Empire Chain is a Python framework that orchestrates all your AI needs by seamlessly integrating LLMs (OpenAI, Anthropic, Groq), vector stores (Qdrant, ChromaDB), document processing, speech-to-text, web crawling, data visualization, and interactive chatbots into a unified interface, making it easy to build powerful AI applications like RAG systems

RAG Chatbot powered by Groq LPU, Ollama and Langchain

a Retrieval-Augmented Generation (RAG) multi-document chatbot application using Llama 3, Langchain, Streamlit, and Groq API

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FastAPI-based AI chatbot with WebSocket streaming, Groq LLM, and RAG over multi-document knowledge using FAISS.

"🚀 Full-stack Medical AI Chatbot using Retrieval-Augmented Generation (RAG). Built with FastAPI, LangChain, and Groq. Features automated CI/CD via GitHub Actions to Docker Hub and scalable vector search with Pinecone."

Built a Retrieval-Augmented Generation (RAG) app leveraging LangChain and Groq/OpenAI LLMs to create an AI chatbot that provides live football transfer updates sourced from Fabrizio Romano’s scraped tweets. Includes ongoing LLM monitoring and evaluation. See README.md for details.

Built a FastAPI backend for an AI chatbot with RAG capabilities, supporting website, PDF, and text data ingestion. Implemented FAISS-based retrieval with embeddings and integrated a Groq LLM for context-aware responses.

A streamlined Retrieval-Augmented Generation (RAG) chatbot using DeepSeek models through Groq's API. This application allows users to upload PDF documents and interact with them through natural language queries, featuring step-by-step reasoning and streaming responses.

AI Lawyer Chatbot App uses RAG to answer legal questions from uploaded PDFs like contracts or policies. It combines FAISS-powered retrieval, Groq LLMs, and a Streamlit UI for fast, accurate document-grounded Q&A. Built with modular agents for scalability and real-world use.

Groq-Gemini powered RAG Chatbot with LangChain: Chat with ANY Website in Real-Time (2024)

DocuMind is an intelligent chatbot powered by cutting-edge AI that lets you converse with multiple PDF documents simultaneously. Built with Mistral embeddings, Groq's lightning-fast LLM, and LangChain's RAG architecture.

Developed a Retrieval-Augmented Generation (RAG) application using Langchain and Groq /OpenAI LLM models to build an AI-powered chatbot that delivers real-time football transfer insights based on Fabrizio Romano's scrapped tweets. LLM monitoring and evaluations are performed. Check README.md

A Retrieval-Augmented Generation (RAG) ChatBot using LangChain, HuggingFace, and Groq’s LLaMA model — allowing interactive Q&A on uploaded PDFs via a sleek Streamlit interface.

This is a simple Q&A chatbot built using Retrieval-Augmented Generation (RAG). The chatbot retrieves relevant information using embeddings and then generates answers using a DeepSeek R1 model via the Groq API. The project is implemented using the LangChain framework in Python.

Helpy – A RAG-powered chatbot for Alliance HPC clusters. Supports OpenAI, Groq, Claude, Gemini, Ollama, and vector search via RAGFlow. Flask + LangChain + WebSocket.

Muti Project Deployment ranging from a super fast Chatbot application using GROQ and Mistral to Corrective RAG Based Application using ollama and langraph to a full planning agent using Langraph, Groq and LLAMA3

I've developed a chatbot that assist user to find the suitable professor by using RAG ,Next.js and Hugging Face embedding model and Groq

Medical Assistant Chatbot using a full-stack RAG (Retrieval-Augmented Generation) pipeline! This project combines powerful tools like LangChain, Pinecone, Google Embeddings, and Groq’s LLaMA 3 to create an intelligent, domain-specific chatbot that answers medical queries using real PDF documents.

An AI-powered medical chatbot built with LangChain, FAISS, and Groq LLMs that uses Retrieval-Augmented Generation (RAG) to answer user questions based on uploaded medical PDF documents. It features document embedding, semantic search, and a Streamlit-based interactive interface for accurate and context-aware responses.

chatbot with rag using groq api

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Document Chatbot with LLAMA 3.1. using Groq , RAG (Langchain, Generative AI)

An end to end RAG application medical chatbot (medibot) - Groq LLM, Langchain and Pinecone vector DB.

RAG Chatbot built with LangChain and powered by the high-speed Groq API. Queries a custom knowledge base

Dental clinic website with RAG-powered AI chatbot using n8n, Groq LLaMA 3.3, vector store, and Google Calendar booking

RAG-powered Document Q&A app using Python, Streamlit, LangChain, FAISS, and HuggingFace embeddings. Supports multi-PDF ingestion, vector search, and high-speed Llama-3/Groq & OpenAI inference for accurate, context-aware answers. Modern Generative AI chatbot for knowledge retrieval.

Production-ready AI agents & chatbots using Google Gemini, Groq, LangChain, CrewAI & LangGraph | 10+ projects covering RAG, document analysis, code exploration & more

An AI-powered document analysis chatbot built with **Gradio** and **Groq LLMs**, using **Retrieval-Augmented Generation (RAG)** and a modern UI

AI chatbot with RAG capabilities built using Django, LangGraph, and LangChain. Features document retrieval, multi-LLM support (Gemini/Groq), and a modern web interface.