RAG retrieval: metadata filtering and cross-encoder reranking explained

Short summary

This article explains two techniques for improving RAG retrieval: metadata filtering and reranking with cross-encoders. Metadata filtering narrows the search space by matching chunk-level metadata before performing vector similarity search, supported by Pinecone, ChromaDB, and Qdrant. Reranking uses a cross-encoder that takes both query and document as joint input to produce relevance scores, reordering results for better accuracy, especially with multimodal content.

• •Metadata filtering narrows vector search by matching chunk metadata before similarity search
• •Cross-encoder reranking jointly encodes query and document to produce relevance scores
• •Reranking is especially useful for multimodal RAG where semantic similarity alone is insufficient

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