embeddings

Ask questions about any data source by leveraging langchains

A ChatGPT integration library for .NET, supporting both OpenAI and Azure OpenAI Service

Text2Text Language Modeling Toolkit

Build, evaluate and observe LLM apps

EntityDB is an in-browser vector database wrapping indexedDB and Transformers.js over WebAssembly

🦀 Prevents outdated Rust code suggestions from AI assistants. This MCP server fetches current crate docs, uses embeddings/LLMs, and provides accurate context via a tool call.

Running local Language Language Models (LLM) to perform Retrieval-Augmented Generation (RAG)

Home of the AI workforce - Multi-agent system, AI agents & tools

Admin UI for Chroma embedding database built with Next.js

Radient turns many data types (not just text) into vectors for similarity search, RAG, regression analysis, and more.

Access the latest AI models like ChatGPT, LLaMA, Deepseek, Diffusion, Hugging face, and beyond through a unified prompt layer and performance evaluation

🦉⚡️Serverless, distributed vector database as an API

Ask questions across your Markdown notes using a fully local Graph RAG engine. Built for Obsidian vaults, works with any folder of Markdown files. Extracts entity-relation triples from wikilinks & YAML frontmatter, retrieves answers via hybrid search (vector + BM25 + temporal). Multilingual. No cloud. Runs on Ollama.

Official source for spanish Language Models and resources made @ BSC-TEMU within the "Plan de las Tecnologías del Lenguaje" (Plan-TL).

GPT Interviewer - Practice interview with AI interviewer based on job descriptions and resume

llama.cpp (GGUF LLMs) and llava.cpp (GGUF VLMs) for ROS 2

Web-optimized vector database (written in Rust).

Notate is a desktop chat application that takes AI conversations to the next level. It combines the simplicity of chat with advanced features like document analysis, vector search, and multi-model AI support - all while keeping your data private.

A modern desktop application for exploring, managing, and analyzing vector databases

Akcio is a demonstration project for Retrieval Augmented Generation (RAG). It leverages the power of LLM to generate responses and uses vector databases to fetch relevant documents to enhance the quality and relevance of the output.