Foundational concepts, practical implementation of semantic search, and the workflow of RAG, highlighting its advantages and versatile applications.
The article provides a step-by-step guide to implementing a basic semantic search using TF-IDF and cosine similarity. This includes preprocessing steps, converting text to embeddings, and searching for relevant documents based on query similarity.
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and contextually rich responses.
This article explains Retrieval Augmented Generation (RAG), a method to reduce the risk of hallucinations in Large Language Models (LLMs) by limiting the context in which they generate answers. RAG is demonstrated using txtai, an open-source embeddings database for semantic search, LLM orchestration, and language model workflows.