Contextual Compression with LLMs

When building RAG (Retrieval Augmented Generation) systems, Large Language Models (LLMs) have a limited context window. Feeding them too much irrelevant information can lead to several problems:

• Performance issues: LLMs might get confused by noise.
• Higher costs: More tokens mean higher API bills.
• Slower responses: More text takes longer to process.

This is where contextual compression comes in!

Contextual compression is a technique used to refine the documents retrieved by your RAG system before they are passed to the LLM. It acts as a smart filter and extractor.

• Filter: Remove entire documents that are less relevant.
• Extract: From the remaining documents, identify and keep only the most pertinent sentences or paragraphs related to the user's query.

Think of it like highlighting the most important parts of a long article.