Building a RAG System in Node.js: Vector Databases, Embeddings & Chunking
watch on portal.gitnation.org ↗https://portal.gitnation.org/contents/building-a-rag-system-in-nodejs-vector-databases-embeddings-and-chunkingsummary
Hands-on coverage of building retrieval-augmented generation (RAG) systems in Node.js, including chunking documents, creating embeddings, and using vector databases such as Chroma for semantic search. It discusses embedding models and language models (Llama), token-based splitting, database storage, re-ranking techniques to improve relevance, and evaluation metrics and experimentation for system performance.
AI-written summary · about
related talks
01h 28mBuilding a RAG System on Node.js
23mNode.js and GenAI: Deep Dive into the RAG Architecture
21mHow RAG is Redefining AI Capabilities
33mVector Databases in the Age of AI: Embracing RAG for Enhanced Search Capabilities
01h 35mTutorial: Build AI-Supercharged RAG Apps with a Vector Database
53mHigh‑Load RAG Architecture: 10 Chunking Optimization Strategies
15mMastering RAG and Vector Search with Elastic
06mWhat makes vector databases essential for RAG and AI apps?