Building Local RAG with Gemma & Qdrant — ft. Tarun R Jain (GDE)

🏗️ Building Local RAG with Gemma & Qdrant — ft. Tarun R Jain (GDE)
Local Intelligence Module initialized. Secure Data Retrieval in progress. 🔐💻

In the Agentverse, privacy and latency are paramount. While cloud-based agents are powerful, many missions require processing sensitive data entirely on-device or within a private infrastructure. Welcome to the technical briefing on Local Retrieval-Augmented Generation (RAG).

Tarun R Jain (Founding Engineer & GDE) leads this deployment, showing you how to combine Google’s open-weight model, Gemma, with Qdrant’s high-performance vector database to build a private, high-speed knowledge engine.

🎙️ Lead Architect:
Tarun R Jain
Founding Engineer | Google Developer Expert (AI)
Tarun is a specialized engineer focused on building efficient AI infrastructure. As a GDE, he is at the forefront of the "Local AI" movement, helping developers deploy sophisticated models like Gemma on edge devices and local servers without sacrificing performance.

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Видео Building Local RAG with Gemma & Qdrant — ft. Tarun R Jain (GDE) канала Google Developer Groups on Campus UTM