Alumnium Integrations

OpenAI’s mission is to ensure that artificial general intelligence (AGI)—by which we mean highly autonomous systems that outperform humans at most economically valuable work—benefits all of humanity. We will attempt to directly build safe and beneficial AGI, but will also consider our mission fulfilled if our work aids others to achieve this outcome. Apply our API to any language task — semantic search, summarization, sentiment analysis, content generation, translation, and more — with only a few examples or by specifying your task in English. One simple integration gives you access to our constantly-improving AI technology. Explore how you integrate with the API with these sample completions.

Gemini is Google's advanced AI chatbot designed to enhance creativity and productivity by engaging in natural language conversations. Accessible via the web and mobile apps, Gemini integrates seamlessly with various Google services, including Docs, Drive, and Gmail, enabling users to draft content, summarize information, and manage tasks efficiently. Its multimodal capabilities allow it to process and generate diverse data types, such as text, images, and audio, providing comprehensive assistance across different contexts. As a continuously learning model, Gemini adapts to user interactions, offering personalized and context-aware responses to meet a wide range of user needs.

The core of extensible programming is defining functions. Python allows mandatory and optional arguments, keyword arguments, and even arbitrary argument lists. Whether you're new to programming or an experienced developer, it's easy to learn and use Python. Python can be easy to pick up whether you're a first-time programmer or you're experienced with other languages. The following pages are a useful first step to get on your way to writing programs with Python! The community hosts conferences and meetups to collaborate on code, and much more. Python's documentation will help you along the way, and the mailing lists will keep you in touch. The Python Package Index (PyPI) hosts thousands of third-party modules for Python. Both Python's standard library and the community-contributed modules allow for endless possibilities.

Selenium automates browsers. That's it! What you do with that power is entirely up to you. Primarily it is for automating web applications for testing purposes, but is certainly not limited to just that. Boring web-based administration tasks can (and should) also be automated as well. If you want to create robust, browser-based regression automation suites and tests, scale and distribute scripts across many environments, then you want to use Selenium WebDriver, a collection of language specific bindings to drive a browser - the way it is meant to be driven. If you want to create quick bug reproduction scripts, create scripts to aid in automation-aided exploratory testing, then you want to use Selenium IDE; a Chrome and Firefox add-on that will do simple record-and-playback of interactions with the browser. If you want to scale by distributing and running tests on several machines and manage multiple environments from a central point.

DeepSeek is a cutting-edge AI assistant powered by the advanced DeepSeek-V3 model, featuring over 600 billion parameters for exceptional performance. Designed to compete with top global AI systems, it offers fast responses and a wide range of features to make everyday tasks easier and more efficient. Available across multiple platforms, including iOS, Android, and the web, DeepSeek ensures accessibility for users everywhere. The app supports multiple languages and has been continually updated to improve functionality, add new language options, and resolve issues. With its seamless performance and versatility, DeepSeek has garnered positive feedback from users worldwide.

Mistral AI is a pioneering artificial intelligence startup specializing in open-source generative AI. The company offers a range of customizable, enterprise-grade AI solutions deployable across various platforms, including on-premises, cloud, edge, and devices. Flagship products include "Le Chat," a multilingual AI assistant designed to enhance productivity in both personal and professional contexts, and "La Plateforme," a developer platform that enables the creation and deployment of AI-powered applications. Committed to transparency and innovation, Mistral AI positions itself as a leading independent AI lab, contributing significantly to open-source AI and policy development.

Claude is an artificial intelligence large language model that can process and generate human-like text. Anthropic is an AI safety and research company that’s working to build reliable, interpretable, and steerable AI systems. Large, general systems of today can have significant benefits, but can also be unpredictable, unreliable, and opaque: our goal is to make progress on these issues. For now, we’re primarily focused on research towards these goals; down the road, we foresee many opportunities for our work to create value commercially and for public benefit.

Playwright supports all modern rendering engines including Chromium, WebKit, and Firefox. Test on Windows, Linux, and macOS, locally or on CI, headless or headed. Playwright waits for elements to be actionable prior to performing actions. It also has a rich set of introspection events. The combination of the two eliminates the need for artificial timeouts - the primary cause of flaky tests. Playwright assertions are created specifically for the dynamic web. Checks are automatically retried until the necessary conditions are met. Configure test retry strategy, capture execution trace, videos, screenshots to eliminate flakes. Browsers run web content belonging to different origins in different processes. Playwright is aligned with the modern browsers architecture and runs tests out-of-process. This makes Playwright free of the typical in-process test runner limitations.

Llama (Large Language Model Meta AI) is a state-of-the-art foundational large language model designed to help researchers advance their work in this subfield of AI. Smaller, more performant models such as Llama enable others in the research community who don’t have access to large amounts of infrastructure to study these models, further democratizing access in this important, fast-changing field. Training smaller foundation models like Llama is desirable in the large language model space because it requires far less computing power and resources to test new approaches, validate others’ work, and explore new use cases. Foundation models train on a large set of unlabeled data, which makes them ideal for fine-tuning for a variety of tasks. We are making Llama available at several sizes (7B, 13B, 33B, and 65B parameters) and also sharing a Llama model card that details how we built the model in keeping with our approach to Responsible AI practices.