GitHub topics: retreival-augmented-generation

Repositories

ashishbamania/Tutorials-On-Artificial-Intelligence

A collection of AI tutorials from Dr. Ashish Bamania

Language: Jupyter Notebook - Size: 202 KB - Last synced at: 1 day ago - Pushed at: 1 day ago - Stars: 7 - Forks: 0

VivekMalipatel/RAG_Application

Retrieval-Augmented Generation system implementing a hybrid dense-sparse vector and knowledge graph based search architecture.

Language: Python - Size: 2.37 MB - Last synced at: 3 days ago - Pushed at: 3 days ago - Stars: 0 - Forks: 0

oracle-samples/ai-optimizer

GenAI/RAG Explorer for experimentation using Oracle Database AI Vector Search

Language: Python - Size: 22.2 MB - Last synced at: 4 days ago - Pushed at: 4 days ago - Stars: 36 - Forks: 17

rupeshtr78/rag-agent-rust

LanceDB Vector embeddings using cli and integrating LLM models for Retrieval-Augmented Generation (RAG) workflows for data storage, retrieval, and AI-driven chat.

Language: Rust - Size: 688 KB - Last synced at: 7 days ago - Pushed at: 7 days ago - Stars: 0 - Forks: 0

hoangsonww/AI-RAG-Assistant-Chatbot

👨🏻‍💻 Meet Lumina – my personal chatbot assistant designed to answer any questions. Powered by Optuna, RAG, LangChain, and Pinecone vector database, Lumina offers friendly support and smart solutions tailored for all conversations. Created as part of the mid-term project for COMP-488 at UNC.

Language: TypeScript - Size: 9.89 MB - Last synced at: 8 days ago - Pushed at: 9 days ago - Stars: 27 - Forks: 20

Abdelrahman-Elshahed/News_Summerization_Using_RAG--Graduation_Project_DEPI

News Summerization RAG system developed with Meta-Llama-3-8B-Instruct (LLM), ChromaDB (Vector Database), and the BART summarizer (Hugging Face).

Language: Jupyter Notebook - Size: 29.5 MB - Last synced at: 13 days ago - Pushed at: 13 days ago - Stars: 0 - Forks: 0

yussufbiyik/langchain-chromadb-rag-example

My attempt at implementing retreival augmented generation on Ollama and other LLM services using chromadb and langchain while also providing an easy to understand, clean code for others since nobody else does

Language: Python - Size: 39.1 KB - Last synced at: 21 days ago - Pushed at: 21 days ago - Stars: 24 - Forks: 3

Aniike-t/HybridRAGLLM

Integrating Traditional IR with Neural Retrieval for Enhanced Question Answering in Large Documents

Language: Python - Size: 16.6 KB - Last synced at: 23 days ago - Pushed at: 23 days ago - Stars: 0 - Forks: 0

RihanArfan/chat-with-pdf

Chat with PDF lets you ask questions to PDF documents. Built and deployed with NuxtHub, and powered by Cloudflare Workers AI and Vectorize.

Language: TypeScript - Size: 879 KB - Last synced at: 19 days ago - Pushed at: 3 months ago - Stars: 84 - Forks: 6

lixx21/legal-document-assistant

A Retrieval-Augmented Generation (RAG) application for querying legal documents. It uses PostgreSQL, Elasticsearch, and LLM to provide summaries and suggestions based on user queries. Features data ingestion with Airflow, real-time monitoring with Grafana, and a Streamlit interface.

Language: Jupyter Notebook - Size: 16.1 MB - Last synced at: 19 days ago - Pushed at: 8 months ago - Stars: 59 - Forks: 13

nitrotap/city-code-assistant

Search the City of Trinidad, Colorado's municipal code. Ask an AI assistant questions about the city, like create a checklist for a special events permit. Uses OpenAI and Retrieval Augmented Generation to query a vector store with the Municipal Code.

Language: JavaScript - Size: 3.87 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

ziweek/hire-me-please-gpt

💻 Toy Project - 🤖🙋🏻‍♂️ A RAG chatbot for my resume | The ultimate job-hunting sidekick RAG chatbot that knows your resume better than you #Google #Gemini-API #retrieval-augmented-generation #streamlit

Language: Python - Size: 5.75 MB - Last synced at: 30 days ago - Pushed at: 2 months ago - Stars: 1 - Forks: 0

dark-horiznz/AI-Research-Assistant-RAG

This Streamlit application serves as a comprehensive AI-powered research tool featuring three main modes: Research Assistant (fetching information from arXiv papers and Wikipedia), Document Q&A (allowing users to upload PDFs and ask questions about them using RAG), and Web Search (retrieving and summarizing web content)

Language: Python - Size: 3.84 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

kobikotasa/LangChain-Pinecone-RAG

LangChain-Pinecone-RAG is a collaborative project exploring the intersection of natural language processing, blockchain technology, and artificial intelligence. The team aims to leverage advanced text generation models to develop innovative solutions for data security and decentralized information exchange.

Size: 1000 Bytes - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

anjalichennupati/Ad_Placement_Optimization_using_RAG_and_LLMs

A project that integrates RAG and LLMs for targeted ad campaign recommendations. It extracts data via web scraping, processes it using LangChain, and enhances accuracy with FAISS. Users can input queries through a Streamlit-based UI, generating AI-powered marketing strategies and custom ad creatives with DALL·E.

Language: Jupyter Notebook - Size: 3.58 MB - Last synced at: about 1 month ago - Pushed at: about 2 months ago - Stars: 1 - Forks: 0

kanavgoyal898/contextSynth

contextSynth is a simple Retrieval-Augmented Generation (RAG) application that scrapes Wikipedia documents and answers relevant questions based on retrieved information. It leverages language models for embeddings, vector stores for indexing, and a retrieval-based question-answering system.

Language: Jupyter Notebook - Size: 1.04 MB - Last synced at: about 1 month ago - Pushed at: about 2 months ago - Stars: 0 - Forks: 0

rohit180497/Coffee-Shop-AI-Agents

This project is an innovative coffee shop application designed to bring an engaging and personalized experience to coffee lovers. The app leverages AI-powered agents for chat-based interactions and integrates modern web and mobile development techniques to provide seamless ordering and delivery services.

Language: Jupyter Notebook - Size: 86.7 MB - Last synced at: about 1 month ago - Pushed at: 3 months ago - Stars: 3 - Forks: 1

ajitsingh98/Building-RAG-System-with-Deepseek-R1-Locally

This repository contains an end-to-end Retrieval-Augmented Generation (RAG) system that leverages Deepseek and Ollama to provide intelligent responses based on any pdf content.

Language: Python - Size: 1000 Bytes - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

Abdelrahman-Amen/RAG_Agent

This project uses LangChain agents and Google Generative AI to build a RAG system, combining LLMs with tools like Wikipedia, Arxiv, and custom retrievers for accurate, real-time answers.

Language: Jupyter Notebook - Size: 12.7 KB - Last synced at: 21 days ago - Pushed at: 3 months ago - Stars: 0 - Forks: 1

Abdelrahman-Amen/RAG_Groq_Integration

In this project, we implement a Retrieval-Augmented Generation (RAG) system using the Groq API. The application dynamically retrieves and processes context from documents, enabling accurate and context-aware question-answering powered by advanced AI embeddings and a language model.

Language: Python - Size: 4.96 MB - Last synced at: 29 days ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

BilalM04/MultiAI

A versatile AI-powered chatbot built with Streamlit, integrating multiple LLMs for chat, web search, and file-based Q&A.

Language: Python - Size: 47.9 KB - Last synced at: 7 days ago - Pushed at: 3 months ago - Stars: 0 - Forks: 2

Abdelrahman-Amen/Retriever_in_RAG

This project is a PDF Question Answering App that enables users to upload any PDF and ask questions about its content. Using a retriever-augmented generation (RAG) approach, it efficiently retrieves relevant information and generates human-like answers, powered by Streamlit and Google Generative AI.

Language: Python - Size: 2.69 MB - Last synced at: 21 days ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

Abdelrahman-Amen/Question_Answering_from_Any_URL_with_RAG

In this project, users can input any URL and ask a question related to its content. Using Retrieval-Augmented Generation (RAG) and LangChain, the app retrieves the most relevant answer from the webpage.

Language: Python - Size: 1.84 MB - Last synced at: about 1 month ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

Abdelrahman-Amen/Simple_PDF_RAG

This project showcases how Retrieval-Augmented Generation (RAG) can be applied to create an efficient and user-friendly system for querying large text documents. With this approach, users can easily extract and interact with relevant information from PDFs in real time.

Language: Python - Size: 1.87 MB - Last synced at: 28 days ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0