Update app.py

app.py

@@ -116,17 +116,14 @@ class Chatbot():
         openai.api_key = os.getenv('OPENAI_API_KEY')
         embedding_model = "text-embedding-ada-002"
         # Get the embeddings for each text element in the dataframe
-        embeddings = df.text.apply([lambda x: get_embedding(x, engine=embedding_model)])
+        embeddings = df.text.apply(lambda x: get_embedding(x, engine=embedding_model))
         embeddings = np.array(embeddings, dtype=np.float32)
         return embeddings    
 
     def search_embeddings(self, embeddings, df, query, n=3, pprint=True):
         
         # Step 1. Get an embedding for the question being asked to the PDF
-        query_embedding = get_embedding(
-            query,
-            engine="text-embedding-ada-002"
-        )
+        query_embedding = get_embedding(query, engine="text-embedding-ada-002")
         query_embedding = np.array(query_embedding, dtype=np.float32)
         # Step 2. Create a FAISS index and add the embeddings
         d = embeddings.shape[1]