How to Fine-Tune Embedding Models for Retrieval-Augmented Generation (RAG)

Embedding models form the backbone of Retrieval-Augmented Generation (RAG) systems. While pre-trained models are valuable, they often lack domain-specific focus. Fine-tuning these models on targeted datasets can dramatically improve their performance for specialized applications, like finance or healthcare.

This guide walks you through fine-tuning an embedding model for a domain-specific RAG system. We’ll use modern techniques like Matryoshka Representation Learning (MRL) to optimize storage and retrieval performance. The workflow includes:

1. Preparing the embedding dataset.
2. Creating a baseline and evaluating the pre-trained model.
3. Applying Matryoshka Representation Learning.
4. Fine-tuning the embedding model.
5. Comparing fine-tuned results with the baseline.

Setting Up Your Environment

Let’s start by installing the required libraries:

# Install core libraries
pip install torch==2.1.2 sentence-transformers transformers datasets tensorboard

We’ll use Hugging Face Hub for model versioning. Log in using your API token:

from huggingface_hub import login…