A powerful multi-document(PDF,DOCX,PPT,MD,EXCEL,TEXT) chatbot that parses and chunks diverse files, indexes them with vector embeddings, and enables conversational retrieval with memory. Built with LangChain, LlamaParse, HuggingFace, Chroma, and Groq LLM API for seamless Q&A over documents.

A Retrieval-Augmented Generation (RAG) chatbot that combines LLMs with multi-document search for context-aware, accurate responses. Designed for intelligent interactions with persistent memory and optimized vector storage.

RAG-Ollama-Chroma is a Retrieval-Augmented Generation (RAG) system using ChromaDB for vector storage, Ollama LLM, and LangChain for document retrieval. It supports multi-format ingestion (.txt, .pdf, .docx), semantic search, and AI-generated responses via Gradio UI and Chainlit chatbot with multi-turn memory. 🚀

Multi-turn chatbot with memory using LangChain, incorporating vector store retrieval with mock documents.

Multi-agent AI chatbot for legal questions on data privacy (GDPR, UU PDP, internal policies), powered by document search, conversational memory, and LangChain.

A RAG based chatbot using Gemini API for intelligent multi PDF querying, enabling contextual Q&A over uploaded documents with vector search and memory support.

Production-ready RAG chatbot using LangChain, FastAPI, and OpenAI. Upload documents and get cited answers. Supports multi-turn memory, Pinecone and Chroma, multiple LLMs, React UI, Docker, and pytest.

AI chatbot using LangChain with session-based memory and RAG. Supports multi-turn conversation, dynamic language prompts, and document retrieval via ChromaDB. Powered by Groq's Gemma2 & LLaMA3, with HuggingFace embeddings. Ideal for exploring chat memory + retrieval.

Production-ready AI Support Chatbot built with RAG, FastAPI, and vector databases. Features document ingestion (PDF/TXT), semantic search, multi-turn memory, and modular LLM architecture for scalable, context-aware responses.

🧠 Multi-Turn Chatbot with LangChain, Ollama, FAISS, and Streamlit: A conversational AI assistant with memory, document-aware responses, and local LLM integration via Ollama. Perfect for building context-rich, intelligent chat applications.

A multi-user Retrieval-Augmented Generation (RAG) chatbot built with Streamlit, Ollama, Elasticsearch, and Redis, enabling users to upload PDFs or crawl web pages, index them, and chat intelligently with document-based context and persistent memory.

Flask + LangChain powered RAG chatbot with docs, images & memory. This Model is a production-ready Retrieval-Augmented Generation (RAG) application built with Flask, LangChain, and ChromaDB. Supports multi-document uploads, deduplicated embeddings, chat with memory, image-aware context, evaluation metrics, and a clean WhatsApp-style UI.

A sophisticated Retrieval Augmented Generation (RAG) customer service chatbot combining FastAPI backend, Pinecone vector database, Groq LLM inference, and Streamlit frontend. Features intelligent agentic decision-making, multi-format document support, real-time streaming responses, and conversation memory.

An AI-powered FAQ chatbot built using LangChain, Google Generative AI (Gemini), Cohere, and Qdrant. This chatbot can: 📚 Retrieve answers from your documents/PDFs 🎯 Use multi-query + reranking for more accurate responses 💾 Maintain conversation memory for context-aware answers 🌐 Reply concisely in multiple languages

LangChain fundamentals: starting with models, prompts, and parsers, then handling documents using loaders and splitters.Added memory for chatbots, built multi-step chains, and created agents with tools transforming a raw language model into a smarter, context-aware system.

ASK IIIT is an agentic RAG (Retrieval-Augmented Generation) chatbot built for IIITDM Jabalpur. It uses a LangGraph multi-node agent with query classification, decomposition, and retrieval to answer college-related questions from ingested PDF documents. The system supports real-time streaming, multi-turn conversation threads, and persistent memory.

NeuraVault is a scalable AI-powered chatbot backend that enables real-time conversational AI over company documents. It integrates LangChain memory, vector embeddings, and LLMs for retrieval-augmented chat, supporting PDFs, Excel, TXT, and more. Designed for multi-user, production-ready deployments

Built AskMyDocs, an AI-powered chatbot to upload and query multiple PDFs using LangChain and OpenAI. Implemented retrieval-augmented generation (RAG) with FAISS embeddings for accurate, context-aware answers. Developed an interactive Streamlit app enabling seamless multi-document conversations with conversational memory.

Developed an AI-powered conversational chatbot that answers user queries based on uploaded PDF documents using Retrieval-Augmented Generation (RAG). Built with LangChain, HuggingFace embeddings, Chroma vector database, and deployed via Streamlit. Includes multi-turn conversation memory, enabling contextual follow-ups across chat sessions.

A multi-document RAG chatbot supporting PDF, PPTX, and DOCX files. It uses hybrid retrieval (FAISS + BM25) with SentenceTransformer embeddings and a lightweight open-source LLM to generate context-aware answers. Includes short-term conversational memory and source citation. Fully local, free, and GPU-ready.

AI-powered multi-agent system that builds tailored resumes, cover letters, and interview prep from uploaded CVs. Uses CrewAI agents, Pinecone vector DB for user memory, FastAPI backend, and Next.js frontend. Users upload documents once; chatbot retrieves context intelligently and crafts job-specific applications in real-time.

Built an intelligent Al chatbot using Python (LangChain, OpenAI GPT-3.5, ChromaDB) with a full RAG pipeline that processes any PDF document, performs semantic search using vector embeddings, integrates real- time web search via Tavily API, handles scanned PDFs using Tesseract OCR, and maintains multi-turn conversation memory like ChatGPT.

This is a Voice-enabled RAG chatbot where PDFs are indexed into a vector database using embeddings. User queries are converted into vectors, relevant document chunks are retrieved, and a large language model generates context-aware answers. The system supports multi-chat sessions, conversational memory, and voice responses using text-to-speech.

An end-to-end RAG chatbot that retrieves information from your documents and generates accurate, context-aware answers using NVIDIA NIM and Gemma-3n-e4b-it. Features include multi-file upload, FAISS in-memory vector store, chat history, hallucination prevention rules, and support for PDF, DOCX, TXT, and CSV files — all wrapped in a fast Streamlit i