World's Fair 2024
RAG at scale: production ready GenAI apps with Azure AI Search
Overview
This talk focuses on scaling Retrieval Augmented Generation (RAG) applications for production use, specifically highlighting the capabilities of Azure AI Search. It addresses the challenges that arise when moving from prototypes to production, such as increased data volume, higher data change rates, and more complex multi-step workflows. The presentation emphasizes how Azure AI Search provides integrated solutions for these scaling dimensions, including advanced vector search, hybrid search, and efficient data ingestion.
Who should watch
- AI Engineers building and deploying RAG applications.
- Product Managers evaluating the feasibility of large-scale GenAI features.
- Builders seeking to understand production-level retrieval system challenges.
- Developers needing to integrate domain-specific knowledge into LLM applications.
- Those concerned with data volume, velocity, and query load in AI systems.
Key takeaways
- RAG applications require robust retrieval systems when scaling to production, moving beyond simple prototypes.
- Azure AI Search offers integrated vector search with approximate and exhaustive nearest neighbor capabilities, alongside traditional keyword search.
- Hybrid search, combining vector and keyword retrieval, followed by a re-ranking stage using cross-encoders, significantly improves result quality.
- Azure AI Search supports massive scale, with increased limits allowing for multi-billion vector applications and enhanced vector density.
- Quantization techniques, including int8 and even single-bit quantization, reduce storage and improve query speed with minimal quality loss, especially when combined with re-ranking.
- An integrated ingestion system in Azure AI Search automates data connection, change tracking, file format handling, chunking, and vectorization from various Azure data sources.
- The system aims to abstract away data pipeline complexities, allowing developers to focus on the RAG stack and workflow.
- Production-ready RAG requires addressing scale in data volume, data change rate, query load, and workflow complexity.
Notable quotes
*When you go to production, you go from oh this demo is really cool to all these users are using it at the same time and they want more.*
*The prevailing pattern right now is to use retrieval augmented generation.*
*The most important one is quality: your application works well for your users when they ask a question and they get the answer they're looking for.*
Unofficial community note. Prefer the recording for nuance.