Athina AI Integrations

Mistral AI is an advanced artificial intelligence company focused on open-source generative AI solutions. Offering adaptable, enterprise-level AI tools, the company enables deployment across cloud, on-premises, edge, and device-based environments. Key offerings include "Le Chat," a multilingual AI assistant designed for enhanced efficiency in both professional and personal settings, and "La Plateforme," a development platform for building and integrating AI-powered applications. With a strong emphasis on transparency and innovation, Mistral AI continues to drive progress in open-source AI and contribute to shaping AI policy.

OpenAI's mission, which is to ensure artificial general intelligence (AGI), benefits all people. This refers to highly autonomous systems that outperform humans in most economically valuable work. While we will try to build safe and useful AGI, we will also consider our mission accomplished if others are able to do the same. Our API can be used to perform any language task, including summarization, sentiment analysis and content generation. You can specify your task in English or use a few examples. Our constantly improving AI technology is available to you with a simple integration. These sample completions will show you how to integrate with the API.

Definitive functions are the heart of extensible programming. Python supports keyword arguments, mandatory and optional arguments, as well as arbitrary argument lists. It doesn't matter if you are a beginner or an expert programmer, Python is easy to learn. Python is easy to learn, whether you are a beginner or an expert in other languages. These pages can be a helpful starting point to learn Python programming. The community hosts meetups and conferences to share code and much more. The documentation for Python will be helpful and the mailing lists will keep in touch. The Python Package Index (PyPI), hosts thousands of third-party Python modules. Both Python's standard library and the community-contributed modules allow for endless possibilities.

Claude is an artificial intelligence language model that can generate text with human-like processing. Anthropic is an AI safety company and research firm that focuses on building reliable, interpretable and steerable AI systems. While large, general systems can provide significant benefits, they can also be unpredictable, unreliable and opaque. Our goal is to make progress in these areas. We are currently focusing on research to achieve these goals. However, we see many opportunities for our work in the future to create value both commercially and for the public good.

AWS offers a wide range of services, including database storage, compute power, content delivery, and other functionality. This allows you to build complex applications with greater flexibility, scalability, and reliability. Amazon Web Services (AWS), the world's largest and most widely used cloud platform, offers over 175 fully featured services from more than 150 data centers worldwide. AWS is used by millions of customers, including the fastest-growing startups, large enterprises, and top government agencies, to reduce costs, be more agile, and innovate faster. AWS offers more services and features than any other cloud provider, including infrastructure technologies such as storage and databases, and emerging technologies such as machine learning, artificial intelligence, data lakes, analytics, and the Internet of Things. It is now easier, cheaper, and faster to move your existing apps to the cloud.

Google Cloud is an online service that lets you create everything from simple websites to complex apps for businesses of any size. Customers who are new to the system will receive $300 in credits for testing, deploying, and running workloads. Customers can use up to 25+ products free of charge. Use Google's core data analytics and machine learning. All enterprises can use it. It is secure and fully featured. Use big data to build better products and find answers faster. You can grow from prototypes to production and even to planet-scale without worrying about reliability, capacity or performance. Virtual machines with proven performance/price advantages, to a fully-managed app development platform. High performance, scalable, resilient object storage and databases. Google's private fibre network offers the latest software-defined networking solutions. Fully managed data warehousing and data exploration, Hadoop/Spark and messaging.

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection. Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.

Microsoft Azure is a cloud computing platform that allows you to quickly develop, test and manage applications. Azure. Invent with purpose. With more than 100 services, you can turn ideas into solutions. Microsoft continues to innovate to support your development today and your product visions tomorrow. Open source and support for all languages, frameworks and languages allow you to build what you want and deploy wherever you want. We can meet you at the edge, on-premises, or in the cloud. Services for hybrid cloud enable you to integrate and manage your environments. Secure your environment from the ground up with proactive compliance and support from experts. This is a trusted service for startups, governments, and enterprises. With the numbers to prove it, the cloud you can trust.

Cohere is an AI company that provides advanced language models designed to help businesses and developers create intelligent text-based applications. Their models support tasks like text generation, summarization, and semantic search, with options such as the Command family for high-performance applications and Aya Expanse for multilingual capabilities across 23 languages. Cohere emphasizes flexibility and security, offering deployment on cloud platforms, private environments, and on-premises systems. The company partners with major enterprises like Oracle and Salesforce to enhance automation and customer interactions through generative AI. Additionally, its research division, Cohere For AI, contributes to machine learning innovation by fostering global collaboration and open-source advancements.

Mistral 7B is a cutting-edge 7.3-billion-parameter language model designed to deliver superior performance, surpassing larger models like Llama 2 13B on multiple benchmarks. It leverages Grouped-Query Attention (GQA) for optimized inference speed and Sliding Window Attention (SWA) to effectively process longer text sequences. Released under the Apache 2.0 license, Mistral 7B is openly available for deployment across a wide range of environments, from local systems to major cloud platforms. Additionally, its fine-tuned variant, Mistral 7B Instruct, excels in instruction-following tasks, outperforming models such as Llama 2 13B Chat in guided responses and AI-assisted applications.

Mistral Large is a state-of-the-art language model developed by Mistral AI, designed for advanced text generation, multilingual reasoning, and complex problem-solving. Supporting multiple languages, including English, French, Spanish, German, and Italian, it provides deep linguistic understanding and cultural awareness. With an extensive 32,000-token context window, the model can process and retain information from long documents with exceptional accuracy. Its strong instruction-following capabilities and native function-calling support make it an ideal choice for AI-driven applications and system integrations. Available via Mistral’s platform, Azure AI Studio, and Azure Machine Learning, it can also be self-hosted for privacy-sensitive use cases. Benchmark results position Mistral Large as one of the top-performing models accessible through an API, second only to GPT-4.

Ragas is a framework that allows you to test and evaluate applications that use the Large Language Model. It provides automatic metrics for assessing performance and robustness. Synthetic test data is generated according to specific requirements. Workflows are also available to ensure quality in development and production monitoring. Ragas integrates seamlessly into existing stacks and provides insights to enhance LLM application. The platform is maintained and developed by a passionate team of individuals who use cutting-edge engineering practices and cutting-edge research to empower visionaries to redefine LLM possibilities. Synthesize high-quality, diverse evaluation data tailored to your needs. Evaluation and quality assurance of your LLM application during production. Use insights to improve the application. Automatic metrics to help you understand performance and robustness of the LLM application.

Llama 3.3, the latest in the Llama language model series, was developed to push the limits of AI-powered communication and understanding. Llama 3.3, with its enhanced contextual reasoning, improved generation of language, and advanced fine tuning capabilities, is designed to deliver highly accurate responses across diverse applications. This version has a larger dataset for training, refined algorithms to improve nuanced understanding, and reduced biases as compared to previous versions. Llama 3.3 excels at tasks such as multilingual communication, technical explanations, creative writing and natural language understanding. It is an indispensable tool for researchers, developers and businesses. Its modular architecture enables customization in specialized domains and ensures performance at scale.

Amazon Bedrock is a managed AWS service designed to make building and scaling generative AI applications easier by providing access to a diverse range of foundation models (FMs) from leading providers such as AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon itself. Through a single API, developers can test, fine-tune, and customize these models to meet specific use cases using advanced techniques like Retrieval Augmented Generation (RAG). The platform allows for the creation of intelligent agents that seamlessly integrate with enterprise systems and data sources, enabling enhanced automation and decision-making. Bedrock’s serverless architecture removes the need for infrastructure management, ensuring high scalability and minimal operational complexity. With a focus on security, data privacy, and responsible AI, Amazon Bedrock empowers organizations to accelerate innovation while maintaining trust and compliance. It represents a powerful tool for businesses aiming to integrate cutting-edge AI solutions into their operations effortlessly.

Our dashboard allows you to dig deeper into analytics, allowing you to verify the information you need to enter requests into Guardrails. Our library of ready-to-use validators will help you unlock efficiency. Validation for diverse use cases can optimize your workflow. Boost your projects by leveraging a dynamic framework that allows you to create, manage, and reuse custom validators. The versatility of the software is matched by its ease of use, allowing it to be used for a wide range innovative applications. You can quickly generate another output option by indicating the error and verifying it. Assures that the outcomes are in line, with expectations, accuracy, correctness, reliability, and interactions with LLMs.

Llama (Large Language Model meta AI) is a state of the art foundational large language model that was created to aid researchers in this subfield. Llama allows researchers to use smaller, more efficient models to study these models. This further democratizes access to this rapidly-changing field. Because it takes far less computing power and resources than large language models, such as Llama, to test new approaches, validate other's work, and explore new uses, training smaller foundation models like Llama can be a desirable option. Foundation models are trained on large amounts of unlabeled data. This makes them perfect for fine-tuning for many tasks. We make Llama available in several sizes (7B-13B, 33B and 65B parameters), and also share a Llama card that explains how the model was built in line with our Responsible AI practices.