Picking the model flavor, embedding model, retriever, and other pieces a RAG application needs

A RAG application in Databricks is packaged as an MLflow model that ties together an embedding model, a retriever, and the underlying LLM into a single logged artifact. When logging this model, engineers must correctly specify the model flavor, dependencies, input example, and signature so the model can be validated, reproduced, and deployed via Model Serving.

1 · Learn the must-know

• 1The model flavor (e.g., pyfunc or langchain) determines how MLflow serializes and loads the RAG chain, and pyfunc is the most flexible choice for custom retrieval-augmented logic.
• 2The embedding model converts both documents and user queries into vector representations and must be the same model used to build the vector index to ensure compatible embeddings.
• 3The retriever is typically a Databricks Vector Search index queried for similarity search, and it must be referenced by its endpoint/index name when the model is logged so it can reconnect at inference time.
• 4Dependencies (pip requirements or conda environment) must be explicitly captured when logging the model so the exact library versions are reproduced in the serving environment.
• 5An input_example should be provided at logging time because it lets MLflow validate the model can run end-to-end and helps auto-infer the model signature.
• 6The model signature defines expected input and output schemas; without a correct signature, Model Serving may reject malformed requests or fail to validate responses properly.

2 · Check your understanding