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

Era of Text Generation: RAG, LangChain, and Vector Databases

Daiasuki_uchiha (@daiasuki_uchiha) / X

Advanced RAG Implementation on Custom Data Using Hybrid Search, Embed Caching And Mistral-AI, by Plaban Nayak

Building a Retrieval-Augmented Generation (RAG) Model from Scratch

Jorge Irsay (@jirsay) / X

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

Building a Retrieval-Augmented Generation (RAG) Model from Scratch

freeCodeCamp on LinkedIn: How to Use MySQL Stored Procedures to Simplify Database Operations

How to Build an LLM RAG Pipeline with Upstash Vector Database