There are 0 repository under retrival-augmented-generation topic.
🔥 Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation 🔥. Our toolkit integrates 40 pre-retrieved benchmark datasets and supports 7+ retrieval techniques, 24+ state-of-the-art Reranking models, and multiple RAG methods.
✨ AI interface for tinkerers (Ollama, Haystack RAG, Python)
BestRAG: A library for hybrid RAG, combining dense, sparse, and late interaction methods for efficient document storage and search.
Multimodal Document Processing RAG with LangChain
Dataviz AI is an AI powered web application that enables users to generate animated infographic videos based on input Data ,files. This MVP leverages the gen ai models for video content and incorporates advanced natural language processing (NLP) techniques, including LangChain and stable diffusion techniques, to analyze and create visual impact.
A self-hosted, privacy-focused RAG (Retrieval-Augmented Generation) interface for intelligent document interaction. Turn any document into a knowledge base you can chat with.
Efficiently search and retrieve information from PDF documents using a Retrieval-Augmented Generation (RAG) approach. This project leverages DeepSeek-R1 (1.5B) for advanced language understanding, FAISS for high-speed vector search, and Hugging Face’s ecosystem for enhanced NLP capabilities. With an intuitive Streamlit interface and Ollama for mode
Intent API enables the management of network devices with the help of ChatGPT by utilizing Netmiko for SSH control and NetBox for centralized network data management to perform vendor-agnostic, intent-based operations.
Just some initial learning for usage of AI models within .NET platform with Semantic Kernel APIs
An AI-powered email assistant that retrieves emails and generates intelligent responses
A medical NLP project developed for the WS2023 course, focusing on extracting, processing, and analyzing medical text data using advanced NLP techniques. This project aims to improve information retrieval and decision-making in healthcare.
Data-Science-and-Insight-Agent-RAG-LLama3-Lava-LLM-Django-WebApplication is an advanced AI-driven chatbot designed to assist in data science, document analysis, and image interpretation.This repository contain the Django based Web Application of this project.
Needle components for Haystack projects.
Pack and unpack source projects of any language into portable text files
Simple rag implementation for any WordPress blog. Leverages the bootstrap, python, milvus vector dB, and configurable options for LLM providers (Open AI, Anthropic) and embeddings.
A football domain-specific knowledge dialogue model implemented using Retrieval-Augmented Generation (RAG) + GPT-3.5 API + Gradio frontend~
A platform built to make learning easy and collaborative. With Library, users get filtered YouTube content for better understanding, real-time doubt-solving, and quick revision tools—all in one place.
The Django-based-Website-Code-generation-with-RAG-Llama3-Multi-AGI-of-Software-development leverages advanced technologies and specialized agents to streamline the entire software development lifecycle. This repository contains Django based Web Application
Retrieval-Augmented Generation on YouTube transcripts and PDFs to deliver accurate and contextual answers.
RAG (Retrieval-augmented generation) app made with Flutter, Firebase, Gemini, LangChain and Pinecone.
An interactive AI-powered study assistant that helps users engage with their study materials through a chat interface. The application uses LangChain and Ollama to provide intelligent responses based on uploaded PDF documents.
A question-answering framework empowered with a custom retrieval-augmented generation (RAG) pipeline to answer queries on local documents
This is a chatbot QA RAG project implemented using LangChain, which answers based only on the context and is flexible to update and integrate with different LLM models and various vector databases
An on-going chatbot project. Deployed on Heroku and Github Page.
The modern web development landscape is plagued by a peculiar paradox: despite the abundance of UI components and design systems, developers still spend countless hours reimplementing similar interfaces. S0 addresses this challenge by introducing a novel approach that combines advanced vector search capabilities.
A RAG based application to chat with documents uploaded to the app
Built and hosted a Conversational AI Chatbot on Streamlit, powered by Large Language Models (LLMs). Implemented Cortex LLM & Cortex Analyst for real-time natural language processing (NLP) and optimized vector embeddings for enhanced contextual understanding.
An AI-powered platform that dynamically generates and grades exam questions using Retrieval-Augmented Generation (RAG). It leverages NLP, document retrieval, and a user-friendly interface for seamless exam creation
This is a RAG based smart search tool for recommending free online courses for upskilling
This is a full-stack web application that enables users to upload documents, query an index built from those documents and retrieve responses powered by a Large Language Model (LLM) using Retrieval-Augmented Generation (RAG).
SentriAI streamlines BPO operations with AI-powered task prioritization, automated ticketing, and an intelligent knowledge base. It enhances efficiency, reduces workload, and ensures faster resolutions through RAG-based document retrieval.
The goal of the project is to build a robust generative search system capable of effectively and accurately answering questions from a policy document.
AI agent that interacts with a Kubernetes cluster to answer queries about its deployed applications
Git Your Code implements a cutting-edge Retrieval-Augmented Generation (RAG) architecture designed for deep semantic analysis of GitHub repositories. The system leverages vector embeddings, natural language processing, and machine learning to provide intelligent code comprehension and query capabilities.
An innovative AI chatbot for order-taking, menu guidance, query filtering, and product recommendations using Market Basket Analysis, prompt engineering, and LLM-based NLP techniques.