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.
This is the Python backend for InsightAI
Chainlit app for RAG chat with documents Parsing PDF documents using LlamaParse, Qdrant, and the Groq model
"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."
Building and deploying a document Q&A application on the Hugging Face cloud using Docker.
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
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
Automated Interview Filtering System
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