修改LM Studio向量错误问题

embedding_adapters.py

@@ -120,18 +120,59 @@ class OllamaEmbeddingAdapter(BaseEmbeddingAdapter):
             return []
 
 class MLStudioEmbeddingAdapter(BaseEmbeddingAdapter):
+    """
+    基于 LM Studio 的 embedding 适配器
+    """
     def __init__(self, api_key: str, base_url: str, model_name: str):
-        self._embedding = OpenAIEmbeddings(
-            openai_api_key=api_key,
-            openai_api_base=ensure_openai_base_url_has_v1(base_url),
-            model=model_name
-        )
+        self.url = ensure_openai_base_url_has_v1(base_url)
+        if not self.url.endswith('/embeddings'):
+            self.url = f"{self.url}/embeddings"
+        
+        self.headers = {
+            "Authorization": f"Bearer {api_key}",
+            "Content-Type": "application/json"
+        }
+        self.model_name = model_name
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        return self._embedding.embed_documents(texts)
+        try:
+            payload = {
+                "input": texts,
+                "model": self.model_name
+            }
+            response = requests.post(self.url, json=payload, headers=self.headers)
+            response.raise_for_status()
+            result = response.json()
+            if "data" not in result:
+                logging.error(f"Invalid response format from LM Studio API: {result}")
+                return [[]] * len(texts)
+            return [item.get("embedding", []) for item in result["data"]]
+        except requests.exceptions.RequestException as e:
+            logging.error(f"LM Studio API request failed: {str(e)}")
+            return [[]] * len(texts)
+        except (KeyError, IndexError, ValueError, TypeError) as e:
+            logging.error(f"Error parsing LM Studio API response: {str(e)}")
+            return [[]] * len(texts)
 
     def embed_query(self, query: str) -> List[float]:
-        return self._embedding.embed_query(query)
+        try:
+            payload = {
+                "input": query,
+                "model": self.model_name
+            }
+            response = requests.post(self.url, json=payload, headers=self.headers)
+            response.raise_for_status()
+            result = response.json()
+            if "data" not in result or not result["data"]:
+                logging.error(f"Invalid response format from LM Studio API: {result}")
+                return []
+            return result["data"][0].get("embedding", [])
+        except requests.exceptions.RequestException as e:
+            logging.error(f"LM Studio API request failed: {str(e)}")
+            return []
+        except (KeyError, IndexError, ValueError, TypeError) as e:
+            logging.error(f"Error parsing LM Studio API response: {str(e)}")
+            return []
 
 class GeminiEmbeddingAdapter(BaseEmbeddingAdapter):
     """