RAG integrates private documents with LLMs using vector databases for semantic search

This article explains Retrieval-Augmented Generation (RAG) and the role of Vector Databases. RAG involves breaking down private documents into chunks, which are then processed by an embedding model to generate multi-dimensional points representing their semantic meaning. Vector databases store these points, enabling semantic search by identifying points that are close to each other based on distance metrics like Cosine Similarity. When a query is made, it's converted into a point, and the vector database efficiently retrieves the most relevant data points. AI

IMPACT Explains core concepts of RAG and Vector Databases, crucial for understanding LLM application development.

RANK_REASON This article explains a technical concept (RAG and Vector DBs) without announcing a new product, model, or research finding.

Read on dev.to — LLM tag →

• ChromaDB
• Cosine Similarity
• embedding model
• Euclidean
• FAISS
• LLM
• Quadrant
• Manhattan distance
• Vector DB
• Pinecone

• other

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