table support added in api

index.py

@@ -7,8 +7,12 @@ app = Flask(__name__)
 @app.route('/vector-search', methods=['GET'])
 def get_data():
     query = request.args.get('query', default='Can i work from home ?')
+    table = request.args.get('table', default='products_embeddings')
+    if table != 'products_embeddings' and table != 'product_sentence_embeddings':
+        return 'Not a valid table.'
+
     query_embeddings = embed_text([query], "RETRIEVAL_DOCUMENT", 256)
-    data = vector_search_in_bigquery(query_embeddings[0])
+    data = vector_search_in_bigquery(query_embeddings[0], table)
     # Return the data as a JSON response
     return jsonify(data)
 

text_embeddings.py

@@ -99,11 +99,11 @@ def save_to_bq(data):
         print("Errors:", errors)
 
 
-def vector_search_in_bigquery(query_embedding):
+def vector_search_in_bigquery(query_embedding, table):
     sql_query = f"""
         SELECT base.text, distance
         FROM VECTOR_SEARCH(
-   TABLE ai_practice_dataset.ai_poc_data , 'embeddings',
+   TABLE DATSET.{table} , 'embeddings',
    (SELECT {query_embedding} as embed) , top_k => 5, distance_type => 'COSINE')
     """