🤖 The RAG application retrieves data from Notion

Capstone-Project - End-to-End AI Engineering Bootcamp (https://maven.com/swirl-ai/end-to-end-ai-engineering)

This project integrates LangFlow as a backend API with a Streamlit frontend for a chatbot interface. It also includes RAGAS evaluation for measuring the performance of RAG (Retrieval-Augmented Generation) pipelines.

A practical guide for building and evaluating an end-to-end Retrieval Augmented Generation (RAG) system with memory and more!

Langchain, Ollama, ChromaDB, Llama 3.1 for PDF RAG Chat Interaction

Implements a Retrieval-Augmented Generation (RAG) system.

Senor 2.0 is an LLM-powered chatbot trained on Indian legal documents, designed to assist Indian citizens in understanding and navigating legal procedures.

Retrieval-Augmented Generation (RAG) system for extracting information from legal documents such as NDAs, contracts, and privacy policies. Includes preprocessing, EDA, vector search using ChromaDB, and evaluation with ROUGE, BLEU, and RAGAS metrics.

Finetuning LLMs using Unsloth on text-to-sql tasks with minimal compute

Create syntetic datasets for RAG evaluation