Context Compression — Reduce Context Without Information Loss

I will implement context compression for your LLM application — using extractive compression to retain only the most query-relevant sentences from retrieved documents, abstractive compression using a smaller model to produce information-dense summaries, and selective compression that applies different compression levels based on content importance. Context compression differs from simple truncation — truncation discards content at arbitrary boundaries, losing information uniformly; compression applied at the semantic level retains the highest-information content from each source, maintaining more useful context within the same token budget.

The implementation covers extractive compression using sentence-level relevance scoring, a smaller-model abstractive compression pipeline for lower-priority content, compression level calibration based on content importance, compression quality evaluation, and token savings vs quality trade-off analysis.