FastGPT is a knowledge-based platform built on the LLMs, offers a comprehensive suite of out-of-the-box capabilities such as data processing, RAG retrieval, and visual AI workflow orchestration, letting you easily develop and deploy complex question-answering systems without the need for extensive setup or configuration.

Hashtag is a knowledge-based platform built on the LLMs, offers a comprehensive suite of out-of-the-box capabilities such as data processing, RAG retrieval, and visual AI workflow orchestration, letting you easily develop and deploy complex question-answering systems without the need for extensive setup or configuration.

Cutting-edge financial analysis tool using Retrieval-Augmented Generation (RAG) to process complex financial data. This project combines AI-powered document retrieval with contextual response generation for scalable, insightful financial analytics.

🌳 A Python engine for orchestrating complex LLM workflows, from simple data processing to RAG agents.

Agentic DSPy RAG is a production-ready, multi-agent Retrieval-Augmented Generation (RAG) system built with the DSPy framework. It features vision-based data ingestion, advanced intent classification, specialized agent routing, multi-step reasoning, and a robust FastAPI backend. Designed for high-quality, context-aware conversational AI over complex

A powerful workflow orchestration framework designed for building complex processing pipelines, including RAG systems, multi-agent collaborations, and data processing workflows.

This repository contains the implementation of our research on optimizing Retrieval-Augmented Generation (RAG) systems for technical domains. Our work addresses the unique challenges of precise information extraction from complex, domain-specific documents by introducing token-aware evaluation metrics and synthetic data generation pipeline.

A RAG system designed for complex enterprise document corpora - Runner up solution to the Argusa AI Data Challenge

FloatChat is an AI-powered oceanographic data analysis system that enables natural language querying, intelligent visualization selection, and context-aware insights over ARGO float datasets using an advanced RAG pipeline. It transforms complex marine data into interactive, research-ready analytics for scientists and professionals.

This project demonstrates a lightweight, browser-based Agentic RAG application designed for a simulated smart factory environment. It showcases how an AI agent can intelligently combine information from diverse data sources to provide comprehensive, context-rich responses to complex natural language queries.

AI/ML Full Stack Development Intern: RAG-based Document QA System: - Built complete pipeline with LlamaIndex & Ollama processing HR policy documents for the company. - Combined three separate AI components (Llama3, mxbai-embed-large) together with OpenWebUI. - Demonstrates experience with complex data processing and AI integration.

Vehicle trajectory prediction is vital for autonomous driving, but supervised models degrade outside training data. We train an LSTM baseline on NGSIM, then build a retrieval DB from NGSIM, nuScenes, and Waymo. Retrieved similar trajectories are fused via Static or Adaptive RAG, improving accuracy, especially for curves and complex maneuvers.

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RAG for complex financial data

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A Knowledge Graph-based RAG pipeline for complex enterprise data retrieval and multi-hop reasoning.

This repo consists of an efficient RAG pipeline for retrieving and evaluating on a complex enterprise data

An RAG application with Persistent Directory creation and storing the vector and indexed data in it for complex query and retrieval.

AI-powered RAG assistant for biomedical maintenance. Specialized in extracting data from complex technical PDFs (tables, diagrams) to provide instant troubleshooting for lab equipment.

Conversational Data Storytelling for Monumental Trees – An interactive web platform combining modern web technologies, data storytelling, and LLM-based chatbots (RAG) to make complex environmental and cultural data accessible, engaging, and personalized for users.

"A specialized RAG-powered computational engine for oncology research. Caduceus Compute leverages semantic retrieval and LLMs to synthesize insights from complex pathology literature and clinical data."

Next-gen Agentic Data Infrastructure for automated auditing and structural reasoning. Built with Python, Gemini, and advanced RAG pipelines to eliminate information gaps in complex B2B ecosystems.

A production-ready RAG assistant for industrial engineering manuals. Extracts structured troubleshooting data from complex PDFs with exact source page traceability using LlamaIndex, OpenAI, and OOP principles.

A comprehensive research management ecosystem for SRIC, IIT Kharagpur. It features a React-based data analytics dashboard for project tracking, integrated with an AI-powered RAG chatbot for navigating complex procurement manuals.

A powerful offline RAG chatbot that accurately extracts data from tables and complex documents without hallucinations. Built for privacy with local LLMs to provide strictly grounded, document-based answers.

Hybrid RAG assistant that fuses Microsoft SQL Server data and local docs (PDF/TXT). Uses FAISS for fast vector retrieval and SQLite for metadata. Answers complex queries across structured organizational data and unstructured knowledge using Gemini 2.5 Flash.

An end-to-end RAG pipeline built with Python to extract text from complex PDFs/documents, analyze data using LLMs, and store high-dimensional embeddings in a Vector Database for semantic search.

A WhatsApp-integrated Retrieval-Augmented Generation (RAG) engine designed for technical teams. It ingests PDFs and markdown documentation, chunks and vectorizes the data, and answers complex queries with precise citations and source links.

Developed RAG for semi-structured data which is an advanced form of Retrieval-Augmented Generation that employs specialized techniques to accurately process information which addresses the limitations of standard RAG, which primarily handles plain text and struggles with complex layouts such as tables and CSV / Excel files.

❄️ Winter-Agent: A framework integrating RAG for deep knowledge retrieval and ReAct for iterative task execution. Solves complex queries through an "Observe-Think-Act" loop with grounded external data. High-performance, modular, and easy to deploy.