#Chatbot

Intel Extension for Transformers improves Transformer model efficiency across platforms such as Intel Gaudi2, CPU, and GPU. Offering seamless Hugging Face API integration for model compression and software optimizations, it enhances models like GPT-J, BLOOM, and T5 for faster inference. The toolkit includes a flexible chatbot framework and expands low-bit inference capabilities, offering robust support for developers working with GenAI/LLM technologies.

NLP.js supports language detection, sentiment analysis, and named entity recognition in 40 languages naturally and 104 with BERT integration. It introduces modular packages and a flexible plugin system in version 4, ideal for building multilingual chatbots. With connectors like Microsoft Bot Framework, NLP.js facilitates operations from tokenization to sentiment analysis. This versatile framework integrates smoothly into various systems, enhancing user interaction capabilities.

Feishu-OpenAI-Stream-Chatbot seamlessly combines Feishu with OpenAI to provide a sophisticated chatbot experience. It features real-time text streaming, multi-topic discussions, and rich text responses. Predefined scenarios and role-playing enhance interaction, while context retention ensures coherent conversations. Suitable for both private and group chats, with automatic dialogue termination. Future enhancements include Feishu-ChatBot integration, conversation history rollback, direct topic-to-PPT conversion, and optimized prompts, offering cultural relevance for users in China.

MemoryBot enhances chatbot functionality by allowing conversation memory retention and data management. Users can quickly customize and deploy chatbots using Databutton templates, with features to memorize, save, and download conversations. By leveraging OpenAI, LangChain, and Streamlit, the project offers resources for building personalized and interactive chatbots. Explore further through detailed blogs, instructional videos, and live demos, presented objectively.

Efficiently set up a sophisticated customer service system using Flask, LLM, and RAG within minutes. This comprehensive solution includes both frontend and backend, compatible with cloud and on-premises LLMs, providing a customizable and straightforward interface. It adeptly handles various knowledge bases, such as websites, isolated URLs, and local files. Improve interactions with a customizable UI and simplified management through an admin console. Access a live demo with detailed guidance for smooth deployment.

openai-whisper-talk leverages OpenAI's cutting-edge technologies like Whisper for ASR, Chat Completions for interaction simulations, and Text-to-Speech for lifelike audio. Developed on the Nuxt framework, it integrates features such as Schedule Management and Long-Term Memory to facilitate efficient event handling and information storage. Supporting varied chatbot profiles and languages, this app enhances interactive experiences by enabling seamless event modification and memory recall, refining communication with AI-driven solutions.

The project facilitates setting up a locally-hosted language model system with a ChatGPT-like interface on consumer-grade hardware. It supports various LLMs such as Ollama, llama.cpp, and vLLM, providing OpenAI API compatibility. The system features a chatbot capable of summarizing content, accessing news, displaying stock data, and utilizing vector databases. Compatible with multiple hardware setups, it supports CPUs from Intel, AMD, Apple Silicon, and GPUs like NVIDIA GTX 1060 or Apple M1/M2. This setup integrates with several operating systems, serving as a tool for advanced AI applications without needing extensive infrastructure.

Clause offers an open-source solution for semantic understanding in chatbot development using deep learning, NLP, and search engine technologies. It allows for the management of multiple bots, custom intent creation, and supports integration with various programming language interfaces, making it ideal for customer service and intelligent QA systems.

EverydayWechat leverages Python and Itchat to send automated daily messages, including weather updates and reminders, to WeChat contacts and groups. The tool offers smart auto-replies and group assistant features that assist in social engagements with useful responses. This project, ideal for users at all skill levels, requires access to WeChat's web version and is non-commercial.

The Django Chatbot utilizes Django and Django Channels to enable efficient WebSocket communication alongside Celery for task automation, adaptable for deployment both locally using Docker and on Heroku. It employs Redis for optimal message management and supports straightforward commands to perform calculations and website checks. Join the Slack channel for additional collaboration and support. Explore its simple setup process and deployment documentation.

Discover how to build a website chatbot with LangChain and Supabase using Next.js and TypeScript. This guide offers detailed steps for setting up a database with Supabase, scraping data, and converting it into vectors with OpenAI's embeddings. Learn customization techniques for chatbot integration, perfect for developers aiming to improve website user interaction with a robust AI chatbot.

The project fine-tunes the Falcon-7B language model with QLoRA on a specialized mental health dataset, derived from FAQs and healthcare blogs, ensuring anonymized, realistic patient-doctor dialogues. Utilizing sharded models, tuning is efficient on both Nvidia A100 and T4 GPUs, achieving a 0.031 training loss after 320 steps. This refined model enhances chatbot support for mental health, providing non-judgmental assistance as a complement to professional services. Available for further exploration with Gradio, this work integrates AI breakthroughs into mental health, fostering greater empathy and understanding.

Learn how to utilize GPT-4 and LangChain to build advanced chatbots capable of handling multiple large PDF files efficiently. This project uses Pinecone, Typescript, and Next.js to guide the creation of scalable AI applications. It includes comprehensive instructions on repository cloning, package installation, setup, and transforming PDFs into embeddings for effective data retrieval. Additionally, it offers troubleshooting tips to ensure proper integration of key components like Pinecone vectorstore and the OpenAI API. Suitable for developers aiming to leverage AI for large-scale document management, this repository provides a detailed approach to modern chatbot development.

Engage with research documents via an AI-chat assistant using OpenAI's Chat API and semantic search through vector databases. Effortlessly upload PDF files, converse with AI, and obtain detailed, context-aware answers. Discover efficient research tools such as Vector Database Retrieval, Arxiv Search, and Document Summarization, available for easy setup both locally and on Streamlit Cloud.

EduChat is a sophisticated chatbot system designed for intelligent education, leveraging pre-trained large-scale language models fine-tuned with a variety of educational data. It offers services such as automated assignment grading, emotional support, tutoring, and exam guidance to improve personalized education. Developed by the EduNLP team at East China Normal University, the project focuses on aligning educational values and providing comprehensive educational tools. Its features cater to teachers, students, and parents, promoting fair and engaging education.

This project features a chatbot implementation primarily for question answering within LangChain documentation, utilizing LangChain.js and Next.js frameworks. It incorporates advanced tools for document processing and query handling, including RecursiveUrlLoader, SitemapLoader, and Weaviate vectorstore, complemented by GPT-3.5 for generating relevant responses. The project is easily deployable locally with adaptable front-end and back-end configurations and is supported by detailed documentation that facilitates modification and deployment.