This repository demonstrates a simple OpenAI Swarm-based system for multi-agent orchestration with Retrieval-Augmented Generation (RAG). It handles tasks like summarization, sentiment analysis, keyword extraction, and document search using FAISS and OpenAI models, showcasing the power of collaborative agents.

Intelligent Document Analyzer built using Retrieval-Augmented Generation (RAG) to enable context-aware question answering, summarization, and insight extraction from large document collections.

"A Retrieval-Augmented Generation (RAG) system for document query and summarization using vector-based search and language models.

ChatPDF leverages Retrieval Augmented Generation (RAG) to let users chat with their PDF documents using natural language. Simply upload a PDF, and interactively query its content with ease. Perfect for extracting information, summarizing text, and enhancing document accessibility.

RAGent Chatbot is a smart AI assistant that combines Retrieval-Augmented Generation (RAG) with a tool-using LLM agent. It can answer user queries using uploaded documents, perform web searches, summarize text, solve math expressions, check weather, and more — all powered by LangChain, Qdrant, and Gemini.

This repository contain flask based rfp document summarizer and chatbot using OPEN AI GPT 3.5 Turbo , the RAG (Retrieval Augmented Generation) pipeline was built using Langchian.

building a question-answering application using Retrieval-Augmented Generation (RAG), LangChain, and LLMs to summarize private company documents. The process involves indexing documents by loading, splitting, and embedding them using Hugging Face and ChromaDB.

An intelligent research article assistant using Retrieval-Augmented Generation (RAG) to summarize and extract key insights from academic papers and documents with high contextual accuracy.

A privacy-focused AI tool built with DeepSeek 8B (via Ollama) and Retrieval-Augmented Generation (RAG). It processes unstructured documents, enabling semantic search and summarization using ChromaDB and LangChain. Features a Streamlit web interface for offline document uploads, queries, and results—ensuring security and efficiency.

A summarizer system that processes PDF documents into searchable chunks using RAG (Retrieval Augmented Generation) for summarization, Ollama for embedding generation, and MongoDB Atlas for vector storage and search.

A Retrieval-Augmented Generation (RAG) system using LangChain, Gemini 1.5 Flash, and ChromaDB to extract and summarize information from web documents.

Chatbot using Retrieval-Augmented Generation (RAG) and LangChain to provide intelligent, document-based responses, with a focus on efficient document retrieval, summarization, and interaction through a Streamlit UI

A CLI-based PDF summarization tool using Retrieval-Augmented Generation (RAG) with ChromaDB and OpenAI’s GPT-4 for efficient document analysis.

An intelligent document analysis tool powered by RAG (Retrieval-Augmented Generation). Features include PDF/DOCX summarization, interactive Q&A, table extraction, and grammar analysis using LangChain, FastAPI, and ChromaDB.

Demonstrates a Retrieval-Augmented Generation (RAG) pipeline using Whisper for transcription, Ollama for language models, and Langchain for document processing. It extracts audio, transcribes it, and applies NLP for translation and summarization.

RAG-Based Summarizer & MultiQuery QnA Tool uses Retrieval-Augmented Generation to generate precise answers from uploaded documents, text, or URLs. Supports Word, PDF, JSON, and plain text. Built with Python and deployed using Next.js for a seamless user experience, it enables efficient multi-query Q&A and document summarization

The RAG-based Local LLM for PDFs project uses a Retrieval-Augmented Generation model to enable intelligent querying and summarization of PDF content locally, ensuring data privacy. It extracts and indexes document content, combining retrieval and generative capabilities for contextual answers, enhancing efficiency in document automation workflows.

LegalMindRAG is an advanced Retrieval-Augmented Generation (RAG) system designed to analyze and summarize legal, commercial, governance, and compliance documents with high accuracy and contextual relevance. Built using LangChain, Ollama, and Chroma, it leverages local LLMs to extract and explain contract clauses in natural language.

nsightAI is an AI-powered assistant that lets you chat with your documents and webpages. Upload a PDF or enter a URL, and it uses RAG (Retrieval-Augmented Generation) with Google Gemini, LangChain, and Qdrant to retrieve, summarize, and answer questions based on your content.

Built an intelligent document summarization and question-answering system using Retrieval-Augmented Generation (RAG) with LangChain and Google Gemini 2.5 Flash, enabling accurate, context-aware understanding of large documents. 🤖 AI-Powered Document Summarization & Q&A Python · LangChain · Gemini 2.5 Flash · HuggingFace · ChromaDB

Document Summarization using Retrieval-Augmented Generation (RAG)

An AI-powered assistant for summarizing and answering questions about Arabic legal documents using Retrieval-Augmented Generation (RAG) and Cache-Augmented Generation (CAG).

A document summarization application that uses RAG (Retrieval-Augmented Generation) to compare results across various language models.

GovChat is a retrieval augmented generation (RAG) app that uses GenAI to chat with and summarize government documents.

A Python-based implementation of Retrieval-Augmented Generation (RAG) using BERT for document embeddings, FAISS for efficient retrieval, and BART for dynamic summarization.

AI-powered large document analyzer using Retrieval-Augmented Generation (RAG) for intelligent search, summarization, and question answering over long PDFs.

Modular Retrieval-Augmented Generation (RAG) system for document QA, summarization, and information extraction using FAISS, sentence-transformers, and LLM backends.

PDF Summarizer with RAG – Developed a GenAI-based application to summarize PDF documents using Retrieval-Augmented Generation. Implemented embeddings, vector search, and context-aware response generation with Streamlit interface.

An AI-powered tool for summarizing and querying legal documents using Retrieval-Augmented Generation (RAG) and the GPT-4 language model.

Doc Summarizer RAG is an intelligent document understanding tool that uses Retrieval-Augmented Generation (RAG) to generate accurate, context-aware summaries from PDFs, DOCX, and text files.