Advanced Retrieval: Extract Metadata from Queries to Improve Retrieval
In Retrieval-Augmented Generation (RAG) applications, the retrieval step, which provides relevant context to your large language model (LLM), is vital for generating high-quality responses. There are possible ways of improving retrieval and metadata filtering is one of the easiest ways. Metadata filtering, the approach of limiting the search space based on some concrete metadata, can really enhance the quality of the retrieved documents. Here are some advantages of using metadata filtering:
You have two options for applying the metadata filter: you can either specify it directly when running the pipeline or, you can extract it from the query itself. In this article, we’ll focus on extracting filters from a query to improve the quality of generated responses in RAG applications. Let’s get started.
First things first, what is metadata? Metadata (or meta tag) is actually data about your data, used to categorize, sort, and filter information based on various attributes such as date, topic, source, or any other information that you find relevant. After incorporating meta information into your data, you can apply filters to queries used with Retrievers to limit the scope of your search based on this metadata and ensure that your answers come from a specific slice of your data.
