An analysis of the tradeoffs between building bespoke code-based agents and using major agent frameworks such as LangGraph and LlamaIndex Workflows for creating autonomous AI systems.
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.
The authors map the landscape of frameworks for abstracting interactions with and between large language models, and suggest two systems of organization for reasoning about the various approaches to, and philosophies of, LLM abstraction.
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
open-source LLM tools offer transparency, flexibility, cost-effectiveness, and heightened data security.
