Chat with PDF lets you ask questions to PDF documents. Built and deployed with NuxtHub, and powered by Cloudflare Workers AI and Vectorize.

A Retrieval-Augmented Generation (RAG) application for querying legal documents. It uses PostgreSQL, Elasticsearch, and LLM to provide summaries and suggestions based on user queries. Features data ingestion with Airflow, real-time monitoring with Grafana, and a Streamlit interface.

A new novel multi-modality (Vision) RAG architecture

GenAI/RAG Sandbox for experimentation using Oracle Database AI Vector Search

A very CPU-friendly RAG implementation

A RAG based LLM assistant for australian first home buyers

A very simple RAG implementation

Agentic Rag with Customizable Sub-Agents.

Explore web scrapping and search engine for thesis search, combine with RAG

ChatBot for live scores of cricket matches.

Building Retrieval Augmented Generation Pipeline from scratch

A RAG based approach to building a chatbot, that uses llama3 at its core, and can enable users to chat with pdfs, by storing pdf data in a vectordb (Chroma) and retrieves using FAISS

The goal of this project is to develop a RAG system using Agent from LangGraph to improve the travelling experience of tourists.

Retrieval Augment Generation, Chat with your document using lang chain and open ai.

Search the City of Trinidad, Colorado's municipal code. Ask an AI assistant questions about the city, like create a checklist for a special events permit. Uses OpenAI and Retrieval Augmented Generation to query a vector store with the Municipal Code.

💻 Toy Project - 🤖🙋🏻‍♂️ A RAG chatbot for my resume | The ultimate job-hunting sidekick RAG chatbot that knows your resume better than you #Google #Gemini-API #retrieval-augmented-generation #streamlit

A Retrieval-Augmented Generation (RAG) app for chatting with content from uploaded PDFs. Built using Streamlit (frontend), FAISS (vector store), Langchain (conversation chains), and local models for word embeddings. Hugging Face API powers the LLM, supporting natural language queries to retrieve relevant PDF information.

Learning Generative AI from scratch with step-by-step Google Colab notebooks. Build scalable architectures for enterprise-level solutions, starting with the basics of RAG systems.

A lightweight toolkit for managing and querying a Pinecone vectorstore with PDF documents. This repository contains two main components: importer which pulls and tracks pdfs from a google cloud storage bucket, & retriever which is a terminal app for rag querying

A versatile AI-powered chatbot built with Streamlit, integrating multiple LLMs for chat, web search, and file-based Q&A.