《Rasa实战：构建开源对话机器人》官方随书代码 | The official source code of Rasa in Action: Building Open Source Conversational AI

Discord AI Chatbot using DialoGPT, trained on the game transcript of The World Ends With You

AI ChatBot using Python Tensorflow and Natural Language Processing (NLP) along side TFLearn

一个基于 Rasa 的中文天气情况问询机器人(chatbot), 带 Web UI 界面

Qiscus provide everything you need to power up your app with chats. And it's now made simple.

Tensorflow chatbot which is capable of interacting with user through Rest Api, Web interface, GUI and CLI.

Chat with GPT LLMs over voice, UI & terminal, all with access to the internet. Powered by OpenAI.

A multimodal chat interface with many tools.

A chatbot that fetches events details from a conference's website

This chatbot developed using Dialoglow,python,flask,MongoDB and deployed on Telegram and pivotal cloud foundary. search @bestcovid19_bot on telegram app

Virtual Assistant

Meet MultiPDF 📚 Chat AI App! 🚀 Chat seamlessly with Multiple PDFs using Langchain, Google Gemini Pro & FAISS Vector DB with Seamless Streamlit Deployment. Get instant, accurate responses from Awesome Google Gemini OpenSource language Model. 📚💬 Transform your PDF experience now! 🔥✨

Voice Assistant based on Whisper ASR and ChatGPT API

It is a Multi Guild AI Chatbot which can speak in all languages. It is made using discordjs v13 by Supreme#2401(diwasatreya)

This chatbot lets you use your microphone to communicate with GPT-4. It uses the OpenAI text to speech to respond with a voice. It uses Pinecone to store long term information and retrieves it to create context. API keys for OpenAI and Pinecone required. Tested on Windows

Open source platform for robots based on LLM

LLM chatbot server with ChatGPT plugins

LLM-based chatbot capable of interfacing with external systems for knowledge retrieval and command execution

Artificial Intelligent ChatBot using Tensorflow and NLP that understand the Context and Intent of Human Language.

Build a chatbot using deep learning techniques. The chatbot will be trained on the dataset which contains categories (intents), pattern and responses. We use a special recurrent neural network (LSTM) to classify which category the user’s message belongs to and then we will give a random response from the list of responses.