import sys
import time
import uuid

def main():
    report = {
        "status": "running",
        "steps": [],
        "env_info": {},
        "vector_pipeline_test": "pending"
    }
    
    try:
        # ==========================================
        # 1. 环境自检
        # ==========================================
        import numpy
        import requests
        import langchain
        import langchain_community
        import pydantic
        
        report["env_info"] = {
            "python": sys.version.split()[0],
            "numpy": numpy.__version__,
            "langchain": langchain.__version__,
            "pydantic": pydantic.__version__
        }
        report["steps"].append("✅ 基础环境库加载成功")

        # ==========================================
        # 2. 定义 Mock 组件 (模拟向量生成)
        # ==========================================
        from langchain_core.embeddings import Embeddings
        from langchain_core.documents import Document
        
        class MockEmbeddings(Embeddings):
            """生成固定维度的随机向量，模拟 OpenAI/HuggingFace"""
            def __init__(self, dim=1536):
                self.dim = dim
                
            def embed_documents(self, texts):
                # 返回 N 个随机向量
                return [numpy.random.rand(self.dim).tolist() for _ in texts]
                
            def embed_query(self, text):
                # 返回 1 个随机向量
                return numpy.random.rand(self.dim).tolist()
        
        embeddings = MockEmbeddings(dim=768)
        report["steps"].append("✅ 向量嵌入模型 (Mock) 初始化成功")

        # ==========================================
        # 3. 实战测试：使用 Qdrant 内存模式跑全流程
        # ==========================================
        # 这一步能验证 LangChain 和底层库的数据流转是否正常
        try:
            from langchain_community.vectorstores import Qdrant
            
            # 准备假数据
            docs = [
                Document(page_content="Hello Dify", metadata={"id": 1}),
                Document(page_content="Vector Database Test", metadata={"id": 2}),
                Document(page_content="Conflict Check", metadata={"id": 3}),
            ]
            
            # --- 关键步骤：在内存中建库 ---
            # Qdrant 支持 location=":memory:"，不需要外部服务器
            db = Qdrant.from_documents(
                docs,
                embeddings,
                location=":memory:", 
                collection_name="test_collection"
            )
            
            # --- 关键步骤：执行向量检索 ---
            results = db.similarity_search("Hello", k=1)
            
            if results and len(results) > 0:
                report["vector_pipeline_test"] = "✅ Success (Qdrant In-Memory)"
                report["steps"].append(f"✅ 向量存取测试通过: 检索到 '{results[0].page_content}'")
            else:
                report["vector_pipeline_test"] = "❌ Failed (No results)"
                
        except Exception as e:
            report["vector_pipeline_test"] = f"❌ Failed: {str(e)}"
            # 如果这一步挂了，说明依赖有深层冲突
            
        # ==========================================
        # 4. 其他数据库驱动加载测试
        # ==========================================
        # 这一步测试是否能正确 import，确保驱动都在
        drivers = {}
        
        # [PostgreSQL / PGVector]
        try:
            import psycopg2
            from langchain_community.vectorstores import PGVector
            drivers["postgres"] = "✅ Loaded"
        except Exception as e:
            drivers["postgres"] = f"❌ Error: {str(e)}"

        # [Milvus]
        try:
            import pymilvus
            from langchain_community.vectorstores import Milvus
            drivers["milvus"] = "✅ Loaded"
        except Exception as e:
            drivers["milvus"] = f"❌ Error: {str(e)}"

        # [Redis]
        try:
            import redis
            from langchain_community.vectorstores import Redis
            drivers["redis"] = "✅ Loaded"
        except Exception as e:
            drivers["redis"] = f"❌ Error: {str(e)}"

        # [Pinecone]
        try:
            import pinecone # 新版包名
            from langchain_community.vectorstores import Pinecone
            drivers["pinecone"] = "✅ Loaded"
        except Exception as e:
            drivers["pinecone"] = f"❌ Error: {str(e)}"

        report["drivers_status"] = drivers

        # ==========================================
        # 5. 最终判定
        # ==========================================
        if "❌" not in str(drivers.values()) and "Success" in report["vector_pipeline_test"]:
            report["status"] = "success"
            report["message"] = "🎉 完美验证！所有库已就绪，且向量计算逻辑运行正常。"
        else:
            report["status"] = "warning"
            report["message"] = "存在潜在问题，请检查详细信息。"

    except Exception as e:
        report["status"] = "error"
        report["message"] = f"全局错误: {str(e)}"

    return report

if __name__ == "__main__":
    report = main()
    print(report)