测试修改

backend/rag_core/config.py

@@ -6,7 +6,8 @@ RAG Core 配置管理模块
 """
 
 import os
-
+import dotenv
+dotenv.load_dotenv()
 
 # ========== 辅助函数：类型转换 ==========
 def _get_str(key: str) -> str | None:

backend/rag_core/embedders.py

@@ -21,6 +21,9 @@ class LlamaCppEmbedder:
         self.base_url = LLAMACPP_EMBEDDING_URL
         self.api_key = LLAMACPP_API_KEY
         self.model = model
+        print(f"初始化 base_url: { self.base_url}")
+
+
 
     def as_langchain_embeddings(self) -> Embeddings:
         """创建 LangChain 兼容的嵌入实例。"""
@@ -41,13 +44,14 @@ class LlamaCppEmbedder:
 
     def _call_embedding_api(self, texts: List[str]) -> List[List[float]]:
         """直接调用 llama.cpp 嵌入 API。"""
+        headers = {"Content-Type": "application/json"}
+        if self.api_key:
+            headers["Authorization"] = f"Bearer {self.api_key}"
+
         base = self.base_url.rstrip("/")
         if not base.endswith("/v1"):
             base = base + "/v1"
 
-        headers = {"Content-Type": "application/json"}
-        if self.api_key:
-            headers["Authorization"] = f"Bearer {self.api_key}"
 
         payload = {
             "input": texts,

rag_indexer/__init__.py

@@ -42,7 +42,7 @@ import sys
 from pathlib import Path
 sys.path.insert(0, str(Path(__file__).parent.parent / "backend"))
 
-from rag_core import (
+from backend.rag_core import (
     LlamaCppEmbedder,
     QdrantVectorStore,
     PostgresDocStore,

rag_indexer/index_builder.py

@@ -94,7 +94,6 @@ class IndexBuilder:
         # 初始化向量存储
         self.vector_store = QdrantVectorStore(
             collection_name=config.collection_name,
-            embeddings=self.embeddings,
         )
 
         # 根据切分类型初始化相关组件

test/test_rag.py

@@ -18,18 +18,14 @@ from dotenv import load_dotenv
 load_dotenv()
 
 # 添加项目根目录到路径
-sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../.."))
-
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+from pydantic import SecretStr
+from langchain_openai import ChatOpenAI
 from rag_indexer.index_builder import IndexBuilderConfig
 from rag_indexer.splitters import SplitterType
-from .pipeline import RAGPipeline
-from .tools import create_rag_tool_sync
-from pydantic import SecretStr
-# 使用本地 LLM（通过 OpenAI 兼容接口）
-from langchain_openai import ChatOpenAI
-from rag_core.retriever_factory import create_parent_retriever
-
-load_dotenv()
+from backend.app.rag.pipeline import RAGPipeline
+from backend.app.rag.tools import create_rag_tool_sync
+from backend.rag_core.retriever_factory import create_parent_retriever
 
 def create_llm():
         """创建本地 vLLM 服务 LLM"""
@@ -113,7 +109,7 @@ async def demonstrate_tool_creation():
         collection_name="rag_documents",
         splitter_type=SplitterType.PARENT_CHILD,
     )
-    retriever = retriever = create_parent_retriever(collection_name="rag_documents", search_k=5)
+    retriever = create_parent_retriever(collection_name="rag_documents", search_k=5)
 
     # 2. 创建 LLM
     llm = create_llm()

test/test_rag_indexer_result.py

@@ -41,41 +41,6 @@ async def test_index_builder():
     else:
         print(f"测试文件不存在: {test_file}")
   
-    # 测试搜索功能
-    print("\n测试搜索功能...")
-    try:
-        results = builder.search("吕布", k=3)
-        print(f"搜索结果数量: {len(results)}")
-        for i, result in enumerate(results):
-            print(f"\n结果 {i+1}:")
-            print(f"内容: {result.page_content[:100]}...")
-    except Exception as e:
-        print(f"搜索测试失败: {e}")
-    
-    # 测试带父块上下文的搜索
-    print("\n测试带父块上下文的搜索...")
-    try:
-        results = await builder.search_with_parent_context("吕布", k=3)
-        print(f"搜索结果数量: {len(results)}")
-        for i, result in enumerate(results):
-            print(f"\n结果 {i+1}:")
-            print(f"内容: {result.page_content[:100]}...")
-    except Exception as e:
-        print(f"带父块上下文的搜索测试失败: {e}")
-    
-    # 测试统一检索接口
-    print("\n测试统一检索接口...")
-    try:
-        # 返回父块
-        results_parent = await builder.retrieve("吕布", return_parent=True)
-        print(f"返回父块的结果数量: {len(results_parent)}")
-        
-        # 返回子块
-        results_child = await builder.retrieve("吕布", return_parent=False)
-        print(f"返回子块的结果数量: {len(results_child)}")
-    except Exception as e:
-        print(f"统一检索接口测试失败: {e}")
-    
     # 关闭资源
     builder.close()
     print("\n测试完成")