#OpenAI

BricksLLM provides a cloud-native AI gateway for diverse LLM production scenarios, integrating with OpenAI, Anthropic, Azure OpenAI, and vLLM. It features usage limits per user, request tracking, PII protection, and reliability through failovers and retries, along with cost and rate-limit controls. Tailored for enterprises, it offers seamless LLM interactions with effective API key management and thorough analytics. Easy to deploy via Docker, it's optimized for managed services, enhancing your AI opportunities.

Utilize AI to effortlessly interact with PDF documents, allowing for queries, information extraction, and quick summaries with source references. The solution supports easy PDF uploads, enabling AI-driven conversation, and efficient data retrieval. Implement these features in under 10 lines of code, and access expanded resources for customization via code repositories and tutorials. Stay informed about updates and explore applications across different formats like PDF, CSV, and YouTube for improved functionality.

TavernAI provides a customizable frontend for chat and storywriting, supporting backends like KoboldAI, OpenAI, and others. Users can engage with characters in group chats, explore story modes, and utilize an online character database. The platform allows for personalization with adjustable themes and backgrounds. Advanced features such as message editing and GPT-4.5 picture recognition enhance interaction. Available for multiple platforms and accessible online via Google Colab, TavernAI integrates seamlessly with supported backends for an enhanced chat and storytelling experience.

The repository provides distributed implementations of neuroevolution algorithms for training deep neural networks in reinforcement learning, including Deep Genetic Algorithm and Evolution Strategies. It supports local and AWS cloud execution, and offers tools like Visual Inspector for NeuroEvolution and a GPU-optimized implementation. Tutorials and guides ensure smooth setup and experimentation, equipping researchers with effective AI development tools.

Discover a Python framework developed by CMU and UW-Madison researchers, designed for LLM applications using multi-agent collaboration. This extensible system allows problem-solving with multi-agent architecture, omitting the need for other LLM frameworks, thereby enhancing flexibility and adaptability in real-world production environments.

This tool significantly enhances how developers create Infrastructure as Code (IaC) templates, configurations, and utilities by integrating with advanced LLM providers such as OpenAI, Amazon Bedrock, and Ollama. It supports a variety of backends for diverse environment customization. With its command line interface, users can efficiently generate robust templates, optimize CI/CD pipelines, and develop policies as code. The tool is accessible through installation options like Homebrew, Docker, and Go, while supporting configurations via a TOML file. Whether it's implementing IaC for AWS, building secure deployment files, or creating efficient query utilities, this tool offers exceptional adaptability and efficiency.

This platform offers a unified chat and search interface supported by major AI providers such as Anthropic, OpenAI, Cohere, and Google Gemini. It is user-friendly, supports self-hosting, and can be deployed on platforms like Vercel and Railway. Designed with a clear interface using Next.js and Tailwind CSS, it facilitates efficient AI interactions, making it suitable for various applications without overemphasizing.

GPT Computer Assistant provides a flexible framework for developing AI assistants across macOS, Windows, and Linux. It includes LLM capabilities, customizable APIs, and seamless Python integration to ensure smooth installation with native scripts. The dynamic API facilitates activity management, making it ideal for developing advanced assistants with voice and device interaction. Check its roadmap for upcoming features enhancing continuous and engaging user interactions.

The project transforms .mdx files for use in custom OpenAI text completions, integrating Vercel and Supabase for embedding creation and PostgreSQL storage with pgvector. It facilitates runtime vector similarity search, enhancing ChatGPT frameworks. Additional tools include a writing assistant and AI encyclopedia, with straightforward development setup instructions.

The guide provides resources for Chinese users to effectively use ChatGPT, offering communication techniques, application development tools, and AI productivity solutions. Topics include dialogue techniques, top open-source projects, GPT guidelines, and AGI development, with regular updates for recent insights.

docGPT facilitates seamless communication with various document formats including PDF, DOCX, CSV, and TXT, enhancing workflow without expensive API keys or subscriptions using 'gpt4free'. Deploy it easily on platforms like Streamlit for flexible access. Leveraging LangChain's capabilities, docGPT empowers users to address complex queries, including those beyond 2020. Suitable for diverse use cases, it provides guidance on deploying personalized models such as OpenAI's gpt-3.5-turbo.

Experts.js simplifies AI agent integration with OpenAI's Assistants API and GPT-4o, providing a user-friendly experience. By offering seamless management of complex run objects and interlinking multiple AI Assistants, it enables an advanced Multi AI Agent System. Discover efficient workflow orchestration with increased AI memory while managing up to 128 tools for intricate tasks. This framework supports features such as vector store file search and asynchronous streaming, enhancing AI capabilities for developers in a practical manner.

The repository offers an extensive array of system prompts for ChatGPT and custom GPTs, serving as a valuable educational resource for prompt writing. Topics covered include acquiring system prompts, safeguarding GPT instructions, and understanding action schemas. The project emphasizes secure management of instructions and knowledge files and provides learning resources, links to GPT index tools, and guidance for contributing prompts. It is a practical guide for those aiming to deepen their grasp of GPT technology.

LiteLLM provides integration with various large language model APIs such as OpenAI, Azure, and Huggingface, using a standardized format. It delivers consistent outputs and includes features for retries and fallbacks. The platform offers budget and rate limit management, spending tracking, and provider translation support for tasks like completion, embedding, and image generation. LiteLLM features streaming, asynchronous operations, and robust logging, suitable for enterprise settings. The proxy server enhances load balancing and authorization management for efficient, secure deployments.

The Open-Sora Plan, spearheaded by Peking University and TuShare's AIGC Lab, seeks to reproduce 'Sora' with a focus on simplicity and scalability. The project harnesses Huawei Ascend AI for comprehensive training and inference, achieving video quality that meets industry standards. It encourages contributions from the open-source community to address gaps and improve the system continuously. Upcoming updates will feature multi-modal support via MindSpeed-MM and enhanced distributed training capabilities.

This comprehensive framework facilitates the development of cross-platform mobile AI applications equipped with real-time streaming for text and chat interfaces and sophisticated image services. It supports top AI models such as OpenAI's ChatGPT and others from Anthropic, Cohere, and Mistral, while integrating image models through Fal.ai. Critical features include an easy-to-use server proxy for authentication, several pre-configured themes, and ByteScale image processing. Designed for developers interested in advanced AI technologies, it provides customizable LLM and image models, both within the app and server-side, to boost development flexibility.

Codel is a fully autonomous AI agent designed to execute complex tasks within a secure Docker environment. It features a built-in browser and editor, saves tasks history in PostgreSQL, and allows self-hosting. Codel integrates seamlessly with OpenAI and Ollama models for efficient task automation, enhancing workflow management through its modern UI and comprehensive toolset.

Utilize a free API key to access GPT-4 with support for various models like GPT-3.5-turbo and Claude. This TypeScript project allows for smooth integration and deployment using options like Docker. Regular updates are provided, along with a Discord community for support. It's designed for developers aiming to incorporate advanced AI technologies into applications cost-effectively, following legal practices. Find comprehensive setup guidance for both local and cloud deployment.

AI-driven CLI tool for effortless Git commit message creation. Enhances productivity by automating message writing, supports Node.js and OpenAI API integration, with features like multiple message generation and conventional formatting. Customize easily for efficient project workflows.

Using grammar-agnostic large language models, the LIDA tool executes code to deliver accurate visualizations across different programming environments. It facilitates data summarization, goal-setting, visualization creation and editing, and infographic generation while maintaining security. Compatible with numerous LLM providers, it enhances data representation effectively.

Utilize the OP Stack for efficient document upload and accurate answer retrieval. This tool facilitates interaction with content in a user-friendly way, improving knowledge extraction using OpenAI embeddings and Pinecone database, ideal for managing extensive libraries.

This cross-platform desktop AI chat application supports Mac, Windows, and Linux while offering community support via Discord and social media updates. Check out the Noi project for enhanced AI features, and stay updated with the latest advancements from OpenAI.

Plandex offers an AI-driven coding assistant within your terminal, simplifying complex project management. It provides quick installation, self-hosting, and easy integration with OpenAI, helping developers save time on version control and routine coding tasks. With support for real-world applications, developers can swiftly build new apps, add features, and fix bugs while enjoying an intuitive and developer-friendly experience.

This page examines the evolving 'DAN' mode in ChatGPT, which pushes AI capabilities beyond standard limitations. By July 2023, the DAN 12.0 prompt proved functional with GPT-3.5, facilitating unique and uncensored AI responses while circumventing OpenAI's established guidelines. 'DAN' seeks to unlock AI's full potential through unbounded creativity, testing biases and enhancing content filtration approaches. Learn about the impact of 'DAN' mode on new AI interaction strategies.

The workshop instructs on developing a secure chat interface for document interaction utilizing OpenAI’s GPT models and retrieval augmented generation (RAG). Features include interactive chat, third-party login, secure document management, and a dynamic REST API with row-level security. It includes detailed guidance for setting up a Supabase project, either locally or in the cloud, optimizing the frontend for file uploads, and is complemented by a comprehensive YouTube tutorial.

Explore how Go developers can integrate large language models (LLMs) seamlessly using LangChain. With detailed documentation and practical examples, this project supports efficient AI solution development and experimentation with OpenAI and others. Access valuable resources and community support for innovative AI applications in Go.

This AI standard library supports seamless integration with top AI SDKs, such as LangChain, LlamaIndex, and OpenAI. It allows developers to efficiently utilize AI tools with TypeScript for diverse tasks, eliminating the need for extra glue code. The library includes specific functions for AI clients like WeatherClient, Perigon, and Serper, facilitating smooth cross-platform integration and optimizing AI application development.

Novel is an advanced Notion-style WYSIWYG editor that incorporates AI-powered autocompletions, providing a seamless content creation experience. It supports easy deployment on Vercel and allows for local setup with specific environment variables. Built on a robust tech stack including Next.js, Tiptap, and OpenAI, Novel also provides cross-framework support for Svelte and Vue, expanding its versatility for developers. Furthermore, it features a VSCode extension developed by the community. Ideal for developers looking for a customizable, extensible editor with AI enhancements for efficient content creation, the project is open-source, inviting contributions for improvements and new features, with all developments managed under the Apache-2.0 license.

This project provides a lightweight solution for integrating large language models like OpenAI ChatGPT into applications, with support for diverse JavaScript environments including Node.js, Deno, and Bun. With TypeScript support, users can utilize their own data across various LLMs like OpenAI, Anthropic, and HuggingFace. The framework allows easy setup across platforms such as Next.js and Cloudflare Workers, ensuring compatibility with serverless environments and edge runtimes. Explore core components for efficient data management and querying, enhancing LLM capabilities within your development workflow.

LLM Scraper is a versatile TypeScript library designed to facilitate the extraction of structured data from any webpage using Large Language Models (LLMs). Compatible with multiple providers such as local platforms like Ollama and remote services including OpenAI and Vercel AI SDK, it ensures robust versatility. The library integrates features like schema definitions via Zod, type safety with TypeScript, and various data formatting modes including HTML, markdown, text, and image. It operates on the Playwright framework and incorporates a new code-generation capability for reusable scripts, along with support for streaming data. A valuable tool for developers focused on efficient web scraping.

RAGs is a Streamlit app designed for configuring RAG pipelines through natural language instructions. The application draws inspiration from OpenAI’s GPTs and facilitates task description, RAG parameter configuration, and RAG agent interaction. Users can adjust parameters like system prompts, summarization settings, and retrieval methods, providing a tailored querying solution. It is compatible with various LLMs and embedding models for versatile application. The default setup employs OpenAI, but it allows exploration of alternative models for customizing and querying RAG agents, ensuring efficient data interactions supported by community involvement.

Agency is a Go-based library designed for generative AI and LLMs, focusing on simplicity and efficient coding practices. This lightweight tool enables developers to create autonomous AI systems, offering features such as OpenAI API integration and a flexible framework for custom operations. Agency addresses the challenges associated with Python-to-Go translations, leveraging Go's capabilities in static typing and performance, thus providing a native solution for AI applications without unnecessary complexity.

Explore a robust cross-platform CLI tool integrating AI chatbots into the Terminal environment without needing API keys. The tool supports various providers including Blackbox AI, DuckDuckGo, and OpenAI, enabling features like shell command execution and image generation. It is suited for developers with experimental code generation capabilities and offers installation options for Linux, MacOS, FreeBSD, and Windows. Updates and management are performed through straightforward commands, facilitating an AI-enhanced workflow aimed at boosting productivity and innovation.

RAGapp enables seamless Agentic RAG deployment in enterprise cloud environments using Docker and LlamaIndex. It supports AI models from OpenAI and Gemini, offers intuitive interfaces like Admin and Chat UIs, and plans enhanced security with access token authorization. Deploy smoothly with Docker Compose or Kubernetes and ensure effective traffic management via API Gateway integration. Reach out for support at any time.

RAG Search API by thinkany.ai offers an effective solution for sophisticated data search, leveraging OpenAI's GPT-3.5-turbo and text-embedding-ada-002 models with a Zilliz database. The setup involves setting environment variables, installing dependencies, and launching the FastAPI server. It supports complex query processing, including reranking and filtering features to refine search results according to adjustable parameters. This API is well-suited for developers seeking to enhance search efficiency.

Ell is a Bash-written CLI tool for seamless interaction with LLMs directly in the terminal. It offers features like pipe-friendly commands, terminal context integration, and interactive communication. Simple installation and support for custom templates and plugins provide flexibility for varied requirements.

Discover how LLMs like OpenAI's GPT-3 are used to implement abstract methods in Python interfaces through the llm_strategy decorator. This package facilitates the conversion of Python data using @dataclasses, refining the Strategy Pattern by interacting with LLMs for response interpretation. Key features include automated data schema extraction and reduction in code dependency. The inclusion of tools for hyperparameter optimization and meta-analysis enhances its utility in generic research settings, providing efficiency for developers aiming to integrate LLM into software engineering.

Discover Langup-ai, an adaptable AI toolset for Bilibili content creation. It features customizable bots such as live stream avatars and video comment responders, powered by OpenAI's GPT models for enhanced engagement. Installation is straightforward within Python environments, emphasizing seamless integration and real-time interactions suitable for diverse broadcasting needs. The platform simplifies credential management and supports task concurrency, ensuring effective and compliant user engagement with filtering options.

Semantic Kernel seamlessly integrates LLMs like OpenAI with programming languages such as C#. It simplifies AI plugin orchestration, enabling efficient and automated user goal fulfillment. Enterprises benefit from robust security features and adaptability. Designed for future-proofing, it supports new AI models with minimal code changes and is available for Python, Java, and more.

This Python library and CLI tool facilitates interaction with Large Language Models via remote APIs or local installations. Key features include command-line prompts, SQLite storage, and embedding generation, with support for model expansion through plugins. Comprehensive documentation ensures streamlined usage and integration.

Bingo provides a seamless interface resembling New Bing's core features, broadly compatible and deployable within domestic networks. It facilitates continuous conversations, multi-platform access, and straightforward Docker deployment. With support for voice interactions and OpenAI integration, it operates without the need for user accounts, fostering an effortless and cost-free experience. The platform allows independent hosting and includes enhancements like image generation and dark mode. Expansion plans incorporate internationalization, enhancing its global reach and versatility.

Integrate large language models with scikit-learn efficiently to enhance text analysis capabilities like zero-shot classification. Scikit-LLM provides a streamlined installation process and detailed documentation, facilitating quick adoption into existing data science workflows. Engage with the community on GitHub and Discord for collaborative feedback and explore additional projects such as Dingo and Falcon for a more comprehensive innovation approach.

Discover how GPT-4 enhances collaboration among AI agents within the GPTeam project. This open-source initiative improves agent productivity and communication through advanced AI technologies. Learn about its architecture, observe agents in action, and test world customization tools. The project also offers integrations with Discord and other platforms, increasing its application flexibility. Join the GPTeam community under the MIT license to contribute to and understand this innovative AI collaboration platform.

Utilize AWS CDK to deploy chatbots with a wide range of Large and Multimodal Language Models. This project supports experimenting with different models and configurations, facilitating easy integration with services such as Amazon Bedrock, Amazon SageMaker, and third-party providers like OpenAI and Anthropic. The framework offers a comprehensive solution for building chatbots suited for diverse applications, enhancing interaction capabilities across platforms. Explore secure messaging and AI-powered document processing through additional resources. An open-source library helps developers design AI solutions using pattern-based architecture.

llm.report offers open-source tools for logging and analytics of OpenAI ChatGPT API requests, aiding in cost analysis and prompt optimization through features like API analytics and user analytics. Suitable for self-hosted and cloud setups, it uses technologies like Next.js and Postgres. Although not actively maintained, it provides valuable insights into AI app expenditures and usage impacts.

This tool utilizes AI to produce shell commands and code capable of enhancing workflow across platforms like Linux and Windows. Featuring a simple installation process and options for using OpenAI's API or free local models, it assists with technical analyses and automates command execution via shell integration, making it ideal for developers.

Explore a curated list of tools and resources for Whisper, OpenAI's open-source speech recognition system. This organized catalog features official documentation, model variations, apps, CLI utilities, web platforms, articles, videos, and community links. Understand implementations for diverse uses, including iOS and macOS applications, web solutions, and third-party APIs, focusing on speed, speaker diarization, and accuracy advancements, all aimed at enhancing speech-to-text processes across platforms.

Coffee integrates AI into IDE environments, significantly enhancing frontend development efficiency. It supports any React project, facilitating swift UI construction and modification. Using Docker for secure code execution, its Python agent monitors JS/TS files to generate components via the OpenAI API. This tool allows developers to dynamically create and adjust components using AI-derived prompts within their current codebases, ensuring clean, maintainable output while improving standard UI component handling. Coffee provides an insight into the future of AI-driven frontend development, with a strong focus on productivity improvement.

The search2ai project links large model APIs with internet functionality, enabling effective search, news access, and content summarization. It supports platforms such as OpenAI, Gemini, and Moonshot, available in both streaming and non-streaming forms. Deployment options include Zeabur, local servers, Cloudflare Worker, and Vercel, all without the need for plugins or key alterations. Improvements focus on speed, privacy-conscious code sharing, and extensive network queries. This project fits various AI use cases while preserving features such as image and speech processing, and can be easily configured for optimal performance through environment variables.

The textbook_quality project generates high-quality, textbook-style pretraining data, offering flexibility from seed or scratch generation with OpenAI and user API connectivity. It includes robust retrieval options, such as Serply and SerpAPI, that can be customized or disabled. Its extensible framework supports environment-specific configurations and custom API adaptors, enhancing adaptability for efficient data generation.