627 lines
18 KiB
Python
Executable File
627 lines
18 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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批量生成多个Agent
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生成一批不同类型的Agent，展示各种工作流模式
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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_text_summary_agent(db: Session, user: User):
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"""生成文本摘要Agent"""
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nodes = []
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edges = []
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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": 300},
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"data": {"label": "开始", "output_format": "json"}
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}
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nodes.append(start_node)
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summary_node = {
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"id": "llm-summary",
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"type": "llm",
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"position": {"x": 250, "y": 300},
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"data": {
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"label": "文本摘要",
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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": "2000",
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"prompt": """你是一个专业的文本摘要专家。请对以下文本进行摘要。
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文本内容：{{query}}
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请生成：
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1. 核心要点（3-5条）
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2. 简要摘要（100-200字）
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3. 关键词（5-10个）
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请以Markdown格式输出。"""
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}
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}
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nodes.append(summary_node)
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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": 450, "y": 300},
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"data": {"label": "结束"}
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}
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nodes.append(end_node)
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edges.append({"id": "e1", "source": "start-1", "target": "llm-summary", "sourceHandle": "right", "targetHandle": "left"})
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edges.append({"id": "e2", "source": "llm-summary", "target": "end-1", "sourceHandle": "right", "targetHandle": "left"})
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return {
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"name": "文本摘要Agent",
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"description": "智能文本摘要工具，能够提取文本核心要点、生成简要摘要和关键词。",
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"workflow_config": {"nodes": nodes, "edges": edges}
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}
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def generate_code_review_agent(db: Session, user: User):
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"""生成代码审查Agent"""
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nodes = []
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edges = []
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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": 300},
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"data": {"label": "开始", "output_format": "json"}
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}
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nodes.append(start_node)
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analysis_node = {
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"id": "llm-analysis",
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"type": "llm",
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"position": {"x": 250, "y": 300},
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"data": {
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"label": "代码分析",
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"provider": "deepseek",
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"model": "deepseek-chat",
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"temperature": "0.2",
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"max_tokens": "3000",
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"prompt": """你是一个资深的代码审查专家。请审查以下代码。
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代码：{{query}}
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请检查：
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1. 代码规范（命名、格式、注释）
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2. 潜在bug和错误
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3. 性能问题
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4. 安全性问题
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5. 最佳实践建议
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请以Markdown格式输出，包含问题列表和改进建议。"""
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}
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}
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nodes.append(analysis_node)
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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": 450, "y": 300},
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"data": {"label": "结束"}
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}
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nodes.append(end_node)
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edges.append({"id": "e1", "source": "start-1", "target": "llm-analysis", "sourceHandle": "right", "targetHandle": "left"})
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edges.append({"id": "e2", "source": "llm-analysis", "target": "end-1", "sourceHandle": "right", "targetHandle": "left"})
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return {
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"name": "代码审查Agent",
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"description": "专业的代码审查工具，能够检查代码规范、潜在bug、性能问题和安全性。",
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"workflow_config": {"nodes": nodes, "edges": edges}
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}
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def generate_translation_agent(db: Session, user: User):
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"""生成翻译Agent"""
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nodes = []
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edges = []
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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": 300},
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"data": {"label": "开始", "output_format": "json"}
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}
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nodes.append(start_node)
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detect_node = {
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"id": "llm-detect",
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"type": "llm",
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"position": {"x": 250, "y": 300},
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"data": {
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"label": "语言检测",
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"provider": "deepseek",
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"model": "deepseek-chat",
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"temperature": "0.1",
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"max_tokens": "500",
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"prompt": """请检测以下文本的语言类型。
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文本：{{query}}
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请输出JSON格式：
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{
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"language": "检测到的语言（中文/英文/日文等）",
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"confidence": "置信度（高/中/低）"
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}"""
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}
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}
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nodes.append(detect_node)
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translate_node = {
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"id": "llm-translate",
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"type": "llm",
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"position": {"x": 450, "y": 300},
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"data": {
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"label": "翻译",
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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": "2000",
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"prompt": """你是一个专业的翻译专家。请翻译以下文本。
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原文：{{query}}
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语言信息：{{output}}
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请提供：
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1. 翻译结果
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2. 翻译说明（如有特殊处理）
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请以Markdown格式输出。"""
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}
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}
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nodes.append(translate_node)
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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": 650, "y": 300},
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"data": {"label": "结束"}
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}
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nodes.append(end_node)
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edges.append({"id": "e1", "source": "start-1", "target": "llm-detect", "sourceHandle": "right", "targetHandle": "left"})
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edges.append({"id": "e2", "source": "llm-detect", "target": "llm-translate", "sourceHandle": "right", "targetHandle": "left"})
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edges.append({"id": "e3", "source": "llm-translate", "target": "end-1", "sourceHandle": "right", "targetHandle": "left"})
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return {
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"name": "智能翻译Agent",
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"description": "多语言翻译工具，支持语言自动检测和高质量翻译。",
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"workflow_config": {"nodes": nodes, "edges": edges}
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}
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def generate_qa_agent(db: Session, user: User):
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"""生成问答助手Agent"""
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nodes = []
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edges = []
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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": 300},
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"data": {"label": "开始", "output_format": "json"}
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}
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nodes.append(start_node)
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understand_node = {
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"id": "llm-understand",
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"type": "llm",
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"position": {"x": 250, "y": 300},
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"data": {
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"label": "问题理解",
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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": "1000",
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"prompt": """请分析用户的问题，提取关键信息。
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用户问题：{{query}}
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请输出JSON格式：
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{
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"question_type": "问题类型（技术/生活/学习等）",
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"keywords": ["关键词1", "关键词2"],
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"intent": "用户意图"
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}"""
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}
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}
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nodes.append(understand_node)
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answer_node = {
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"id": "llm-answer",
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"type": "llm",
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"position": {"x": 450, "y": 300},
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"data": {
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"label": "生成答案",
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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": "2000",
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"prompt": """你是一个知识渊博的助手。请回答用户的问题。
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用户问题：{{query}}
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问题分析：{{output}}
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请提供：
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1. 直接答案
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2. 详细解释
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3. 相关建议
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请以Markdown格式输出，确保答案准确、清晰、有用。"""
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}
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}
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nodes.append(answer_node)
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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": 650, "y": 300},
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"data": {"label": "结束"}
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}
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nodes.append(end_node)
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edges.append({"id": "e1", "source": "start-1", "target": "llm-understand", "sourceHandle": "right", "targetHandle": "left"})
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edges.append({"id": "e2", "source": "llm-understand", "target": "llm-answer", "sourceHandle": "right", "targetHandle": "left"})
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edges.append({"id": "e3", "source": "llm-answer", "target": "end-1", "sourceHandle": "right", "targetHandle": "left"})
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return {
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"name": "智能问答助手",
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"description": "智能问答系统，能够理解问题意图并提供详细准确的答案。",
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"workflow_config": {"nodes": nodes, "edges": edges}
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}
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def generate_document_agent(db: Session, user: User):
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"""生成文档生成Agent"""
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nodes = []
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edges = []
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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": 300},
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"data": {"label": "开始", "output_format": "json"}
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}
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nodes.append(start_node)
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plan_node = {
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"id": "llm-plan",
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"type": "llm",
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"position": {"x": 250, "y": 300},
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"data": {
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"label": "文档规划",
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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": "1500",
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"prompt": """请根据用户需求规划文档结构。
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用户需求：{{query}}
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请输出JSON格式的文档大纲：
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{
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"title": "文档标题",
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"sections": [
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{"name": "章节1", "content": "内容描述"},
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{"name": "章节2", "content": "内容描述"}
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]
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}"""
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}
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}
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nodes.append(plan_node)
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generate_node = {
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"id": "llm-generate",
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"type": "llm",
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"position": {"x": 450, "y": 300},
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"data": {
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"label": "生成文档",
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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": "4000",
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"prompt": """请根据文档规划生成完整的文档内容。
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用户需求：{{query}}
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文档规划：{{output}}
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请生成结构完整、内容详实的Markdown文档。"""
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}
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}
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nodes.append(generate_node)
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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": 650, "y": 300},
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"data": {"label": "结束"}
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}
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nodes.append(end_node)
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edges.append({"id": "e1", "source": "start-1", "target": "llm-plan", "sourceHandle": "right", "targetHandle": "left"})
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edges.append({"id": "e2", "source": "llm-plan", "target": "llm-generate", "sourceHandle": "right", "targetHandle": "left"})
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edges.append({"id": "e3", "source": "llm-generate", "target": "end-1", "sourceHandle": "right", "targetHandle": "left"})
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return {
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"name": "文档生成Agent",
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"description": "智能文档生成工具，能够根据需求规划文档结构并生成完整内容。",
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"workflow_config": {"nodes": nodes, "edges": edges}
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}
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def generate_data_analysis_agent(db: Session, user: User):
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"""生成数据分析Agent"""
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nodes = []
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edges = []
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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": 300},
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"data": {"label": "开始", "output_format": "json"}
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}
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nodes.append(start_node)
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parse_node = {
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"id": "llm-parse",
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"type": "llm",
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"position": {"x": 250, "y": 300},
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"data": {
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"label": "数据解析",
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"provider": "deepseek",
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"model": "deepseek-chat",
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"temperature": "0.2",
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"max_tokens": "2000",
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"prompt": """请解析用户提供的数据。
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数据内容：{{query}}
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请输出JSON格式：
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{
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"data_type": "数据类型（表格/列表/文本等）",
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"structure": "数据结构描述",
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"key_fields": ["字段1", "字段2"]
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}"""
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}
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}
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nodes.append(parse_node)
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analysis_node = {
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"id": "llm-analysis",
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"type": "llm",
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"position": {"x": 450, "y": 300},
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"data": {
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"label": "数据分析",
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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": "3000",
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"prompt": """请对数据进行深入分析。
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原始数据：{{query}}
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数据解析：{{output}}
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请提供：
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1. 数据概览
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2. 关键指标
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3. 趋势分析
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4. 洞察建议
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请以Markdown格式输出，包含数据表格和图表描述。"""
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}
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}
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nodes.append(analysis_node)
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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": 650, "y": 300},
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"data": {"label": "结束"}
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}
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nodes.append(end_node)
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edges.append({"id": "e1", "source": "start-1", "target": "llm-parse", "sourceHandle": "right", "targetHandle": "left"})
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edges.append({"id": "e2", "source": "llm-parse", "target": "llm-analysis", "sourceHandle": "right", "targetHandle": "left"})
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edges.append({"id": "e3", "source": "llm-analysis", "target": "end-1", "sourceHandle": "right", "targetHandle": "left"})
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return {
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"name": "数据分析Agent",
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"description": "智能数据分析工具，能够解析数据、提取关键指标并提供深度洞察。",
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"workflow_config": {"nodes": nodes, "edges": edges}
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}
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def generate_creative_writing_agent(db: Session, user: User):
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"""生成创意写作Agent"""
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nodes = []
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edges = []
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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": 300},
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"data": {"label": "开始", "output_format": "json"}
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}
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nodes.append(start_node)
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brainstorm_node = {
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"id": "llm-brainstorm",
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"type": "llm",
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"position": {"x": 250, "y": 300},
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"data": {
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"label": "头脑风暴",
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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": "1500",
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"prompt": """你是一个创意写作专家。请根据用户需求进行头脑风暴。
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用户需求：{{query}}
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请提供：
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1. 创意主题（3-5个）
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2. 故事大纲
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3. 角色设定
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4. 写作风格建议
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请以Markdown格式输出。"""
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}
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}
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nodes.append(brainstorm_node)
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write_node = {
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"id": "llm-write",
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"type": "llm",
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"position": {"x": 450, "y": 300},
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"data": {
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"label": "创作内容",
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"provider": "deepseek",
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"model": "deepseek-chat",
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"temperature": "0.9",
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"max_tokens": "4000",
|
||
"prompt": """请根据创意方案进行创作。
|
||
|
||
用户需求：{{query}}
|
||
创意方案：{{output}}
|
||
|
||
请创作一篇完整的作品（文章、故事、诗歌等），确保内容生动、有趣、有创意。"""
|
||
}
|
||
}
|
||
nodes.append(write_node)
|
||
|
||
end_node = {
|
||
"id": "end-1",
|
||
"type": "end",
|
||
"position": {"x": 650, "y": 300},
|
||
"data": {"label": "结束"}
|
||
}
|
||
nodes.append(end_node)
|
||
|
||
edges.append({"id": "e1", "source": "start-1", "target": "llm-brainstorm", "sourceHandle": "right", "targetHandle": "left"})
|
||
edges.append({"id": "e2", "source": "llm-brainstorm", "target": "llm-write", "sourceHandle": "right", "targetHandle": "left"})
|
||
edges.append({"id": "e3", "source": "llm-write", "target": "end-1", "sourceHandle": "right", "targetHandle": "left"})
|
||
|
||
return {
|
||
"name": "创意写作Agent",
|
||
"description": "创意写作助手，能够进行头脑风暴并创作各种类型的创意内容。",
|
||
"workflow_config": {"nodes": nodes, "edges": edges}
|
||
}
|
||
|
||
|
||
def generate_batch_agents(db: Session, username: str = "admin"):
|
||
"""批量生成Agent"""
|
||
print("=" * 60)
|
||
print("批量生成Agent")
|
||
print("=" * 60)
|
||
print()
|
||
|
||
# 查找用户
|
||
user = db.query(User).filter(User.username == username).first()
|
||
if not user:
|
||
print(f"❌ 未找到用户 '{username}'，请先创建该用户")
|
||
return
|
||
print(f"✅ 找到用户: {user.username} (ID: {user.id})")
|
||
print()
|
||
|
||
# 定义要生成的Agent列表
|
||
agent_generators = [
|
||
generate_text_summary_agent,
|
||
generate_code_review_agent,
|
||
generate_translation_agent,
|
||
generate_qa_agent,
|
||
generate_document_agent,
|
||
generate_data_analysis_agent,
|
||
generate_creative_writing_agent,
|
||
]
|
||
|
||
created_count = 0
|
||
updated_count = 0
|
||
failed_count = 0
|
||
|
||
for generator in agent_generators:
|
||
try:
|
||
agent_data = generator(db, user)
|
||
agent_name = agent_data["name"]
|
||
|
||
# 检查Agent是否已存在
|
||
existing_agent = db.query(Agent).filter(
|
||
Agent.name == agent_name,
|
||
Agent.user_id == user.id
|
||
).first()
|
||
|
||
if existing_agent:
|
||
existing_agent.workflow_config = agent_data["workflow_config"]
|
||
existing_agent.description = agent_data["description"]
|
||
existing_agent.updated_at = datetime.now()
|
||
existing_agent.status = "published"
|
||
updated_count += 1
|
||
print(f"⚠️ 更新Agent: {agent_name}")
|
||
else:
|
||
agent = Agent(
|
||
id=str(uuid.uuid4()),
|
||
name=agent_name,
|
||
description=agent_data["description"],
|
||
workflow_config=agent_data["workflow_config"],
|
||
status="published",
|
||
user_id=user.id,
|
||
version=1
|
||
)
|
||
db.add(agent)
|
||
created_count += 1
|
||
print(f"✅ 创建Agent: {agent_name}")
|
||
except Exception as e:
|
||
failed_count += 1
|
||
print(f"❌ 生成Agent失败: {generator.__name__} - {str(e)}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
|
||
try:
|
||
db.commit()
|
||
print()
|
||
print("=" * 60)
|
||
print("✅ 批量生成完成！")
|
||
print("=" * 60)
|
||
print(f" - 新建: {created_count} 个")
|
||
print(f" - 更新: {updated_count} 个")
|
||
print(f" - 失败: {failed_count} 个")
|
||
print()
|
||
print("📋 生成的Agent列表：")
|
||
for generator in agent_generators:
|
||
agent_data = generator(db, user)
|
||
print(f" • {agent_data['name']}")
|
||
print()
|
||
except Exception as e:
|
||
db.rollback()
|
||
print(f"❌ 提交失败: {str(e)}")
|
||
import traceback
|
||
traceback.print_exc()
|
||
|
||
|
||
def main():
|
||
"""主函数"""
|
||
db = SessionLocal()
|
||
try:
|
||
generate_batch_agents(db, username="admin")
|
||
finally:
|
||
db.close()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|