RAG combines LLMs with information retrieval systems. Explore top RAG tools and learn how to choose the best one for your specific use case.

Rapidly deploy your GenAI apps with these top 17 RAG software platforms. Explore your options and choose the right tool for your project today!

"This thing is a tangled mess." I was relieved to hear the presenter say the words I was thinking. He had just finished walking me through a new AI agent, which I'm going to call Sherpa throughout this article. Sherpa was a proof of concept for a new AI agent their team had been working

Up to this point, we've covered agentic system architecture, how to organize your system into sub-agents and to build uniform mechanisms to standardize communication. Today we'll turn our attention to the tool layer and one of the most important aspects of agentic system design you'll need to consider: data retrieval. Data Retrieval and Agentic RAG

The article discusses creating uniform interaction models in modular agentic systems for effective request dispatching among agents and subagents.

The article looks at the benefits of modularity in agentic systems, enhancing clarity, maintainability, and reducing complexity.

RAG pipelines are the key to providing your LLM-powered apps with fresh, accurate data. In this guide, we explore best practices and antipatterns.

This article delves into the technical intricacies of Agentic AI Architecture, exploring its core components, key principles, development phases, technological integrations, applications, challenges, and future directions.

This guide outlines how to create efficient agentic systems by focusing on three layers: tools, reasoning, and action. Each layer presents unique challenges that can impact overall system performance.

The Automated Design of Agentic Systems (ADAS) is a unique approach in AI that enables the creation of agents capable of designing, testing, and refining themselves.

Explore essential AI tools for R programming to enhance your low-code development capabilities and streamline your projects. | Restackio

Introduction. Few days ago I discovered the OpenAI Gym library which is a bunch of standardized AI environments written in Python to benchmark the skills of AI programs and scripts, aka agents.

Like many of you, my first real exposure to AI was when ChatGPT dropped. I spent way too much time prompting it with random stuff, used it for some PowerShell, and tried out the voice feature when that launched. Mostly, I’ve used AI for things like writing docs, double-checking my grammar and English, and making some funny pictures.

Let’s start this with some confessions: I’m at best an enthusiastic amateur with AI. I know more than most, and I know nothing in the grand scheme. Example: I’m not sure I have any idea of what an AI agent is. I think I do, but there’s so much marketing hype around them that I

Use Meta Prompting to rapidly generate results in the GenAI Age - README.md

Generating diagrams with AI / LLMs

Replace 'hub' with 'ingest' in any GitHub URL for a prompt-friendly text.

A flexible, feature-rich yet light-weight logging framework based on R6 classes. It supports hierarchical loggers, custom log levels, arbitrary data fields in log events, logging to plaintext, JSON, (rotating) files, memory buffers. For extra appenders that support logging to databases, email and push notifications see the the package lgr.app.

Explore essential logging practices for AI applications to ensure compliance and enhance software reliability. | Restackio

Please remember you are using a beta version of mcpx and mcp.run services.

Hi Obsidian Community! https://github.com/PublikPrinciple/obsidian-mcp-rest I’m working on implementing an MCP (Model Context Protocol) server that integrates with Obsidian’s Local REST API plugin. The goal is to allow AI assistants like Claude to interact directly with Obsidian vaults in a secure, local manner. Project Overview GitHub Repository: GitHub - PublikPrinciple/obsidian-mcp-rest: An MCP server implementation for accessing Obsidian via local REST API Purpose: Enable AI assistants ...

Batch processing framework for ellmer chat model interactions. Enables sequential and parallel processing of chat completions. Core capabilities include error handling with backoff, state persistence, progress tracking, and retry management. Parallel processing is implemented via the future framework. Additional features include structured data extraction, tool integration, timeout handling, verbosity control, and sound notifications. Includes methods for returning chat texts, chat objects, progress status, and structured data.

This guide outlines my approach to building software projects using Cursor IDE, leveraging Gemini (gemini-exp-1206, gemini-2.0-flash-exp) and Claude (3.5 sonnet and 3 opus) for effective development and maintaining code integrity. 1. Project Setup: Laying the Foundation for AI Collaboration Think of your project as a well-organized workshop where you and your AI collaborators (Gemini and Claude) will build something amazing. The first step is to set up the workshop properly. 1.1. Define the Ru...

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