Jupyter Notebook agents

🚀 Explore Happy-LLM, a tool designed to enhance interactions with language models, offering a user-friendly experience in Chinese.

This project leverages the Phi3 model and ChromaDB to create a Retrieval-Augmented Generation (RAG) application.

KG1 is a social knowledge management application, infused with AI.

🤖GraphRAG v2 API (From Local to Global: arxiv.org/abs/2404.16130) Playground 🥦

A PoC of using Weaviate + DSPy to make a BookStore CoPilot

The Zoomcamp LLM Course focuses on tools for working with LLMs and RAG, including OpenAI API, HuggingFace, Elasticsearch, and Streamlit. It covers vector search, embedding creation, data ingestion with Mage, and monitoring using Grafana, emphasizing practical applications and best practices.

A RAG system designed for law firms to enable lawyers to efficiently "talk to their data"

A Tool that Allows You Run PyTest within Jupyter Notebook Cells

The goal of this project is to develop a RAG system using Agent from LangGraph to improve the travelling experience of tourists.

This project consist on ChatBot implementation with two approach: Retrival-Augmented Generation (RAG) and Fine-Tuning

Notebooks that walk through modern LangChain and LangGraph patterns using the latest v1.x APIs. Content is organized to start from fundamentals and build up on that.

Next-Gen Indonesia IT Law Q&A: Knowledge Graphs & LLMs. ArangoDB Hackathon Project Repository: Building the Next-Gen Agentic App with GraphRAG & NVIDIA cuGraph.

🗯️LLM toolkit for RAG, tuning, agents, and more

Innovative AI agent implementations using LangGraph—featuring ReAct, RAG (Corrective, Self, Agentic), chatbots, microagents, and more, with multi-AI agent systems on the horizon! 🤖🚀

Build your own AI chatbot from scratch using Google Colab, TensorFlow, and a interactive neon matrix interface.

Explore Mistral AI's extensive collection of models. Learn to select, prompt, and integrate Mistral's open-source and commercial models for tasks like classification, coding, and Retrieval Augmented Generation (RAG).

This project aims to develop a high-precision legal expert system for contract Q&A using Retrieval-Augmented Generation (RAG). The system leverages advanced natural language processing (NLP) techniques to provide accurate and context-aware answers to questions about legal contracts and integrates a powerful language model with a custom retrieval

AI-powered multi-agent financial advisor system with LangGraph orchestration across nine specialized agents including retrieval, portfolio analysis, market desk, due diligence, and compliance, routing through a LiteLLM gateway, computing real portfolio risk metrics, and holding risky answers for human review before they reach the user.

A RAG-based chatbot that extracts insights, summaries, and topic clusters from YouTube videos, providing comprehensive understanding and answering user queries.

Step-by-step implementations for building cognitive financial agents

LLMlight is a lightweight Python library for running local language models with built-in memory, retrieval, and prompt optimization, requiring minimal dependencies.

Hands-on Generative AI: 15 RAG modules from document loaders to guardrails, plus Papeer — a research paper assistant with citations and claim verification. Also LangGraph workflows and LangSmith tracing.

Agentic AI refers to AI systems capable of autonomous decision-making, planning, and executing tasks based on goals—acting like intelligent agents. These systems combine LLMs with tools, memory, and feedback loops to complete complex workflows with minimal human input.

✅ 의료 데이터를 활용한 챗봇과 다양한 파이프라인을 제작합니다.

Collection of generative AI projects & applications including: multiple LLMs, AI agents, RAG

An AI-powered crypto analytics platform integrating forecasting, sentiment, and on-chain intelligence, built with FastAPI, MCP protocol, and MLflow in a monolithic architecture.

The LangGraph project implements a "Reflection Agent" designed to iteratively refine answers to user queries using a Large Language Model (LLM) and web search. It simulates a research process where an initial answer is generated, critiqued, and revised based on information gathered from web searches, all managed by a LangGraph workflow.

AI agents, trained by the minds that move the world.

This is a YouTube Q&A Chatbot powered by a Large Language Model (LLM) and FastAPI. Users can enter a YouTube video URL and ask questions — the system generates accurate answers using the video transcript.

This is a document question answering app made with LangChain and deployed on Streamlit where you can upload a .txt or .docx file, ask questions based on the file and an LLM like Falcon-7B or Dolly-V2-3B answers it. ChromaDB is used as the vector database.

GenAI demo with Amazon Kendra, 🦜️🔗 LangChain and Amazon SageMaker JumpStart

AI-powered book recommendations engine that fuses semantic understanding and emotion intelligence to deliver deeply relevant book suggestions. Powered by LangChain, OpenAI embeddings, and emotion-aware NLP across ~7,000 titles, with an interactive Gradio experience for intuitive exploration.

Interactive Book Recommendation System using RAG and RecSys

This project seeks to create a comprehensive system for summarising research papers by harnessing the latest advancements in AI and NLP. By merging abstractive text summarization with LLMs and the RAG methodology, we anticipate developing a unique and effective approach to extracting valuable insights from research papers

This project implements a RAG (Retrieval-Augmented Generation) application to answer questions about phenotypes using biological and genomic data. The pipeline integrates information retrieval with response generation via language models (LLM), facilitating accurate analysis of phenotypic data.

Series of generative artificial intelligence (AI) for creating new content, including audio, code, images, text, simulations, and videos.

All CPU efficient GPU-less Financial Analysis RAG Model with Qdrant, Langchain and GPT4All x Mistral-7B, run RAG without any GPU support!

The Agentic AI Workshop by ADP Brazil Labs.

Creating RAG from Scratch . Creating RAG using the langchain. Creating RAG using llama indexing and Qdrant db

AI Engineering Bootcamp for Programmers - A 19-module, 18-week immersive study plan designed for programmers looking to master AI engineering through real-world projects, foundational theory, and practical tools.

A Typeform-inspired Chat Feature with Chat Agents for Engaging Data Collection, Powered by AutoGen and Langchain. Includes REST API Integration for Messaging Systems.

Unlock the potential of AI-driven solutions and delve into the world of Large Language Models. Explore cutting-edge concepts, real-world applications, and best practices to build powerful systems with these state-of-the-art models.

Record voice, transcribe a prompt, picturize the prompt, create variations, get description of a celebrity and upload, other use cases on KB

🚀 Build a production-ready Agentic RAG system with LangGraph using minimal code and streamline your AI development process.

A ChatBot powered by OpenAI GPT-3.5 which can answer questions on E-commerce data

This repository contains various techniques for Retrieval-Augmented Generation (RAG) systems.

A Streamlit app for document question answering and text summarization.

A practical guide to getting started with local 🤖 Large Language Models using Python, Ollama, & Streamlit. Includes basic chatbot setup, RAG-enabled PDF querying, & vectorstore visualization. Ideal for experimenting with LLMs on your own machine—no cloud req. 🦙

Extracting Product Insights from Unstructured text data using LLMs with LangChain

The LLM Dataset Generator is an open source tool for generating text data compatible with various language models supported by LangChain. You can customize it to meet your specific needs, making it a valuable resource for researchers, developers, and organizations working on NLP applications.

Automate complex business workflows with our Multi-AI-Agent Systems using crewAI. This framework leverages autonomous, role-specific AI agents to collaboratively perform multi-step tasks, enhancing efficiency and accuracy across various domains. Ideal for applications in resume tailoring, website design, research, customer support, and more.

Gemini Pro LLM and Pinecone Vector Database for fast and performant Retrieval Augmented Generation (RAG) with LlamaIndex

GROQ LPU Powered LLM chat engine

Techniques and workflows for building an LLM-powered RAG application using LlamaIndex.

【🔥🔥NLP-LLM-Agent🔥🔥】: projects, analysis, source code, model training and testing.

AI and Data Science Course

Agentic Framework examples for SAP Gen AI Hub via LiteLLM

Agentic Search Engine For Real-Time IoT Data

Labs for agentic AI — covering Azure AI Foundry, Foundry Agent Service, Microsoft Agent Framework, AI Agents, RAG, Azure AI Search, and Azure Container Apps(ACA)

This repository documents my hackathon solution, hands-on experiments, and technical notes from the IEEE YP Agentic AI Summit Dec 2025

Use LLM/AI agent for Web scraping (collection data) and analysis data with deep research

LLM App to demystify and summarize Terms and Conditions agreements

Implementation of "Building Agentic RAG with LlamaIndex" offered by DeepLearning.AI focusing on developing intelligent research agents using the Retrieval-Augmented Generation (RAG) framework.

A Retrieval-Augmented Generation (RAG) application for querying legal documents. It uses PostgreSQL, Elasticsearch, and LLM to provide summaries and suggestions based on user queries. Features data ingestion with Airflow, real-time monitoring with Grafana, and a Streamlit interface.

RAG pipeline with Agents for multiround conversation consistancy

This project is a Document Retrieval application that utilizes Retrieval-Augmented Generation (RAG) techniques to enable users to interact with uploaded PDF documents. By leveraging a Large Language Model (LLM), users can ask questions about the content of the documents and receive accurate answers based on the information retrieved.

A simple Docker based playground that brings together popular open source data and AI tools to help others get started with data lakehouse architecture and GenAI development on their local machines.

Loreer is wise AI assistance that has the vast knowledge of all lores, whether that is movie, series, game, or book related. Powered by LLMs and AI.

SAIRA Project, Generative Artificial Intelligence, Fall 2023, Innopolis University

Unlock the power of vector databases with the "Vector Databases: from Embeddings to Applications" course! A journey that will equip you with essential skills to leverage vector databases for various applications.

RAG/LLM-based movie recommendation system with

llm_works

An implementation of Retrieval-Augmented Generation (RAG) indexing using Rust and LanceDB. It leverages the power of Rust for efficient and safe performance, combined with LanceDB for advanced indexing capabilities.

关于 LangChain 的深度学习与实践教程仓库，涵盖了从基础入门到高级 Agent 架构的多个阶段。本项目旨在通过实际的代码示例和 Notebook 教程，帮助开发者掌握 LangChain 生态系统的核心概念与最新特性。

Explore Functions, Tools and Agents with LangChain along with LangChain Expression Language

Explore the fundamentals of MLLMs and emblematic models. This repository covers practical techniques for preprocessing, prompt engineering, and building multimodal pipelines using LangChain and LangGraph, alongside future trends and challenges in AI.

Intelligent Document Reranking RAG System

Demo building Restaurant Recommendation Chatbot based on Yelp Public Data

The goal of this projest is to present 3 different approaches for text classification (trained ML model, zer-shot transformer, RAG).

🎓 AI-powered academic tutor using RAG to provide contextual answers from course textbooks across multiple subjects with intelligent query enhancement 🤖✨

An end-to-end Machine Learning and Agentic AI system to predict customer churn and generate structured retention strategies using LangGraph and Open-Source LLMs.

GRAPHRAG with Langchain

This repository features a collection of generative AI applications designed to showcase the capabilities of AI across various domains.

BENCHMARKS WITHOUT BORDERS

Security Agent Benchmarking and Evaluation Research

AI agents, trained by the minds that move the world.

Agentic AI for Cold-Chain Sustainability: LSTM-Based Predictive Logistics

Hands-on LangGraph tutorials: 6 agentic design patterns + Agentic RAG, with runnable notebooks and the official diagrams.

Educational Tutor AI Assistant using Google's Gemini language model. The assistant will include features like generating quizzes, creating flashcards, providing grammar corrections, and offering project guidance.

🎓 RAG Agent: Intelligent University Policy Assistant

【WWW2024】The MMO Economist: AI Empowers Robust, Healthy, and Sustainable P2W MMO Economies

AI Agent for Real estate recommendation system

LangChain Documentation based practice

This repository provides a LangChain-powered chatbot for customer support, enabling businesses to deliver efficient and personalized support experiences to their customers. Leverage the power of natural language processing (NLP) to handle customer inquiries, resolve issues promptly, and enhance overall customer satisfaction.

An AI-based system that helps manage healthcare appointments using different agents for tasks like booking, cancelling, and answering questions.

Intelligent cooking companion that finds recipes based on your ingredients using advanced AI. Built for Google's Gen AI Intensive Course 2025Q1, showcasing 8 AI capabilities: LangGraph agents, RAG with ChromaDB, voice commands, nutrition API integration, Google Search grounding, and more.

A curated list of prediction market APIs, tools, datasets, and resources for developers and AI agents