Vector Search and RAG Tutorial – Using LLMs with Your Data

You can use Vector Search and embeddings to easily combine your data with large language models like GPT-4. I just published a course on the channel that will teach you how to implement Vector Search on three different projects. First, you will learn about the concepts and then
You can use Vector Search and embeddings to easily combine your data with large language models like GPT-4. I just published a course on the channel that will teach you how to implement Vector Search on three different projects. First, you will learn about the concepts and then I'll guide you through developing three projects. In the first project we build a semantic search feature to find movies using natural language queries. For this we use Python, machine learning

Concept Retrieval Augmented Generation (RAG) approach and the Embed recipe - Dataiku Knowledge Base

Concept Retrieval Augmented Generation (RAG) approach and the Embed recipe - Dataiku Knowledge Base

freeCodeCamp on LinkedIn: What is the Nullish Coalescing Operator in JavaScript, and how is it useful

freeCodeCamp on LinkedIn: Ace Your Deep Learning Job Interview

Gartner RAG Tips for Grounding LLMs with Relevant Internal Data

media.licdn.com/dms/image/D4D12AQH7QUIAoNej_w/arti

Primer on Vector Databases and Retrieval-Augmented Generation (RAG) using Langchain, Pinecone & HuggingFace, by Jayita Bhattacharyya

freeCodeCamp on LinkedIn: Signals in Angular – How to Write More Reactive Code

Hands-On with RAG: Step-by-Step Guide to Integrating Retrieval Augmented Generation in LLMs, by Necati Demir

Rodney Lamar (@rodenylamar) / X

freeCodeCamp on LinkedIn: How to Use GitHub Actions to Automate Open-Source Projects

Nathi Ndlovu (@NATHINDLOVU_SA) / X