修复嵌入服务，使用自定义 ZhipuAI 嵌入实现

backend/app/model_services/embedding_services.py

@@ -120,14 +120,31 @@ class ZhipuEmbeddingProvider(BaseServiceProvider[Embeddings]):
             Embeddings: LangChain 兼容的嵌入实例
         """
         if self._service_instance is None:
-            from langchain_zhipu import ZhipuAIEmbeddings
+            self._service_instance = _SimpleZhipuAIEmbeddings(
-            self._service_instance = ZhipuAIEmbeddings(
                 model=self._model,
                 api_key=ZHIPUAI_API_KEY
             )
         return self._service_instance
 
 
+class _SimpleZhipuAIEmbeddings(Embeddings):
+    """
+    简单的智谱 AI 嵌入实现，直接用 zhipuai 库
+    """
+    def __init__(self, model: str, api_key: str):
+        from zhipuai import ZhipuAI
+        self.model = model
+        self.client = ZhipuAI(api_key=api_key)
+    
+    def embed_documents(self, texts: list[str]) -> list[list[float]]:
+        response = self.client.embeddings.create(model=self.model, input=texts)
+        return [item.embedding for item in response.data]
+    
+    def embed_query(self, text: str) -> list[float]:
+        response = self.client.embeddings.create(model=self.model, input=[text])
+        return response.data[0].embedding
+
+
 class LocalLlamaCppEmbedder:
     """
     通过 OpenAI 兼容 API 封装 llama.cpp 嵌入服务

backend/requirements.txt

@@ -13,6 +13,9 @@ langgraph==1.1.6
 langgraph-checkpoint-postgres==3.0.5
 tiktoken>=0.12.0
 
+# Zhipu AI
+zhipuai==2.0.1
+
 # Vector DB
 qdrant-client==1.17.1
 
@@ -36,7 +39,7 @@ tenacity==9.1.4
 rich==15.0.0
 PyYAML==6.0.3
 numpy>=1.26.2
-pyjwt==2.10.1
+pyjwt==2.8.0
 
 # Document Processing
 unstructured==0.22.21