An exploration of Retrieval-Augmented Generation (RAG) using Langchain and LlamaIndex, explaining how these tools can enhance Large Language Models (LLMs) by combining retrieval and generation techniques.
Jupyter notebook for using LlamaIndex with arXiv papers for retrieval-augmented generation (RAG).
This article guides you through the process of building a local RAG (Retrieval-Augmented Generation) system using Llama 3, Ollama for model management, and LlamaIndex as the RAG framework. The tutorial demonstrates how to get a basic local RAG system up and running with just a few lines of code.
A CLI tool for interacting with local or remote LLMs to retrieve information about files, execute queries, and perform other tasks in a Retrieval-Augmented Generation (RAG) fashion.
LlamaIndex comes with a built-in indexing feature, which allows developers to index large datasets efficiently. This makes it easier to search and retrieve information from these datasets, ultimately improving the overall performance of LLM-based applications.
A step-by-step guide on deploying LlamaIndex RAGs to AWS ECS fargate
A deep dive into model quantization with GGUF and llama.cpp and model evaluation with LlamaIndex