Question

A GenAI engineer built a RAG chain in a Databricks notebook using LangChain. The chain references a custom Python helper module (retrieval_utils.py) that lives alongside the notebook. When logging the chain with MLflow using mlflow.langchain.log_model() by passing the in-memory chain object directly, the model deploys successfully but fails at serving time with a ModuleNotFoundError for retrieval_utils. What is the recommended way to log the model so it serves correctly?

Accepted Answer

Use MLflow's code-based (Models from Code) logging by passing the path to a Python script that defines and sets the chain, and include the helper module via code_paths so dependencies are captured.

Answer

Increase the serving endpoint compute size so it has enough memory to import the helper module at runtime.

Answer

Manually copy retrieval_utils.py into the Databricks workspace root directory after the endpoint is created.

Answer

Set scale-to-zero to false so the endpoint keeps the notebook session warm and can access the helper module.