Optimizing LLM Context Windows: Implementing Lossless Compression Strategies for RAG Agents

Short summary

This article tackles the context window bottleneck in RAG pipelines, explaining why bigger windows increase cost quadratically and degrade quality through the 'lost in the middle' phenomenon. It introduces lossless compression strategies including semantic hashing and MinHash-based deduplication to merge redundant retrieved chunks before injecting them into the LLM. A Python implementation using sentence-transformers and datasketch demonstrates how to identify and filter near-duplicate chunks by embedding similarity, preserving information density while cutting token footprint.

• •Context window bloat inflates inference cost quadratically and degrades output via middle-loss bias
• •Semantic hashing and MinHash deduplication merge redundant chunks without losing information density
• •Python implementation with sentence-transformers and datasketch filters near-duplicate chunks by cosine similarity

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