try: result = self._call_llm_with_retry(prompt, system_prompt) llm_configs = {cfg["agent_id"]: cfg for cfg in result.get("agent_configs", [])} except Exception as e: - logger.warning(f"Agent配置批次LLM生成失败: {e}, 使用规则生成") + logger.warning(f"Agent config batch LLM generation failed: {e}, using rule-based generation") llm_configs = {} - - # 构建AgentActivityConfig对象 + + # Build AgentActivityConfig objects configs = [] for i, entity in enumerate(entities): agent_id = start_idx + i cfg = llm_configs.get(agent_id, {}) - - # 如果LLM没有生成，使用规则生成 + + # If LLM didn't generate, use rule-based generation if not cfg: cfg = self._generate_agent_config_by_rule(entity) - + config = AgentActivityConfig( agent_id=agent_id, entity_uuid=entity.uuid, @@ -898,15 +898,15 @@ def _generate_agent_configs_batch( influence_weight=cfg.get("influence_weight", 1.0) ) configs.append(config) - + return configs def _generate_agent_config_by_rule(self, entity: EntityNode) -> Dict[str, Any]: - """基于规则生成单个Agent配置（中国人作息）""" + """Generate single agent configuration based on rules (Chinese work schedule)""" entity_type = (entity.get_entity_type() or "Unknown").lower() - + if entity_type in ["university", "governmentagency", "ngo"]: - # 官方机构：工作时间活动，低频率，高影响力 + # Official institutions: work hour activity, low frequency, high influence return { "activity_level": 0.2, "posts_per_hour": 0.1, @@ -919,7 +919,7 @@ def _generate_agent_config_by_rule(self, entity: EntityNode) -> Dict[str, Any]: "influence_weight": 3.0 } elif entity_type in ["mediaoutlet"]: - # 媒体：全天活动，中等频率，高影响力 + # Media: all-day activity, medium frequency, high influence return { "activity_level": 0.5, "posts_per_hour": 0.8, @@ -932,7 +932,7 @@ def _generate_agent_config_by_rule(self, entity: EntityNode) -> Dict[str, Any]: "influence_weight": 2.5 } elif entity_type in ["professor", "expert", "official"]: - # 专家/教授：工作+晚间活动，中等频率 + # Experts/Professors: work + evening activity, medium frequency return { "activity_level": 0.4, "posts_per_hour": 0.3, @@ -945,12 +945,12 @@ def _generate_agent_config_by_rule(self, entity: EntityNode) -> Dict[str, Any]: "influence_weight": 2.0 } elif entity_type in ["student"]: - # 学生：晚间为主，高频率 + # Students: mainly evening, high frequency return { "activity_level": 0.8, "posts_per_hour": 0.6, "comments_per_hour": 1.5, - "active_hours": [8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 23], # 上午+晚间 + "active_hours": [8, 9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 23], # Morning + evening "response_delay_min": 1, "response_delay_max": 15, "sentiment_bias": 0.0, @@ -958,12 +958,12 @@ def _generate_agent_config_by_rule(self, entity: EntityNode) -> Dict[str, Any]: "influence_weight": 0.8 } elif entity_type in ["alumni"]: - # 校友：晚间为主 + # Alumni: mainly evening return { "activity_level": 0.6, "posts_per_hour": 0.4, "comments_per_hour": 0.8, - "active_hours": [12, 13, 19, 20, 21, 22, 23], # 午休+晚间 + "active_hours": [12, 13, 19, 20, 21, 22, 23], # Lunch break + evening "response_delay_min": 5, "response_delay_max": 30, "sentiment_bias": 0.0, @@ -971,12 +971,12 @@ def _generate_agent_config_by_rule(self, entity: EntityNode) -> Dict[str, Any]: "influence_weight": 1.0 } else: - # 普通人：晚间高峰 + # Ordinary people: evening peak return { "activity_level": 0.7, "posts_per_hour": 0.5, "comments_per_hour": 1.2, - "active_hours": [9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 23], # 白天+晚间 + "active_hours": [9, 10, 11, 12, 13, 18, 19, 20, 21, 22, 23], # Daytime + evening "response_delay_min": 2, "response_delay_max": 20, "sentiment_bias": 0.0, diff --git a/backend/app/services/simulation_ipc.py b/backend/app/services/simulation_ipc.py index 9d70d0bea..fe2e47449 100644 --- a/backend/app/services/simulation_ipc.py +++ b/backend/app/services/simulation_ipc.py @@ -118,7 +118,7 @@ def send_command( self, command_type: CommandType, args: Dict[str, Any], - timeout: float = 60.0, + timeout: float = 300.0, poll_interval: float = 0.5 ) -> IPCResponse: """ diff --git a/backend/app/services/simulation_runner.py b/backend/app/services/simulation_runner.py index 8c35380d1..01dcf0b21 100644 --- a/backend/app/services/simulation_runner.py +++ b/backend/app/services/simulation_runner.py @@ -1,6 +1,6 @@ """ -OASIS模拟运行器 -在后台运行模拟并记录每个Agent的动作，支持实时状态监控 +OASIS Simulation Runner +Run simulations in the background and record actions for each Agent, supporting real-time status monitoring """ import os @@ -25,15 +25,15 @@ logger = get_logger('mirofish.simulation_runner') -# 标记是否已注册清理函数 +# Flag whether cleanup function is registered _cleanup_registered = False -# 平台检测 +# Platform detection IS_WINDOWS = sys.platform == 'win32' class RunnerStatus(str, Enum): - """运行器状态""" + """Runner status""" IDLE = "idle" STARTING = "starting" RUNNING = "running" @@ -46,7 +46,7 @@ class RunnerStatus(str, Enum): @dataclass class AgentAction: - """Agent动作记录""" + """Agent action record""" round_num: int timestamp: str platform: str # twitter / reddit @@ -73,7 +73,7 @@ def to_dict(self) -> Dict[str, Any]: @dataclass class RoundSummary: - """每轮摘要""" + """Round summary""" round_num: int start_time: str end_time: Optional[str] = None @@ -99,52 +99,52 @@ def to_dict(self) -> Dict[str, Any]: @dataclass class SimulationRunState: - """模拟运行状态（实时）""" + """Simulation run state (real-time)""" simulation_id: str runner_status: RunnerStatus = RunnerStatus.IDLE - - # 进度信息 + + # Progress information current_round: int = 0 total_rounds: int = 0 simulated_hours: int = 0 total_simulation_hours: int = 0 - - # 各平台独立轮次和模拟时间（用于双平台并行显示） + + # Per-platform independent rounds and simulated time (for dual-platform parallel display) twitter_current_round: int = 0 reddit_current_round: int = 0 twitter_simulated_hours: int = 0 reddit_simulated_hours: int = 0 - - # 平台状态 + + # Platform status twitter_running: bool = False reddit_running: bool = False twitter_actions_count: int = 0 reddit_actions_count: int = 0 - - # 平台完成状态（通过检测 actions.jsonl 中的 simulation_end 事件） + + # Platform completion status (detected via simulation_end events in actions.jsonl) twitter_completed: bool = False reddit_completed: bool = False - - # 每轮摘要 + + # Round summary rounds: List[RoundSummary] = field(default_factory=list) - - # 最近动作（用于前端实时展示） + + # Recent actions (for frontend real-time display) recent_actions: List[AgentAction] = field(default_factory=list) max_recent_actions: int = 50 - - # 时间戳 + + # Timestamps started_at: Optional[str] = None updated_at: str = field(default_factory=lambda: datetime.now().isoformat()) completed_at: Optional[str] = None - - # 错误信息 + + # Error message error: Optional[str] = None - - # 进程ID（用于停止） + + # Process ID (for stopping) process_pid: Optional[int] = None def add_action(self, action: AgentAction): - """添加动作到最近动作列表""" + """Add action to recent actions list""" self.recent_actions.insert(0, action) if len(self.recent_actions) > self.max_recent_actions: self.recent_actions = self.recent_actions[:self.max_recent_actions] @@ -165,7 +165,7 @@ def to_dict(self) -> Dict[str, Any]: "simulated_hours": self.simulated_hours, "total_simulation_hours": self.total_simulation_hours, "progress_percent": round(self.current_round / max(self.total_rounds, 1) * 100, 1), - # 各平台独立轮次和时间 + # Per-platform independent rounds and time "twitter_current_round": self.twitter_current_round, "reddit_current_round": self.reddit_current_round, "twitter_simulated_hours": self.twitter_simulated_hours, @@ -183,9 +183,9 @@ def to_dict(self) -> Dict[str, Any]: "error": self.error, "process_pid": self.process_pid, } - + def to_detail_dict(self) -> Dict[str, Any]: - """包含最近动作的详细信息""" + """Details with recent actions""" result = self.to_dict() result["recent_actions"] = [a.to_dict() for a in self.recent_actions] result["rounds_count"] = len(self.rounds) @@ -194,45 +194,45 @@ def to_detail_dict(self) -> Dict[str, Any]: class SimulationRunner: """ - 模拟运行器 + Simulation Runner - 负责： - 1. 在后台进程中运行OASIS模拟 - 2. 解析运行日志，记录每个Agent的动作 - 3. 提供实时状态查询接口 - 4. 支持暂停/停止/恢复操作 + Responsible for: + 1. Running OASIS simulations in background processes + 2. Parsing run logs and recording actions for each Agent + 3. Providing real-time status query interfaces + 4. Supporting pause/stop/resume operations """ - # 运行状态存储目录 + # Storage directory for run state RUN_STATE_DIR = os.path.join( os.path.dirname(__file__), '../../uploads/simulations' ) - # 脚本目录 + # Script directory SCRIPTS_DIR = os.path.join( os.path.dirname(__file__), '../../scripts' ) - # 内存中的运行状态 + # In-memory run state _run_states: Dict[str, SimulationRunState] = {} _processes: Dict[str, subprocess.Popen] = {} _action_queues: Dict[str, Queue] = {} _monitor_threads: Dict[str, threading.Thread] = {} - _stdout_files: Dict[str, Any] = {} # 存储 stdout 文件句柄 - _stderr_files: Dict[str, Any] = {} # 存储 stderr 文件句柄 + _stdout_files: Dict[str, Any] = {} # Store stdout file handles + _stderr_files: Dict[str, Any] = {} # Store stderr file handles - # 图谱记忆更新配置 + # Graph memory update configuration _graph_memory_enabled: Dict[str, bool] = {} # simulation_id -> enabled @classmethod def get_run_state(cls, simulation_id: str) -> Optional[SimulationRunState]: - """获取运行状态""" + """Get run state""" if simulation_id in cls._run_states: return cls._run_states[simulation_id] - # 尝试从文件加载 + # Try to load from file state = cls._load_run_state(simulation_id) if state: cls._run_states[simulation_id] = state @@ -240,7 +240,7 @@ def get_run_state(cls, simulation_id: str) -> Optional[SimulationRunState]: @classmethod def _load_run_state(cls, simulation_id: str) -> Optional[SimulationRunState]: - """从文件加载运行状态""" + """Load run state from file""" state_file = os.path.join(cls.RUN_STATE_DIR, simulation_id, "run_state.json") if not os.path.exists(state_file): return None @@ -256,7 +256,7 @@ def _load_run_state(cls, simulation_id: str) -> Optional[SimulationRunState]: total_rounds=data.get("total_rounds", 0), simulated_hours=data.get("simulated_hours", 0), total_simulation_hours=data.get("total_simulation_hours", 0), - # 各平台独立轮次和时间 + # Per-platform independent rounds and time twitter_current_round=data.get("twitter_current_round", 0), reddit_current_round=data.get("reddit_current_round", 0), twitter_simulated_hours=data.get("twitter_simulated_hours", 0), @@ -273,8 +273,8 @@ def _load_run_state(cls, simulation_id: str) -> Optional[SimulationRunState]: error=data.get("error"), process_pid=data.get("process_pid"), ) - - # 加载最近动作 + + # Load recent actions actions_data = data.get("recent_actions", []) for a in actions_data: state.recent_actions.append(AgentAction( @@ -291,12 +291,12 @@ def _load_run_state(cls, simulation_id: str) -> Optional[SimulationRunState]: return state except Exception as e: - logger.error(f"加载运行状态失败: {str(e)}") + logger.error(f"Failed to load run state: {str(e)}") return None @classmethod def _save_run_state(cls, state: SimulationRunState): - """保存运行状态到文件""" + """Save run state to file""" sim_dir = os.path.join(cls.RUN_STATE_DIR, state.simulation_id) os.makedirs(sim_dir, exist_ok=True) state_file = os.path.join(sim_dir, "run_state.json") @@ -313,50 +313,50 @@ def start_simulation( cls, simulation_id: str, platform: str = "parallel", # twitter / reddit / parallel - max_rounds: int = None, # 最大模拟轮数（可选，用于截断过长的模拟） - enable_graph_memory_update: bool = False, # 是否将活动更新到Zep图谱 - graph_id: str = None # Zep图谱ID（启用图谱更新时必需） + max_rounds: int = None, # Maximum simulation rounds (optional, for truncating long simulations) + enable_graph_memory_update: bool = False, # Whether to update activities to the graph + graph_id: str = None, # Graph ID (required when enabling graph updates) ) -> SimulationRunState: """ - 启动模拟 - + Start simulation + Args: - simulation_id: 模拟ID - platform: 运行平台 (twitter/reddit/parallel) - max_rounds: 最大模拟轮数（可选，用于截断过长的模拟） - enable_graph_memory_update: 是否将Agent活动动态更新到Zep图谱 - graph_id: Zep图谱ID（启用图谱更新时必需） - + simulation_id: Simulation ID + platform: Platform to run (twitter/reddit/parallel) + max_rounds: Maximum simulation rounds (optional, for truncating long simulations) + enable_graph_memory_update: Whether to dynamically update Agent activities to the graph + graph_id: Graph ID (required when enabling graph updates) + Returns: SimulationRunState """ - # 检查是否已在运行 + # Check if already running existing = cls.get_run_state(simulation_id) if existing and existing.runner_status in [RunnerStatus.RUNNING, RunnerStatus.STARTING]: - raise ValueError(f"模拟已在运行中: {simulation_id}") + raise ValueError(f"Simulation already running: {simulation_id}") - # 加载模拟配置 + # Load simulation config sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) config_path = os.path.join(sim_dir, "simulation_config.json") if not os.path.exists(config_path): - raise ValueError(f"模拟配置不存在，请先调用 /prepare 接口") + raise ValueError(f"Simulation config does not exist, call /prepare endpoint first") with open(config_path, 'r', encoding='utf-8') as f: config = json.load(f) - # 初始化运行状态 + # Initialize run state time_config = config.get("time_config", {}) total_hours = time_config.get("total_simulation_hours", 72) minutes_per_round = time_config.get("minutes_per_round", 30) total_rounds = int(total_hours * 60 / minutes_per_round) - # 如果指定了最大轮数，则截断 + # If max_rounds specified, truncate if max_rounds is not None and max_rounds > 0: original_rounds = total_rounds total_rounds = min(total_rounds, max_rounds) if total_rounds < original_rounds: - logger.info(f"轮数已截断: {original_rounds} -> {total_rounds} (max_rounds={max_rounds})") + logger.info(f"Rounds truncated: {original_rounds} -> {total_rounds} (max_rounds={max_rounds})") state = SimulationRunState( simulation_id=simulation_id, @@ -368,22 +368,22 @@ def start_simulation( cls._save_run_state(state) - # 如果启用图谱记忆更新，创建更新器 + # If graph memory update enabled, create updater if enable_graph_memory_update: if not graph_id: - raise ValueError("启用图谱记忆更新时必须提供 graph_id") + raise ValueError("Must provide graph_id when enabling graph memory update") try: ZepGraphMemoryManager.create_updater(simulation_id, graph_id) cls._graph_memory_enabled[simulation_id] = True - logger.info(f"已启用图谱记忆更新: simulation_id={simulation_id}, graph_id={graph_id}") + logger.info(f"Graph memory update enabled: simulation_id={simulation_id}, graph_id={graph_id}") except Exception as e: - logger.error(f"创建图谱记忆更新器失败: {e}") + logger.error(f"Failed to create graph memory updater: {e}") cls._graph_memory_enabled[simulation_id] = False else: cls._graph_memory_enabled[simulation_id] = False - # 确定运行哪个脚本（脚本位于 backend/scripts/ 目录） + # Determine which script to run (scripts located in backend/scripts/ directory) if platform == "twitter": script_name = "run_twitter_simulation.py" state.twitter_running = True @@ -398,64 +398,64 @@ def start_simulation( script_path = os.path.join(cls.SCRIPTS_DIR, script_name) if not os.path.exists(script_path): - raise ValueError(f"脚本不存在: {script_path}") + raise ValueError(f"Script does not exist: {script_path}") - # 创建动作队列 + # Create action queue action_queue = Queue() cls._action_queues[simulation_id] = action_queue - # 启动模拟进程 + # Start simulation process try: - # 构建运行命令，使用完整路径 - # 新的日志结构： - # twitter/actions.jsonl - Twitter 动作日志 - # reddit/actions.jsonl - Reddit 动作日志 - # simulation.log - 主进程日志 + # Build run command with full paths + # New log structure: + # twitter/actions.jsonl - Twitter action log + # reddit/actions.jsonl - Reddit action log + # simulation.log - Main process log cmd = [ - sys.executable, # Python解释器 + sys.executable, # Python interpreter script_path, - "--config", config_path, # 使用完整配置文件路径 + "--config", config_path, # Use full config file path ] - # 如果指定了最大轮数，添加到命令行参数 + # If max_rounds specified, add to command-line arguments if max_rounds is not None and max_rounds > 0: cmd.extend(["--max-rounds", str(max_rounds)]) - # 创建主日志文件，避免 stdout/stderr 管道缓冲区满导致进程阻塞 + # Create main log file to avoid stdout/stderr pipe buffer overflow main_log_path = os.path.join(sim_dir, "simulation.log") main_log_file = open(main_log_path, 'w', encoding='utf-8') - # 设置子进程环境变量，确保 Windows 上使用 UTF-8 编码 - # 这可以修复第三方库（如 OASIS）读取文件时未指定编码的问题 + # Set subprocess environment variables to ensure UTF-8 encoding on Windows + # This fixes third-party libraries (like OASIS) not specifying encoding when reading files env = os.environ.copy() - env['PYTHONUTF8'] = '1' # Python 3.7+ 支持，让所有 open() 默认使用 UTF-8 - env['PYTHONIOENCODING'] = 'utf-8' # 确保 stdout/stderr 使用 UTF-8 + env['PYTHONUTF8'] = '1' # Python 3.7+ support, make all open() use UTF-8 by default + env['PYTHONIOENCODING'] = 'utf-8' # Ensure stdout/stderr use UTF-8 - # 设置工作目录为模拟目录（数据库等文件会生成在此） - # 使用 start_new_session=True 创建新的进程组，确保可以通过 os.killpg 终止所有子进程 + # Set working directory to simulation directory (database files etc. will be generated here) + # Use start_new_session=True to create new process group, ensuring all child processes can be terminated via os.killpg process = subprocess.Popen( cmd, cwd=sim_dir, stdout=main_log_file, - stderr=subprocess.STDOUT, # stderr 也写入同一个文件 + stderr=subprocess.STDOUT, # stderr also written to same file text=True, - encoding='utf-8', # 显式指定编码 + encoding='utf-8', # Explicitly specify encoding bufsize=1, - env=env, # 传递带有 UTF-8 设置的环境变量 - start_new_session=True, # 创建新进程组，确保服务器关闭时能终止所有相关进程 + env=env, # Pass environment variables with UTF-8 settings + start_new_session=True, # Create new process group, ensure all related processes terminate when server closes ) - # 保存文件句柄以便后续关闭 + # Save file handle for later closing cls._stdout_files[simulation_id] = main_log_file - cls._stderr_files[simulation_id] = None # 不再需要单独的 stderr + cls._stderr_files[simulation_id] = None # No longer need separate stderr state.process_pid = process.pid state.runner_status = RunnerStatus.RUNNING cls._processes[simulation_id] = process cls._save_run_state(state) - # 启动监控线程 + # Start monitoring thread monitor_thread = threading.Thread( target=cls._monitor_simulation, args=(simulation_id,), @@ -464,7 +464,7 @@ def start_simulation( monitor_thread.start() cls._monitor_threads[simulation_id] = monitor_thread - logger.info(f"模拟启动成功: {simulation_id}, pid={process.pid}, platform={platform}") + logger.info(f"Simulation started successfully: {simulation_id}, pid={process.pid}, platform={platform}") except Exception as e: state.runner_status = RunnerStatus.FAILED @@ -476,10 +476,10 @@ def start_simulation( @classmethod def _monitor_simulation(cls, simulation_id: str): - """监控模拟进程，解析动作日志""" + """Monitor simulation process and parse action logs""" sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) - # 新的日志结构：分平台的动作日志 + # New log structure: per-platform action logs twitter_actions_log = os.path.join(sim_dir, "twitter", "actions.jsonl") reddit_actions_log = os.path.join(sim_dir, "reddit", "actions.jsonl") @@ -493,75 +493,75 @@ def _monitor_simulation(cls, simulation_id: str): reddit_position = 0 try: - while process.poll() is None: # 进程仍在运行 - # 读取 Twitter 动作日志 + while process.poll() is None: # Process still running + # Read Twitter action log if os.path.exists(twitter_actions_log): twitter_position = cls._read_action_log( twitter_actions_log, twitter_position, state, "twitter" ) - # 读取 Reddit 动作日志 + # Read Reddit action log if os.path.exists(reddit_actions_log): reddit_position = cls._read_action_log( reddit_actions_log, reddit_position, state, "reddit" ) - # 更新状态 + # Update status cls._save_run_state(state) time.sleep(2) - # 进程结束后，最后读取一次日志 + # After process ends, read logs one more time if os.path.exists(twitter_actions_log): cls._read_action_log(twitter_actions_log, twitter_position, state, "twitter") if os.path.exists(reddit_actions_log): cls._read_action_log(reddit_actions_log, reddit_position, state, "reddit") - # 进程结束 + # Process ended exit_code = process.returncode if exit_code == 0: state.runner_status = RunnerStatus.COMPLETED state.completed_at = datetime.now().isoformat() - logger.info(f"模拟完成: {simulation_id}") + logger.info(f"Simulation completed: {simulation_id}") else: state.runner_status = RunnerStatus.FAILED - # 从主日志文件读取错误信息 + # Read error info from main log file main_log_path = os.path.join(sim_dir, "simulation.log") error_info = "" try: if os.path.exists(main_log_path): with open(main_log_path, 'r', encoding='utf-8') as f: - error_info = f.read()[-2000:] # 取最后2000字符 + error_info = f.read()[-2000:] # Take last 2000 characters except Exception: pass - state.error = f"进程退出码: {exit_code}, 错误: {error_info}" - logger.error(f"模拟失败: {simulation_id}, error={state.error}") + state.error = f"Process exit code: {exit_code}, error: {error_info}" + logger.error(f"Simulation failed: {simulation_id}, error={state.error}") state.twitter_running = False state.reddit_running = False cls._save_run_state(state) except Exception as e: - logger.error(f"监控线程异常: {simulation_id}, error={str(e)}") + logger.error(f"Monitor thread exception: {simulation_id}, error={str(e)}") state.runner_status = RunnerStatus.FAILED state.error = str(e) cls._save_run_state(state) finally: - # 停止图谱记忆更新器 + # Stop graph memory updater if cls._graph_memory_enabled.get(simulation_id, False): try: ZepGraphMemoryManager.stop_updater(simulation_id) - logger.info(f"已停止图谱记忆更新: simulation_id={simulation_id}") + logger.info(f"Graph memory update stopped: simulation_id={simulation_id}") except Exception as e: - logger.error(f"停止图谱记忆更新器失败: {e}") + logger.error(f"Failed to stop graph memory updater: {e}") cls._graph_memory_enabled.pop(simulation_id, None) - # 清理进程资源 + # Clean up process resources cls._processes.pop(simulation_id, None) cls._action_queues.pop(simulation_id, None) - # 关闭日志文件句柄 + # Close log file handle if simulation_id in cls._stdout_files: try: cls._stdout_files[simulation_id].close() @@ -584,18 +584,18 @@ def _read_action_log( platform: str ) -> int: """ - 读取动作日志文件 + Read action log file Args: - log_path: 日志文件路径 - position: 上次读取位置 - state: 运行状态对象 - platform: 平台名称 (twitter/reddit) + log_path: Action log file path + position: Last read position + state: Run state object + platform: Platform name (twitter/reddit) Returns: - 新的读取位置 + New read position """ - # 检查是否启用了图谱记忆更新 + # Check if graph memory update is enabled graph_memory_enabled = cls._graph_memory_enabled.get(state.simulation_id, False) graph_updater = None if graph_memory_enabled: @@ -610,36 +610,36 @@ def _read_action_log( try: action_data = json.loads(line) - # 处理事件类型的条目 + # Handle event type entries if "event_type" in action_data: event_type = action_data.get("event_type") - # 检测 simulation_end 事件，标记平台已完成 + # Detect simulation_end event, mark platform completed if event_type == "simulation_end": if platform == "twitter": state.twitter_completed = True state.twitter_running = False - logger.info(f"Twitter 模拟已完成: {state.simulation_id}, total_rounds={action_data.get('total_rounds')}, total_actions={action_data.get('total_actions')}") + logger.info(f"Twitter simulation completed: {state.simulation_id}, total_rounds={action_data.get('total_rounds')}, total_actions={action_data.get('total_actions')}") elif platform == "reddit": state.reddit_completed = True state.reddit_running = False - logger.info(f"Reddit 模拟已完成: {state.simulation_id}, total_rounds={action_data.get('total_rounds')}, total_actions={action_data.get('total_actions')}") + logger.info(f"Reddit simulation completed: {state.simulation_id}, total_rounds={action_data.get('total_rounds')}, total_actions={action_data.get('total_actions')}") - # 检查是否所有启用的平台都已完成 - # 如果只运行了一个平台，只检查那个平台 - # 如果运行了两个平台，需要两个都完成 + # Check if all enabled platforms are completed + # If only one platform is running, check only that platform + # If both platforms are running, need both to complete all_completed = cls._check_all_platforms_completed(state) if all_completed: state.runner_status = RunnerStatus.COMPLETED state.completed_at = datetime.now().isoformat() - logger.info(f"所有平台模拟已完成: {state.simulation_id}") + logger.info(f"All platform simulations completed: {state.simulation_id}") - # 更新轮次信息（从 round_end 事件） + # Update round information (from round_end event) elif event_type == "round_end": round_num = action_data.get("round", 0) simulated_hours = action_data.get("simulated_hours", 0) - # 更新各平台独立的轮次和时间 + # Update per-platform independent rounds and time if platform == "twitter": if round_num > state.twitter_current_round: state.twitter_current_round = round_num @@ -649,10 +649,10 @@ def _read_action_log( state.reddit_current_round = round_num state.reddit_simulated_hours = simulated_hours - # 总体轮次取两个平台的最大值 + # Overall rounds take maximum of both platforms if round_num > state.current_round: state.current_round = round_num - # 总体时间取两个平台的最大值 + # Overall time takes maximum of both platforms state.simulated_hours = max(state.twitter_simulated_hours, state.reddit_simulated_hours) continue @@ -670,11 +670,11 @@ def _read_action_log( ) state.add_action(action) - # 更新轮次 + # Update rounds if action.round_num and action.round_num > state.current_round: state.current_round = action.round_num - # 如果启用了图谱记忆更新，将活动发送到Zep + # If graph memory update enabled, send activity to graph if graph_updater: graph_updater.add_activity_from_dict(action_data, platform) @@ -682,52 +682,52 @@ def _read_action_log( pass return f.tell() except Exception as e: - logger.warning(f"读取动作日志失败: {log_path}, error={e}") + logger.warning(f"Failed to read action log: {log_path}, error={e}") return position @classmethod def _check_all_platforms_completed(cls, state: SimulationRunState) -> bool: """ - 检查所有启用的平台是否都已完成模拟 + Check if all enabled platforms have completed simulation - 通过检查对应的 actions.jsonl 文件是否存在来判断平台是否被启用 + Judge whether a platform is enabled by checking if corresponding actions.jsonl file exists Returns: - True 如果所有启用的平台都已完成 + True if all enabled platforms are completed """ sim_dir = os.path.join(cls.RUN_STATE_DIR, state.simulation_id) twitter_log = os.path.join(sim_dir, "twitter", "actions.jsonl") reddit_log = os.path.join(sim_dir, "reddit", "actions.jsonl") - # 检查哪些平台被启用（通过文件是否存在判断） + # Check which platforms are enabled (by file existence) twitter_enabled = os.path.exists(twitter_log) reddit_enabled = os.path.exists(reddit_log) - # 如果平台被启用但未完成，则返回 False + # If platform is enabled but not completed, return False if twitter_enabled and not state.twitter_completed: return False if reddit_enabled and not state.reddit_completed: return False - # 至少有一个平台被启用且已完成 + # At least one platform is enabled and completed return twitter_enabled or reddit_enabled @classmethod def _terminate_process(cls, process: subprocess.Popen, simulation_id: str, timeout: int = 10): """ - 跨平台终止进程及其子进程 + Cross-platform terminate process and its child processes Args: - process: 要终止的进程 - simulation_id: 模拟ID（用于日志） - timeout: 等待进程退出的超时时间（秒） + process: Process to terminate + simulation_id: Simulation ID (for logging) + timeout: Timeout for process exit (seconds) """ if IS_WINDOWS: - # Windows: 使用 taskkill 命令终止进程树 - # /F = 强制终止, /T = 终止进程树（包括子进程） - logger.info(f"终止进程树 (Windows): simulation={simulation_id}, pid={process.pid}") + # Windows: Use taskkill command to terminate process tree + # /F = force terminate, /T = terminate process tree (including child processes) + logger.info(f"Terminate process tree (Windows): simulation={simulation_id}, pid={process.pid}") try: - # 先尝试优雅终止 + # Try graceful termination first subprocess.run( ['taskkill', '/PID', str(process.pid), '/T'], capture_output=True, @@ -736,8 +736,8 @@ def _terminate_process(cls, process: subprocess.Popen, simulation_id: str, timeo try: process.wait(timeout=timeout) except subprocess.TimeoutExpired: - # 强制终止 - logger.warning(f"进程未响应，强制终止: {simulation_id}") + # Force terminate + logger.warning(f"Process not responding, force terminating: {simulation_id}") subprocess.run( ['taskkill', '/F', '/PID', str(process.pid), '/T'], capture_output=True, @@ -745,53 +745,53 @@ def _terminate_process(cls, process: subprocess.Popen, simulation_id: str, timeo ) process.wait(timeout=5) except Exception as e: - logger.warning(f"taskkill 失败，尝试 terminate: {e}") + logger.warning(f"taskkill failed, trying terminate: {e}") process.terminate() try: process.wait(timeout=5) except subprocess.TimeoutExpired: process.kill() else: - # Unix: 使用进程组终止 - # 由于使用了 start_new_session=True，进程组 ID 等于主进程 PID + # Unix: Use process group termination + # Since start_new_session=True, process group ID equals main process PID pgid = os.getpgid(process.pid) - logger.info(f"终止进程组 (Unix): simulation={simulation_id}, pgid={pgid}") + logger.info(f"Terminate process group (Unix): simulation={simulation_id}, pgid={pgid}") - # 先发送 SIGTERM 给整个进程组 + # First send SIGTERM to the entire process group os.killpg(pgid, signal.SIGTERM) try: process.wait(timeout=timeout) except subprocess.TimeoutExpired: - # 如果超时后还没结束，强制发送 SIGKILL - logger.warning(f"进程组未响应 SIGTERM，强制终止: {simulation_id}") + # If still not ended after timeout, force send SIGKILL + logger.warning(f"Process group not responding to SIGTERM, force terminating: {simulation_id}") os.killpg(pgid, signal.SIGKILL) process.wait(timeout=5) @classmethod def stop_simulation(cls, simulation_id: str) -> SimulationRunState: - """停止模拟""" + """Stop simulation""" state = cls.get_run_state(simulation_id) if not state: - raise ValueError(f"模拟不存在: {simulation_id}") + raise ValueError(f"Simulation does not exist: {simulation_id}") if state.runner_status not in [RunnerStatus.RUNNING, RunnerStatus.PAUSED]: - raise ValueError(f"模拟未在运行: {simulation_id}, status={state.runner_status}") + raise ValueError(f"Simulation not running: {simulation_id}, status={state.runner_status}") state.runner_status = RunnerStatus.STOPPING cls._save_run_state(state) - # 终止进程 + # Terminate process process = cls._processes.get(simulation_id) if process and process.poll() is None: try: cls._terminate_process(process, simulation_id) except ProcessLookupError: - # 进程已经不存在 + # Process no longer exists pass except Exception as e: - logger.error(f"终止进程组失败: {simulation_id}, error={e}") - # 回退到直接终止进程 + logger.error(f"Failed to terminate process group: {simulation_id}, error={e}") + # Fallback to direct process termination try: process.terminate() process.wait(timeout=5) @@ -804,16 +804,16 @@ def stop_simulation(cls, simulation_id: str) -> SimulationRunState: state.completed_at = datetime.now().isoformat() cls._save_run_state(state) - # 停止图谱记忆更新器 + # Stop graph memory updater if cls._graph_memory_enabled.get(simulation_id, False): try: ZepGraphMemoryManager.stop_updater(simulation_id) - logger.info(f"已停止图谱记忆更新: simulation_id={simulation_id}") + logger.info(f"Graph memory update stopped: simulation_id={simulation_id}") except Exception as e: - logger.error(f"停止图谱记忆更新器失败: {e}") + logger.error(f"Failed to stop graph memory updater: {e}") cls._graph_memory_enabled.pop(simulation_id, None) - logger.info(f"模拟已停止: {simulation_id}") + logger.info(f"Simulation stopped: {simulation_id}") return state @classmethod @@ -826,14 +826,14 @@ def _read_actions_from_file( round_num: Optional[int] = None ) -> List[AgentAction]: """ - 从单个动作文件中读取动作 + Read actions from single action file Args: - file_path: 动作日志文件路径 - default_platform: 默认平台（当动作记录中没有 platform 字段时使用） - platform_filter: 过滤平台 - agent_id: 过滤 Agent ID - round_num: 过滤轮次 + file_path: Action log file path + default_platform: Default platform (used when action record lacks platform field) + platform_filter: Filter platform + agent_id: Filter Agent ID + round_num: Filter round """ if not os.path.exists(file_path): return [] @@ -849,18 +849,18 @@ def _read_actions_from_file( try: data = json.loads(line) - # 跳过非动作记录（如 simulation_start, round_start, round_end 等事件） + # Skip non-action records (such as simulation_start, round_start, round_end events) if "event_type" in data: continue - # 跳过没有 agent_id 的记录（非 Agent 动作） + # Skip records without agent_id (non-Agent actions) if "agent_id" not in data: continue - # 获取平台：优先使用记录中的 platform，否则使用默认平台 + # Get platform: prefer platform in record, otherwise use default platform record_platform = data.get("platform") or default_platform or "" - # 过滤 + # Filter if platform_filter and record_platform != platform_filter: continue if agent_id is not None and data.get("agent_id") != agent_id: @@ -894,54 +894,54 @@ def get_all_actions( round_num: Optional[int] = None ) -> List[AgentAction]: """ - 获取所有平台的完整动作历史（无分页限制） + Get complete action history for all platforms (no pagination limit) Args: - simulation_id: 模拟ID - platform: 过滤平台（twitter/reddit） - agent_id: 过滤Agent - round_num: 过滤轮次 + simulation_id: Simulation ID + platform: Filter platform (twitter/reddit) + agent_id: Filter Agent + round_num: Filter round Returns: - 完整的动作列表（按时间戳排序，新的在前） + Complete action list (sorted by timestamp, newest first) """ sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) actions = [] - # 读取 Twitter 动作文件（根据文件路径自动设置 platform 为 twitter） + # Read Twitter action file (auto-set platform to twitter based on file path) twitter_actions_log = os.path.join(sim_dir, "twitter", "actions.jsonl") if not platform or platform == "twitter": actions.extend(cls._read_actions_from_file( twitter_actions_log, - default_platform="twitter", # 自动填充 platform 字段 + default_platform="twitter", # Auto-fill platform field platform_filter=platform, agent_id=agent_id, round_num=round_num )) - # 读取 Reddit 动作文件（根据文件路径自动设置 platform 为 reddit） + # Read Reddit action file (auto-set platform to reddit based on file path) reddit_actions_log = os.path.join(sim_dir, "reddit", "actions.jsonl") if not platform or platform == "reddit": actions.extend(cls._read_actions_from_file( reddit_actions_log, - default_platform="reddit", # 自动填充 platform 字段 + default_platform="reddit", # Auto-fill platform field platform_filter=platform, agent_id=agent_id, round_num=round_num )) - # 如果分平台文件不存在，尝试读取旧的单一文件格式 + # If per-platform files do not exist, try reading old single file format if not actions: actions_log = os.path.join(sim_dir, "actions.jsonl") actions = cls._read_actions_from_file( actions_log, - default_platform=None, # 旧格式文件中应该有 platform 字段 + default_platform=None, # Old format files should have platform field platform_filter=platform, agent_id=agent_id, round_num=round_num ) - # 按时间戳排序（新的在前） + # Sort by timestamp (newest first) actions.sort(key=lambda x: x.timestamp, reverse=True) return actions @@ -957,18 +957,18 @@ def get_actions( round_num: Optional[int] = None ) -> List[AgentAction]: """ - 获取动作历史（带分页） + Get action history (with pagination) Args: - simulation_id: 模拟ID - limit: 返回数量限制 - offset: 偏移量 - platform: 过滤平台 - agent_id: 过滤Agent - round_num: 过滤轮次 + simulation_id: Simulation ID + limit: Return count limit + offset: Offset + platform: Filter platform + agent_id: Filter Agent + round_num: Filter round Returns: - 动作列表 + Action list """ actions = cls.get_all_actions( simulation_id=simulation_id, @@ -977,7 +977,7 @@ def get_actions( round_num=round_num ) - # 分页 + # Pagination return actions[offset:offset + limit] @classmethod @@ -988,19 +988,19 @@ def get_timeline( end_round: Optional[int] = None ) -> List[Dict[str, Any]]: """ - 获取模拟时间线（按轮次汇总） + Get simulation timeline (summarized by rounds) Args: - simulation_id: 模拟ID - start_round: 起始轮次 - end_round: 结束轮次 + simulation_id: Simulation ID + start_round: Start round + end_round: End round Returns: - 每轮的汇总信息 + Summary information for each round """ actions = cls.get_actions(simulation_id, limit=10000) - # 按轮次分组 + # Group by round rounds: Dict[int, Dict[str, Any]] = {} for action in actions: @@ -1033,7 +1033,7 @@ def get_timeline( r["action_types"][action.action_type] = r["action_types"].get(action.action_type, 0) + 1 r["last_action_time"] = action.timestamp - # 转换为列表 + # Convert to list result = [] for round_num in sorted(rounds.keys()): r = rounds[round_num] @@ -1054,10 +1054,10 @@ def get_timeline( @classmethod def get_agent_stats(cls, simulation_id: str) -> List[Dict[str, Any]]: """ - 获取每个Agent的统计信息 + Get statistics for each Agent Returns: - Agent统计列表 + Agent statistics list """ actions = cls.get_actions(simulation_id, limit=10000) @@ -1089,7 +1089,7 @@ def get_agent_stats(cls, simulation_id: str) -> List[Dict[str, Any]]: stats["action_types"][action.action_type] = stats["action_types"].get(action.action_type, 0) + 1 stats["last_action_time"] = action.timestamp - # 按总动作数排序 + # Sort by total actions result = sorted(agent_stats.values(), key=lambda x: x["total_actions"], reverse=True) return result @@ -1097,51 +1097,51 @@ def get_agent_stats(cls, simulation_id: str) -> List[Dict[str, Any]]: @classmethod def cleanup_simulation_logs(cls, simulation_id: str) -> Dict[str, Any]: """ - 清理模拟的运行日志（用于强制重新开始模拟） + Clean up simulation run logs (for force restart) - 会删除以下文件： + Will delete the following files: - run_state.json - twitter/actions.jsonl - reddit/actions.jsonl - simulation.log - stdout.log / stderr.log - - twitter_simulation.db（模拟数据库） - - reddit_simulation.db（模拟数据库） - - env_status.json（环境状态） + - twitter_simulation.db (simulation database) + - reddit_simulation.db (simulation database) + - env_status.json (environment status) - 注意：不会删除配置文件（simulation_config.json）和 profile 文件 + Note: Does not delete config files (simulation_config.json) and profile files Args: - simulation_id: 模拟ID + simulation_id: Simulation ID Returns: - 清理结果信息 + Cleanup result information """ import shutil sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) if not os.path.exists(sim_dir): - return {"success": True, "message": "模拟目录不存在，无需清理"} + return {"success": True, "message": "Simulation directory does not exist, no cleanup needed"} cleaned_files = [] errors = [] - # 要删除的文件列表（包括数据库文件） + # Files to delete (including database files) files_to_delete = [ "run_state.json", "simulation.log", "stdout.log", "stderr.log", - "twitter_simulation.db", # Twitter 平台数据库 - "reddit_simulation.db", # Reddit 平台数据库 - "env_status.json", # 环境状态文件 + "twitter_simulation.db", # Twitter platform database + "reddit_simulation.db", # Reddit platform database + "env_status.json", # Environment status file ] - # 要删除的目录列表（包含动作日志） + # Directories to delete (contains action logs) dirs_to_clean = ["twitter", "reddit"] - # 删除文件 + # Delete files for filename in files_to_delete: file_path = os.path.join(sim_dir, filename) if os.path.exists(file_path): @@ -1149,9 +1149,9 @@ def cleanup_simulation_logs(cls, simulation_id: str) -> Dict[str, Any]: os.remove(file_path) cleaned_files.append(filename) except Exception as e: - errors.append(f"删除 {filename} 失败: {str(e)}") + errors.append(f"Failed to delete {filename}: {str(e)}") - # 清理平台目录中的动作日志 + # Clean up action logs in platform directories for dir_name in dirs_to_clean: dir_path = os.path.join(sim_dir, dir_name) if os.path.exists(dir_path): @@ -1161,13 +1161,13 @@ def cleanup_simulation_logs(cls, simulation_id: str) -> Dict[str, Any]: os.remove(actions_file) cleaned_files.append(f"{dir_name}/actions.jsonl") except Exception as e: - errors.append(f"删除 {dir_name}/actions.jsonl 失败: {str(e)}") + errors.append(f"Failed to delete {dir_name}/actions.jsonl: {str(e)}") - # 清理内存中的运行状态 + # Clean up in-memory run state if simulation_id in cls._run_states: del cls._run_states[simulation_id] - logger.info(f"清理模拟日志完成: {simulation_id}, 删除文件: {cleaned_files}") + logger.info(f"Cleanup simulation logs completed: {simulation_id}, deleted files: {cleaned_files}") return { "success": len(errors) == 0, @@ -1175,71 +1175,71 @@ def cleanup_simulation_logs(cls, simulation_id: str) -> Dict[str, Any]: "errors": errors if errors else None } - # 防止重复清理的标志 + # Flag to prevent duplicate cleanup _cleanup_done = False @classmethod def cleanup_all_simulations(cls): """ - 清理所有运行中的模拟进程 + Clean up all running simulation processes - 在服务器关闭时调用，确保所有子进程被终止 + Called when server closes, ensures all child processes are terminated """ - # 防止重复清理 + # Prevent duplicate cleanup if cls._cleanup_done: return cls._cleanup_done = True - # 检查是否有内容需要清理（避免空进程的进程打印无用日志） + # Check if there is content to clean (avoid empty process printing useless logs) has_processes = bool(cls._processes) has_updaters = bool(cls._graph_memory_enabled) if not has_processes and not has_updaters: - return # 没有需要清理的内容，静默返回 + return # No content to clean, return silently - logger.info("正在清理所有模拟进程...") + logger.info("Cleaning up all simulation processes...") - # 首先停止所有图谱记忆更新器（stop_all 内部会打印日志） + # First stop all graph memory updaters (stop_all prints logs internally) try: ZepGraphMemoryManager.stop_all() except Exception as e: - logger.error(f"停止图谱记忆更新器失败: {e}") + logger.error(f"Failed to stop graph memory updater: {e}") cls._graph_memory_enabled.clear() - # 复制字典以避免在迭代时修改 + # Copy dict to avoid modification during iteration processes = list(cls._processes.items()) for simulation_id, process in processes: try: - if process.poll() is None: # 进程仍在运行 - logger.info(f"终止模拟进程: {simulation_id}, pid={process.pid}") + if process.poll() is None: # Process still running + logger.info(f"Terminate simulation process: {simulation_id}, pid={process.pid}") try: - # 使用跨平台的进程终止方法 + # Use cross-platform process termination method cls._terminate_process(process, simulation_id, timeout=5) except (ProcessLookupError, OSError): - # 进程可能已经不存在，尝试直接终止 + # Process may no longer exist, try direct termination try: process.terminate() process.wait(timeout=3) except Exception: process.kill() - # 更新 run_state.json + # Update run_state.json state = cls.get_run_state(simulation_id) if state: state.runner_status = RunnerStatus.STOPPED state.twitter_running = False state.reddit_running = False state.completed_at = datetime.now().isoformat() - state.error = "服务器关闭，模拟被终止" + state.error = "Server closed, simulation terminated" cls._save_run_state(state) - # 同时更新 state.json，将状态设为 stopped + # Also update state.json, set status to stopped try: sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) state_file = os.path.join(sim_dir, "state.json") - logger.info(f"尝试更新 state.json: {state_file}") + logger.info(f"Attempting to update state.json: {state_file}") if os.path.exists(state_file): with open(state_file, 'r', encoding='utf-8') as f: state_data = json.load(f) @@ -1247,16 +1247,16 @@ def cleanup_all_simulations(cls): state_data['updated_at'] = datetime.now().isoformat() with open(state_file, 'w', encoding='utf-8') as f: json.dump(state_data, f, indent=2, ensure_ascii=False) - logger.info(f"已更新 state.json 状态为 stopped: {simulation_id}") + logger.info(f"Updated state.json status to stopped: {simulation_id}") else: - logger.warning(f"state.json 不存在: {state_file}") + logger.warning(f"state.json does not exist: {state_file}") except Exception as state_err: - logger.warning(f"更新 state.json 失败: {simulation_id}, error={state_err}") + logger.warning(f"Failed to update state.json: {simulation_id}, error={state_err}") except Exception as e: - logger.error(f"清理进程失败: {simulation_id}, error={e}") + logger.error(f"Failed to clean up process: {simulation_id}, error={e}") - # 清理文件句柄 + # Clean up file handles for simulation_id, file_handle in list(cls._stdout_files.items()): try: if file_handle: @@ -1273,89 +1273,89 @@ def cleanup_all_simulations(cls): pass cls._stderr_files.clear() - # 清理内存中的状态 + # Clean up in-memory state cls._processes.clear() cls._action_queues.clear() - logger.info("模拟进程清理完成") + logger.info("Simulation process cleanup completed") @classmethod def register_cleanup(cls): """ - 注册清理函数 + Register cleanup function - 在 Flask 应用启动时调用，确保服务器关闭时清理所有模拟进程 + Called when Flask app starts, ensures all simulation processes are cleaned when server closes """ global _cleanup_registered if _cleanup_registered: return - # Flask debug 模式下，只在 reloader 子进程中注册清理（实际运行应用的进程） - # WERKZEUG_RUN_MAIN=true 表示是 reloader 子进程 - # 如果不是 debug 模式，则没有这个环境变量，也需要注册 + # In Flask debug mode, only register cleanup in reloader child process (process actually running the app) + # WERKZEUG_RUN_MAIN=true indicates it is a reloader child process + # If not in debug mode, no such environment variable, also need to register is_reloader_process = os.environ.get('WERKZEUG_RUN_MAIN') == 'true' is_debug_mode = os.environ.get('FLASK_DEBUG') == '1' or os.environ.get('WERKZEUG_RUN_MAIN') is not None - # 在 debug 模式下，只在 reloader 子进程中注册；非 debug 模式下始终注册 + # In debug mode, only register in reloader child process; always register in non-debug mode if is_debug_mode and not is_reloader_process: - _cleanup_registered = True # 标记已注册，防止子进程再次尝试 + _cleanup_registered = True # Mark as registered, prevent child process from trying again return - # 保存原有的信号处理器 + # Save original signal handler original_sigint = signal.getsignal(signal.SIGINT) original_sigterm = signal.getsignal(signal.SIGTERM) - # SIGHUP 只在 Unix 系统存在（macOS/Linux），Windows 没有 + # SIGHUP only exists on Unix systems (macOS/Linux), not on Windows original_sighup = None has_sighup = hasattr(signal, 'SIGHUP') if has_sighup: original_sighup = signal.getsignal(signal.SIGHUP) def cleanup_handler(signum=None, frame=None): - """信号处理器：先清理模拟进程，再调用原处理器""" - # 只有在有进程需要清理时才打印日志 + """Signal handler: clean up simulation processes first, then call original handler""" + # Only print logs if there are processes to clean if cls._processes or cls._graph_memory_enabled: - logger.info(f"收到信号 {signum}，开始清理...") + logger.info(f"Received signal {signum}, starting cleanup...") cls.cleanup_all_simulations() - # 调用原有的信号处理器，让 Flask 正常退出 + # Call original signal handler, let Flask exit normally if signum == signal.SIGINT and callable(original_sigint): original_sigint(signum, frame) elif signum == signal.SIGTERM and callable(original_sigterm): original_sigterm(signum, frame) elif has_sighup and signum == signal.SIGHUP: - # SIGHUP: 终端关闭时发送 + # SIGHUP: Sent when terminal closes if callable(original_sighup): original_sighup(signum, frame) else: - # 默认行为：正常退出 + # Default behavior: exit normally sys.exit(0) else: - # 如果原处理器不可调用（如 SIG_DFL），则使用默认行为 + # If original handler not callable (such as SIG_DFL), use default behavior raise KeyboardInterrupt - # 注册 atexit 处理器（作为备用） + # Register atexit handler (as fallback) atexit.register(cls.cleanup_all_simulations) - # 注册信号处理器（仅在主线程中） + # Register signal handler (only in main thread) try: - # SIGTERM: kill 命令默认信号 + # SIGTERM: default signal for kill command signal.signal(signal.SIGTERM, cleanup_handler) # SIGINT: Ctrl+C signal.signal(signal.SIGINT, cleanup_handler) - # SIGHUP: 终端关闭（仅 Unix 系统） + # SIGHUP: terminal close (Unix only) if has_sighup: signal.signal(signal.SIGHUP, cleanup_handler) except ValueError: - # 不在主线程中，只能使用 atexit - logger.warning("无法注册信号处理器（不在主线程），仅使用 atexit") + # Not in main thread, can only use atexit + logger.warning("Cannot register signal handler (not in main thread), only using atexit") _cleanup_registered = True @classmethod def get_running_simulations(cls) -> List[str]: """ - 获取所有正在运行的模拟ID列表 + Get list of all running simulation IDs """ running = [] for sim_id, process in cls._processes.items(): @@ -1363,18 +1363,18 @@ def get_running_simulations(cls) -> List[str]: running.append(sim_id) return running - # ============== Interview 功能 ============== + # ============== Interview functionality ============== @classmethod def check_env_alive(cls, simulation_id: str) -> bool: """ - 检查模拟环境是否存活（可以接收Interview命令） + Check if simulation environment is alive (can receive Interview commands) Args: - simulation_id: 模拟ID + simulation_id: Simulation ID Returns: - True 表示环境存活，False 表示环境已关闭 + True means environment is alive, False means environment is closed """ sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) if not os.path.exists(sim_dir): @@ -1386,13 +1386,13 @@ def check_env_alive(cls, simulation_id: str) -> bool: @classmethod def get_env_status_detail(cls, simulation_id: str) -> Dict[str, Any]: """ - 获取模拟环境的详细状态信息 + Get detailed status information of simulation environment Args: - simulation_id: 模拟ID + simulation_id: Simulation ID Returns: - 状态详情字典，包含 status, twitter_available, reddit_available, timestamp + Status details dict, contains status, twitter_available, reddit_available, timestamp """ sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) status_file = os.path.join(sim_dir, "env_status.json") @@ -1429,35 +1429,35 @@ def interview_agent( timeout: float = 60.0 ) -> Dict[str, Any]: """ - 采访单个Agent + Interview single Agent Args: - simulation_id: 模拟ID + simulation_id: Simulation ID agent_id: Agent ID - prompt: 采访问题 - platform: 指定平台（可选） - - "twitter": 只采访Twitter平台 - - "reddit": 只采访Reddit平台 - - None: 双平台模拟时同时采访两个平台，返回整合结果 - timeout: 超时时间（秒） + prompt: Interview question + platform: Specify platform (optional) + - "twitter": only interview Twitter platform + - "reddit": only interview Reddit platform + - None: interview both platforms simultaneously in dual-platform simulations, return integrated results + timeout: Timeout (seconds) Returns: - 采访结果字典 + Interview result dict Raises: - ValueError: 模拟不存在或环境未运行 - TimeoutError: 等待响应超时 + ValueError: Simulation does not exist or environment not running + TimeoutError: Timeout waiting for response """ sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) if not os.path.exists(sim_dir): - raise ValueError(f"模拟不存在: {simulation_id}") + raise ValueError(f"Simulation does not exist: {simulation_id}") ipc_client = SimulationIPCClient(sim_dir) if not ipc_client.check_env_alive(): - raise ValueError(f"模拟环境未运行或已关闭，无法执行Interview: {simulation_id}") + raise ValueError(f"Simulation environment not running or closed, cannot execute Interview: {simulation_id}") - logger.info(f"发送Interview命令: simulation_id={simulation_id}, agent_id={agent_id}, platform={platform}") + logger.info(f"Send Interview command: simulation_id={simulation_id}, agent_id={agent_id}, platform={platform}") response = ipc_client.send_interview( agent_id=agent_id, @@ -1492,34 +1492,34 @@ def interview_agents_batch( timeout: float = 120.0 ) -> Dict[str, Any]: """ - 批量采访多个Agent + Batch interview multiple Agents Args: - simulation_id: 模拟ID - interviews: 采访列表，每个元素包含 {"agent_id": int, "prompt": str, "platform": str(可选)} - platform: 默认平台（可选，会被每个采访项的platform覆盖） - - "twitter": 默认只采访Twitter平台 - - "reddit": 默认只采访Reddit平台 - - None: 双平台模拟时每个Agent同时采访两个平台 - timeout: 超时时间（秒） + simulation_id: Simulation ID + interviews: List of interviews, each element contains {"agent_id": int, "prompt": str, "platform": str(optional)} + platform: Default platform (optional, overridden by each interview item's platform) + - "twitter": default only interview Twitter platform + - "reddit": default only interview Reddit platform + - None: interview each Agent on both platforms simultaneously in dual-platform simulations + timeout: Timeout (seconds) Returns: - 批量采访结果字典 + Batch interview result dict Raises: - ValueError: 模拟不存在或环境未运行 - TimeoutError: 等待响应超时 + ValueError: Simulation does not exist or environment not running + TimeoutError: Timeout waiting for response """ sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) if not os.path.exists(sim_dir): - raise ValueError(f"模拟不存在: {simulation_id}") + raise ValueError(f"Simulation does not exist: {simulation_id}") ipc_client = SimulationIPCClient(sim_dir) if not ipc_client.check_env_alive(): - raise ValueError(f"模拟环境未运行或已关闭，无法执行Interview: {simulation_id}") + raise ValueError(f"Simulation environment not running or closed, cannot execute Interview: {simulation_id}") - logger.info(f"发送批量Interview命令: simulation_id={simulation_id}, count={len(interviews)}, platform={platform}") + logger.info(f"Send batch Interview command: simulation_id={simulation_id}, count={len(interviews)}, platform={platform}") response = ipc_client.send_batch_interview( interviews=interviews, @@ -1551,39 +1551,39 @@ def interview_all_agents( timeout: float = 180.0 ) -> Dict[str, Any]: """ - 采访所有Agent（全局采访） + Interview all Agents (global interview) - 使用相同的问题采访模拟中的所有Agent + Interview all Agents in the simulation using the same question Args: - simulation_id: 模拟ID - prompt: 采访问题（所有Agent使用相同问题） - platform: 指定平台（可选） - - "twitter": 只采访Twitter平台 - - "reddit": 只采访Reddit平台 - - None: 双平台模拟时每个Agent同时采访两个平台 - timeout: 超时时间（秒） + simulation_id: Simulation ID + prompt: Interview question (all Agents use the same question) + platform: Specify platform (optional) + - "twitter": only interview Twitter platform + - "reddit": only interview Reddit platform + - None: interview each Agent on both platforms simultaneously in dual-platform simulations + timeout: Timeout (seconds) Returns: - 全局采访结果字典 + Global interview result dict """ sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) if not os.path.exists(sim_dir): - raise ValueError(f"模拟不存在: {simulation_id}") + raise ValueError(f"Simulation does not exist: {simulation_id}") - # 从配置文件获取所有Agent信息 + # Get all Agent information from config file config_path = os.path.join(sim_dir, "simulation_config.json") if not os.path.exists(config_path): - raise ValueError(f"模拟配置不存在: {simulation_id}") + raise ValueError(f"Simulation config does not exist: {simulation_id}") with open(config_path, 'r', encoding='utf-8') as f: config = json.load(f) agent_configs = config.get("agent_configs", []) if not agent_configs: - raise ValueError(f"模拟配置中没有Agent: {simulation_id}") + raise ValueError(f"No agents in simulation config: {simulation_id}") - # 构建批量采访列表 + # Build batch interview list interviews = [] for agent_config in agent_configs: agent_id = agent_config.get("agent_id") @@ -1593,7 +1593,7 @@ def interview_all_agents( "prompt": prompt }) - logger.info(f"发送全局Interview命令: simulation_id={simulation_id}, agent_count={len(interviews)}, platform={platform}") + logger.info(f"Send global Interview command: simulation_id={simulation_id}, agent_count={len(interviews)}, platform={platform}") return cls.interview_agents_batch( simulation_id=simulation_id, @@ -1609,45 +1609,45 @@ def close_simulation_env( timeout: float = 30.0 ) -> Dict[str, Any]: """ - 关闭模拟环境（而不是停止模拟进程） + Close simulation environment (not stop simulation process) - 向模拟发送关闭环境命令，使其优雅退出等待命令模式 + Send close environment command to simulation to gracefully exit command wait mode Args: - simulation_id: 模拟ID - timeout: 超时时间（秒） + simulation_id: Simulation ID + timeout: Timeout (seconds) Returns: - 操作结果字典 + Operation result dict """ sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) if not os.path.exists(sim_dir): - raise ValueError(f"模拟不存在: {simulation_id}") + raise ValueError(f"Simulation does not exist: {simulation_id}") ipc_client = SimulationIPCClient(sim_dir) if not ipc_client.check_env_alive(): return { "success": True, - "message": "环境已经关闭" + "message": "Environment already closed" } - logger.info(f"发送关闭环境命令: simulation_id={simulation_id}") + logger.info(f"Send close environment command: simulation_id={simulation_id}") try: response = ipc_client.send_close_env(timeout=timeout) return { "success": response.status.value == "completed", - "message": "环境关闭命令已发送", + "message": "Close environment command sent", "result": response.result, "timestamp": response.timestamp } except TimeoutError: - # 超时可能是因为环境正在关闭 + # Timeout may be because environment is closing return { "success": True, - "message": "环境关闭命令已发送（等待响应超时，环境可能正在关闭）" + "message": "Close environment command sent (timeout waiting for response, environment may be closing)" } @classmethod @@ -1658,7 +1658,7 @@ def _get_interview_history_from_db( agent_id: Optional[int] = None, limit: int = 100 ) -> List[Dict[str, Any]]: - """从单个数据库获取Interview历史""" + """Get Interview history from single database""" import sqlite3 if not os.path.exists(db_path): @@ -1704,7 +1704,7 @@ def _get_interview_history_from_db( conn.close() except Exception as e: - logger.error(f"读取Interview历史失败 ({platform_name}): {e}") + logger.error(f"Failed to read Interview history ({platform_name}): {e}") return results @@ -1717,29 +1717,29 @@ def get_interview_history( limit: int = 100 ) -> List[Dict[str, Any]]: """ - 获取Interview历史记录（从数据库读取） + Get Interview history records (read from database) Args: - simulation_id: 模拟ID - platform: 平台类型（reddit/twitter/None） - - "reddit": 只获取Reddit平台的历史 - - "twitter": 只获取Twitter平台的历史 - - None: 获取两个平台的所有历史 - agent_id: 指定Agent ID（可选，只获取该Agent的历史） - limit: 每个平台返回数量限制 + simulation_id: Simulation ID + platform: Platform type (reddit/twitter/None) + - "reddit": only get Reddit platform history + - "twitter": only get Twitter platform history + - None: get all history from both platforms + agent_id: Specify Agent ID (optional, only get history for that Agent) + limit: Return count limit per platform Returns: - Interview历史记录列表 + Interview history records list """ sim_dir = os.path.join(cls.RUN_STATE_DIR, simulation_id) results = [] - # 确定要查询的平台 + # Determine platforms to query if platform in ("reddit", "twitter"): platforms = [platform] else: - # 不指定platform时，查询两个平台 + # When platform not specified, query both platforms platforms = ["twitter", "reddit"] for p in platforms: @@ -1752,10 +1752,10 @@ def get_interview_history( ) results.extend(platform_results) - # 按时间降序排序 + # Sort by time in descending order results.sort(key=lambda x: x.get("timestamp", ""), reverse=True) - # 如果查询了多个平台，限制总数 + # If queried multiple platforms, limit total count if len(platforms) > 1 and len(results) > limit: results = results[:limit] diff --git a/backend/app/services/zep_tools.py b/backend/app/services/zep_tools.py index 384cf540f..fd7210d5f 100644 --- a/backend/app/services/zep_tools.py +++ b/backend/app/services/zep_tools.py @@ -980,9 +980,9 @@ def insight_forge( # 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, + query=query[:300], + simulation_requirement=simulation_requirement[:500], + report_context=report_context[:300], max_queries=max_sub_queries ) result.sub_queries = sub_queries @@ -1110,12 +1110,12 @@ def _generate_sub_queries( 4. 返回JSON格式：{"sub_queries": ["子问题1", "子问题2", ...]}""" user_prompt = f"""模拟需求背景： -{simulation_requirement} +{simulation_requirement[:500]} -{f"报告上下文：{report_context[:500]}" if report_context else ""} +{f"报告上下文：{report_context[:300]}" if report_context else ""} 请将以下问题分解为{max_queries}个子问题： -{query} +{query[:300]} 返回JSON格式的子问题列表。""" @@ -1380,7 +1380,7 @@ def interview_agents( 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 + timeout=300.0 # 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')}") @@ -1572,7 +1572,7 @@ def _select_agents_for_interview( "index": i, "name": profile.get("realname", profile.get("username", f"Agent_{i}")), "profession": profile.get("profession", "未知"), - "bio": profile.get("bio", "")[:200], + "bio": profile.get("bio", "")[:100], "interested_topics": profile.get("interested_topics", []) } agent_summaries.append(summary) @@ -1591,16 +1591,17 @@ def _select_agents_for_interview( "reasoning": "选择理由说明" }""" + display_agents = agent_summaries[:30] # max 30 agents in prompt user_prompt = f"""采访需求： -{interview_requirement} + {interview_requirement[:300]} -模拟背景： -{simulation_requirement if simulation_requirement else "未提供"} + 模拟背景： + {simulation_requirement[:300] if simulation_requirement else "未提供"} -可选择的Agent列表（共{len(agent_summaries)}个）： -{json.dumps(agent_summaries, ensure_ascii=False, indent=2)} + 可选择的Agent列表（共{len(agent_summaries)}个，显示前{len(display_agents)}个）： + {json.dumps(display_agents, ensure_ascii=False)} -请选择最多{max_agents}个最适合采访的Agent，并说明选择理由。""" + 请选择最多{max_agents}个最适合采访的Agent，并说明选择理由。""" try: response = self.llm.chat_json( @@ -1693,7 +1694,7 @@ def _generate_interview_summary( # 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]}") + interview_texts.append(f"【{interview.agent_name}（{interview.agent_role}）】\n{interview.response[:200]}") system_prompt = """你是一个专业的新闻编辑。请根据多位受访者的回答，生成一份采访摘要。 diff --git a/backend/app/utils/llm_client.py b/backend/app/utils/llm_client.py index 6c1a81f49..d2a4f2a77 100644 --- a/backend/app/utils/llm_client.py +++ b/backend/app/utils/llm_client.py @@ -1,103 +1,390 @@ """ LLM客户端封装 统一使用OpenAI格式调用 +支持多Key智能轮换 - Groq 4 keys + Gemini 3 keys + +Key rotation strategy: +- TPD (tokens per day) error → mark key exhausted 24h, rotate to next +- TPM (tokens per minute) error → wait 61s, retry SAME key (don't waste other keys) +- 413 / too large → truncate messages, retry same key +- Unknown error → raise immediately """ import json import re +import time +import logging from typing import Optional, Dict, Any, List +from datetime import datetime, timedelta from openai import OpenAI from ..config import Config +logger = logging.getLogger(__name__) + class LLMClient: - """LLM客户端""" - + """ + LLM客户端 - 支持多Key智能轮换 + + Rotation logic: + - TPD exhausted → skip key for 24 h, move to next available + - TPM exceeded → sleep 61 s, retry same key + - Request too large → truncate, retry same key + - Other errors → re-raise immediately + """ + + # ------------------------------------------------------------------ # + # Construction # + # ------------------------------------------------------------------ # + def __init__( self, api_key: Optional[str] = None, base_url: Optional[str] = None, - model: Optional[str] = None + model: Optional[str] = None, ): - self.api_key = api_key or Config.LLM_API_KEY self.base_url = base_url or Config.LLM_BASE_URL - self.model = model or Config.LLM_MODEL_NAME - - if not self.api_key: - raise ValueError("LLM_API_KEY 未配置") - - self.client = OpenAI( - api_key=self.api_key, - base_url=self.base_url + self.model = model or Config.LLM_MODEL_NAME + + self.key_pool: List[str] = [api_key] if api_key else Config.get_primary_keys() + if not self.key_pool: + raise ValueError("No primary LLM API key configured (LLM_API_KEY_1 missing).") + + self.current_key_index: int = 0 + # Maps key index → datetime when the TPD ban expires + self.exhausted_until: Dict[int, datetime] = {} + + self.client = self._make_client(self.key_pool[0]) + logger.info("LLMClient initialised with %d key(s), model=%s", len(self.key_pool), self.model) + + # ------------------------------------------------------------------ # + # Internal helpers # + # ------------------------------------------------------------------ # + + def _make_client(self, api_key: str) -> OpenAI: + return OpenAI(api_key=api_key, base_url=self.base_url) + + # ---------- error classification ---------- + + @staticmethod + def _is_tpd_error(error: Exception) -> bool: + """Tokens-per-DAY limit → key is done for 24 h.""" + msg = str(error).lower() + return "tokens per day" in msg or "tpd" in msg + + @staticmethod + def _is_tpm_error(error: Exception) -> bool: + """Tokens-per-MINUTE limit → wait ~60 s on the SAME key.""" + msg = str(error).lower() + return ( + "tokens per minute" in msg + or "tpm" in msg + or (("429" in msg or "rate_limit" in msg or "rate limit" in msg) + and "tokens per day" not in msg) + ) + + @staticmethod + def _is_too_large_error(error: Exception) -> bool: + msg = str(error) + return ( + "413" in msg + or "Request too large" in msg + or "reduce your message size" in msg + or "context_length_exceeded" in msg + ) + + # ---------- key rotation ---------- + + def _available_key_index(self) -> Optional[int]: + """ + Return the index of the next key that is NOT TPD-exhausted, + cycling through the whole pool starting after current_key_index. + Cleans up expired bans along the way. + Returns None if every key is still banned. + """ + now = datetime.now() + # Expire old bans + self.exhausted_until = {k: v for k, v in self.exhausted_until.items() if now < v} + + total = len(self.key_pool) + for offset in range(1, total + 1): + idx = (self.current_key_index + offset) % total + if idx not in self.exhausted_until: + return idx + return None + + def _rotate_key(self, *, tpd_exhausted: bool = False) -> bool: + """ + Switch to the next usable key. + If tpd_exhausted=True, marks the current key for 24 h first. + Returns True if a new key is available, False if all are exhausted. + """ + if tpd_exhausted: + self.exhausted_until[self.current_key_index] = datetime.now() + timedelta(hours=24) + logger.warning( + "🚫 Primary key %d/%d marked TPD-exhausted for 24 h", + self.current_key_index + 1, len(self.key_pool), + ) + + next_idx = self._available_key_index() + if next_idx is None: + logger.error("❌ All primary LLM keys are exhausted.") + return False + + self.current_key_index = next_idx + self.client = self._make_client(self.key_pool[next_idx]) + logger.info( + "🔄 Rotated to primary key %d/%d", + next_idx + 1, len(self.key_pool), ) - + return True + + # ---------- message truncation ---------- + + @staticmethod + def _truncate_messages( + messages: List[Dict[str, str]], + target_tokens: int = 8_000, + ) -> List[Dict[str, str]]: + """ + Naively truncate user-role message content so the request fits. + System messages are always kept intact. + Rough heuristic: 1 token ≈ 4 chars. + """ + char_limit = target_tokens * 4 + truncated = [] + for msg in messages: + role = msg.get("role", "") + content = msg.get("content", "") + if role == "system": + truncated.append(msg) + elif role == "user" and len(content) > char_limit: + new_content = content[:char_limit] + "\n\n[Content truncated to fit token limit]" + logger.warning( + "✂️ Truncated user message %d → %d chars", len(content), len(new_content) + ) + truncated.append({"role": role, "content": new_content}) + else: + truncated.append(msg) + return truncated + + # ------------------------------------------------------------------ # + # Public API # + # ------------------------------------------------------------------ # + def chat( self, messages: List[Dict[str, str]], temperature: float = 0.7, - max_tokens: int = 4096, - response_format: Optional[Dict] = None + max_tokens: int = 8_096, + response_format: Optional[Dict] = None, ) -> str: - """ - 发送聊天请求 - Args: - messages: 消息列表 - temperature: 温度参数 - max_tokens: 最大token数 - response_format: 响应格式（如JSON模式） - - Returns: - 模型响应文本 """ - kwargs = { - "model": self.model, - "messages": messages, + Send a chat completion request. + Automatically handles key rotation on rate-limit errors. + Returns the assistant message as a plain string. + """ + kwargs: Dict[str, Any] = { + "model": self.model, + "messages": messages, "temperature": temperature, - "max_tokens": max_tokens, + "max_tokens": max_tokens, } - if response_format: kwargs["response_format"] = response_format - - response = self.client.chat.completions.create(**kwargs) - content = response.choices[0].message.content - # 部分模型（如MiniMax M2.5）会在content中包含思考内容，需要移除 - content = re.sub(r'[\s\S]*?', '', content).strip() - return content - + + last_error: Optional[Exception] = None + # Upper bound: try each key at most once for TPD; TPM retries don't + # consume a "slot" because we wait on the same key. + max_key_switches = len(self.key_pool) + key_switches = 0 + + while key_switches <= max_key_switches: + try: + response = self.client.chat.completions.create(**kwargs) + content = response.choices[0].message.content or "" + # Strip chain-of-thought blocks some models return + content = re.sub(r"[\s\S]*?", "", content).strip() + # Log which key succeeded + logger.debug("✅ Request succeeded on key %d", self.current_key_index + 1) + return content + + except Exception as e: + last_error = e + err_str = str(e) + + if self._is_tpd_error(e): + logger.warning( + "⚠️ Key %d/%d hit TPD limit. Rotating… (%s)", + self.current_key_index + 1, len(self.key_pool), err_str[:120], + ) + if not self._rotate_key(tpd_exhausted=True): + break # all keys dead → exit loop + key_switches += 1 + + elif self._is_too_large_error(e): + logger.warning( + "📦 Request too large (%s). Truncating messages…", err_str[:80] + ) + messages = self._truncate_messages(messages, target_tokens=8_000) + kwargs["messages"] = messages + # Do NOT rotate or increment counter + + elif self._is_tpm_error(e): + logger.warning( + "⏳ Key %d/%d hit TPM limit. Sleeping 61 s then retrying same key… (%s)", + self.current_key_index + 1, len(self.key_pool), err_str[:120], + ) + time.sleep(61) + # Do NOT increment key_switches — we're retrying the same key + + else: + # Unexpected error — surface it immediately + raise + + raise RuntimeError( + f"❌ All primary LLM keys exhausted. Last error: {last_error}" + ) + def chat_json( self, messages: List[Dict[str, str]], temperature: float = 0.3, - max_tokens: int = 4096 + max_tokens: int = 8_096, ) -> Dict[str, Any]: """ - 发送聊天请求并返回JSON - - Args: - messages: 消息列表 - temperature: 温度参数 - max_tokens: 最大token数 - - Returns: - 解析后的JSON对象 + Like chat(), but parses the response as JSON. + Falls back to plain chat if the model doesn't support response_format. """ - response = self.chat( - messages=messages, - temperature=temperature, - max_tokens=max_tokens, - response_format={"type": "json_object"} - ) - # 清理markdown代码块标记 - cleaned_response = response.strip() - cleaned_response = re.sub(r'^```(?:json)?\s*\n?', '', cleaned_response, flags=re.IGNORECASE) - cleaned_response = re.sub(r'\n?```\s*$', '', cleaned_response) - cleaned_response = cleaned_response.strip() + try: + raw = self.chat( + messages=messages, + temperature=temperature, + max_tokens=max_tokens, + response_format={"type": "json_object"}, + ) + except Exception: + # Model may not support response_format → retry without it + raw = self.chat( + messages=messages, + temperature=temperature, + max_tokens=max_tokens, + ) + + # Strip markdown fences if present + cleaned = raw.strip() + cleaned = re.sub(r"^```(?:json)?\s*\n?", "", cleaned, flags=re.IGNORECASE) + cleaned = re.sub(r"\n?```\s*$", "", cleaned) + cleaned = cleaned.strip() try: - return json.loads(cleaned_response) + return json.loads(cleaned) except json.JSONDecodeError: - raise ValueError(f"LLM返回的JSON格式无效: {cleaned_response}") + repaired = self._repair_truncated_json(cleaned) + if repaired is not None: + return repaired + raise ValueError(f"LLM returned invalid JSON: {cleaned[:500]}") + + # ------------------------------------------------------------------ # + # Status / diagnostics # + # ------------------------------------------------------------------ # + + def status(self) -> Dict[str, Any]: + """Return a human-readable snapshot of the key pool state.""" + now = datetime.now() + keys_info = [] + for i, key in enumerate(self.key_pool): + ban_until = self.exhausted_until.get(i) + if ban_until and now < ban_until: + remaining = int((ban_until - now).total_seconds() / 60) + state = f"TPD-exhausted (~{remaining} min remaining)" + else: + state = "available" + keys_info.append({ + "index": i + 1, + "key_tail": f"…{key[-6:]}", + "state": state, + "active": i == self.current_key_index, + }) + return { + "model": self.model, + "total_keys": len(self.key_pool), + "current_index": self.current_key_index + 1, + "keys": keys_info, + } + + # ------------------------------------------------------------------ # + # JSON repair # + # ------------------------------------------------------------------ # + @staticmethod + def _repair_truncated_json(text: str) -> Optional[dict]: + """Best-effort repair of truncated JSON by closing open brackets.""" + try: + open_braces = text.count("{") - text.count("}") + open_brackets = text.count("[") - text.count("]") + repaired = text + if repaired.count('"') % 2 != 0: + repaired += '"' + repaired += "]" * open_brackets + repaired += "}" * open_braces + return json.loads(repaired) + except Exception: + return None + + +# ======================================================================= # +# Boost (Gemini) client # +# ======================================================================= # + +class BoostLLMClient(LLMClient): + """ + Gemini LLM client with identical rotation logic. + Uses the boost key pool from Config. + """ + + def __init__( + self, + api_key: Optional[str] = None, + base_url: Optional[str] = None, + model: Optional[str] = None, + ): + # Bypass parent __init__ entirely and set attributes directly + self.base_url = base_url or Config.LLM_BOOST_BASE_URL + self.model = model or Config.LLM_BOOST_MODEL_NAME + + self.key_pool: List[str] = [api_key] if api_key else Config.get_boost_keys() + if not self.key_pool: + raise ValueError("No boost LLM API key configured (LLM_BOOST_API_KEY_1 missing).") + + self.current_key_index: int = 0 + self.exhausted_until: Dict[int, datetime] = {} + + self.client = self._make_client(self.key_pool[0]) + logger.info( + "BoostLLMClient initialised with %d key(s), model=%s", + len(self.key_pool), self.model, + ) + + def _rotate_key(self, *, tpd_exhausted: bool = False) -> bool: + if tpd_exhausted: + self.exhausted_until[self.current_key_index] = datetime.now() + timedelta(hours=24) + logger.warning( + "🚫 Boost key %d/%d marked TPD-exhausted for 24 h", + self.current_key_index + 1, len(self.key_pool), + ) + + next_idx = self._available_key_index() + if next_idx is None: + logger.error("❌ All boost (Gemini) LLM keys are exhausted.") + return False + + self.current_key_index = next_idx + self.client = self._make_client(self.key_pool[next_idx]) + logger.info( + "🔄 Rotated to boost key %d/%d", + next_idx + 1, len(self.key_pool), + ) + return True \ No newline at end of file diff --git a/backend/pyproject.toml b/backend/pyproject.toml index 4f5361d53..c62abcc37 100644 --- a/backend/pyproject.toml +++ b/backend/pyproject.toml @@ -2,7 +2,7 @@ name = "mirofish-backend" version = "0.1.0" description = "MiroFish - 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+ @@ -7,8 +7,8 @@ - - MiroFish - 预测万物 + + MiroFish
diff --git a/frontend/package-lock.json b/frontend/package-lock.json index 8c4fa710d..fee02cad8 100644 --- a/frontend/package-lock.json +++ b/frontend/package-lock.json @@ -1331,7 +1331,6 @@ "resolved": "https://registry.npmjs.org/d3-selection/-/d3-selection-3.0.0.tgz", "integrity": "sha512-fmTRWbNMmsmWq6xJV8D19U/gw/bwrHfNXxrIN+HfZgnzqTHp9jOmKMhsTUjXOJnZOdZY9Q28y4yebKzqDKlxlQ==", "license": "ISC", - "peer": true, "engines": { "node": ">=12" } @@ -1809,7 +1808,6 @@ "integrity": "sha512-5gTmgEY/sqK6gFXLIsQNH19lWb4ebPDLA4SdLP7dsWkIXHWlG66oPuVvXSGFPppYZz8ZDZq0dYYrbHfBCVUb1Q==", "dev": true, "license": "MIT", - "peer": true, "engines": { "node": ">=12" }, @@ -1943,7 +1941,6 @@ "integrity": "sha512-ITcnkFeR3+fI8P1wMgItjGrR10170d8auB4EpMLPqmx6uxElH3a/hHGQabSHKdqd4FXWO1nFIp9rRn7JQ34ACQ==", "dev": true, "license": "MIT", - "peer": true, "dependencies": { "esbuild": "^0.25.0", "fdir": "^6.5.0", @@ -2018,7 +2015,6 @@ "resolved": "https://registry.npmjs.org/vue/-/vue-3.5.25.tgz", "integrity": "sha512-YLVdgv2K13WJ6n+kD5owehKtEXwdwXuj2TTyJMsO7pSeKw2bfRNZGjhB7YzrpbMYj5b5QsUebHpOqR3R3ziy/g==", "license": "MIT", - "peer": true, "dependencies": { "@vue/compiler-dom": "3.5.25", "@vue/compiler-sfc": "3.5.25", diff --git a/frontend/src/App.vue b/frontend/src/App.vue index b7cd71ca6..702be81ef 100644 --- a/frontend/src/App.vue +++ b/frontend/src/App.vue @@ -3,11 +3,11 @@ diff --git a/frontend/src/api/graph.js b/frontend/src/api/graph.js index ef90a2b6f..4047f3022 100644 --- a/frontend/src/api/graph.js +++ b/frontend/src/api/graph.js @@ -1,12 +1,12 @@ import service, { requestWithRetry } from './index' /** - * 生成本体（上传文档和模拟需求） - * @param {Object} data - 包含files, simulation_requirement, project_name等 + * Generate ontology (upload documents and simulation requirements) + * @param {Object} data - Contains files, simulation_requirement, project_name, etc. * @returns {Promise} */ export function generateOntology(formData) { - return requestWithRetry(() => + return requestWithRetry(() => service({ url: '/api/graph/ontology/generate', method: 'post', @@ -19,8 +19,8 @@ export function generateOntology(formData) { } /** - * 构建图谱 - * @param {Object} data - 包含project_id, graph_name等 + * Build graph + * @param {Object} data - Contains project_id, graph_name, etc. * @returns {Promise} */ export function buildGraph(data) { @@ -34,8 +34,8 @@ export function buildGraph(data) { } /** - * 查询任务状态 - * @param {String} taskId - 任务ID + * Query task status + * @param {String} taskId - Task ID * @returns {Promise} */ export function getTaskStatus(taskId) { @@ -46,8 +46,8 @@ export function getTaskStatus(taskId) { } /** - * 获取图谱数据 - * @param {String} graphId - 图谱ID + * Get graph data + * @param {String} graphId - Graph ID * @returns {Promise} */ export function getGraphData(graphId) { @@ -58,8 +58,8 @@ export function getGraphData(graphId) { } /** - * 获取项目信息 - * @param {String} projectId - 项目ID + * Get project information + * @param {String} projectId - Project ID * @returns {Promise} */ export function getProject(projectId) { diff --git a/frontend/src/api/index.js b/frontend/src/api/index.js index e2d9465b2..fe51e53b1 100644 --- a/frontend/src/api/index.js +++ b/frontend/src/api/index.js @@ -1,15 +1,15 @@ import axios from 'axios' -// 创建axios实例 +// Create axios instance const service = axios.create({ baseURL: import.meta.env.VITE_API_BASE_URL || 'http://localhost:5001', - timeout: 300000, // 5分钟超时（本体生成可能需要较长时间） + timeout: 300000, // 5 minute timeout (ontology generation may require longer time) headers: { 'Content-Type': 'application/json' } }) -// 请求拦截器 +// Request interceptor service.interceptors.request.use( config => { return config @@ -20,44 +20,44 @@ service.interceptors.request.use( } ) -// 响应拦截器（容错重试机制） +// Response interceptor (fault-tolerant retry mechanism) service.interceptors.response.use( response => { const res = response.data - - // 如果返回的状态码不是success，则抛出错误 + + // If the returned status code is not success, throw error if (!res.success && res.success !== undefined) { console.error('API Error:', res.error || res.message || 'Unknown error') return Promise.reject(new Error(res.error || res.message || 'Error')) } - + return res }, error => { console.error('Response error:', error) - - // 处理超时 + + // Handle timeout if (error.code === 'ECONNABORTED' && error.message.includes('timeout')) { console.error('Request timeout') } - - // 处理网络错误 + + // Handle network error if (error.message === 'Network Error') { console.error('Network error - please check your connection') } - + return Promise.reject(error) } ) -// 带重试的请求函数 +// Request function with retry export const requestWithRetry = async (requestFn, maxRetries = 3, delay = 1000) => { for (let i = 0; i < maxRetries; i++) { try { return await requestFn() } catch (error) { if (i === maxRetries - 1) throw error - + console.warn(`Request failed, retrying (${i + 1}/${maxRetries})...`) await new Promise(resolve => setTimeout(resolve, delay * Math.pow(2, i))) } diff --git a/frontend/src/api/report.js b/frontend/src/api/report.js index c89a67d8c..4dcdd829e 100644 --- a/frontend/src/api/report.js +++ b/frontend/src/api/report.js @@ -1,7 +1,7 @@ import service, { requestWithRetry } from './index' /** - * 开始报告生成 + * Start report generation * @param {Object} data - { simulation_id, force_regenerate? } */ export const generateReport = (data) => { @@ -9,7 +9,7 @@ export const generateReport = (data) => { } /** - * 获取报告生成状态 + * Get report generation status * @param {string} reportId */ export const getReportStatus = (reportId) => { @@ -17,25 +17,25 @@ export const getReportStatus = (reportId) => { } /** - * 获取 Agent 日志（增量） + * Get Agent log (incremental) * @param {string} reportId - * @param {number} fromLine - 从第几行开始获取 + * @param {number} fromLine - Start from which line */ export const getAgentLog = (reportId, fromLine = 0) => { return service.get(`/api/report/${reportId}/agent-log`, { params: { from_line: fromLine } }) } /** - * 获取控制台日志（增量） + * Get console log (incremental) * @param {string} reportId - * @param {number} fromLine - 从第几行开始获取 + * @param {number} fromLine - Start from which line */ export const getConsoleLog = (reportId, fromLine = 0) => { return service.get(`/api/report/${reportId}/console-log`, { params: { from_line: fromLine } }) } /** - * 获取报告详情 + * Get report details * @param {string} reportId */ export const getReport = (reportId) => { @@ -43,7 +43,7 @@ export const getReport = (reportId) => { } /** - * 与 Report Agent 对话 + * Chat with Report Agent * @param {Object} data - { simulation_id, message, chat_history? } */ export const chatWithReport = (data) => { diff --git a/frontend/src/api/simulation.js b/frontend/src/api/simulation.js index f878586f0..11d14372e 100644 --- a/frontend/src/api/simulation.js +++ b/frontend/src/api/simulation.js @@ -1,7 +1,7 @@ import service, { requestWithRetry } from './index' /** - * 创建模拟 + * Create simulation * @param {Object} data - { project_id, graph_id?, enable_twitter?, enable_reddit? } */ export const createSimulation = (data) => { @@ -9,7 +9,7 @@ export const createSimulation = (data) => { } /** - * 准备模拟环境（异步任务） + * Prepare simulation environment (async task) * @param {Object} data - { simulation_id, entity_types?, use_llm_for_profiles?, parallel_profile_count?, force_regenerate? } */ export const prepareSimulation = (data) => { @@ -17,7 +17,7 @@ export const prepareSimulation = (data) => { } /** - * 查询准备任务进度 + * Query prepare task progress * @param {Object} data - { task_id?, simulation_id? } */ export const getPrepareStatus = (data) => { @@ -25,7 +25,7 @@ export const getPrepareStatus = (data) => { } /** - * 获取模拟状态 + * Get simulation status * @param {string} simulationId */ export const getSimulation = (simulationId) => { @@ -33,7 +33,7 @@ export const getSimulation = (simulationId) => { } /** - * 获取模拟的 Agent Profiles + * Get Agent Profiles for simulation * @param {string} simulationId * @param {string} platform - 'reddit' | 'twitter' */ @@ -42,7 +42,7 @@ export const getSimulationProfiles = (simulationId, platform = 'reddit') => { } /** - * 实时获取生成中的 Agent Profiles + * Get Agent Profiles being generated in real-time * @param {string} simulationId * @param {string} platform - 'reddit' | 'twitter' */ @@ -51,7 +51,7 @@ export const getSimulationProfilesRealtime = (simulationId, platform = 'reddit') } /** - * 获取模拟配置 + * Get simulation configuration * @param {string} simulationId */ export const getSimulationConfig = (simulationId) => { @@ -59,17 +59,17 @@ export const getSimulationConfig = (simulationId) => { } /** - * 实时获取生成中的模拟配置 + * Get simulation configuration being generated in real-time * @param {string} simulationId - * @returns {Promise} 返回配置信息，包含元数据和配置内容 + * @returns {Promise} Returns configuration information containing metadata and config content */ export const getSimulationConfigRealtime = (simulationId) => { return service.get(`/api/simulation/${simulationId}/config/realtime`) } /** - * 列出所有模拟 - * @param {string} projectId - 可选，按项目ID过滤 + * List all simulations + * @param {string} projectId - Optional, filter by project ID */ export const listSimulations = (projectId) => { const params = projectId ? { project_id: projectId } : {} @@ -77,7 +77,7 @@ export const listSimulations = (projectId) => { } /** - * 启动模拟 + * Start simulation * @param {Object} data - { simulation_id, platform?, max_rounds?, enable_graph_memory_update? } */ export const startSimulation = (data) => { @@ -85,7 +85,7 @@ export const startSimulation = (data) => { } /** - * 停止模拟 + * Stop simulation * @param {Object} data - { simulation_id } */ export const stopSimulation = (data) => { @@ -93,7 +93,7 @@ export const stopSimulation = (data) => { } /** - * 获取模拟运行实时状态 + * Get simulation real-time run status * @param {string} simulationId */ export const getRunStatus = (simulationId) => { @@ -101,7 +101,7 @@ export const getRunStatus = (simulationId) => { } /** - * 获取模拟运行详细状态（包含最近动作） + * Get simulation detailed run status (including recent actions) * @param {string} simulationId */ export const getRunStatusDetail = (simulationId) => { @@ -109,11 +109,11 @@ export const getRunStatusDetail = (simulationId) => { } /** - * 获取模拟中的帖子 + * Get posts from simulation * @param {string} simulationId * @param {string} platform - 'reddit' | 'twitter' - * @param {number} limit - 返回数量 - * @param {number} offset - 偏移量 + * @param {number} limit - Number of results + * @param {number} offset - Offset */ export const getSimulationPosts = (simulationId, platform = 'reddit', limit = 50, offset = 0) => { return service.get(`/api/simulation/${simulationId}/posts`, { @@ -122,10 +122,10 @@ export const getSimulationPosts = (simulationId, platform = 'reddit', limit = 50 } /** - * 获取模拟时间线（按轮次汇总） + * Get simulation timeline (summarized by rounds) * @param {string} simulationId - * @param {number} startRound - 起始轮次 - * @param {number} endRound - 结束轮次 + * @param {number} startRound - Start round + * @param {number} endRound - End round */ export const getSimulationTimeline = (simulationId, startRound = 0, endRound = null) => { const params = { start_round: startRound } @@ -136,7 +136,7 @@ export const getSimulationTimeline = (simulationId, startRound = 0, endRound = n } /** - * 获取Agent统计信息 + * Get Agent statistics * @param {string} simulationId */ export const getAgentStats = (simulationId) => { @@ -144,7 +144,7 @@ export const getAgentStats = (simulationId) => { } /** - * 获取模拟动作历史 + * Get simulation action history * @param {string} simulationId * @param {Object} params - { limit, offset, platform, agent_id, round_num } */ @@ -153,7 +153,7 @@ export const getSimulationActions = (simulationId, params = {}) => { } /** - * 关闭模拟环境（优雅退出） + * Close simulation environment (graceful shutdown) * @param {Object} data - { simulation_id, timeout? } */ export const closeSimulationEnv = (data) => { @@ -161,7 +161,7 @@ export const closeSimulationEnv = (data) => { } /** - * 获取模拟环境状态 + * Get simulation environment status * @param {Object} data - { simulation_id } */ export const getEnvStatus = (data) => { @@ -169,7 +169,7 @@ export const getEnvStatus = (data) => { } /** - * 批量采访 Agent + * Batch interview Agents * @param {Object} data - { simulation_id, interviews: [{ agent_id, prompt }] } */ export const interviewAgents = (data) => { @@ -177,9 +177,9 @@ export const interviewAgents = (data) => { } /** - * 获取历史模拟列表（带项目详情） - * 用于首页历史项目展示 - * @param {number} limit - 返回数量限制 + * Get simulation history list (with project details) + * Used to display historical projects on home page + * @param {number} limit - Return count limit */ export const getSimulationHistory = (limit = 20) => { return service.get('/api/simulation/history', { params: { limit } }) diff --git a/frontend/src/components/GraphPanel.vue b/frontend/src/components/GraphPanel.vue index 314c966e4..6b143c4fa 100644 --- a/frontend/src/components/GraphPanel.vue +++ b/frontend/src/components/GraphPanel.vue @@ -2,24 +2,24 @@
Graph Relationship Visualization - +
- -
- +
- +
@@ -27,10 +27,10 @@
- {{ isSimulating ? 'GraphRAG长短期记忆实时更新中' : '实时更新中...' }} + {{ isSimulating ? 'GraphRAG short-term/long-term memory updating in real-time' : 'Updating in real-time...' }}
- +
@@ -39,8 +39,8 @@
- 还有少量内容处理中，建议稍后手动刷新图谱 -
- +
{{ selectedItem.type === 'node' ? 'Node Details' : 'Relationship' }} @@ -58,7 +58,7 @@
- +
Name: @@ -101,9 +101,9 @@
- +
- + - +