#!/usr/bin/env python3
"""
RAG 系统使用示例

演示如何使用 app/rag 模块进行知识检索。
"""

import sys
import os

# 添加项目根目录到路径
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", ".."))

from langchain_openai import OpenAIEmbeddings
from langchain_community.llms import VLLMOpenAI


def setup_environment():
    """设置环境变量"""
    # 设置 Qdrant 连接信息（根据实际情况修改）
    os.environ.setdefault("QDRANT_URL", "http://115.190.121.151:6333")
    # 如果需要 API 密钥，请设置 QDRANT_API_KEY
    
    print("环境变量已设置")
    print(f"QDRANT_URL: {os.environ.get('QDRANT_URL')}")


def demonstrate_basic_rag():
    """演示基础 RAG 功能"""
    print("\n" + "="*60)
    print("演示: 基础 RAG 检索 (Level 1)")
    print("="*60)
    
    # 创建嵌入模型（使用 OpenAI 兼容的本地模型）
    embeddings = OpenAIEmbeddings(
        openai_api_base="http://localhost:8000/v1",  # 本地 VLLM 服务
        openai_api_key="no-key-needed",
        model="text-embedding-ada-002",  # 假设的模型名称
    )
    
    # 创建 RAG 流水线
    from app.rag import RAGPipeline, RAGConfig, RAGLevel
    
    config = RAGConfig(
        collection_name="documents",  # 你的集合名称
        rag_level=RAGLevel.BASIC,
        verbose=True,
    )
    
    pipeline = RAGPipeline(
        embeddings=embeddings,
        config=config,
    )
    
    # 示例查询
    query = "公司报销流程是什么？"
    print(f"\n查询: {query}")
    
    try:
        result = pipeline.retrieve(query)
        print(f"找到 {len(result.documents)} 个相关文档")
        
        # 格式化上下文
        context = pipeline.format_context(result.documents)
        print(f"\n上下文预览:\n{context[:500]}...")
        
    except Exception as e:
        print(f"检索失败: {e}")
        print("请确保 Qdrant 服务正常运行且集合存在")


def demonstrate_hybrid_rag():
    """演示混合 RAG 功能"""
    print("\n" + "="*60)
    print("演示: 混合 RAG 检索 (Level 2)")
    print("="*60)
    
    embeddings = OpenAIEmbeddings(
        openai_api_base="http://localhost:8000/v1",
        openai_api_key="no-key-needed",
        model="text-embedding-ada-002",
    )
    
    from app.rag import RAGPipeline, RAGConfig, RAGLevel
    
    config = RAGConfig(
        collection_name="documents",
        rag_level=RAGLevel.HYBRID,
        dense_k=10,
        sparse_k=10,
        rerank_top_n=5,
        verbose=True,
    )
    
    pipeline = RAGPipeline(
        embeddings=embeddings,
        config=config,
    )
    
    query = "如何申请年假？"
    print(f"\n查询: {query}")
    
    try:
        result = pipeline.retrieve(query)
        print(f"找到 {len(result.documents)} 个重排序后的文档")
        
    except Exception as e:
        print(f"检索失败: {e}")


def demonstrate_rag_fusion():
    """演示 RAG-Fusion 功能"""
    print("\n" + "="*60)
    print("演示: RAG-Fusion (Level 3)")
    print("="*60)
    
    embeddings = OpenAIEmbeddings(
        openai_api_base="http://localhost:8000/v1",
        openai_api_key="no-key-needed",
        model="text-embedding-ada-002",
    )
    
    # 创建语言模型用于查询改写
    llm = VLLMOpenAI(
        openai_api_base="http://localhost:8000/v1",
        openai_api_key="no-key-needed",
        model_name="Qwen2.5-7B-Instruct",  # 你的本地模型
        temperature=0.3,
        max_tokens=512,
    )
    
    from app.rag import RAGPipeline, RAGConfig, RAGLevel
    
    config = RAGConfig(
        collection_name="documents",
        rag_level=RAGLevel.FUSION,
        num_queries=3,
        verbose=True,
    )
    
    pipeline = RAGPipeline(
        embeddings=embeddings,
        llm=llm,
        config=config,
    )
    
    query = "项目上线需要哪些审批？"
    print(f"\n查询: {query}")
    
    try:
        result = pipeline.retrieve(query)
        print(f"找到 {len(result.documents)} 个文档 (经过多路查询改写和重排序)")
        
    except Exception as e:
        print(f"检索失败: {e}")


def demonstrate_agentic_rag():
    """演示 Agentic RAG 功能"""
    print("\n" + "="*60)
    print("演示: Agentic RAG (Level 4)")
    print("="*60)
    
    embeddings = OpenAIEmbeddings(
        openai_api_base="http://localhost:8000/v1",
        openai_api_key="no-key-needed",
        model="text-embedding-ada-002",
    )
    
    llm = VLLMOpenAI(
        openai_api_base="http://localhost:8000/v1",
        openai_api_key="no-key-needed",
        model_name="Qwen2.5-7B-Instruct",
        temperature=0.3,
        max_tokens=512,
    )
    
    from app.rag import create_agentic_rag_pipeline
    
    try:
        # 创建 Agentic RAG 流水线
        agentic_rag = create_agentic_rag_pipeline(
            embeddings=embeddings,
            agent_llm=llm,
            config={
                "collection_name": "documents",
                "verbose": True,
            },
        )
        
        print("Agentic RAG 流水线创建成功！")
        print(f"- 绑定的模型: {agentic_rag['llm']}")
        print(f"- RAG 工具: {agentic_rag['tool'].name}")
        
        # 演示工具调用
        print("\n工具调用示例:")
        response = agentic_rag["tool"].invoke({"query": "员工福利有哪些？"})
        print(f"工具响应预览: {response[:200]}...")
        
    except Exception as e:
        print(f"创建 Agentic RAG 失败: {e}")
        import traceback
        traceback.print_exc()


def main():
    """主函数"""
    print("RAG 系统演示")
    print("="*60)
    
    # 设置环境
    setup_environment()
    
    # 演示各级功能
    demonstrate_basic_rag()
    demonstrate_hybrid_rag()
    demonstrate_rag_fusion()
    demonstrate_agentic_rag()
    
    print("\n" + "="*60)
    print("演示完成!")
    print("="*60)
    
    print("\n使用说明:")
    print("1. 确保 Qdrant 服务运行且集合已创建")
    print("2. 根据需要修改 embeddings 和 llm 配置")
    print("3. 在 Agent 系统中导入并使用 app.rag.tools.search_knowledge_base_tool")
    print("4. 将工具绑定到你的 Agent 模型")


if __name__ == "__main__":
    main()