The OpenSource Alternative to Glean's Workplace AI

RAG-GPT, leveraging LLM and RAG technology, learns from user-customized knowledge bases to provide contextually relevant answers for a wide range of queries, ensuring rapid and accurate information retrieval.

An agentic AI application that allows you to chat with your papers and gather also information from papers on ArXiv and on PubMed

Building and deploying a document Q&A application on the Hugging Face cloud using Docker.

Getting Started with GPT4 API, GPT4 RAG, OpenAI GPT4 Assistant, OpenAI Models

Turn PDF into Notes in seconds📝

This is the Python backend for InsightAI

User-friendly interface for creating effective Retrieval Augmented Generation (RAGs)

Chainlit app for RAG chat with documents Parsing PDF documents using LlamaParse, Qdrant, and the Groq model

Simplify context preparation for large language models. This Streamlit application uses llama-parse and files-to-prompt to combine PDF and text files into a single, Claude XML-formatted context file for LLMs.

A RAG app with streamlit as UI app, flask as backend api. Bot trả lời về document, về data cụ thể. Bot trả lời về document của công ty, trả lời về tờ hướng dẫn sử dụng hay gì đó. Data: bot có khả năng query để lấy dữ liệu (dạng dữ liệu có cấu trúc) như csv

A Laravel client for Llamaparse Cloud API

Parse documents using AI - any document converted to markdown suitable for RAG applications

Llama Parse PDF: Extract tables from PDF documents and convert them into Excel format, simplifying the process of managing transaction data.

SmartRAG-Assistant/GenAI-Assistant leverages advanced LLM models and Nvidia APIs for efficient query handling and document summarization. It integrates LlamaParse for structured data extraction, HuggingFace embeddings for vectorization, and PineconeDB for efficient retrieval, ensuring precise answers to user queries.

using Llama Parse to read pdf and convert into mark down or text

RAG application to track and analyze Safaricom Mpesa transactions from using LLMs.

This repository contains my learning experience and practical implementation of the LlamaIndex 🦙 framework in Python. This project explores the core components of LlamaIndex, its unique approach to context augmentation, and the process of creating LLM (Large Language Model) applications.

Automated Interview Filtering System

An AI-powered platform that helps job seekers optimize resumes and find best-fit jobs using semantic search and local LLMs, while enabling recruiters to post jobs. Easy to run with Docker or locally, leveraging FastAPI, Streamlit, FAISS, and Ollama.