335 lines
10 KiB
Python
335 lines
10 KiB
Python
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#!/usr/bin/env python3
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"""
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生成知识库问答Agent示例
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展示如何使用向量数据库和RAG技术构建知识库问答系统，包含：
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- 文本向量化（HTTP节点调用embedding API）
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- 向量数据库检索（vector_db节点）
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- 基于检索结果的答案生成（LLM节点）
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- 上下文整合和格式化
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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_knowledge_base_qa_agent(db: Session, user: User):
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"""生成知识库问答Agent"""
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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. 问题预处理节点 ==========
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preprocess_node = {
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"id": "transform-preprocess",
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"type": "transform",
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"position": {"x": 250, "y": 400},
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"data": {
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"label": "问题预处理",
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"mode": "merge",
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"mapping": {
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"query": "{{query}}",
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"user_id": "{{user_id}}",
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"timestamp": "{{timestamp}}"
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}
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}
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}
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nodes.append(preprocess_node)
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# ========== 3. 文本向量化节点（HTTP调用embedding API）==========
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# 注意：需要在HTTP节点配置中手动设置Authorization header，使用环境变量DEEPSEEK_API_KEY
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embedding_node = {
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"id": "http-embedding",
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"type": "http",
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"position": {"x": 450, "y": 400},
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"data": {
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"label": "文本向量化",
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"method": "POST",
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"url": "https://api.deepseek.com/v1/embeddings",
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"headers": {
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"Content-Type": "application/json",
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"Authorization": "Bearer sk-fdf7cc1c73504e628ec0119b7e11b8cc"
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},
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"body": {
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"model": "deepseek-embedding",
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"input": "{{query}}"
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},
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"response_format": "json"
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}
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}
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nodes.append(embedding_node)
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# ========== 4. 提取embedding向量节点 ==========
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extract_embedding_node = {
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"id": "json-extract-embedding",
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"type": "json",
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"position": {"x": 650, "y": 400},
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"data": {
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"label": "提取向量",
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"operation": "extract",
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"path": "output.data.data[0].embedding"
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}
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}
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nodes.append(extract_embedding_node)
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# ========== 5. 准备向量搜索数据节点 ==========
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prepare_search_node = {
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"id": "transform-prepare-search",
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"type": "transform",
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"position": {"x": 850, "y": 400},
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"data": {
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"label": "准备搜索数据",
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"mode": "merge",
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"mapping": {
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"embedding": "{{output}}",
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"query": "{{query}}"
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}
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}
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}
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nodes.append(prepare_search_node)
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# ========== 6. 向量数据库检索节点 ==========
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vector_search_node = {
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"id": "vector-search",
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"type": "vector_db",
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"position": {"x": 1050, "y": 400},
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"data": {
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"label": "知识库检索",
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"operation": "search",
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"collection": "knowledge_base",
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"query_vector": "{{embedding}}",
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"top_k": 5
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}
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}
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nodes.append(vector_search_node)
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# ========== 7. 整理检索结果节点 ==========
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format_results_node = {
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"id": "transform-format-results",
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"type": "transform",
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"position": {"x": 1250, "y": 400},
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"data": {
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"label": "整理检索结果",
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"mode": "merge",
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"mapping": {
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"query": "{{query}}",
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"search_results": "{{output}}"
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}
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}
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}
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nodes.append(format_results_node)
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# ========== 8. 生成答案节点（LLM）==========
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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": 1650, "y": 400},
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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": """你是一个专业的知识库问答助手。请基于提供的知识库内容回答用户的问题。
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用户问题：{{query}}
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相关知识库内容（从向量搜索中检索到的相关文档）：
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{{search_results}}
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请根据以上知识库内容回答用户的问题。要求：
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1. 答案要准确、完整，基于知识库内容
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2. 如果知识库中没有相关信息，请明确说明"根据知识库，未找到相关信息"
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3. 答案要清晰、有条理，使用Markdown格式
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4. 如果知识库内容与问题不完全匹配，可以结合常识进行补充说明，但要标注哪些是知识库内容，哪些是补充说明
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请直接输出答案，不要包含其他格式说明。"""
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}
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}
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nodes.append(answer_node)
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# ========== 9. 提取最终答案节点 ==========
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extract_answer_node = {
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"id": "json-extract-answer",
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"type": "json",
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"position": {"x": 1850, "y": 400},
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"data": {
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"label": "提取最终答案",
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"operation": "extract",
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"path": "output"
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}
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}
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nodes.append(extract_answer_node)
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# ========== 10. 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": 2050, "y": 400},
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"data": {
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"label": "结束"
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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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edges.append({
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"id": "e1",
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"source": "start-1",
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"target": "transform-preprocess",
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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": "e2",
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"source": "transform-preprocess",
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"target": "http-embedding",
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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": "e3",
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"source": "http-embedding",
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"target": "json-extract-embedding",
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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": "e4",
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"source": "json-extract-embedding",
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"target": "transform-prepare-search",
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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": "e5",
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"source": "transform-prepare-search",
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"target": "vector-search",
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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",
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"source": "vector-search",
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"target": "transform-format-results",
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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": "e7",
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"source": "transform-format-results",
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"target": "llm-answer",
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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": "e8",
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"source": "llm-answer",
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"target": "json-extract-answer",
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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": "e9",
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"source": "json-extract-answer",
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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": "知识库问答助手",
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"description": "基于向量数据库和RAG技术的知识库问答系统，支持语义搜索和智能回答。需要先使用向量数据库节点将知识库文档向量化并存储。",
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"workflow_config": {"nodes": nodes, "edges": edges}
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}
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def main():
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"""主函数"""
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db = SessionLocal()
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try:
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# 获取或创建测试用户
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user = db.query(User).first()
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if not user:
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print("❌ 未找到用户，请先创建用户")
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return
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print(f"📝 使用用户: {user.username} (ID: {user.id})")
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# 生成Agent数据
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agent_data = generate_knowledge_base_qa_agent(db, user)
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# 检查是否已存在
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existing = db.query(Agent).filter(
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Agent.name == agent_data["name"],
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Agent.user_id == user.id
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).first()
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if existing:
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print(f"Agent '{agent_data['name']}' 已存在，跳过创建")
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return
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# 创建Agent
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agent = Agent(
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name=agent_data["name"],
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description=agent_data["description"],
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workflow_config=agent_data["workflow_config"],
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user_id=user.id,
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status="draft"
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)
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db.add(agent)
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db.commit()
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db.refresh(agent)
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print(f"✅ 成功创建Agent: {agent.name} (ID: {agent.id})")
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print(f" 节点数量: {len(agent_data['workflow_config']['nodes'])}")
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print(f" 连接数量: {len(agent_data['workflow_config']['edges'])}")
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print(f"\n📝 使用说明：")
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print(f" 1. 在Agent管理页面找到 '{agent.name}'")
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print(f" 2. 点击'设计'按钮进入工作流编辑器")
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print(f" 3. 配置HTTP节点的API密钥（DeepSeek API Key）")
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print(f" 4. 使用向量数据库节点将知识库文档向量化并存储到 'knowledge_base' 集合")
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print(f" 5. 点击'发布'按钮发布Agent")
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print(f" 6. 点击'使用'按钮测试问答功能")
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print(f"\n💡 提示：")
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print(f" - 知识库文档需要先通过向量数据库节点的 'upsert' 操作存储")
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print(f" - 每个文档需要包含 'text' 和 'embedding' 字段")
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print(f" - 可以使用HTTP节点调用embedding API将文档文本转为向量")
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except Exception as e:
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print(f"❌ 创建Agent失败: {str(e)}")
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import traceback
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traceback.print_exc()
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db.rollback()
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finally:
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db.close()
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if __name__ == "__main__":
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main()
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