203 lines
5.6 KiB
Python
203 lines
5.6 KiB
Python
#!/usr/bin/env python3
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"""
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RAG 系统使用示例
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演示如何使用 app/rag 模块进行知识检索。
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"""
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import sys
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import os
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from dotenv import load_dotenv
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# 加载环境变量
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load_dotenv()
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# 添加项目根目录到路径
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))
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from langchain_openai import OpenAIEmbeddings
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from langchain_community.llms import VLLMOpenAI
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def setup_environment():
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"""设置环境变量"""
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# 设置 Qdrant 连接信息（根据实际情况修改）
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os.environ.setdefault("QDRANT_URL", "http://115.190.121.151:6333")
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# 设置 Qdrant API 密钥（根据实际情况修改）
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os.environ.setdefault("QDRANT_API_KEY", "your-api-key-here")
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# 如果需要 API 密钥，请设置 QDRANT_API_KEY
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print("环境变量已设置")
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print(f"QDRANT_URL: {os.environ.get('QDRANT_URL')}")
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print(f"QDRANT_API_KEY: {'***' if os.environ.get('QDRANT_API_KEY') else '未设置'}")
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def demonstrate_basic_rag():
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"""演示基础 RAG 功能"""
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print("\n" + "="*60)
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print("演示: 基础 RAG 检索 (Level 1)")
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print("="*60)
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# 创建嵌入模型（使用本地 LlamaCpp 模型）
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from rag_core import LlamaCppEmbedder
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embedder = LlamaCppEmbedder()
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embeddings = embedder.as_langchain_embeddings()
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# 创建 RAG 流水线
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from app.rag import RAGPipeline, RAGLevel
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pipeline = RAGPipeline(
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embeddings=embeddings,
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config={
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"collection_name": "rag_documents", # 你的集合名称
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"rag_level": RAGLevel.BASIC.value,
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}
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)
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# 示例查询
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query = "吕布"
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print(f"\n查询: {query}")
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try:
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documents = pipeline.retrieve(query)
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print(f"找到 {len(documents)} 个相关文档")
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# 格式化上下文
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context = pipeline.format_context(documents)
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print(f"\n上下文预览:\n{context[:500]}...")
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except Exception as e:
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print(f"检索失败: {e}")
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print("请确保 Qdrant 服务正常运行且集合存在")
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def demonstrate_hybrid_rag():
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"""演示混合 RAG 功能"""
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print("\n" + "="*60)
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print("演示: 混合 RAG 检索 (Level 2)")
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print("="*60)
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from rag_core import LlamaCppEmbedder
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embedder = LlamaCppEmbedder()
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embeddings = embedder.as_langchain_embeddings()
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from app.rag import RAGPipeline, RAGLevel
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pipeline = RAGPipeline(
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embeddings=embeddings,
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config={
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"collection_name": "rag_documents",
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"rag_level": RAGLevel.RERANK.value,
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"rerank_top_n": 5,
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}
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)
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query = "吕布"
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print(f"\n查询: {query}")
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try:
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documents = pipeline.retrieve(query)
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print(f"找到 {len(documents)} 个重排序后的文档")
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# 格式化上下文
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context = pipeline.format_context(documents)
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print(f"\n上下文预览:\n{context[:500]}...")
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except Exception as e:
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print(f"检索失败: {e}")
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def demonstrate_rag_fusion():
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"""演示 RAG-Fusion 功能"""
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print("\n" + "="*60)
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print("演示: RAG-Fusion (Level 3)")
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print("="*60)
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from rag_core import LlamaCppEmbedder
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embedder = LlamaCppEmbedder()
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embeddings = embedder.as_langchain_embeddings()
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# 创建语言模型用于查询改写（使用 OpenAI 兼容的本地模型）
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from langchain_openai import ChatOpenAI
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llm = ChatOpenAI(
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openai_api_base="http://localhost:8000/v1",
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openai_api_key="no-key-needed",
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model="Qwen2.5-7B-Instruct", # 你的本地模型
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temperature=0.3,
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max_tokens=512,
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)
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from app.rag import RAGPipeline, RAGLevel
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pipeline = RAGPipeline(
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embeddings=embeddings,
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llm=llm,
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config={
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"collection_name": "rag_documents",
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"rag_level": RAGLevel.FUSION.value,
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"num_queries": 3,
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}
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)
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query = "吕布"
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print(f"\n查询: {query}")
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try:
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documents = pipeline.retrieve(query)
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print(f"找到 {len(documents)} 个文档 (经过多路查询改写和重排序)")
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# 格式化上下文
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context = pipeline.format_context(documents)
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print(f"\n上下文预览:\n{context[:500]}...")
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except Exception as e:
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print(f"检索失败: {e}")
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def demonstrate_agentic_rag():
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"""演示 Agentic RAG 功能"""
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print("\n" + "="*60)
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print("演示: Agentic RAG (Level 4)")
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print("="*60)
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from app.rag import search_knowledge_base_sync
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try:
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# 演示工具调用
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print("工具调用示例:")
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response = search_knowledge_base_sync("吕布")
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print(f"工具响应预览: {response[:200]}...")
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except Exception as e:
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print(f"工具调用失败: {e}")
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import traceback
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traceback.print_exc()
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def main():
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"""主函数"""
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print("RAG 系统演示")
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print("="*60)
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# 设置环境
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setup_environment()
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# 演示基础功能
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demonstrate_basic_rag()
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demonstrate_hybrid_rag()
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# demonstrate_rag_fusion() # 需要本地 LLM 服务
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# demonstrate_agentic_rag() # 需要本地 LLM 服务
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print("\n" + "="*60)
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print("演示完成!")
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print("="*60)
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print("\n使用说明:")
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print("1. 确保 Qdrant 服务运行且集合已创建")
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print("2. 已使用本地 LlamaCpp 嵌入模型")
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print("3. 在 Agent 系统中导入并使用 app.rag.tools.search_knowledge_base")
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print("4. 将工具绑定到你的 Agent 模型")
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if __name__ == "__main__":
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main() |