Leveraging LLM to build Conversational UIs
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Updated
Mar 23, 2025 - TypeScript
Leveraging LLM to build Conversational UIs
x0-GPT is an advanced AI-powered tool that enables you to interact seamlessly with any website or document (including PDFs) using natural language. Whether you're looking to extract specific data, automate tasks, or gain insights, x0-GPT makes it possible with ease. Best of all, it's free and accessible to everyone.
A Dockerized Streamlit app leveraging a RAG LLM with FAISS to offer answers from uploaded markdown files, deployed on GCP Cloud.
Paste any public website URL, ingest its content into Upstash Vector & Jina, and chat with grounded answers via a multi-provider LLM fallback chain (Gemini, Groq, OpenRouter free models, Hugging Face). Built with Next.js 16, React 19, & TypeScript, with live token streaming & Jina (SPA JS-heavy page ingestion), Redis-backed session history
Learn Retrieval-Augmented Generation (RAG) from Scratch using LLMs from Hugging Face and Langchain or Python
AI-Enhanced Enterprise Knowledge Base Platform
This python powered AI based RAG Scraper allows you to ask question based on PDF/URL provided to the software using local Ollama powered LLMs
This project implements a Retrieval-Augmented Generation (RAG) based chatbot designed to handle university-related queries using natural language understanding. It combines semantic search with generative AI to provide precise, context-aware answers to students, faculty, and visitors.
AI-powered invoice processing system using multi-agent pipeline, RAG, OCR, and LLM for automated extraction, validation, and fraud detection.
My low-key LLM-friendly wrapper for search engines
An intelligent GraphRAG system that transforms any database into a knowledge graph using Neo4j, combines pgvector semantic search with graph reasoning, and enables natural language question answering over structured data.
This repository contains the implementation of a Retrieval-Augmented Generation (RAG) agent using Large Language Models (LLMs). RAG agents combine the power of information retrieval with text generation, enabling applications such as intelligent question-answering systems, and more.
Ce projet est un logiciel de Retrieval-Augmented Generation (RAG) développé en Node.js avec LangChain et Ollama.
RAG-LLM enables interactive question answering leveraging RAG architecture and Large Language Models (LLMs) applied to custom dataset regarding Medium articles.
A repo for my MS Project titled "Fake-news detection".
A document-grounded question answering CLI over PDFs and text files, storing chunks in Supabase pgvector and auditing every answer with a second model for hallucinations and relevance.
Evaluation data for the "Scrum-Guided Development of an Intelligent Payroll Management System with Rule-Based Automation and RAG Chatbot Support " paper.
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