Improve your Tabular Data Ingestion for RAG with Reranking

This article demonstrates how to enhance RAG system accuracy by adding a reranker to select the most relevant context chunks from tabular data, addressing the challenge of context mismatches in retrieval-augmented generation.

Key Insights

• Reranking Solution: Adds an additional scoring step after initial retrieval to prioritize the most relevant context chunks before LLM processing.
• Data Pipeline: Covers complete workflow from PDF extraction through indexing with both unified and distributed context collection strategies.
• Practical Implementation: Provides code examples using ChromaDB and demonstrates improved relevance scoring with real billionaire data from Wikipedia.