Retrieval-augmented-generation Packages

LU (Layer Understanding) is a lightweight framework for controlled chatbot interactions with LLMs, action orchestration, and retrieval-augmented generation (RAG).

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

Evaluate responses from MongoDB chatbot server

A chatbot server for retrieval augmented generation (RAG).

UI React components for the MongoDB Chatbot Framework

Common elements used by MongoDB Chatbot Framework components.

MongoDB Ingest CLI for the MongoDB Chatbot Framework.

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

A modular toolkit for building RAG (Retrieval-Augmented Generation) pipelines in Node.js

RAG (Retrieval-Augmented Generation) library for NestJS with vector database integration

The `@anyparser/core` Typescript SDK enables developers to quickly extract structured data from a wide variety of file formats like PDFs, images, websites, audio, and videos.

The Retrieval-Augmented Generation (RAG) module contains document processing and embedding utilities.