%sql
CREATE OR REPLACE FUNCTION kaushal.kaushal.vector_similarity_search(
  query_text STRING,
  filter_id INT,
  num_results INT
)
RETURNS STRING
LANGUAGE PYTHON
COMMENT "Vector similarity search using authenticated client"
ENVIRONMENT (
  dependencies = '["databricks-vectorsearch", "databricks-sdk"]',
  environment_version = 'None'
)
AS $$
import json
import os

# Get credentials from Databricks secrets
# You'll need to set these up first
def get_secret(scope, key):
    from databricks.sdk import WorkspaceClient
    w = WorkspaceClient()
    return w.secrets.get_secret(scope=scope, key=key).value

# Alternative: if dbutils is available in UDF context
# token = dbutils.secrets.get(scope="your-scope", key="databricks-token")
# host = dbutils.secrets.get(scope="your-scope", key="databricks-host")

# Set environment variables for authentication
os.environ['DATABRICKS_HOST'] = get_secret("your-scope", "databricks-host")
os.environ['DATABRICKS_TOKEN'] = get_secret("your-scope", "databricks-token")

from databricks.vector_search.client import VectorSearchClient

# Initialize client - should now pick up environment variables
client = VectorSearchClient()
index = client.get_index(
    endpoint_name="vector-search-demo-endpoint-kaushal",
    index_name="kaushal.kaushal.my_text_data_index"
)

results = index.similarity_search(
    query_text=query_text,
    columns=["id", "content"],
    filters={"id": [filter_id]},
    num_results=num_results
)

return json.dumps(results.get('result', {}).get('data_array', []))
$$;

Then run your UC function with SQL, and you should get the expected results.