debug query embed

services/qdrant_searcher.py

@@ -6,19 +6,22 @@ from qdrant_client.http.models import Filter, FieldCondition
 
 class QdrantSearcher:
     def __init__(self, qdrant_url, access_token):
-        # Removed the encoder since embeddings are precomputed externally
         self.client = QdrantClient(url=qdrant_url, api_key=access_token)
 
     def search_documents(self, collection_name, query_embedding, user_id, limit=3):
         logging.info("Starting document search")
 
-        # Ensure the query_embedding is in the correct format (list)
+        # Ensure the query_embedding is in the correct format (flat list of floats)
         if isinstance(query_embedding, torch.Tensor):
-            query_embedding = query_embedding.detach().numpy().tolist()
-            logging.info("Converted query embedding to list")   
+            query_embedding = query_embedding.detach().numpy().flatten().tolist()
         elif isinstance(query_embedding, np.ndarray):
-            query_embedding = query_embedding.tolist()
-            logging.info("Converted query embedding to list")
+            query_embedding = query_embedding.flatten().tolist()
+        else:
+            raise ValueError("query_embedding must be a torch.Tensor or numpy.ndarray")
+
+        # Validate that all elements in the query_vector are floats
+        if not all(isinstance(x, float) for x in query_embedding):
+            raise ValueError("All elements in query_embedding must be of type float")
 
         # Filter by user_id
         query_filter = Filter(must=[FieldCondition(key="user_id", match={"value": user_id})])