RAG Architecture in Retail (Azure-based) :A Real Enterprise Use Case

It sounds like you're diving deep into the architecture and implementation details of a robust Retrieval-Augmented Generation (RAG) model tailored for retail applications. Here's a summary and some key points to consider as you move forward with your project:

Phase 1: Data Ingestion & Preprocessing

Key Points:

• Data Quality: Ensure that the data ingested is clean, relevant, and comprehensive. This includes product details, customer queries, FAQs, policies, etc.
• Metadata Extraction: Use Azure Document Intelligence to extract structured metadata (like SKU codes, brand names, model numbers) from unstructured text. This can significantly improve retrieval precision.
• Text Splitting & Overlap: Optimize the RecursiveCharacterTextSplitter with an appropriate overlap parameter (e.g., 500 tokens and 60-token overlap). Ensure that tables and structured data are preprocessed to maintain their integrity.

Phase 2: Embedding & Indexing

Key Points:

• Embedding Model Selection: Use text-embedding-3-small or a fine-tuned retail-specific model. This balances storage costs with quality.
• Dual Index Strategy: Implement both dense vector indexing (Pine

Read the full article at Towards AI - Medium

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