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chatbot

CivicTruth is a Retrieval-Augmented Generation (RAG) platform that enables transparent, citation-backed question answering over legislative and policy documents.

Our assistant addresses the challenge of navigating fragmented and massive match data from the IPL 2026 season. Currently, cricket fans and analysts struggle to extract specific match statistics or venue-specific performance metrics from raw CSV datasets without advanced data analysis tools.

Student Study Planner is a Streamlit-based application that enables students to organize their studies efficiently through subject and task management, automated study planning, progress analytics, AI-powered study assistance, and English/Telugu language support.

Lightweight local-first RAG chatbot built with Streamlit, Ollama, ChromaDB, SQLite, and local quality checks.

Local-First Document Q&A with RAG using Ollama — Find-Retrieve-Answer pattern

A Retrieval-Augmented Generation (RAG) chatbot built with Streamlit, LangChain, FAISS, and sentence-transformers. Upload PDFs and ask questions using Gemini or local Llama.cpp LLMs.

A production-ready Retrieval-Augmented Generation (RAG) chatbot built with Streamlit, LangChain, Google Gemini, and ChromaDB. Upload PDFs and ask questions — the system retrieves relevant document chunks and answers using Gemini's LLM.

AI-powered decision simulation platform — multi-expert adversarial debate with real-time streaming, structured scoring, and AI-synthesised verdicts. Built for the Swecha Internship Hackathon.

Fully offline document Q&A system using PyMuPDF4LLM, Ollama, and llama.cpp. Dual LLM backend with pre-commit quality gates.

RAG Chatbot built using LangChain, FAISS, Groq LLM, and Flask. Supports PDF document ingestion and intelligent question answering through Retrieval-Augmented Generation.

AI-powered PDF Chatbot using Retrieval Augmented Generation (RAG), FAISS, and OpenAI APIs.

Local PDF RAG system using TinyLlama, FAISS, Streamlit, and Sentence Transformers.

Local-First PDF Q&A Assistant built using PyMuPDF4LLM and Llama.cpp. Supports PDF upload, document retrieval, and question answering using a local language model.

Production-ready RAG chatbot built with Streamlit, LangChain, FAISS, and local LLM support. Features document Q&A, PDF processing, and AGPLv3 compliance.

Local PDF Question Answering System using TinyLlama, Llama.cpp, Streamlit, and Retrieval-Augmented Generation (RAG).

Multimodal Retrieval-Augmented Generation (RAG) application built with Streamlit, LangChain and ChromaDB for intelligent document question answering.

A dual-mode RAG (Retrieval-Augmented Generation) chatbot. Mode 1 uses hybrid search (pgvector + BM25) with Ollama for fully local, free LLM inference. Mode 2 uses a lightweight Find-Retrieve-Answer workflow with PyMuPDF4LLM and llama.cpp for structured PDF Q&A with zero embeddings or vector database.

GRAM Connect is a platform that helps citizens identify and apply for eligible government welfare schemes based on their profile, location, income, and category. It provides scheme recommendations, eligibility checking, and awareness of government benefits.

Local Streamlit RAG research assistant for querying uploaded and preloaded documents with Ollama, LangChain, and ChromaDB.

CineRAG is a Retrieval-Augmented Generation (RAG) application designed to answer cinema-related queries. The platform currently focuses on the Telugu film industry, providing responses based on information stored in a local knowledge repository.