AnythingLLM Embed widget submodule for the main AnythingLLM application
Ce projet est un logiciel de Retrieval-Augmented Generation (RAG) développé en Node.js avec LangChain et Ollama.
SOC Analyst Automation using a RAG model integrates a knowledge retrieval system with generative AI to automate SOC Level-1 tasks. It processes server logs, retrieves relevant security insights, and generates accurate responses, enhancing incident analysis, reducing response times, and improving efficiency in handling cybersecurity threats through
Chat Bot based on Generative AI Technologies including Flask, React, Mistral 7B, ChromaDB, All-MiniLM-L6-V2, PostgreSQL and implemented JWT Authentication.
A production-ready AI-powered e-commerce support chatbot using Vercel AI SDK, Google Gemini, Prisma, Neon DB, and Zod validation.
AI Chatbot - Utilizes Ollama to have a 100% local AI Chatbot that you can use to Chat with your own domain knowledge. This Chatbot uses a custom 3D Background that dynamically changes based on the emotional state of the chatbot, which is determined based on sentiment analysis.
A fast, efficient and simple chatbot for answering domain or organization specific questions using a dataset of FAQs.
HaloSatoshi is a 24/7 AI-powered banking chatbot that can handle both general inquiries and specific requests like mobile token replacement or transfer error reports. It uses Retrieval-Augmented Generation with cosine similarity and a defined threshold to ensure accurate responses. Powered by Ollama, it delivers natural, human-like answers.
An AI-driven assistant that enables users to interact with PDF documents through natural language queries. Leveraging Retrieval-Augmented Generation (RAG), this application combines the capabilities of React for the frontend and FastAPI for the backend to provide accurate and context-aware responses based on the content of uploaded PDFs.
A citation-aware AI RAG chatbot that reads & analyzes technical PDFs using LangChain + FastAPI.
A chatbot assistant that provides tailored products guidance to customers, using custom RAG from custom data.
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