完成后端配置，用于部署

scripts/test_env.py

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+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)