feat: optimize MilvusSearchResponse

app/utils/milvus_utils.py

@@ -19,6 +19,17 @@ class KnowledgeQueryParam(BaseModel):
   top_k: int = Field(default=5, description="返回结果数量")
 
 
+class MilvusSearchNode(BaseModel):
+  text: str = Field(..., description="文档内容")
+  metadata: dict = Field(..., description="文档元信息")
+  score: float = Field(..., description="相似度分数")
+
+
+class MilvusSearchResponse(BaseModel):
+  answer: str = Field(..., description="根据用户问题从检索结果中提取的内容")
+  nodes: List[MilvusSearchNode] = Field(..., description="搜索结果")
+
+
 class MilvusService:
   def __init__(self):
     milvus_vector_store: Optional[MilvusVectorStore] = None
@@ -74,7 +85,7 @@ class MilvusService:
     return vector_index
 
   # 检索milvus文档
-  async def async_search(self, param: KnowledgeQueryParam) -> List[dict]:
+  async def async_search(self, param: KnowledgeQueryParam) -> MilvusSearchResponse:
     """异步向量搜索"""
     # 创建查询引擎，设置top_k参数
     vector_index = VectorStoreIndex.from_vector_store(
@@ -113,20 +124,16 @@ class MilvusService:
       """) | create_llm() | StrOutputParser()
 
     chain_input = {"context": "\n".join([node.node.text for node in result_nodes]), "question": param.question}
-    print("chain_input", chain_input)
+
     relative_content = await chain.ainvoke(chain_input)
 
-    return {
-      "answer": relative_content,
-      "sources": [
-        {
-          "text": node.node.text,
-          "metadata": node.node.metadata,
-          "score": node.score
-        }
+    return MilvusSearchResponse(
+      answer=relative_content,
+      nodes=[
+        MilvusSearchNode(text=node.node.text, metadata=node.node.metadata, score=node.score)
         for node in result_nodes
       ]
-    }
+    )
 
   async def async_delete(self, doc_id: str):
     vector_index = VectorStoreIndex.from_vector_store(