R

Retrieval Augmented Generation

https://github.com/run-llama/llama_index LlamaIndex is a data framework for your LLM applications

🔧🔗https://github.com/mem0ai/mem0 Memory for AI Agents; SOTA in AI Agent Memory; Announcing OpenMemory MCP - local and secure memory management.

🔧🔗https://github.com/patchy631/ai-engineering-hub In-depth tutorials on LLMs, RAGs and real-world AI agent applications.

🔧🔗https://github.com/mendableai/firecrawl-app-examples 🔥 This repository contains complete application examples, including websites and other projects, developed using Firecrawl.

🔧🔗https://github.com/HKUDS/MiniRAG

"MiniRAG: Making RAG Simpler with Small and Free Language Models"

🔧🔗https://github.com/llmware-ai/llmware

Unified framework for building enterprise RAG pipelines with small, specialized models

🕸️🔗https://llmware-ai.github.io/llmware/

🔧🔗https://github.com/decodingml/production-llm-rag-course

Second Brain Semantic AI Engine: Powered by LLMs & RAG

🔧🔗https://github.com/aws-samples/Reducing-Hallucinations-in-LLM-Agents-with-a-Verified-Semantic-Cache

This repository contains sample code demonstrating how to implement a verified semantic cache using Amazon Bedrock Knowledge Bases to prevent hallucinations in Large Language Model (LLM) responses while improving latency and reducing costs.

🔧🔗 Ragdaemon Ragdaemon is a Retrieval-Augmented Generation (RAG) system for code. It runs a daemon (background process) to watch your active code, put it in a knowledge graph Generate and render a 3d call graph for a Python project

https://github.com/run-llama/finetune-embedding Fine-Tuning Embedding for RAG with Synthetic Data deprecated

https://github.com/run-llama/ai-engineer-workshop Building, Evaluating, and Optimizing your RAG App for Production

https://github.com/run-llama/modal_finetune_sql finettune llama against a sqldb

https://github.com/run-llama/rag-bedrock Advanced Retrieval Augmented Generation patterns with AWS Bedrock and LlamaIndex www.madhats.ai/