Fine-Tuning Text Embeddings For Domain-Specific Search

An overview with Python code

Embedding models represent text as semantically meaningful vectors. Although they can be readily used for countless use cases (e.g. retrieval, classification), general-purpose embedding models may perform poorly on domain-specific tasks. One way to overcome this limitation is via fine-tuning. In this article, I will discuss the key ideas behind this technique and share a concrete example of fine-tuning embeddings for matching queries to AI job postings.

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A popular use of text embedding models is in retrieval augmented generation (RAG). This is where, given an input to an LLM-based system (e.g. a customer question), the relevant context (e.g. FAQs) is automatically retrieved from a knowledge base and passed to an LLM.

Embeddings enable the retrieval process through a 3-step process.

1. Vector representations (i.e. embeddings) are computed for all items in a knowledge base
2. Input text is translated into a vector representation (using the same…