rag-implementation

To build a grounded document Q&A system, you integrate external knowledge into LLM workflows using a RAG pipeline. This involves indexing documents into vector databases, retrieving relevant context via embeddings, and generating accurate answers with source citations.

Improve retrieval accuracy for semantic search by applying diverse strategies like dense, sparse, hybrid, multi-query, or HyDE approaches. You can further refine top results by applying cross-encoder reranking to ensure the most relevant context is passed to the LLM.

Embeddings convert text into numerical vectors, which are then stored and efficiently retrieved from vector databases. This mechanism allows the RAG pipeline to perform semantic search, finding contextually relevant information rather than relying on exact keyword matches.

Yes, you can assemble end-to-end pipelines that incorporate diverse retrieval strategies like HyDE and multi-query alongside indexing and reranking. This approach ensures robust retrieval, allowing the system to handle varied query structures and improve overall grounding.

Evaluate a RetrievalQA flow by assembling an end-to-end pipeline that includes built-in evaluation mechanisms. After indexing a document set and running the flow, you can verify the grounded answers and measure how accurately the system retrieves and generates responses.

Apply cross-encoder reranking in a RAG system after the initial retrieval phase to improve top results. It is essential when basic semantic search returns broadly relevant but poorly ordered documents, ensuring only the most precise context is used for generation.