544 lines
17 KiB
Python
544 lines
17 KiB
Python
#!/usr/bin/env python3
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"""
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生成智能聊天Agent示例
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展示如何使用平台能力构建一个完整的聊天智能体，包含：
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- 记忆管理（缓存节点）
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- 意图识别（LLM节点）
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- 多分支路由（Switch节点）
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- 上下文传递（Transform节点）
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- 多轮对话支持
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"""
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import sys
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import os
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from sqlalchemy.orm import Session
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from app.core.database import SessionLocal
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from app.models.agent import Agent
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from app.models.user import User
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from datetime import datetime
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import uuid
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def generate_chat_agent(db: Session, user: User):
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"""生成智能聊天Agent - 完整示例"""
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nodes = []
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edges = []
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# ========== 1. 开始节点 ==========
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start_node = {
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"id": "start-1",
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"type": "start",
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"position": {"x": 50, "y": 400},
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"data": {
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"label": "开始",
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"output_format": "json"
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}
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}
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nodes.append(start_node)
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# ========== 2. 查询记忆节点 ==========
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query_memory_node = {
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"id": "cache-query",
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"type": "cache",
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"position": {"x": 250, "y": 400},
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"data": {
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"label": "查询记忆",
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"operation": "get",
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"key": "user_memory_{user_id}",
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"default_value": '{"conversation_history": [], "user_profile": {}, "context": {}}'
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}
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}
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nodes.append(query_memory_node)
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# ========== 3. 合并用户输入和记忆 ==========
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merge_context_node = {
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"id": "transform-merge",
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"type": "transform",
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"position": {"x": 450, "y": 400},
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"data": {
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"label": "合并上下文",
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"mode": "merge",
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"mapping": {
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"user_input": "{{query}}",
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"memory": "{{output}}",
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"timestamp": "{{timestamp}}"
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}
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}
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}
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nodes.append(merge_context_node)
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# ========== 4. 意图理解节点 ==========
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intent_node = {
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"id": "llm-intent",
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"type": "llm",
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"position": {"x": 650, "y": 400},
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"data": {
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"label": "意图理解",
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"provider": "deepseek",
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"model": "deepseek-chat",
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"temperature": "0.3",
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"max_tokens": "200",
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"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。
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用户输入：{{user_input}}
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对话历史：{{memory.conversation_history}}
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用户画像：{{memory.user_profile}}
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请以JSON格式输出分析结果：
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{
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"intent": "意图类型（greeting/question/emotion/request/goodbye/other）",
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"emotion": "情感状态（positive/neutral/negative）",
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"keywords": ["关键词1", "关键词2"],
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"topic": "话题主题",
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"needs_response": true
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}
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请确保输出是有效的JSON格式，不要包含其他文字。"""
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}
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}
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nodes.append(intent_node)
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# ========== 5. Switch节点 - 根据意图分支 ==========
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switch_node = {
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"id": "switch-intent",
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"type": "switch",
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"position": {"x": 850, "y": 400},
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"data": {
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"label": "意图路由",
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"field": "intent",
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"cases": {
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"greeting": "greeting-handle",
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"question": "question-handle",
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"emotion": "emotion-handle",
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"request": "request-handle",
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"goodbye": "goodbye-handle"
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},
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"default": "general-handle"
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}
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}
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nodes.append(switch_node)
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# ========== 6. 问候处理分支 ==========
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greeting_node = {
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"id": "llm-greeting",
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"type": "llm",
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"position": {"x": 1050, "y": 200},
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"data": {
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"label": "问候回复",
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"provider": "deepseek",
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"model": "deepseek-chat",
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"temperature": "0.7",
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"max_tokens": "200",
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"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。
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用户输入：{{user_input}}
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对话历史：{{memory.conversation_history}}
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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。"""
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}
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}
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nodes.append(greeting_node)
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# ========== 7. 问题处理分支 ==========
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question_node = {
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"id": "llm-question",
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"type": "llm",
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"position": {"x": 1050, "y": 300},
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"data": {
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"label": "问题回答",
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"provider": "deepseek",
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"model": "deepseek-chat",
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"temperature": "0.5",
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"max_tokens": "500",
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"prompt": """你是一个知识渊博、乐于助人的AI助手。请简洁、准确地回答用户的问题。
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用户问题：{{user_input}}
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对话历史：{{memory.conversation_history}}
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意图分析：{{output}}
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回答要求：
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1. 直接给出核心答案，避免冗长描述
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2. 如果是介绍类问题（如"你能做什么"），用简洁的要点列举，控制在100字以内
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3. 如果是知识性问题，提供准确答案和简要说明，控制在150字以内
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4. 如果问题不明确，友好地询问更多信息，控制在50字以内
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请以自然、简洁的方式回答，避免重复和冗余。直接输出回答内容，无需额外格式化。"""
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}
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}
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nodes.append(question_node)
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# ========== 8. 情感处理分支 ==========
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emotion_node = {
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"id": "llm-emotion",
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"type": "llm",
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"position": {"x": 1050, "y": 400},
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"data": {
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"label": "情感回应",
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"provider": "deepseek",
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"model": "deepseek-chat",
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"temperature": "0.8",
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"max_tokens": "500",
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"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。
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用户输入：{{user_input}}
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情感状态：{{output.emotion}}
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对话历史：{{memory.conversation_history}}
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请根据用户的情感：
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- 如果是积极情感：给予鼓励和共鸣
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- 如果是消极情感：给予理解、安慰和支持
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- 如果是中性情感：给予关注和陪伴
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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。"""
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}
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}
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nodes.append(emotion_node)
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# ========== 9. 请求处理分支 ==========
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request_node = {
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"id": "llm-request",
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"type": "llm",
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"position": {"x": 1050, "y": 500},
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"data": {
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"label": "请求处理",
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"provider": "deepseek",
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"model": "deepseek-chat",
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"temperature": "0.4",
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"max_tokens": "800",
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"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。
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用户请求：{{user_input}}
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意图分析：{{output}}
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对话历史：{{memory.conversation_history}}
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请：
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1. 理解用户的请求内容
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2. 如果可以满足，说明如何满足
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3. 如果无法满足，友好地说明原因并提供替代方案
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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。"""
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}
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}
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nodes.append(request_node)
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# ========== 10. 告别处理分支 ==========
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goodbye_node = {
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"id": "llm-goodbye",
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"type": "llm",
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"position": {"x": 1050, "y": 600},
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"data": {
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"label": "告别回复",
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"provider": "deepseek",
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"model": "deepseek-chat",
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"temperature": "0.6",
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"max_tokens": "150",
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"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。
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用户输入：{{user_input}}
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对话历史：{{memory.conversation_history}}
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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。"""
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}
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}
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nodes.append(goodbye_node)
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# ========== 11. 通用处理分支 ==========
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general_node = {
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"id": "llm-general",
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"type": "llm",
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"position": {"x": 1050, "y": 700},
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"data": {
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"label": "通用回复",
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"provider": "deepseek",
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"model": "deepseek-chat",
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"temperature": "0.6",
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"max_tokens": "500",
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"prompt": """你是一个友好、专业的AI助手。请回应用户的输入。
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用户输入：{{user_input}}
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对话历史：{{memory.conversation_history}}
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意图分析：{{output}}
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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。"""
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}
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}
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nodes.append(general_node)
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# ========== 12. Merge节点 - 合并所有分支结果 ==========
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merge_response_node = {
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"id": "merge-response",
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"type": "merge",
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"position": {"x": 1250, "y": 400},
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"data": {
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"label": "合并回复",
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"mode": "merge_first",
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"strategy": "object"
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}
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}
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nodes.append(merge_response_node)
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# ========== 13. 更新记忆节点 ==========
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update_memory_node = {
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"id": "cache-update",
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"type": "cache",
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"position": {"x": 1450, "y": 400},
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"data": {
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"label": "更新记忆",
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"operation": "set",
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"key": "user_memory_{user_id}",
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"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}}}',
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"ttl": 86400
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}
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}
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nodes.append(update_memory_node)
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# ========== 14. JSON提取节点 - 提取最终回答文本 ==========
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json_extract_node = {
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"id": "json-extract",
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"type": "json",
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"position": {"x": 1650, "y": 400},
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"data": {
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"label": "提取回答",
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"operation": "extract",
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"path": "right.right.right"
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}
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}
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nodes.append(json_extract_node)
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# ========== 15. 结束节点 ==========
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end_node = {
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"id": "end-1",
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"type": "end",
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"position": {"x": 1850, "y": 400},
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"data": {
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"label": "结束",
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"output_format": "text"
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}
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}
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nodes.append(end_node)
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# ========== 连接边 ==========
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# 开始 -> 查询记忆
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edges.append({
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"id": "e1",
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"source": "start-1",
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"target": "cache-query",
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"sourceHandle": "right",
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"targetHandle": "left"
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})
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# 查询记忆 -> 合并上下文
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edges.append({
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"id": "e2",
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"source": "cache-query",
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"target": "transform-merge",
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"sourceHandle": "right",
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"targetHandle": "left"
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})
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# 合并上下文 -> 意图理解
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edges.append({
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"id": "e3",
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"source": "transform-merge",
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"target": "llm-intent",
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"sourceHandle": "right",
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"targetHandle": "left"
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})
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# 意图理解 -> Switch路由
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edges.append({
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"id": "e4",
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"source": "llm-intent",
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"target": "switch-intent",
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"sourceHandle": "right",
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"targetHandle": "left"
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})
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# Switch -> 各分支处理节点
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edges.append({
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"id": "e5-greeting",
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"source": "switch-intent",
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"target": "llm-greeting",
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"sourceHandle": "greeting-handle",
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"targetHandle": "left"
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})
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edges.append({
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"id": "e5-question",
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"source": "switch-intent",
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"target": "llm-question",
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"sourceHandle": "question-handle",
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"targetHandle": "left"
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})
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edges.append({
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"id": "e5-emotion",
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"source": "switch-intent",
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"target": "llm-emotion",
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"sourceHandle": "emotion-handle",
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"targetHandle": "left"
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})
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edges.append({
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"id": "e5-request",
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"source": "switch-intent",
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"target": "llm-request",
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"sourceHandle": "request-handle",
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"targetHandle": "left"
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})
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edges.append({
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"id": "e5-goodbye",
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"source": "switch-intent",
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"target": "llm-goodbye",
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"sourceHandle": "goodbye-handle",
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"targetHandle": "left"
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})
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edges.append({
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"id": "e5-general",
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"source": "switch-intent",
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"target": "llm-general",
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"sourceHandle": "default",
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"targetHandle": "left"
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})
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# 各分支 -> Merge节点
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edges.append({
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"id": "e6-greeting",
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"source": "llm-greeting",
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"target": "merge-response",
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"sourceHandle": "right",
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"targetHandle": "left"
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})
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edges.append({
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"id": "e6-question",
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"source": "llm-question",
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"target": "merge-response",
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"sourceHandle": "right",
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"targetHandle": "left"
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})
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edges.append({
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"id": "e6-emotion",
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"source": "llm-emotion",
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"target": "merge-response",
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"sourceHandle": "right",
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"targetHandle": "left"
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})
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edges.append({
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"id": "e6-request",
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"source": "llm-request",
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"target": "merge-response",
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"sourceHandle": "right",
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"targetHandle": "left"
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})
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edges.append({
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"id": "e6-goodbye",
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"source": "llm-goodbye",
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"target": "merge-response",
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"sourceHandle": "right",
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"targetHandle": "left"
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})
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edges.append({
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"id": "e6-general",
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"source": "llm-general",
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"target": "merge-response",
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"sourceHandle": "right",
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"targetHandle": "left"
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})
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# Merge -> 更新记忆
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edges.append({
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"id": "e7",
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"source": "merge-response",
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"target": "cache-update",
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"sourceHandle": "right",
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"targetHandle": "left"
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})
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# 更新记忆 -> JSON提取
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edges.append({
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"id": "e8",
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"source": "cache-update",
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"target": "json-extract",
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"sourceHandle": "right",
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"targetHandle": "left"
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})
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# JSON提取 -> 结束
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edges.append({
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"id": "e9",
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"source": "json-extract",
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"target": "end-1",
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"sourceHandle": "right",
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"targetHandle": "left"
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})
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return {
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"name": "智能聊天助手（完整示例）",
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"description": """一个完整的聊天智能体示例，展示平台的核心能力：
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- ✅ 记忆管理：使用缓存节点存储和查询对话历史
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- ✅ 意图识别：使用LLM节点分析用户意图
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- ✅ 多分支路由：使用Switch节点根据意图分发到不同处理分支
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- ✅ 上下文传递：使用Transform节点合并数据
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- ✅ 多轮对话：支持上下文记忆和连贯对话
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- ✅ 个性化回复：根据不同意图生成针对性回复
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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()
|