retrieval-augmented-generation

AgentSearch is a framework for powering search agents and enabling customizable local search.

AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.

A Survey on Multimodal Retrieval-Augmented Generation

Dataset and benchmark for RAG on company internal documents.

Data-Driven Evaluation for LLM-Powered Applications

💡 Awesome RAG: A resource of Retrieval-Augmented Generation (RAG) for LLMs, focusing on the development of technology.

Transformers 3rd Edition

Manifold is an experimental platform for enabling long horizon workflow automation using teams of AI assistants.

Hybrid RAG system combining vector search, knowledge graph (LightRAG), and cross-encoder reranking — with Docling document parsing, visual intelligence (image/table captioning), agentic streaming chat, and inline citations. Powered by Gemini or local Ollama models.

🚀 Retrieval Augmented Generation (RAG) with txtai. Combine search and LLMs to find insights with your own data.

In-Context Learning for eXtreme Multi-Label Classification (XMC) using only a handful of examples.

LightningRAG is a full-stack Vue + Gin starter with a decoupled frontend and backend, plus built-in, extensible RAG (retrieval-augmented generation): knowledge bases, vector search, and integrations with many LLM and vector-store providers

MLX-Embeddings is the best package for running Vision and Language Embedding models locally on your Mac using MLX.

A curated list of retrieval-augmented generation (RAG) in large language models

中文优先的企业 RAG 知识库：可控解析、治理、切块、混合检索、重排、引用、图谱、评测与 Dify 接入。

Redis Vector Library (RedisVL) -- the AI-native Python client for Redis.

RAG evaluation without the need for "golden answers"

Program that lets you ask questions about your documents, audio, and video files.

AI Client for chat, RAG, plans, MCP tools, and agents. Support multiple LLMs (Anthropic, OpenAI, VertexAI, vLLM, Nvidia NIM, Gemini, OpenRouter)

Generative AI Application Builder on AWS facilitates the development, rapid experimentation, and deployment of generative artificial intelligence (AI) applications without requiring deep experience in AI. The solution includes integrations with Amazon Bedrock and its included LLMs, such as Amazon Titan, and pre-built connectors for 3rd-party LLMs.