Your RAG Treats a 3-Year-Old Doc the Same as Yesterday's - Here's How to Fix It

This comprehensive guide outlines an advanced approach to implementing Recency-Aware Retrieval-Augmented Generation (RAG) using Databricks' ecosystem, specifically focusing on integrating recency scoring into a Vector Search-based retrieval system. Here’s a summary of the key points and steps involved:

Key Components

1. Data Ingestion:

   • Use binaryFile to read new PDFs from Unity Catalog (UC) volumes.
   • Capture actual modification timestamps (source_modified_at) for each document.

2. Document Processing:

   • Extract text, tables, and figures using ai_parse_document().
   • Chunk the extracted content into smaller segments suitable for vector search indexing.

3. Change Data Feed (CDF):

   • Use CDF to propagate changes in the dataset to the vector index.
   • Soft-delete outdated documents by setting is_active to false instead of hard-deleting them.

4. Vector Search Indexing:

   • Create and manage a vector search index that filters out inactive (is_active = false) chunks during retrieval.

5. Recency Scoring:

   • Implement exponential decay scoring based on the actual modification timestamp.
   • Adjust weights to balance similarity

Read the full article at Towards AI - Medium

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