An open source framework for building AI-powered apps with familiar code-centric patterns. Genkit makes it easy to integrate, test, and deploy sophisticated AI features to Firebase or Google Cloud.

🌌 Fast, dependency-free, full-text and vector search engine with typo tolerance, filters, facets, stemming, and more. Works with any JavaScript runtime, browser, server, service!

This is an GraphRAG - Knowledge Graph based and out-of-the-box conversational search tool that leverages the vector storage capabilities of TiDB Serverless. It provides a seamless way to embed a powerful question-answering (QA) bot directly on your website, requiring only a simple copy-and-paste of a JavaScript snippet. Demo: https://tidb.ai

Private & local AI personal knowledge management app.

The GUI for Milvus

Community Plugins for Genkit (OpenAI, Groq, Anthropic, Cohere, etc)

The official TypeScript/Node client for the Pinecone vector database

A NodeJS RAG framework to easily work with LLMs and embeddings

Admin UI for Chroma embedding database built with Next.js

Official Weaviate TypeScript Client

Example of building a chatbot with Langchain and Supabase Vector.

Chat with PDF 📃

This project uses advanced NLP and semantic search to navigate YouTube video transcripts. By segmenting transcripts and using a vector store, it provides contextually relevant search results. Integrating ChatGPT for query processing ensures high-quality answers, enhancing user experience. Ideal for educational platforms, content creators, and viewe

Your new mate on Discord and Slack. Powered by AI.

🚀 Hermes harnesses the power of GPT technology to create interactive conversations with your PDF documents. This innovative open-source tool transforms your PDFs into dynamic chatbots!

Kubernetes operator for producing vector embeddings based on source code repositories.

This is a repository to let you know the implementation of a basic RAG pipeline using LangChain in Supabase Edge Functions.

Genkit AI framework plugin for HNSW vector database. save data into vector store for Retrieval Augmented Generation (RAG) implementation in Generative AI

A simple and efficient product filtering system, experiencing the latest technologies, streamlines your search effortlessly.

An elegant hybrid search engine that significantly enhances search precision by seamlessly querying semantically related results using embedding AI models. For experiencing AI models and integrations.