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Jupyter Notebook agents
2,100 Jupyter Notebook AI agents indexed on MeshKore — the most complete public catalog, ranked by popularity and updated daily.
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Jupyter Notebook agents — page 17 of 21
Este repositório contém um exemplo de como criar agentes de IA usando a biblioteca CrewAI, o modelo Llama3 e a API Groq em Python. O objetivo é fornecer uma estrutura básica para configurar e executar agentes de IA.
Data Interpreter for autoGen
Materiales de la charla "Agentes inteligentes"
Advanced lecture of MIT 16.412 - Cognitive Robotics - in Spring 2019
An AI agent in your terminal that can connect to databases and generate insights
Hands-on workshop for building a Deep Research workflow with LangGraph.
A comprehensive guide and resource collection for building sophisticated agentic AI systems with LangGraph.
🐉 Dungeons & Dragons RAG Exploration 🎲
A Retrieval-Augmented Generation (RAG) pipeline using ADE. RAG enables LLMs to answer questions about documents by retrieving relevant chunks and generating answers grounded in that context.
This GitHub repository contains a Python script for an AI Therapist Chatbot powered by OpenAI's GPT-3.5 Turbo model. The chatbot is designed to engage with users in a supportive and empathetic manner, providing responses to user queries and messages.
Tensorflow Chatbot trained with Cornell movie-dialog corpus
NLP project myself, and Ye Fan developed for ECE 657.
This project seeks to fine-tune the GPT-2.5 model with a personal touch. By training it on personal Telegram chats, we aim to capture the essence of individualized writing styles. To spice things up, we've also fed the model a sprinkle of anecdotes and a dash of math tasks.
A curated collection of advanced AI, ML, and NLP projects—including recommenders, chatbots, prediction systems, and more. Each project is end-to-end, production-ready, and demonstrates real-world applications of modern data science and generative AI. Perfect for learning, showcasing, or building upon!
Watcharapon Weeraborirak Computer Engineering AI Developer (Machine Learning)
A multi-language virtual assistant
Generative AI nano degree program
A functional PoC chatbot inspired from Google's DialogFlow
An attention-based seq2seq neural network chatbot with PyTorch, trained on Microsoft's MetaLWOz dataset.
Virtual AI Chatbot. Automate customer service at your car dealership site. Let the chatbot be your service booking agent and sales assistant.
simply a chatbot ! (Maybe not so simple ;-) )
Repository of OpenClassrooms' AI Engineer path, project #10 : create a flight booking chatbot, integrate and deploy it as a web app
An easy to implement chatbot that can be used to enhance customer experience immediately on any website.
Whatsapp chatbot automation with Python and Selenium for sending messages, photos
A multi-layer bidirectional seq-2-seq chatbot with bahdanau attention.
QuizGPT is an interactive AI tutor that engages students with adaptive questions based on school materials, creating a fun chat experience.
对于pytorch官网chatbot的教程做了模块化的处理，包含大量的注释
Voice Chatbot, Course Project, Speech Processing
chatbot using NLP and RNN
A chatbot that can predict disease by knowing symptoms from you. It can predict almost 41 disease. It can also tell you description, precautions and symptoms for various diseases.
This project is a simple deep learning-based chatbot that uses a three-layer neural network to predict the intent of user inputs. The model architecture consists of the following layers:
A smart Solution for mental health checkup with aid of ML , applied with Firebase to store data and a dedicated ARCore App to make it interactive. Feature includes Dialogflow implementation and AudioPlaying capabilities to highlight the features presented
pytorch前馈网络分类预测chatbot
한국어 Open Domain 챗봇 개발을 위한 Pytorch Lightning 기반 코드
System Retrieval Augmented Generation
Bernard combines a sophisticated neural network machine learning model and scripted dialogue content. Using transformers and Decoding methods, it mimics the talking style of the user.
Chatbot Developed with the help of NLP and Python
MTech Project with collaboration with WNS Global Service, main goal of this project is to achieve two things: firstly, to create a reliable and open-source system that can efficiently extract information from private PDF files, and secondly, to develop a user-friendly web application that utilizes web scraping techniques to gather relevant data.
Simple Educational Implementation of OpenAI CLIP in PyTorch
🔊 PiperGen: Pretrain Piper TTS with OpenAI voices! Capture, convert, and fine-tune models for rich, natural speech synthesis. 🗣✨
Using Deep Reinforcement Learning to Find the Best Strategy in Blackjack
A Python Package for retrieving Federal Reserve Economic Data at Scale and feeding it to OpenAI
This is a Voice Assistant like Google-Assistant or Alexa using Open AI's ChatGPT. This project is developed using Python & Gradio on Google Colab.
Descubre como mejorar los modelos de lenguaje con técnicas de prompt engineering y aprovechar el poder de OpenAI para tareas de procesamiento de lenguaje natural.
PaliGemma FineTuning
Benchmarking Large Language Models for GxP Healthcare Compliance
This project explores the use of various NLP techniques to classify companies by analyzing their textual descriptions
Explore new metrics and best practices to monitor your LLM systems and ensure safety and quality
Authenticating with Entra ID (former Azure AD) to access Azure OpenAI models in Python SDK v1.x
ML-based career counsellor Streamlit app that uses GPT-3 to suggest jobs based on user skills
you will find brief code implementations of some of the latest developments in AI, including Stable Diffusion, Whisper, YOLO and HuggigFace Transformers
Simulation of ML interview with plain OpenAI and based on interview-related articles. Use of LangChain framework, OpenAI text-davinci-003 LLM and ChromaDB database for answering questions about loaded texts.
The Text-to-Image Generator is a tool that allows users to generate images from text descriptions using the OpenAI API. The purpose of this project is to make it easier for people to create images without having to be skilled in graphic design or photography. This project was built using Python, Streamlit, PIL, and urllib.
This is a a hands-on course teaching GenAI Foundations including Deep Learning, NLP, LLMs, Prompt Engineering, Fine Tuning, RAG, and Optimization (distillation, pruning, and quantization).
gpt2 Colab Notebook and Dataset + Model to Generate Fake Trump Tweets
Which NBA player does r/NBA hate the most? Sentiment analysis of 1.57M Reddit comments from the 2024-25 season.
LLM is a very powerful tool. It often performs more than required (hallucinations) and may tend to generate output in a pattern it finds best. We need RAG to harness the power of LLM in a controlled manner. In this work we implement a simple RAG system with Codegemma and an in-memory Vector Database.
Generating embedding for 1000s of PDF Documents, in Qdrant using FastEmbed with distributed Computing in Ray
Multimodal-VideoRAG: Using BridgeTower Embeddings and Large Vision Language Models
A hands-on security lab demonstrating how to poison a Retrieval-Augmented Generation (RAG) system by injecting malicious data into its vector database. Learn RAG architecture, attack surfaces, poisoning techniques, detection methods, and mitigation strategies through a practical, lightweight demo.
A clean and simple implementation of Retrieval Augmented Generation (RAG) to enhanced LLaMA chat model to answer questions from a private knowledge base. We use Tesla user manuals to build the knowledge base, and use open-source embedding and Cross-Encoders reranking models from Sentence Transformers in this project.
Course material for the DSD bootcamp on combining large language models with vector databases.
RAG based QA system based on domain knowledge from local PDFs, useful for reviewing for exams and interviews :)
A Retrieval-Augmented Generation (RAG) chatbot built in Python using embeddings, PostgreSQL with pgvector, and Hugging Face language models. This project allows a chatbot to answer user queries by retrieving relevant information from a corpus of text documents.
Lets you ask question about topics in video and gives summary of the YouTube video.
Demo showing how the Trustworthy Language Model add reliability to LLM outputs and improves RAG, agents, and data enrichment worfklows. can be used to improve fine-tuning of LLMs, accuracy of LLM outputs, and smart routing for RAG and agents.
A vanilla from scratch Retrieval Augmented Generation (RAG) implementation that includes a web interface to control it.
🧠 [ACM-TORS] A Resource for Multi-Modal Learning in Visual RAGs
My first Multi-Modal RAG pipeline....Dummy version
A Retrieval-Augmented Generation (RAG) system for automating Security Operations Center (SOC) log analysis. This project combines NLP techniques with vector search to process security logs, enabling semantic querying and visualization through an interactive Streamlit interface.
Python-llama Agents, LLM-Rag-Application, Aenerative-AI, Machine-Learning. Model Training, Implementing various machine learning algorithms such as Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting. Model Evaluation: Assessing model performance
NLP (Natural Language Processing)
A RAG system for Contract Q&A that enables chatting with a contract and asking questions about the contract. It has an interface build with React and FastAPI in backend integrating rag-pipeline with Autogen agents and websockets for communication. Evaluation of the RAG is done using RAGAS.
A Visual and Interactive tool to learn and explore Hybrid RAG (Vector + Graph DBs) and Agentic RAG systems.
My personal notes, code and projects of the Udacity Generative AI Nanodegree.
Investigating the vulnerability of Large Language Models (LLMs) to misinformation in Retrieval-Augmented Generation (RAG) systems by poisoning vector databases and analyzing LLM responses to identify potential weaknesses and exploitation risks.
Practical AI recipes built on Eden AI runnable notebooks, 200+ models, swappable providers
此代码仓库收录了我根据 LangChain Academy《LangChain Essentials》课程整理的练习与笔记，已全面同步至 LangChain v1.1.0 版本。所有 Notebook、脚本与附加资料均可直接运行，适合作为“保姆级”上手手册，每个 Notebook 都围绕构建 LangChain LLM 应用的关键主题展开，从基础 Agent 配置、工具调用到中间件与人类介入流程。
An advanced AI mental health assistant that combines voice interaction, fine-tuned psychology models, and intelligent knowledge retrieval to provide comprehensive psychological support.
🧠 Personal POC demonstrating agentic workflows with LangGraph and real-time legal research via Tavily. ⚖️📚 An autonomous assistant that helps users find applicable laws 📜, case precedents 🏛️, and document templates ✍️ based on jurisdiction 🌍, legal topic 📂, and specific requirements ✅.
A Question Answering(Q/A) Chatbot on Insurance Documents. Powered by Retrieval Augmented Generation(RAG), LlamaIndex and LangGraph. Inspired from my Upgrad_IIITB PG Course.
Experimental RAG that consumes Cyber Security articles via RSS
Python fundamentals for LangChain: focused notebooks covering the essential concepts you need to start building AI applications. Quick and practical.
This repository is my platform to learn, experiment, and innovate with LLMs. Here I try to dive in and discover diverse applications, research experiments, and projects fueled by the power of language models.
An end-to-end LangGraph pipeline that fetches Facebook ads, downloads and processes video content, transcribes Urdu audio via Whisper, analyzes marketing techniques and visuals with GPT-4o, and produces structured marketing insights.
This repository contains various implementations of AI agents using LangGraph, demonstrating different patterns and use cases for building intelligent, tool-enabled conversational systems. Each project explores different aspects of agentic AI development, from basic chatbots to sophisticated RAG (Retrieval-Augmented Generation) systems.
Hooplytics turns NBA box-score data into player intelligence through machine learning, interactive analytics, and visual workflows for exploring trends, projections, and performance signals.
This repository presents a project focused on developing a high-precision legal expert LLM application called Contract Advisor RAG. The project's goal is to create a Retrieval Augmented Generation (RAG) system for Contract Q&A, enabling users to interact with contracts by asking questions and receiving accurate, context-rich responses.
Hybrid RAG system for translating Ancient Egyptian transliterations using the TLA dataset.
Smart AI Based Food RAG is an intelligent food search system that utilizes cutting-edge technologies to provide users with efficient and intuitive text and image search capabilities. The system combines the power of Retrieval Augmented Generation (RAG) techniques, computer vision.
LLM powered Mental Health Support
A multimodal RAG application using Qwen 2.5 VL, ColPali, and QdrantDB for text and image-based retrieval.
An intelligent financial advisory system showcasing 5 key Gen AI capabilities: Structured Output/JSON Mode, RAG (Retrieval Augmented Generation), Embeddings, Function Calling, and LangGraph Agents. Analyzes spending patterns, provides personalized recommendations, and answers finance questions with evidence-based advice.
The Claude Certified Architect – Foundations certification validates that practitioners can make informed decisions about tradeoffs when implementing real-world solutions with Claude. This exam test foundational knowledge across Claude Code, the Agent SDK, APIs, and MCP — the core technologies used to build production-grade applications with Claude
AI Agent and Agentic Ai Development Notebooks
AmritaGPT is a chatbot designed to answer all Amrita Viswa Vidyapeetham related questions, covering topics such as clubs, placements, entrance exams, and more. The system facilitates text-to-text conversation as well as speech-to-text and text-to-speech functionalities. 🤖📚🎙️
🍽️ SFPPy - Python Framework for Food Contact Compliance and Risk Assessment🍏⏩🍎
Colab notebook and source code used to fine-tune Microsoft's Phi-3-mini to understand, translate, and converse in the Igbo language while retaining general English capabilities. Plus script for safely resuming training after timeouts.