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#

chatbot-application

Here are 494 public repositories matching this topic...

《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

  • UpdatedFeb 15, 2023
  • Python

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

  • UpdatedDec 20, 2022
  • Jupyter Notebook

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

  • UpdatedFeb 27, 2019

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

  • UpdatedJun 29, 2023
  • Python

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! 🔥✨

  • UpdatedApr 23, 2024
  • Python

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

  • UpdatedNov 15, 2020
  • CSS

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

  • UpdatedOct 11, 2023
  • HTML

A customizable AI support chatbot template with Arcjet rate limiting and bot protection. Made completely free with Google AI Studio

  • UpdatedSep 11, 2025
  • TypeScript

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

  • UpdatedApr 29, 2022
  • JavaScript

Build-An-LLM-RAG-Chatbot-With-LangChain-Python

  • UpdatedNov 13, 2024
  • Python

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

  • UpdatedDec 6, 2023
  • Python

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.

  • UpdatedJan 5, 2020
  • Python

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