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.
Getting Started with GPT4 API, GPT4 RAG, OpenAI GPT4 Assistant, OpenAI Models
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
Q&A Chatbot capable of answering questions related to the financial reports of large public companies. This chatbot can be directly hosted in Heroku platform. The RAG (Retrieval-Augmented Generation) approach is used.
Parsing PDF, PPT, and Txt documents using LlamaParse, Qdrant, and the Groq model
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.
using Llama Parse to read pdf and convert into mark down or text
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
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