The article explores whether combining a command-line agent (like Claude Code or Gemini CLI) with Unix-like file system tools and SemTools is sufficient for complex tasks, particularly document search. It details a benchmark testing the limits of coding agents with and without SemTools, focusing on search, cross-referencing, and temporal analysis. The conclusion is that CLI access is powerful and SemTools enhances agent capabilities for document search and RAG.
This repository contains the source code for the summarize-and-chat project. This project provides a unified document summarization and chat framework with LLMs, aiming to address the challenges of building a scalable solution for document summarization while facilitating natural language interactions through chat interfaces.
LlamaIndex, founded by former Uber research scientists, offers a cloud service for building custom agents over unstructured data. The service includes features like data connectors, utilities for transforming unstructured data, and role-based access control.
This blueprint demonstrates how to build an AI agent that automates blog post creation using LlamaIndex and NVIDIA's language and retrieval models, ensuring high-quality, well-researched content.
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).
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.
