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
-
Updated
Jun 6, 2024 - TypeScript
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
AWS Generative AI CDK Constructs are sample implementations of AWS CDK for common generative AI patterns.
MongoDB Chatbot Framework. Powered by MongoDB and Atlas Vector Search.
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.
Build your own serverless AI Chat with Retrieval-Augmented-Generation using LangChain.js, TypeScript and Azure
⚡ Cloud-native, AI-powered, document processing pipelines on AWS.
Chrome Extension to Summarize or Chat with Web Pages/Local Documents Using locally running LLMs. Keep all of your data and conversations private. 🔐
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.
Research Assistant Fronted App
A demonstration of RAG that utilizes Next.js, LangChain, and OpenAI. RAG (Retrieval Augmented Generation) is a technique that combines information retrieval and language generation to produce more informed and contextual responses.
RAGを導入した授業プラットフォーム
AI powered Video-Frame Analysis
Chat application with PDF integration, offering users a seamless experience to engage in discussions around uploaded PDF files.
Using LLMs and RAG to generate and optimize BigQuery searches
Build a generative AI application using Azure Functions and LangChain.js
Library to generate vector embeddings in NodeJS
Create documents to improve the accuracy of large language models which employ retrieval augmented generation.
Learning NextJs 14 with app router. Messing with proper server/client component architecture, Next API routes, Next streaming, Next cache beta, retrieval augmented generation with OpenAI embeddings API, PineconeDB to store context/query embeddings, and chatbot with OpenAI prompt.
Add a description, image, and links to the retrieval-augmented-generation topic page so that developers can more easily learn about it.
To associate your repository with the retrieval-augmented-generation topic, visit your repo's landing page and select "manage topics."