Session brief

Building efficient hybrid context query for LLM grounding: Simrat Hanspal

Overview

This talk introduces a method for building efficient hybrid context queries for Retrieval Augmented Generation (RAG) applications, specifically demonstrated with an e-commerce product search use case. It highlights the limitations of traditional keyword search and the need for natural language understanding. The core idea is to leverage Large Language Models (LLMs) by providing them with relevant data context, enabling more accurate and grounded responses. The presentation showcases how a unified data API can handle semantic, structured, and hybrid queries, enhancing RAG pipelines.

Who should watch

• AI Engineers
• Product Managers
• Builders working on RAG applications
• Those interested in secure data retrieval for GenAI
• Developers looking to integrate LLMs with diverse data sources

Key takeaways

• Traditional keyword search in e-commerce is limited; natural language queries offer richer intent capture.
• LLMs require external data context (RAG) to answer questions about unseen or dynamic data.
• RAG pipelines must be production-ready and secure, preventing data leaks even with malicious prompts.
• Unlike static data-driven applications, RAG applications require data to adapt to dynamic user queries.
• Hura enables the creation of unified, dynamic, and secure GraphQL APIs over multiple data sources, supporting semantic, structured, and hybrid queries.
• Hura can automatically vectorize records into a vector database upon insertion using its event system.
• Role-based access control in Hura is crucial for security, allowing granular permissions to prevent unauthorized data modifications or access.
• By defining restricted roles, Hura can block malicious insert mutations, ensuring data integrity.

Notable quotes

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Unofficial community note. Prefer the recording for nuance.