Improving Retrieval in RAG with Reranking

Retrieval Augmented Generation (RAG) pipelines often encounter issues with retrieving the most relevant information, leading to inaccuracies in generated content, particularly when handling complex queries. The text discusses enhancing a basic RAG setup by incorporating a reranking step, which evaluates and reorders retrieved document chunks by semantic relevance, thereby improving the quality of the context used by language models like GPT-4. This walkthrough describes a process where raw patent documents are parsed and chunked using the Unstructured platform, stored in Pinecone as vector embeddings, and then queried for relevant information. While a simple retrieval method is fast, it can struggle with precision, especially for intricate queries, so reranking is proposed as a solution to refine search results by over-fetching and narrowing down the most pertinent data before it is fed into the language model. The text concludes that while reranking may not be necessary for every use case, it is a valuable addition for scenarios involving complex queries or verbose documents, as it enhances retrieval accuracy without significantly altering the existing pipeline.