Enhance OASIS simulation capabilities and profile generation
- Updated README.md to include detailed descriptions of new features, including Zep mixed search functionality and detailed persona generation for individual and group entities. - Implemented a robust mechanism for checking simulation preparation status to avoid redundant profile generation. - Added support for parallel profile generation, improving efficiency in creating OASIS Agent Profiles. - Enhanced the simulation configuration generator to adopt a stepwise approach, ensuring better handling of complex configurations. - Introduced error handling and retry mechanisms for LLM calls, improving the reliability of profile generation. - Updated simulation management to support new API parameters for controlling profile generation behavior.
This commit is contained in:
666ghj
@@ -1,6 +1,11 @@
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"""
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OASIS Agent Profile生成器
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将Zep图谱中的实体转换为OASIS模拟平台所需的Agent Profile格式
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优化改进：
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1. 调用Zep检索功能二次丰富节点信息
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2. 优化提示词生成非常详细的人设
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3. 区分个人实体和抽象群体实体
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"""
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import json
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@@ -10,6 +15,7 @@ from dataclasses import dataclass, field
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from datetime import datetime
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from openai import OpenAI
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from zep_cloud.client import Zep
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from ..config import Config
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from ..utils.logger import get_logger
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@@ -137,6 +143,11 @@ class OasisProfileGenerator:
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OASIS Profile生成器
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将Zep图谱中的实体转换为OASIS模拟所需的Agent Profile
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优化特性：
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1. 调用Zep图谱检索功能获取更丰富的上下文
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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）
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3. 区分个人实体和抽象群体实体
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"""
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# MBTI类型列表
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@@ -153,11 +164,25 @@ class OasisProfileGenerator:
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"Canada", "Australia", "Brazil", "India", "South Korea"
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]
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# 个人类型实体（需要生成具体人设）
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INDIVIDUAL_ENTITY_TYPES = [
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"student", "alumni", "professor", "person", "publicfigure",
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"expert", "faculty", "official", "journalist", "activist"
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]
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# 群体/机构类型实体（需要生成群体代表人设）
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GROUP_ENTITY_TYPES = [
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"university", "governmentagency", "organization", "ngo",
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"mediaoutlet", "company", "institution", "group", "community"
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]
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def __init__(
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self,
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api_key: Optional[str] = None,
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base_url: Optional[str] = None,
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model_name: Optional[str] = None
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model_name: Optional[str] = None,
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zep_api_key: Optional[str] = None,
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graph_id: Optional[str] = None
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):
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self.api_key = api_key or Config.LLM_API_KEY
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self.base_url = base_url or Config.LLM_BASE_URL
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@@ -170,6 +195,17 @@ class OasisProfileGenerator:
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api_key=self.api_key,
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base_url=self.base_url
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)
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# Zep客户端用于检索丰富上下文
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self.zep_api_key = zep_api_key or Config.ZEP_API_KEY
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self.zep_client = None
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self.graph_id = graph_id
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if self.zep_api_key:
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try:
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self.zep_client = Zep(api_key=self.zep_api_key)
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except Exception as e:
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logger.warning(f"Zep客户端初始化失败: {e}")
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def generate_profile_from_entity(
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self,
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@@ -245,28 +281,195 @@ class OasisProfileGenerator:
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suffix = random.randint(100, 999)
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return f"{username}_{suffix}"
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def _search_zep_for_entity(self, entity: EntityNode) -> Dict[str, Any]:
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"""
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使用Zep图谱混合搜索功能获取实体相关的丰富信息
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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。
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使用并行请求同时搜索，提高效率。
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Args:
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entity: 实体节点对象
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Returns:
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包含facts, node_summaries, context的字典
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"""
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import concurrent.futures
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if not self.zep_client:
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return {"facts": [], "node_summaries": [], "context": ""}
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entity_name = entity.name
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results = {
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"facts": [],
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"node_summaries": [],
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"context": ""
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}
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# 必须有graph_id才能进行搜索
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if not self.graph_id:
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logger.debug(f"跳过Zep检索：未设置graph_id")
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return results
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comprehensive_query = f"关于{entity_name}的所有信息、活动、事件、关系和背景"
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def search_edges():
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"""搜索边（事实/关系）"""
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try:
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return self.zep_client.graph.search(
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query=comprehensive_query,
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graph_id=self.graph_id,
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limit=30,
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scope="edges",
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reranker="rrf"
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)
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except Exception as e:
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logger.debug(f"Zep边搜索失败: {e}")
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return None
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def search_nodes():
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"""搜索节点（实体摘要）"""
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try:
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return self.zep_client.graph.search(
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query=comprehensive_query,
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graph_id=self.graph_id,
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limit=20,
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scope="nodes",
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reranker="rrf"
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)
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except Exception as e:
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logger.debug(f"Zep节点搜索失败: {e}")
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return None
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try:
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# 并行执行edges和nodes搜索
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with concurrent.futures.ThreadPoolExecutor(max_workers=2) as executor:
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edge_future = executor.submit(search_edges)
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node_future = executor.submit(search_nodes)
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# 获取结果
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edge_result = edge_future.result(timeout=30)
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node_result = node_future.result(timeout=30)
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# 处理边搜索结果
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all_facts = set()
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if edge_result and hasattr(edge_result, 'edges') and edge_result.edges:
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for edge in edge_result.edges:
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if hasattr(edge, 'fact') and edge.fact:
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all_facts.add(edge.fact)
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results["facts"] = list(all_facts)
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# 处理节点搜索结果
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all_summaries = set()
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if node_result and hasattr(node_result, 'nodes') and node_result.nodes:
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for node in node_result.nodes:
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if hasattr(node, 'summary') and node.summary:
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all_summaries.add(node.summary)
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if hasattr(node, 'name') and node.name and node.name != entity_name:
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all_summaries.add(f"相关实体: {node.name}")
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results["node_summaries"] = list(all_summaries)
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# 构建综合上下文
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context_parts = []
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if results["facts"]:
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context_parts.append("事实信息:\n" + "\n".join(f"- {f}" for f in results["facts"][:20]))
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if results["node_summaries"]:
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context_parts.append("相关实体:\n" + "\n".join(f"- {s}" for s in results["node_summaries"][:10]))
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results["context"] = "\n\n".join(context_parts)
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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")
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except concurrent.futures.TimeoutError:
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logger.warning(f"Zep检索超时 ({entity_name})")
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except Exception as e:
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logger.warning(f"Zep检索失败 ({entity_name}): {e}")
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return results
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def _build_entity_context(self, entity: EntityNode) -> str:
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"""构建实体的上下文信息"""
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"""
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构建实体的完整上下文信息
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包括：
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1. 实体本身的边信息（事实）
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2. 关联节点的详细信息
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3. Zep混合检索到的丰富信息
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"""
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context_parts = []
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# 添加相关边信息
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# 1. 添加实体属性信息
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if entity.attributes:
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attrs = []
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for key, value in entity.attributes.items():
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if value and str(value).strip():
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attrs.append(f"- {key}: {value}")
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if attrs:
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context_parts.append("### 实体属性\n" + "\n".join(attrs))
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# 2. 添加相关边信息（事实/关系）
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existing_facts = set()
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if entity.related_edges:
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relationships = []
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for edge in entity.related_edges[:10]: # 最多取10条
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if edge.get("fact"):
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relationships.append(edge["fact"])
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for edge in entity.related_edges: # 不限制数量
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fact = edge.get("fact", "")
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edge_name = edge.get("edge_name", "")
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direction = edge.get("direction", "")
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if fact:
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relationships.append(f"- {fact}")
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existing_facts.add(fact)
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elif edge_name:
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if direction == "outgoing":
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relationships.append(f"- {entity.name} --[{edge_name}]--> (相关实体)")
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else:
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relationships.append(f"- (相关实体) --[{edge_name}]--> {entity.name}")
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if relationships:
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context_parts.append("Related facts:\n" + "\n".join(f"- {r}" for r in relationships))
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context_parts.append("### 相关事实和关系\n" + "\n".join(relationships))
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# 添加关联节点信息
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# 3. 添加关联节点的详细信息
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if entity.related_nodes:
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related_names = [n["name"] for n in entity.related_nodes[:5]]
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if related_names:
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context_parts.append(f"Related to: {', '.join(related_names)}")
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related_info = []
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for node in entity.related_nodes: # 不限制数量
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node_name = node.get("name", "")
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node_labels = node.get("labels", [])
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node_summary = node.get("summary", "")
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# 过滤掉默认标签
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custom_labels = [l for l in node_labels if l not in ["Entity", "Node"]]
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label_str = f" ({', '.join(custom_labels)})" if custom_labels else ""
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if node_summary:
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related_info.append(f"- **{node_name}**{label_str}: {node_summary}")
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else:
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related_info.append(f"- **{node_name}**{label_str}")
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if related_info:
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context_parts.append("### 关联实体信息\n" + "\n".join(related_info))
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# 4. 使用Zep混合检索获取更丰富的信息
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zep_results = self._search_zep_for_entity(entity)
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if zep_results.get("facts"):
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# 去重：排除已存在的事实
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new_facts = [f for f in zep_results["facts"] if f not in existing_facts]
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if new_facts:
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context_parts.append("### Zep检索到的事实信息\n" + "\n".join(f"- {f}" for f in new_facts[:15]))
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if zep_results.get("node_summaries"):
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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]))
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return "\n\n".join(context_parts)
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def _is_individual_entity(self, entity_type: str) -> bool:
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"""判断是否是个人类型实体"""
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return entity_type.lower() in self.INDIVIDUAL_ENTITY_TYPES
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def _is_group_entity(self, entity_type: str) -> bool:
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"""判断是否是群体/机构类型实体"""
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return entity_type.lower() in self.GROUP_ENTITY_TYPES
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def _generate_profile_with_llm(
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self,
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entity_name: str,
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@@ -275,63 +478,271 @@ class OasisProfileGenerator:
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entity_attributes: Dict[str, Any],
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context: str
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) -> Dict[str, Any]:
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"""使用LLM生成详细人设"""
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"""
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使用LLM生成非常详细的人设
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prompt = f"""Based on the following entity information, generate a detailed social media user profile for simulation purposes.
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根据实体类型区分：
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- 个人实体：生成具体的人物设定
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- 群体/机构实体：生成代表性账号设定
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"""
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is_individual = self._is_individual_entity(entity_type)
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if is_individual:
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prompt = self._build_individual_persona_prompt(
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entity_name, entity_type, entity_summary, entity_attributes, context
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)
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else:
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prompt = self._build_group_persona_prompt(
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entity_name, entity_type, entity_summary, entity_attributes, context
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)
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Entity Information:
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- Name: {entity_name}
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- Type: {entity_type}
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- Summary: {entity_summary}
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- Attributes: {json.dumps(entity_attributes, ensure_ascii=False)}
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Context:
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{context}
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Generate a JSON object with the following fields:
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{{
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"bio": "A short bio (max 150 chars) suitable for social media",
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"persona": "A detailed persona description (2-3 sentences) describing personality, interests, and behavior patterns",
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"age": <integer between 18-65, or null if not applicable>,
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"gender": "<male/female/other, or null if not applicable>",
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"mbti": "<MBTI type like INTJ, ENFP, etc., or null>",
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"country": "<country name, or null>",
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"profession": "<profession/occupation, or null>",
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"interested_topics": ["topic1", "topic2", ...]
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}}
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Important:
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- The profile should be consistent with the entity type and context
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- Make the persona feel realistic and suitable for social media simulation
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- If the entity is an organization, institution, or non-person, adapt the profile accordingly (e.g., as an official account)
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- Return ONLY the JSON object, no additional text"""
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try:
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# 使用重试机制调用LLM API
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from ..utils.retry import RetryableAPIClient
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retry_client = RetryableAPIClient(max_retries=3, initial_delay=1.0)
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def call_llm():
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return self.client.chat.completions.create(
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# 尝试多次生成，直到成功或达到最大重试次数
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max_attempts = 3
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last_error = None
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for attempt in range(max_attempts):
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try:
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response = self.client.chat.completions.create(
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model=self.model_name,
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messages=[
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{"role": "system", "content": "You are a profile generator for social media simulation. Generate realistic user profiles based on entity information."},
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{"role": "system", "content": self._get_system_prompt(is_individual)},
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{"role": "user", "content": prompt}
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],
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response_format={"type": "json_object"},
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temperature=0.7
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temperature=0.7 - (attempt * 0.1) # 每次重试降低温度
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# 不设置max_tokens，让LLM自由发挥
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)
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content = response.choices[0].message.content
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# 检查是否被截断（finish_reason不是'stop'）
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finish_reason = response.choices[0].finish_reason
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if finish_reason == 'length':
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logger.warning(f"LLM输出被截断 (attempt {attempt+1}), 尝试修复...")
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content = self._fix_truncated_json(content)
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# 尝试解析JSON
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try:
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result = json.loads(content)
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# 验证必需字段
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if "bio" not in result or not result["bio"]:
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result["bio"] = entity_summary[:200] if entity_summary else f"{entity_type}: {entity_name}"
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if "persona" not in result or not result["persona"]:
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result["persona"] = entity_summary or f"{entity_name}是一个{entity_type}。"
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return result
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except json.JSONDecodeError as je:
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logger.warning(f"JSON解析失败 (attempt {attempt+1}): {str(je)[:80]}")
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# 尝试修复JSON
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result = self._try_fix_json(content, entity_name, entity_type, entity_summary)
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if result.get("_fixed"):
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del result["_fixed"]
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return result
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last_error = je
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except Exception as e:
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logger.warning(f"LLM调用失败 (attempt {attempt+1}): {str(e)[:80]}")
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last_error = e
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import time
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time.sleep(1 * (attempt + 1)) # 指数退避
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logger.warning(f"LLM生成人设失败（{max_attempts}次尝试）: {last_error}, 使用规则生成")
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return self._generate_profile_rule_based(
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entity_name, entity_type, entity_summary, entity_attributes
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)
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def _fix_truncated_json(self, content: str) -> str:
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"""修复被截断的JSON（输出被max_tokens限制截断）"""
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import re
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# 如果JSON被截断，尝试闭合它
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content = content.strip()
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# 计算未闭合的括号
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open_braces = content.count('{') - content.count('}')
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open_brackets = content.count('[') - content.count(']')
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# 检查是否有未闭合的字符串
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# 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
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if content and content[-1] not in '",}]':
|
||||
# 尝试闭合字符串
|
||||
content += '"'
|
||||
|
||||
# 闭合括号
|
||||
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"""
|
||||
import re
|
||||
|
||||
# 1. 首先尝试修复被截断的情况
|
||||
content = self._fix_truncated_json(content)
|
||||
|
||||
# 2. 尝试提取JSON部分
|
||||
json_match = re.search(r'\{[\s\S]*\}', content)
|
||||
if json_match:
|
||||
json_str = json_match.group()
|
||||
|
||||
response = retry_client.call_with_retry(call_llm)
|
||||
result = json.loads(response.choices[0].message.content)
|
||||
return result
|
||||
# 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
|
||||
def fix_string_newlines(match):
|
||||
s = match.group(0)
|
||||
# 替换字符串内的实际换行符为空格
|
||||
s = s.replace('\n', ' ').replace('\r', ' ')
|
||||
# 替换多余空格
|
||||
s = re.sub(r'\s+', ' ', s)
|
||||
return s
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"LLM生成人设失败（已重试）: {str(e)}, 使用规则生成")
|
||||
return self._generate_profile_rule_based(
|
||||
entity_name, entity_type, entity_summary, entity_attributes
|
||||
)
|
||||
# 匹配JSON字符串值
|
||||
json_str = re.sub(r'"[^"\\]*(?:\\.[^"\\]*)*"', fix_string_newlines, json_str)
|
||||
|
||||
# 4. 尝试解析
|
||||
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
|
||||
try:
|
||||
# 移除所有控制字符
|
||||
json_str = re.sub(r'[\x00-\x1f\x7f-\x9f]', ' ', json_str)
|
||||
# 替换所有连续空白
|
||||
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
|
||||
bio_match = re.search(r'"bio"\s*:\s*"([^"]*)"', content)
|
||||
persona_match = re.search(r'"persona"\s*:\s*"([^"]*)', content) # 可能被截断
|
||||
|
||||
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
|
||||
if bio_match or persona_match:
|
||||
logger.info(f"从损坏的JSON中提取了部分信息")
|
||||
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")
|
||||
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}。"
|
||||
}
|
||||
|
||||
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。"
|
||||
return base_prompt
|
||||
|
||||
def _build_individual_persona_prompt(
|
||||
self,
|
||||
entity_name: str,
|
||||
entity_type: str,
|
||||
entity_summary: str,
|
||||
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。
|
||||
|
||||
实体名称: {entity_name}
|
||||
实体类型: {entity_type}
|
||||
实体摘要: {entity_summary}
|
||||
实体属性: {attrs_str}
|
||||
|
||||
上下文信息:
|
||||
{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
|
||||
4. gender: 性别（男/女）
|
||||
5. mbti: MBTI类型
|
||||
6. country: 国家
|
||||
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
|
||||
- 内容要与实体信息保持一致"""
|
||||
|
||||
def _build_group_persona_prompt(
|
||||
self,
|
||||
entity_name: str,
|
||||
entity_type: str,
|
||||
entity_summary: str,
|
||||
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。
|
||||
|
||||
实体名称: {entity_name}
|
||||
实体类型: {entity_type}
|
||||
实体摘要: {entity_summary}
|
||||
实体属性: {attrs_str}
|
||||
|
||||
上下文信息:
|
||||
{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: null（机构不适用）
|
||||
4. gender: null（机构不适用）
|
||||
5. mbti: 可选，用于描述账号风格，如ISTJ代表严谨保守
|
||||
6. country: 国家
|
||||
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、数字或null
|
||||
- persona必须是一段连贯的文字描述，不要使用换行符
|
||||
- 使用中文
|
||||
- 机构账号发言要符合其身份定位"""
|
||||
|
||||
def _generate_profile_rule_based(
|
||||
self,
|
||||
@@ -398,29 +809,46 @@ Important:
|
||||
"interested_topics": ["General", "Social Issues"],
|
||||
}
|
||||
|
||||
def set_graph_id(self, graph_id: str):
|
||||
"""设置图谱ID用于Zep检索"""
|
||||
self.graph_id = graph_id
|
||||
|
||||
def generate_profiles_from_entities(
|
||||
self,
|
||||
entities: List[EntityNode],
|
||||
use_llm: bool = True,
|
||||
progress_callback: Optional[callable] = None
|
||||
progress_callback: Optional[callable] = None,
|
||||
graph_id: Optional[str] = None,
|
||||
parallel_count: int = 5
|
||||
) -> List[OasisAgentProfile]:
|
||||
"""
|
||||
批量从实体生成Agent Profile
|
||||
批量从实体生成Agent Profile（支持并行生成）
|
||||
|
||||
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
|
||||
|
||||
Returns:
|
||||
Agent Profile列表
|
||||
"""
|
||||
profiles = []
|
||||
total = len(entities)
|
||||
import concurrent.futures
|
||||
from threading import Lock
|
||||
|
||||
for idx, entity in enumerate(entities):
|
||||
if progress_callback:
|
||||
progress_callback(idx + 1, total, f"生成 {entity.name} 的人设...")
|
||||
# 设置graph_id用于Zep检索
|
||||
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
|
||||
lock = Lock()
|
||||
|
||||
def generate_single_profile(idx: int, entity: EntityNode) -> tuple:
|
||||
"""生成单个profile的工作函数"""
|
||||
entity_type = entity.get_entity_type() or "Entity"
|
||||
|
||||
try:
|
||||
profile = self.generate_profile_from_entity(
|
||||
@@ -428,23 +856,115 @@ Important:
|
||||
user_id=idx,
|
||||
use_llm=use_llm
|
||||
)
|
||||
profiles.append(profile)
|
||||
|
||||
# 实时输出生成的人设到控制台和日志
|
||||
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
|
||||
profiles.append(OasisAgentProfile(
|
||||
fallback_profile = OasisAgentProfile(
|
||||
user_id=idx,
|
||||
user_name=self._generate_username(entity.name),
|
||||
name=entity.name,
|
||||
bio=f"{entity.get_entity_type() or 'Entity'}: {entity.name}",
|
||||
bio=f"{entity_type}: {entity.name}",
|
||||
persona=entity.summary or f"A participant in social discussions.",
|
||||
source_entity_uuid=entity.uuid,
|
||||
source_entity_type=entity.get_entity_type(),
|
||||
))
|
||||
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}）...")
|
||||
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"{'='*60}\n")
|
||||
|
||||
# 使用线程池并行执行
|
||||
with concurrent.futures.ThreadPoolExecutor(max_workers=parallel_count) as executor:
|
||||
# 提交所有任务
|
||||
future_to_entity = {
|
||||
executor.submit(generate_single_profile, idx, entity): (idx, entity)
|
||||
for idx, entity in enumerate(entities)
|
||||
}
|
||||
|
||||
# 收集结果
|
||||
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]
|
||||
|
||||
if progress_callback:
|
||||
progress_callback(
|
||||
current,
|
||||
total,
|
||||
f"已完成 {current}/{total}: {entity.name}（{entity_type}）"
|
||||
)
|
||||
|
||||
if error:
|
||||
logger.warning(f"[{current}/{total}] {entity.name} 使用备用人设: {error}")
|
||||
else:
|
||||
logger.info(f"[{current}/{total}] 成功生成人设: {entity.name} ({entity_type})")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"处理实体 {entity.name} 时发生异常: {str(e)}")
|
||||
with lock:
|
||||
completed_count[0] += 1
|
||||
profiles[idx] = OasisAgentProfile(
|
||||
user_id=idx,
|
||||
user_name=self._generate_username(entity.name),
|
||||
name=entity.name,
|
||||
bio=f"{entity_type}: {entity.name}",
|
||||
persona=entity.summary or "A participant in social discussions.",
|
||||
source_entity_uuid=entity.uuid,
|
||||
source_entity_type=entity_type,
|
||||
)
|
||||
|
||||
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"{'='*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）"""
|
||||
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'
|
||||
|
||||
output_lines = [
|
||||
f"\n{separator}",
|
||||
f"[已生成] {entity_name} ({entity_type})",
|
||||
f"{separator}",
|
||||
f"用户名: {profile.user_name}",
|
||||
f"",
|
||||
f"【简介】",
|
||||
f"{profile.bio}",
|
||||
f"",
|
||||
f"【详细人设】",
|
||||
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}",
|
||||
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）
|
||||
print(output)
|
||||
|
||||
def save_profiles(
|
||||
self,
|
||||
profiles: List[OasisAgentProfile],
|
||||
@@ -470,10 +990,18 @@ Important:
|
||||
|
||||
def _save_twitter_csv(self, profiles: List[OasisAgentProfile], file_path: str):
|
||||
"""
|
||||
保存Twitter Profile为CSV格式
|
||||
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, user_name, name, bio, friend_count, follower_count, statuses_count, created_at
|
||||
- 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
|
||||
"""
|
||||
import csv
|
||||
|
||||
@@ -484,28 +1012,32 @@ Important:
|
||||
with open(file_path, 'w', newline='', encoding='utf-8') as f:
|
||||
writer = csv.writer(f)
|
||||
|
||||
# 写入表头
|
||||
headers = ['user_id', 'user_name', 'name', 'bio', 'friend_count',
|
||||
'follower_count', 'statuses_count', 'created_at']
|
||||
# 写入OASIS要求的表头
|
||||
headers = ['user_id', 'name', 'username', 'user_char', 'description']
|
||||
writer.writerow(headers)
|
||||
|
||||
# 写入数据行
|
||||
for profile in profiles:
|
||||
# bio需要处理换行符和逗号
|
||||
bio = profile.bio.replace('\n', ' ').replace('\r', ' ')
|
||||
for idx, profile in enumerate(profiles):
|
||||
# user_char: 完整人设（bio + persona），用于LLM系统提示
|
||||
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）
|
||||
user_char = user_char.replace('\n', ' ').replace('\r', ' ')
|
||||
|
||||
# description: 简短简介，用于外部显示
|
||||
description = profile.bio.replace('\n', ' ').replace('\r', ' ')
|
||||
|
||||
row = [
|
||||
profile.user_id,
|
||||
profile.user_name,
|
||||
profile.name,
|
||||
bio,
|
||||
profile.friend_count,
|
||||
profile.follower_count,
|
||||
profile.statuses_count,
|
||||
profile.created_at
|
||||
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）
|
||||
]
|
||||
writer.writerow(row)
|
||||
|
||||
logger.info(f"已保存 {len(profiles)} 个Twitter Profile到 {file_path} (CSV格式)")
|
||||
logger.info(f"已保存 {len(profiles)} 个Twitter Profile到 {file_path} (OASIS CSV格式)")
|
||||
|
||||
def _save_reddit_json(self, profiles: List[OasisAgentProfile], file_path: str):
|
||||
"""
|
||||
|
||||
Reference in New Issue
Block a user