RAG & Vector Search

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Build a Custom AI Agent With NVIDIA Nemotron RAG Models

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by NVIDIA Developer

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RAG & Vector Search

Today’s curated video collection focuses on Retrieval-Augmented Generation (RAG) and Vector Search technologies, showcasing innovative applications and developments in the AI landscape. Notable contributions include NVIDIA's guide on constructing custom AI agents with RAG models, which emphasizes the practical implementation of advanced AI frameworks. Additionally, Rachel Rapp from Qdrant discusses high-throughput, low-latency embedding pipelines, essential for real-world applications, while the lecture on Maximum Marginal Relevance (MMR) introduces techniques to enhance response quality by reducing redundancy. The collection also features insights into hybrid RAG systems aimed at optimizing GDPR compliance and an exploration of image-aware RAG pipelines using Cohere's multi-modal embeddings. This collection underscores the growing significance of RAG systems in AI, particularly in enhancing data processing and retrieval capabilities.

Key Themes Across All Feeds

•Retrieval-Augmented Generation
•Vector Search
•AI Agent Development

RAG & Vector Search

Build a Custom AI Agent With NVIDIA Nemotron RAG Models

Key Themes Across All Feeds

RAG & Vector Search

Build a Custom AI Agent With NVIDIA Nemotron RAG Models

"This is why you need a RAG system" - Apurva Misra - Ep 63

High-Throughput, Low-Latency Embedding Pipelines for Real-World Applications | Baseten | Rachel Rapp

Fine-Tuning Small Language Models: dalle basi al Reasoning in pratica

How to Create an Image-Aware RAG Pipeline with Cohere Multi-Modal Embeddings & LLaMA 4

Optimizing GDPR Compliance Retrieval with Hybrid Graph‑Augmented RAG Systems | KI Reply | Agrawal

Lecture 20: Maximum Marginal Relevance (MMR) in RAG - Eliminating Redundancy for Better AI Responses

MASTER SERIES - RAG 5. DATA INGESTION AND PREPROCESSING

Tongyi DeepResearch: Open Agentic LLM for Research

RAG Pipeline Delta Check: Looping Through Knowledge Base Files in n8n

Real Deep Research #ai #llm #chatgpt

How to Build a Daily Data Health Check for RAG Pipelines (n8n + Supabase) #shorts