Build your own serverless AI Chat with Retrieval-Augmented-Generation using LangChain.js, TypeScript and Azure

MongoDB Chatbot Framework. Powered by MongoDB and Atlas Vector Search.

AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.

Generative AI Application Builder on AWS facilitates the development, rapid experimentation, and deployment of generative artificial intelligence (AI) applications without requiring deep experience in AI. The solution includes integrations with Amazon Bedrock and its included LLMs, such as Amazon Titan, and pre-built connectors for 3rd-party LLMs.

HTML to Markdown converter and crawler.

Chrome Extension to Summarize or Chat with Web Pages/Local Documents Using locally running LLMs. Keep all of your data and conversations private. 🔐

Minimalist web-searching app with an AI assistant that runs directly from your browser. Uses Web-LLM, Ratchet-ML, Wllama and SearXNG. Demo: https://felladrin-minisearch.hf.space

An enterprise-grade AI retriever designed to streamline AI integration into your applications, ensuring cutting-edge accuracy.

⚡ Cloud-native, AI-powered, document processing pipelines on AWS.

RAG document chat with Amazon Bedrock using Typescript on Lambda.

Edge compatible PDF.ai Chat with any PDF document You can ask questions, get summaries, find information, and more. Built with Pinecone, OpenAI, Langchain, Nextjs13, TypeScript, Clerk Auth, Drizzle ORM for edge runtime environment, Shadcn UI.

Library to generate vector embeddings in NodeJS

An automated assignment grading system that leverages LLMs and AI to enhance grading efficiency and reliability. It includes modules for data input, criteria definition, AI integration, consistency checks, and comprehensive reporting, aimed at improving educational outcomes.

RAGを導入した授業プラットフォーム

Automate your customer support using OpenAI Assistants API

Yet Another PDF Chatbot (Also see my ChatPDF repo)

Legal AI advisor based on RAG using +100,000 legal documents and SOTA embeddings and language model.

COSCUP 2023 的議程搜尋系統

A pipeline to convert contextual knowledge stored in documents and databases into text embeddings, and store them in a vector store

Automate your customer support using Retrieval Augmented Generation (RAG): OpenAI + Pinecone