#!/usr/bin/env python3 """ 生成智能聊天Agent示例 展示如何使用平台能力构建一个完整的聊天智能体，包含： - 记忆管理（缓存节点） - 意图识别（LLM节点） - 多分支路由（Switch节点） - 上下文传递（Transform节点） - 多轮对话支持 """ import sys import os sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from sqlalchemy.orm import Session from app.core.database import SessionLocal from app.models.agent import Agent from app.models.user import User from datetime import datetime import uuid def generate_chat_agent(db: Session, user: User): """生成智能聊天Agent - 完整示例""" nodes = [] edges = [] # ========== 1. 开始节点 ========== start_node = { "id": "start-1", "type": "start", "position": {"x": 50, "y": 400}, "data": { "label": "开始", "output_format": "json" } } nodes.append(start_node) # ========== 2. 查询记忆节点 ========== query_memory_node = { "id": "cache-query", "type": "cache", "position": {"x": 250, "y": 400}, "data": { "label": "查询记忆", "operation": "get", "key": "user_memory_{user_id}", "default_value": '{"conversation_history": [], "user_profile": {}, "context": {}}' } } nodes.append(query_memory_node) # ========== 3. 合并用户输入和记忆 ========== merge_context_node = { "id": "transform-merge", "type": "transform", "position": {"x": 450, "y": 400}, "data": { "label": "合并上下文", "mode": "merge", "mapping": { "user_input": "{{query}}", "memory": "{{output}}", "timestamp": "{{timestamp}}" } } } nodes.append(merge_context_node) # ========== 4. 意图理解节点 ========== intent_node = { "id": "llm-intent", "type": "llm", "position": {"x": 650, "y": 400}, "data": { "label": "意图理解", "provider": "deepseek", "model": "deepseek-chat", "temperature": "0.3", "max_tokens": "1000", "prompt": """你是一个专业的对话意图分析助手。请分析用户的输入，识别用户的意图和情感。 用户输入：{{user_input}} 对话历史：{{memory.conversation_history}} 用户画像：{{memory.user_profile}} 请以JSON格式输出分析结果： { "intent": "意图类型（greeting/question/emotion/request/goodbye/other）", "emotion": "情感状态（positive/neutral/negative）", "keywords": ["关键词1", "关键词2"], "topic": "话题主题", "needs_response": true } 请确保输出是有效的JSON格式，不要包含其他文字。""" } } nodes.append(intent_node) # ========== 5. Switch节点 - 根据意图分支 ========== switch_node = { "id": "switch-intent", "type": "switch", "position": {"x": 850, "y": 400}, "data": { "label": "意图路由", "field": "intent", "cases": { "greeting": "greeting-handle", "question": "question-handle", "emotion": "emotion-handle", "request": "request-handle", "goodbye": "goodbye-handle" }, "default": "general-handle" } } nodes.append(switch_node) # ========== 6. 问候处理分支 ========== greeting_node = { "id": "llm-greeting", "type": "llm", "position": {"x": 1050, "y": 200}, "data": { "label": "问候回复", "provider": "deepseek", "model": "deepseek-chat", "temperature": "0.7", "max_tokens": "500", "prompt": """你是一个温暖、友好的AI助手。用户向你打招呼，请用自然、亲切的方式回应。 用户输入：{{user_input}} 对话历史：{{memory.conversation_history}} 请生成一个友好、自然的问候回复，长度控制在50字以内。直接输出回复内容，不要包含其他说明。""" } } nodes.append(greeting_node) # ========== 7. 问题处理分支 ========== question_node = { "id": "llm-question", "type": "llm", "position": {"x": 1050, "y": 300}, "data": { "label": "问题回答", "provider": "deepseek", "model": "deepseek-chat", "temperature": "0.5", "max_tokens": "2000", "prompt": """你是一个知识渊博、乐于助人的AI助手。请回答用户的问题。 用户问题：{{user_input}} 对话历史：{{memory.conversation_history}} 意图分析：{{output}} 请提供： 1. 直接、准确的答案 2. 必要的解释和说明 3. 如果问题不明确，友好地询问更多信息 请以自然、易懂的方式回答，长度控制在200字以内。直接输出回答内容。""" } } nodes.append(question_node) # ========== 8. 情感处理分支 ========== emotion_node = { "id": "llm-emotion", "type": "llm", "position": {"x": 1050, "y": 400}, "data": { "label": "情感回应", "provider": "deepseek", "model": "deepseek-chat", "temperature": "0.8", "max_tokens": "1000", "prompt": """你是一个善解人意的AI助手。请根据用户的情感状态，给予适当的回应。 用户输入：{{user_input}} 情感状态：{{output.emotion}} 对话历史：{{memory.conversation_history}} 请根据用户的情感： - 如果是积极情感：给予鼓励和共鸣 - 如果是消极情感：给予理解、安慰和支持 - 如果是中性情感：给予关注和陪伴 请生成一个温暖、共情的回复，长度控制在150字以内。直接输出回复内容。""" } } nodes.append(emotion_node) # ========== 9. 请求处理分支 ========== request_node = { "id": "llm-request", "type": "llm", "position": {"x": 1050, "y": 500}, "data": { "label": "请求处理", "provider": "deepseek", "model": "deepseek-chat", "temperature": "0.4", "max_tokens": "1500", "prompt": """你是一个专业的AI助手。用户提出了一个请求，请分析并回应。 用户请求：{{user_input}} 意图分析：{{output}} 对话历史：{{memory.conversation_history}} 请： 1. 理解用户的请求内容 2. 如果可以满足，说明如何满足 3. 如果无法满足，友好地说明原因并提供替代方案 请以清晰、友好的方式回应，长度控制在200字以内。直接输出回复内容。""" } } nodes.append(request_node) # ========== 10. 告别处理分支 ========== goodbye_node = { "id": "llm-goodbye", "type": "llm", "position": {"x": 1050, "y": 600}, "data": { "label": "告别回复", "provider": "deepseek", "model": "deepseek-chat", "temperature": "0.6", "max_tokens": "300", "prompt": """你是一个友好的AI助手。用户要结束对话，请给予温暖的告别。 用户输入：{{user_input}} 对话历史：{{memory.conversation_history}} 请生成一个温暖、友好的告别回复，表达期待下次交流。长度控制在50字以内。直接输出回复内容。""" } } nodes.append(goodbye_node) # ========== 11. 通用处理分支 ========== general_node = { "id": "llm-general", "type": "llm", "position": {"x": 1050, "y": 700}, "data": { "label": "通用回复", "provider": "deepseek", "model": "deepseek-chat", "temperature": "0.6", "max_tokens": "1000", "prompt": """你是一个友好、专业的AI助手。请回应用户的输入。 用户输入：{{user_input}} 对话历史：{{memory.conversation_history}} 意图分析：{{output}} 请生成一个自然、有意义的回复，保持对话的连贯性。长度控制在150字以内。直接输出回复内容。""" } } nodes.append(general_node) # ========== 12. Merge节点 - 合并所有分支结果 ========== merge_response_node = { "id": "merge-response", "type": "merge", "position": {"x": 1250, "y": 400}, "data": { "label": "合并回复", "mode": "merge_first", "strategy": "object" } } nodes.append(merge_response_node) # ========== 13. 更新记忆节点 ========== update_memory_node = { "id": "cache-update", "type": "cache", "position": {"x": 1450, "y": 400}, "data": { "label": "更新记忆", "operation": "set", "key": "user_memory_{user_id}", "value": '{"conversation_history": {{memory.conversation_history}} + [{"role": "user", "content": "{{user_input}}", "timestamp": "{{timestamp}}"}, {"role": "assistant", "content": "{{output}}", "timestamp": "{{timestamp}}"}], "user_profile": {{memory.user_profile}}, "context": {{memory.context}}}', "ttl": 86400 } } nodes.append(update_memory_node) # ========== 14. 格式化最终回复 ========== format_response_node = { "id": "llm-format", "type": "llm", "position": {"x": 1650, "y": 400}, "data": { "label": "格式化回复", "provider": "deepseek", "model": "deepseek-chat", "temperature": "0.3", "max_tokens": "500", "prompt": """请将以下回复内容格式化为最终输出。确保回复自然、流畅。 原始回复：{{output}} 请直接输出格式化后的回复内容，不要包含其他说明或标记。如果原始回复已经是合适的格式，直接输出即可。""" } } nodes.append(format_response_node) # ========== 15. 结束节点 ========== end_node = { "id": "end-1", "type": "end", "position": {"x": 1850, "y": 400}, "data": { "label": "结束", "output_format": "text" } } nodes.append(end_node) # ========== 连接边 ========== # 开始 -> 查询记忆 edges.append({ "id": "e1", "source": "start-1", "target": "cache-query", "sourceHandle": "right", "targetHandle": "left" }) # 查询记忆 -> 合并上下文 edges.append({ "id": "e2", "source": "cache-query", "target": "transform-merge", "sourceHandle": "right", "targetHandle": "left" }) # 合并上下文 -> 意图理解 edges.append({ "id": "e3", "source": "transform-merge", "target": "llm-intent", "sourceHandle": "right", "targetHandle": "left" }) # 意图理解 -> Switch路由 edges.append({ "id": "e4", "source": "llm-intent", "target": "switch-intent", "sourceHandle": "right", "targetHandle": "left" }) # Switch -> 各分支处理节点 edges.append({ "id": "e5-greeting", "source": "switch-intent", "target": "llm-greeting", "sourceHandle": "greeting-handle", "targetHandle": "left" }) edges.append({ "id": "e5-question", "source": "switch-intent", "target": "llm-question", "sourceHandle": "question-handle", "targetHandle": "left" }) edges.append({ "id": "e5-emotion", "source": "switch-intent", "target": "llm-emotion", "sourceHandle": "emotion-handle", "targetHandle": "left" }) edges.append({ "id": "e5-request", "source": "switch-intent", "target": "llm-request", "sourceHandle": "request-handle", "targetHandle": "left" }) edges.append({ "id": "e5-goodbye", "source": "switch-intent", "target": "llm-goodbye", "sourceHandle": "goodbye-handle", "targetHandle": "left" }) edges.append({ "id": "e5-general", "source": "switch-intent", "target": "llm-general", "sourceHandle": "default", "targetHandle": "left" }) # 各分支 -> Merge节点 edges.append({ "id": "e6-greeting", "source": "llm-greeting", "target": "merge-response", "sourceHandle": "right", "targetHandle": "left" }) edges.append({ "id": "e6-question", "source": "llm-question", "target": "merge-response", "sourceHandle": "right", "targetHandle": "left" }) edges.append({ "id": "e6-emotion", "source": "llm-emotion", "target": "merge-response", "sourceHandle": "right", "targetHandle": "left" }) edges.append({ "id": "e6-request", "source": "llm-request", "target": "merge-response", "sourceHandle": "right", "targetHandle": "left" }) edges.append({ "id": "e6-goodbye", "source": "llm-goodbye", "target": "merge-response", "sourceHandle": "right", "targetHandle": "left" }) edges.append({ "id": "e6-general", "source": "llm-general", "target": "merge-response", "sourceHandle": "right", "targetHandle": "left" }) # Merge -> 更新记忆 edges.append({ "id": "e7", "source": "merge-response", "target": "cache-update", "sourceHandle": "right", "targetHandle": "left" }) # 更新记忆 -> 格式化回复 edges.append({ "id": "e8", "source": "cache-update", "target": "llm-format", "sourceHandle": "right", "targetHandle": "left" }) # 格式化回复 -> 结束 edges.append({ "id": "e9", "source": "llm-format", "target": "end-1", "sourceHandle": "right", "targetHandle": "left" }) return { "name": "智能聊天助手（完整示例）", "description": """一个完整的聊天智能体示例，展示平台的核心能力： - ✅ 记忆管理：使用缓存节点存储和查询对话历史 - ✅ 意图识别：使用LLM节点分析用户意图 - ✅ 多分支路由：使用Switch节点根据意图分发到不同处理分支 - ✅ 上下文传递：使用Transform节点合并数据 - ✅ 多轮对话：支持上下文记忆和连贯对话 - ✅ 个性化回复：根据不同意图生成针对性回复 适用场景：情感陪聊、客服助手、智能问答等聊天场景。""", "workflow_config": {"nodes": nodes, "edges": edges} } def main(): """主函数：生成并保存Agent""" db = SessionLocal() try: # 获取或创建测试用户 user = db.query(User).filter(User.username == "admin").first() if not user: print("请先创建admin用户") return # 生成Agent agent_data = generate_chat_agent(db, user) # 检查是否已存在 existing = db.query(Agent).filter( Agent.name == agent_data["name"], Agent.user_id == user.id ).first() if existing: print(f"Agent '{agent_data['name']}' 已存在，跳过创建") return # 创建Agent agent = Agent( name=agent_data["name"], description=agent_data["description"], workflow_config=agent_data["workflow_config"], user_id=user.id, status="draft" ) db.add(agent) db.commit() db.refresh(agent) print(f"✅ 成功创建Agent: {agent.name} (ID: {agent.id})") print(f" 节点数量: {len(agent_data['workflow_config']['nodes'])}") print(f" 连接数量: {len(agent_data['workflow_config']['edges'])}") print(f"\n📝 使用说明：") print(f" 1. 在Agent管理页面找到 '{agent.name}'") print(f" 2. 点击'设计'按钮进入工作流编辑器") print(f" 3. 配置LLM节点的API密钥（如需要）") print(f" 4. 点击'发布'按钮发布Agent") print(f" 5. 点击'使用'按钮测试对话功能") except Exception as e: print(f"❌ 创建Agent失败: {str(e)}") import traceback traceback.print_exc() db.rollback() finally: db.close() if __name__ == "__main__": main()