R

RAG

Self-contained offline environment providing local AI chat, offline Wikipedia/content archives, IRC communication, audio streaming, file server, and development tools. Designed for zero internet dependency - download once, run anywhere. Perfect for remote areas, emergency scenarios, or escaping surveillance capitalism.

Multi-agent RAG pipeline with adversarial critique, built with LangGraph, Qdrant, and Groq.

ApolloCore is a self-contained, embeddable knowledge base that runs 100% locally with no external API dependencies. It combines vector search, keyword search, knowledge graphs, entity extraction, and LLM-powered query enhancement into a single high-performance C++23 library.

Fast BM25 full-text search for code repositories with CLI and MCP integration for AI coding agents.

RAGBase is a schema-enforced infrastructure layer for building modular Retrieval-Augmented Generation (RAG) systems. It provides a strict, contract-driven architecture where every component—ingestion, embedding, retrieval, and generation—is defined by explicit input/output schemas and validated through a built-in spec hygiene layer. This ensures predictable behavior, prevents system drift, and enables safe module interchangeability in production-grade AI pipelines. https://roxanneardary.com/ragbase/

Python toolkit for benchmarking RAG quality on Salesforce Data Cloud. Includes retrieval metrics (Hit Rate, MRR, NDCG), generation evaluation, and optional Claude Code skills.

Advanced Conversational Core (ACC) — A multimodal AI framework featuring 5D emotional simulation, semantic RAG memory, cognitive monologue stream, and multi-modal I/O (text, voice, image).

Lightweight vector embeddings store for RAG applications

Мария Михайловна Куджо моя сестра https://t.me/onetech_mashabot

These projects form an open-source suite of AI infrastructure tools built for modularity, security, and self-hosted deployment, allowing users to maintain full ownership and control of their systems and data. They deliver interoperable AI functions including automation, data retrieval, encryption, and identity management that can be applied across many different industries.

A complete RAG demo application in Python with a multi-stage pipeline to minimize hallucinations.

Curated retrieval-augmented generation (RAG) frameworks, tools, and reference implementations.

Curated vector databases, vector search engines, and embedding storage systems for AI.

🧠 Open-source persistent memory engine for AI agents and LLMs with semantic search, automatic deduplication, and intelligent context retrieval.

A persistent-memory AI agent with cryptographically signed, hash-chained, tamper-evident memory. Pre-commit quality scoring quarantines prompt-injection; protected zones guard core records. Scans its own history for recurring gaps and proposes improvements for human review. Provider-agnostic (Claude, GPT, Gemini, DeepSeek, OpenRouter, Ollama).

Production-ready RAG starter: hybrid search, chunking strategies, observability (Prometheus/Grafana), MLflow tracking, drift detection, GDPR deletion, and evaluation. The parts the tutorials skip.

RAG-Powered SOC Assistant - By Ayi NEDJIMI

https://ayinedjimi-consultants.fr

GPU-accelerated embedding server for RAG systems - CUDA, FastAPI, sentence-transformers | Serveur d'embeddings GPU ultra-rapide

https://ayinedjimi-consultants.fr

Yanapa - Asistente AI offline para comunidades originarias de Sudamerica y Centroamerica. Salud, derechos, cultura y medicina tradicional en lenguas indigenas.

Hybrid credit intelligence platform using XGBoost, RAG, Terraform and GitLab CI/CD.