veenaramesh

import mlflow 
from mlflow.genai.scorers import scorer
from mlflow.genai.scorers import Correctness, RelevanceToQuery
from custom_scorer import ToolUsageScorer
import re

# Workaround for serverless compatibility
mlflow.tracking._model_registry.utils._get_registry_uri_from_spark_session = lambda: "databricks-uc"

model = mlflow.pyfunc.load_model(f"models:/{model_name}@agent_latest")

veenaramesh

# Databricks notebook source
import pandas as pd 
import numpy as np 
import cloudpickle 
from datetime import datetime 
import mlflow
import os 
import json

# COMMAND ----------

veenaramesh

common_permissions: &permissions
  permissions:
    - level: CAN_VIEW
      group_name: users


resources:
  jobs:
    clean_up_job:
      name: ${bundle.target}-{{ .input_project_name }}-clean_up_job