newsAWS Machine LearningTrust 88 · LabPublished 11d agoLive · 9d ago
Reduce RAG costs on Amazon Bedrock with query-aware compression
Input tokens are often a meaningful part of the cost of running Retrieval Augmented Generation (RAG) at scale. This post describes a query-aware context compression pattern on Amazon Bedrock: after retrieval, a smaller model filters retrieved chunks against the query before the primary model answers, reducing input tokens and cost while preserving answer quality.
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- PossiblePossibly related (embedding) · 61%aws-samples/bedrock-kb-rag-workshop →
- PossiblePossibly related (embedding) · 58%Ahren09/SARA →
- PossiblePossibly related (embedding) · 58%Covering the Unseen: Information Demand Coverage Optimization for Retrieval-Augmented Generation →
- PossiblePossibly related (embedding) · 55%santushtmatra11/structured-document-retriever →
- PossiblePossibly related (embedding) · 55%Know Before You Fetch: Calibrated Retrieval-Budget Allocation for Retrieval-Augmented Generation →
- PossiblePossibly related (embedding) · 50%Preference Shapes Relevance: Cross-component Hierarchical Semantic Alignment for Personalized Generative Retrieval →
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repoaws-samples/bedrock-kb-rag-workshoprepoAhren09/SARApaperCovering the Unseen: Information Demand Coverage Optimization for Retrieval-Augmented Generationreposantushtmatra11/structured-document-retrieverpaperKnow Before You Fetch: Calibrated Retrieval-Budget Allocation for Retrieval-Augmented Generation
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Related across the graph
reposantushtmatra11/structured-document-retrieverpaperPreference Shapes Relevance: Cross-component Hierarchical Semantic Alignment for Personalized Generative RetrievalpaperKnow Before You Fetch: Calibrated Retrieval-Budget Allocation for Retrieval-Augmented GenerationrepoAhren09/SARApaperCovering the Unseen: Information Demand Coverage Optimization for Retrieval-Augmented Generationrepoaws-samples/bedrock-kb-rag-workshop