Update app.py

app.py

@@ -48,7 +48,7 @@ d = 384  # vectors dimension
 m = 32  # hnsw parameter. Higher is more accurate but takes more time to index (default is 32, 128 should be ok)
 #index = faiss.IndexHNSWFlat(d, m)
 index =  faiss.IndexFlatL2(embedding_dim)
-data.add_faiss_index("embeddings", custom_index=index) 
+data.add_faiss_index(embeddings.shape[1], custom_index=index) 
 # adds an index column that for the embeddings
 
 print("check1")
@@ -77,7 +77,7 @@ terminators = [
 
 def search(query: str, k: int = 3 ):
     """a function that embeds a new query and returns the most probable results"""
-    embedded_query = embedding_model.encode([query]) # create embedding of a new query
+    embedded_query = embedding_model.encode(query) # create embedding of a new query
     scores, retrieved_examples = data.get_nearest_examples( # retrieve results
         "embeddings", embedded_query, # compare our new embedded query with the dataset embeddings
         k=k # get only top k results