RAG Packages

Javascript wrapper for prism-ai (prism-ai.ch) - simple, powerful, RAG.

A NodeJS RAG framework to easily work with LLMs and custom datasets

A code relevance system for finding relevant files

A code relevance system for finding relevant files

Automatically and continuously vectorize your PostgreSQL tables with the flexibility of your own embedding pipelines

Optimize Subtitles for RAG Ingestion

RAG Chat bot library

A NodeJS RAG framework to easily work with LLMs and custom datasets

A customizable React chat widget for RAG applications

Rag chatbot for business

A TypeScript/JavaScript module for implementing Retrieval-Augmented Generation (RAG) using Qdrant vector database, Google's Generative AI embeddings, and Groq LLM.

Retrieval Augmented Generation (RAG) based CLI tool to help you talk with your documents

Quickest way to production grade RAG UI.

A NodeJS RAG framework to easily work with LLMs and custom datasets

The memory layer for Personalized AI in Typescript

An MCP Server for RAG Web Browser Actor

A Model Context Protocol server for fetching and storing documentation in a vector database, enabling semantic search and retrieval to augment LLM capabilities with relevant documentation context.

MCP server for integrating Tavily search API with LLMs, providing web search, RAG context generation, and QnA capabilities

WebAssembly binding for llama.cpp - Enabling on-browser LLM inference

The TypeScript Code Extractor and Analyzer can be handy for RAG (Retrieval-Augmented Generation) systems for codebases. It provides a detailed and structured representation of the codebase that can be converted into embeddings, enabling more effective adv