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Auto Ontology is a Python framework for building semantic, graph-based reasoning layers over data. It enables deterministic query generation, better context retrieval, and more explainable AI for LLM and agent workflows.
RAGFlow is an agent-native database: a Retrieval-Augmented Generation (RAG) engine that grew into context infrastructure.
ConardLi's open-source Skills collection, featuring web design, knowledge retrieval, image generation, and more.
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems.
A powerful MCP toolkit for coding, providing semantic retrieval and editing capabilities - the IDE for your agent
[EMNLP2025] LightRAG: Simple and Fast Retrieval-Augmented Generation
Self-evolving memory OS for LLM & AI Agents: ultra-persistent memory, hybrid-retrieval, and cross-task skill reuse, with 35.24% token savings and DeepSeek Harness support.
A modular graph-based Retrieval-Augmented Generation (RAG) system
Developer-friendly OSS embedded retrieval library for multimodal AI. Search More; Manage Less.
FastGPT is a knowledge-based platform built on the LLMs, offers a comprehensive suite of out-of-the-box capabilities such as data processing, RAG retrieval, and visual AI workflow orchestration, letting you easily develop and deploy complex question-answering systems without the need for extensive setup or configuration.
State-of-the-Art Embeddings, Retrieval, and Reranking
One Postgres for your application data, full-text search, vector retrieval, and aggregations. Home of the pg_search extension.
OpenRAG is a comprehensive, single package Retrieval-Augmented Generation platform built on Langflow, Docling, and Opensearch.
The retrieval layer for production AI systems. Lightning-fast (<10ms) search without vector databases. Built for browser, edge, on-device, and cloud.
A lightweight knowledge base for people and agents, with ultra fast indexing and most precise retrieval.
C++ package retrieval
Timestamp Retrieval for Anime Clips Everywhere
Synapsa Commons: free, hands-on AI courses that run anywhere (Colab, Kaggle, Binder, Codespaces, Jupyter). EU AI Act conformity evidence, model validation, predictive maintenance, document extraction, retrieval and RAG, AI agents and MCP, a simulated humanoid. Every lesson autograded. From the team building Synapsa.
physical optics: integrated modeling, phase retrieval, segmented systems, polynomials and fitting, sequential raytracing...
Deeplake is AI Data Runtime for Agents. It provides serverless postgres with a multimodal datalake, enabling scalable retrieval and training.
Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI
Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.
The codebase for the book "AI-Powered Search" (Manning Publications, 2025) and associated "AI-Powered Search: Modern Retrieval for Humans & Agents" Maven course
A Python framework for performing information retrieval experiments, building on http://terrier.org/
Tevatron - Unified Document Retrieval Toolkit across Scale, Language, and Modality. Demo in SIGIR 2023, SIGIR 2025.
IPFS HTTP Gateway daemon in Go, tuned for public production traffic: retrieval only, no pinning, no providing
[ACM TOMM 2023] - Composed Image Retrieval using Contrastive Learning and Task-oriented CLIP-based Features
A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.
Easily train a good VC model with voice data <= 10 mins!
RAG (Retrieval-augmented generation) ChatBot that provides answers based on contextual information extracted from a collection of Markdown files.