rag-pipelines

Optimize chunking in your RAG pipeline by applying semantic or structure-aware chunking techniques. These methods maximize context retention and retrieval performance by preserving the logical boundaries of your source documents, ensuring that retrieved chunks provide coherent and complete answers.

Hybrid search combines vector similarity and keyword matching scores, while Reciprocal Rank Fusion merges their ranked result lists. This balances semantic understanding with exact term matching to surface highly relevant documents before passing them to a cross-encoder reranker for final precision boosting.

Yes, you can integrate cross-encoder reranking with an existing vector database. The vector database handles initial similarity search to retrieve candidate chunks, and the cross-encoder reranker then evaluates these top candidates to boost precision and ensure the most accurate context is injected into prompts.

Yes, deterministic chunking is needed for processing large document collections to ensure reproducible retrieval results. It applies consistent rules based on document structure, maintaining traceability and stable context boundaries across your entire vector database.

Vector search alone may return irrelevant chunks due to overlapping embeddings or lost context. Implementing structure-aware chunking to preserve boundaries, followed by hybrid search and cross-encoder reranking, filters out low-relevance candidates and significantly boosts final answer precision.