Update app.py

app.py

@@ -86,10 +86,19 @@ def retrieve_document(query):
         print(f"Error fetching metadata: {metadata_.status_code} - {metadata_.text}")
         return None
     
+       # Save the FAISS file temporarily
+    with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+        for chunk in embeddings_.iter_content(chunk_size=8192):
+            temp_file.write(chunk)
+        temp_path = temp_file.name  # Store file path
+
     try:
-        embeddings_file = embeddings_.json()
-    except requests.exceptions.JSONDecodeError as e:
-        print(f"Error decoding embeddings JSON: {e}")
+        # Load FAISS index from file
+        index = faiss.read_index(embeddings_file)
+        print(f"Successfully loaded FAISS index with {index.ntotal} vectors.")
+
+    except Exception as e:
+        print(f"Error loading FAISS index: {e}")
         return None
 
     try:
@@ -98,13 +107,12 @@ def retrieve_document(query):
         print(f"Error decoding metadata JSON: {e}")
         return None
 
-    print(embeddings_file, metadata_file)
+    print(index, metadata_file)
 
     # Generate query embedding
     query_embedding = embedding_model.encode([query]).astype(np.float32)
 
     # Search for the closest document in FAISS index
-    index = faiss.read_index(embeddings_file)
     _, closest_idx = index.search(query_embedding, 1)
 
     with open(metadata_file, "r") as f: