Flock is a workflow-based low-code platform for rapidly building chatbots, RAG, and coordinating multi-agent teams, powered by LangGraph, Langchain, FastAPI, and NextJS.（Flock 是一个基于workflow工作流的低代码平台，用于快速构建聊天机器人、RAG、Agent和Muti-Agent应用，采用 LangGraph、Langchain、FastAPI 和 NextJS 构建。）

This repository contains advanced LLM-based chatbots for Q&A using LLM agents, and Retrieval Augmented Generation (RAG) and with different databases. (VectorDB, GraphDB, SQLite, CSV, XLSX, etc.)

RAG (Retrieval-augmented generation) ChatBot that provides answers based on contextual information extracted from a collection of Markdown files.

A fully custom chatbot built with Agentic RAG (Retrieval-Augmented Generation), combining Gemini models with a local knowledge base for accurate, context-aware, and explainable responses. Features a lightweight, dependency-free frontend and a streamlined FastAPI backend for complete control and simplicity.

Ready-to-use AI Multimodal ChatGPT-based WhatsApp chatbot assistant for your business. Now supports GPT-4o with text + audio + image input, audio responses, and improved RAG + MCP 🤩

A multi-pdf chatbot based on RAG architecture, allows users to upload multiple pdfs and ask questions from them.

(RAG) Developing a chatbot capable of utilizing PDF files as a knowledge base to answer questions. Additionally, we employed the vision capability of GPT-4V to identify and describe graphs from each page. Describing the charts and their illustrated trends improves the chatbot's accuracy in answering and providing additional context.

ChatBot, show how to implement a RAG based on OceanBase or OceanBase seekdb AI capabilities escpecailly hybrid search and AI embedding.

Complete RAG based Medical Chatbot

An AI-powered chatbot that leverages RAG (Retrieval-Augmented Generation) to answer your questions based on the content of uploaded PDFs

Java 23, SpringBoot 3.4.1 Examples using Deep Learning 4 Java & LangChain4J for Generative AI using ChatGPT LLM, RAG and other open source LLMs. Sentiment Analysis, Application Context based ChatBots. Custom Data Handling. LLMs - GPT 3.5 / 4o, Gemini Pro 1.5, Claude 3, Llama 3.1, Phi-3, Gemma 2, Falcon 3, Qwen 2.5, Mistral Nemo, Wizard Math

Rag Chatbot React And Tyepscript base boilerplate

RAG-based chatbot for retail e-commerce.

LifeSpring Clinic Intelligent RAG-Based Healthcare Assistant is an AI-powered chatbot that provides accurate clinic information using Retrieval-Augmented Generation. It answers patient queries about doctors, services, appointments, symptoms, and care policies with reliable, document-based responses.

An LLM Chatbot based on LangGraph and LangChain that dynamically retrieves and processes resumes using RAG to perform resume screening.

RAG based chatbot for Global AI Hub

A Retrieval-Augmented Generation (RAG) chatbot that answers questions from your own documents using local or cloud-based LLMs.

A RAG-Based AI Chatbot for Collage customer support

An open-source OpenAI wrapper for a RAG-based chatbot that seamlessly integrates with your documents.

AI-powered chatbot that provides guidance using YouTube video content as a knowledge base. Built with RAG architecture using LangChain, Qdrant, and Google Gemini AI, ElevenLabs and Streamlit.

RAG Based LLM Chatbot Built using Open Source Stack (Llama 3.2 Model, BGE Embeddings, and Qdrant running locally within a Docker Container)

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This project leverages Language Model (LLM) finetuning, Semantic Chunking, and a Retrieval-Augmented Generation (RAG) based Chatbot framework to provide personalized medical information retrieval for remote patients in the Healthcare 5.0 era.

It is a chatbot for question and answering using RAG based on LLM

A RAG-based AI chatbot that mimics stylistic properties from provided WhatsApp conversations.

This is a RAG based chatbot in which semantic cache and guardrails have been incorporated.

Build a Chatbot which uses Retrieval Augmented Generation (RAG) to answer based on your own data!

RAG-based Chatbot that helps answer questions around healthy eating & lifestyle choices, based on 1200+ science-backed blog posts of NutritionFacts.org.

RAG based chatbot for my university's Global Service Department OGS. LLM + ETL

A RAG-based chatbot withmulti-tenant project support, where specialized agents collaborate over knowledge graphs to deliver accurate, context-aware answers.