**OpenAI Chat API Workflow for Alfred** An Alfred 5 Workflow for using OpenAI Chat API to interact with GPT-3.5/GPT-4 🤖💬 It also allows image generation 🖼️, image understanding 👀, speech-to-text conversion 🎤, and text-to-speech synthesis 🔈 **Features:** * Execute all features using Alfred UI, selected text, or a dedicated web UI * Web UI is constructed by the workflow and runs locally on your Mac 💻 * API call is made directly between the workflow and OpenAI, ensuring your chat messages are not shared online with anyone other than OpenAI 🔒 * OpenAI does not use the data from the API Platform for training 🚫 * Export chat data to a simple JSON format external file 📄 * Continue the chat by importing the exported data later 🔄

WebUI is a user-friendly tool built on Gradio that enhances website accessibility for AI agents. It supports various Large Language Models (LLMs) and allows custom browser integration for seamless interaction. The tool eliminates the need for re-login and authentication challenges, offering high-definition screen recording capabilities.

DeepPavlov is an open-source conversational AI library built on PyTorch. It is designed for the development of production-ready chatbots and complex conversational systems, as well as for research in the area of NLP and dialog systems. The library offers a wide range of models for tasks such as Named Entity Recognition, Intent/Sentence Classification, Question Answering, Sentence Similarity/Ranking, Syntactic Parsing, and more. DeepPavlov also provides embeddings like BERT, ELMo, and FastText for various languages, along with AutoML capabilities and integrations with REST API, Socket API, and Amazon AWS.

ShellChatGPT is a shell wrapper for OpenAI's ChatGPT, DALL-E, Whisper, and TTS, featuring integration with LocalAI, Ollama, Gemini, Mistral, Groq, and GitHub Models. It provides text and chat completions, vision, reasoning, and audio models, voice-in and voice-out chatting mode, text editor interface, markdown rendering support, session management, instruction prompt manager, integration with various service providers, command line completion, file picker dialogs, color scheme personalization, stdin and text file input support, and compatibility with Linux, FreeBSD, MacOS, and Termux for a responsive experience.

VideoDB Python SDK allows you to interact with the VideoDB serverless database. Manage videos as intelligent data, not files. It's scalable, cost-efficient & optimized for AI applications and LLM integration. The SDK provides functionalities for uploading videos, viewing videos, streaming specific sections of videos, searching inside a video, searching inside multiple videos in a collection, adding subtitles to a video, generating thumbnails, and more. It also offers features like indexing videos by spoken words, semantic indexing, and future indexing options for scenes, faces, and specific domains like sports. The SDK aims to simplify video management and enhance AI applications with video data.

Shell-AI (`shai`) is a CLI utility that enables users to input commands in natural language and receive single-line command suggestions. It leverages natural language understanding and interactive CLI tools to enhance command line interactions. Users can describe tasks in plain English and receive corresponding command suggestions, making it easier to execute commands efficiently. Shell-AI supports cross-platform usage and is compatible with Azure OpenAI deployments, offering a user-friendly and efficient way to interact with the command line.

Code2Prompt is a powerful command-line tool that generates comprehensive prompts from codebases, designed to streamline interactions between developers and Large Language Models (LLMs) for code analysis, documentation, and improvement tasks. It bridges the gap between codebases and LLMs by converting projects into AI-friendly prompts, enabling users to leverage AI for various software development tasks. The tool offers features like holistic codebase representation, intelligent source tree generation, customizable prompt templates, smart token management, Gitignore integration, flexible file handling, clipboard-ready output, multiple output options, and enhanced code readability.

Generative Fusion Decoding (GFD) is a novel shallow fusion framework that integrates Large Language Models (LLMs) into multi-modal text recognition systems such as automatic speech recognition (ASR) and optical character recognition (OCR). GFD operates across mismatched token spaces of different models by mapping text token space to byte token space, enabling seamless fusion during the decoding process. It simplifies the complexity of aligning different model sample spaces, allows LLMs to correct errors in tandem with the recognition model, increases robustness in long-form speech recognition, and enables fusing recognition models deficient in Chinese text recognition with LLMs extensively trained on Chinese. GFD significantly improves performance in ASR and OCR tasks, offering a unified solution for leveraging existing pre-trained models through step-by-step fusion.

Airbadge is a Stripe addon for Auth.js that simplifies the process of creating a SaaS site by integrating payment, authentication, gating, self-service account management, webhook handling, trials & free plans, session data, and more. It allows users to launch a SaaS app without writing any authentication or payment code. The project is open source and free to use with optional paid features under the BSL License.

RAG-GPT is a tool that allows users to quickly launch an intelligent customer service system with Flask, LLM, and RAG. It includes frontend, backend, and admin console components. The tool supports cloud-based and local LLMs, enables deployment of conversational service robots in minutes, integrates diverse knowledge bases, offers flexible configuration options, and features an attractive user interface.

STaRK is a large-scale semi-structure retrieval benchmark on Textual and Relational Knowledge Bases. It provides natural-sounding and practical queries crafted to incorporate rich relational information and complex textual properties, closely mirroring real-life scenarios. The benchmark aims to assess how effectively large language models can handle the interplay between textual and relational requirements in queries, using three diverse knowledge bases constructed from public sources.

LLMBox is a comprehensive library designed for implementing Large Language Models (LLMs) with a focus on a unified training pipeline and comprehensive model evaluation. It serves as a one-stop solution for training and utilizing LLMs, offering flexibility and efficiency in both training and utilization stages. The library supports diverse training strategies, comprehensive datasets, tokenizer vocabulary merging, data construction strategies, parameter efficient fine-tuning, and efficient training methods. For utilization, LLMBox provides comprehensive evaluation on various datasets, in-context learning strategies, chain-of-thought evaluation, evaluation methods, prefix caching for faster inference, support for specific LLM models like vLLM and Flash Attention, and quantization options. The tool is suitable for researchers and developers working with LLMs for natural language processing tasks.

The OpenAIApi Plugin provides access to the OpenAI API in Unreal Engine, allowing users to generate images, transcribe speech, and power NPCs using advanced AI models. It offers blueprint nodes for making API calls, setting parameters, and accessing completion values. Users can authenticate using an API key directly or as an environment variable. The plugin supports various tasks such as generating images, transcribing speech, and interacting with NPCs through chat endpoints.

rclip is a command-line photo search tool powered by the OpenAI's CLIP neural network. It allows users to search for images using text queries, similar image search, and combining multiple queries. The tool extracts features from photos to enable searching and indexing, with options for previewing results in supported terminals or custom viewers. Users can install rclip on Linux, macOS, and Windows using different installation methods. The repository follows the Conventional Commits standard and welcomes contributions from the community.

Upgini is an intelligent data search engine with a Python library that helps users find and add relevant features to their ML pipeline from various public, community, and premium external data sources. It automates the optimization of connected data sources by generating an optimal set of machine learning features using large language models, GraphNNs, and recurrent neural networks. The tool aims to simplify feature search and enrichment for external data to make it a standard approach in machine learning pipelines. It democratizes access to data sources for the data science community.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

ONNX Runtime Generative AI is a library that provides the generative AI loop for ONNX models, including inference with ONNX Runtime, logits processing, search and sampling, and KV cache management. Users can call a high level `generate()` method, or run each iteration of the model in a loop. It supports greedy/beam search and TopP, TopK sampling to generate token sequences, has built in logits processing like repetition penalties, and allows for easy custom scoring.

Jupyter AI connects generative AI with Jupyter notebooks. It provides a user-friendly and powerful way to explore generative AI models in notebooks and improve your productivity in JupyterLab and the Jupyter Notebook. Specifically, Jupyter AI offers: * An `%%ai` magic that turns the Jupyter notebook into a reproducible generative AI playground. This works anywhere the IPython kernel runs (JupyterLab, Jupyter Notebook, Google Colab, Kaggle, VSCode, etc.). * A native chat UI in JupyterLab that enables you to work with generative AI as a conversational assistant. * Support for a wide range of generative model providers, including AI21, Anthropic, AWS, Cohere, Gemini, Hugging Face, NVIDIA, and OpenAI. * Local model support through GPT4All, enabling use of generative AI models on consumer grade machines with ease and privacy.

Khoj is an open-source, personal AI assistant that extends your capabilities by creating always-available AI agents. You can share your notes and documents to extend your digital brain, and your AI agents have access to the internet, allowing you to incorporate real-time information. Khoj is accessible on Desktop, Emacs, Obsidian, Web, and Whatsapp, and you can share PDF, markdown, org-mode, notion files, and GitHub repositories. You'll get fast, accurate semantic search on top of your docs, and your agents can create deeply personal images and understand your speech. Khoj is self-hostable and always will be.

LangChain.dart is a Dart port of the popular LangChain Python framework created by Harrison Chase. LangChain provides a set of ready-to-use components for working with language models and a standard interface for chaining them together to formulate more advanced use cases (e.g. chatbots, Q&A with RAG, agents, summarization, extraction, etc.). The components can be grouped into a few core modules: * **Model I/O:** LangChain offers a unified API for interacting with various LLM providers (e.g. OpenAI, Google, Mistral, Ollama, etc.), allowing developers to switch between them with ease. Additionally, it provides tools for managing model inputs (prompt templates and example selectors) and parsing the resulting model outputs (output parsers). * **Retrieval:** assists in loading user data (via document loaders), transforming it (with text splitters), extracting its meaning (using embedding models), storing (in vector stores) and retrieving it (through retrievers) so that it can be used to ground the model's responses (i.e. Retrieval-Augmented Generation or RAG). * **Agents:** "bots" that leverage LLMs to make informed decisions about which available tools (such as web search, calculators, database lookup, etc.) to use to accomplish the designated task. The different components can be composed together using the LangChain Expression Language (LCEL).

Danswer is an open-source Gen-AI Chat and Unified Search tool that connects to your company's docs, apps, and people. It provides a Chat interface and plugs into any LLM of your choice. Danswer can be deployed anywhere and for any scale - on a laptop, on-premise, or to cloud. Since you own the deployment, your user data and chats are fully in your own control. Danswer is MIT licensed and designed to be modular and easily extensible. The system also comes fully ready for production usage with user authentication, role management (admin/basic users), chat persistence, and a UI for configuring Personas (AI Assistants) and their Prompts. Danswer also serves as a Unified Search across all common workplace tools such as Slack, Google Drive, Confluence, etc. By combining LLMs and team specific knowledge, Danswer becomes a subject matter expert for the team. Imagine ChatGPT if it had access to your team's unique knowledge! It enables questions such as "A customer wants feature X, is this already supported?" or "Where's the pull request for feature Y?"

Infinity is an AI-native database designed for LLM applications, providing incredibly fast full-text and vector search capabilities. It supports a wide range of data types, including vectors, full-text, and structured data, and offers a fused search feature that combines multiple embeddings and full text. Infinity is easy to use, with an intuitive Python API and a single-binary architecture that simplifies deployment. It achieves high performance, with 0.1 milliseconds query latency on million-scale vector datasets and up to 15K QPS.

ChatFAQ is an open-source comprehensive platform for creating a wide variety of chatbots: generic ones, business-trained, or even capable of redirecting requests to human operators. It includes a specialized NLP/NLG engine based on a RAG architecture and customized chat widgets, ensuring a tailored experience for users and avoiding vendor lock-in.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

AnythingLLM is a full-stack application that enables you to turn any document, resource, or piece of content into context that any LLM can use as references during chatting. This application allows you to pick and choose which LLM or Vector Database you want to use as well as supporting multi-user management and permissions.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.

Glide is a cloud-native LLM gateway that provides a unified REST API for accessing various large language models (LLMs) from different providers. It handles LLMOps tasks such as model failover, caching, key management, and more, making it easy to integrate LLMs into applications. Glide supports popular LLM providers like OpenAI, Anthropic, Azure OpenAI, AWS Bedrock (Titan), Cohere, Google Gemini, OctoML, and Ollama. It offers high availability, performance, and observability, and provides SDKs for Python and NodeJS to simplify integration.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

ONNX Runtime Generative AI is a library that provides the generative AI loop for ONNX models, including inference with ONNX Runtime, logits processing, search and sampling, and KV cache management. Users can call a high level `generate()` method, or run each iteration of the model in a loop. It supports greedy/beam search and TopP, TopK sampling to generate token sequences, has built in logits processing like repetition penalties, and allows for easy custom scoring.