AI Learning Roadmaps alternatives for AI agents.

Unified multimodal backend for AI data apps

Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training.

Minimalist web-searching platform with an AI assistant that runs directly from your browser. Demo: https://felladrin-minisearch.hf.space

This repository provides an advanced Retrieval-Augmented Generation (RAG) solution for complex question answering. It uses sophisticated graph based algorithm to handle the tasks.

This repository contains examples for customers to get started using the Amazon Bedrock Service. This contains examples for all available foundational models

Full-stack AI app generator — FastAPI + Next.js with AI Agents, RAG, streaming, auth, and 20+ integrations out of the box.

Building blocks for rapid development of GenAI applications

🧭 Architecture-first system design: 26 bilingual tutorials, 25 architecture templates, and 6 end-to-end cases covering distributed systems, AI-native systems, RAG, coding Agents, and production trade-offs.

Knowhere extracts, parses, and outputs structured chunks ready for AI Agents and RAG.

[EMNLP2025] "LightRAG: Simple and Fast Retrieval-Augmented Generation"

Collect, organize, use, and share, all in OmniBox.

🥤 RAGLite is a Python toolkit for Retrieval-Augmented Generation (RAG) with DuckDB or PostgreSQL

AI equity research agent with resilient workflows, Redis Lua single-flight, pgvector RAG, versioned reports, evidence tracing, and RAG evaluation.

An Obsidian plugin to interact with your privacy focused AI-Assistant making your second brain even smarter!

Unify Efficient Fine-tuning of RAG Retrieval, including Embedding, ColBERT, ReRanker.

A practical lab for building, testing, and evaluating apps with Apple's Foundation Models framework.