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Description
Phoenix serves as a comprehensive open-source observability toolkit tailored for experimentation, evaluation, and troubleshooting purposes. It empowers AI engineers and data scientists to swiftly visualize their datasets, assess performance metrics, identify problems, and export relevant data for enhancements. Developed by Arize AI, the creators of a leading AI observability platform, alongside a dedicated group of core contributors, Phoenix is compatible with OpenTelemetry and OpenInference instrumentation standards. The primary package is known as arize-phoenix, and several auxiliary packages cater to specialized applications. Furthermore, our semantic layer enhances LLM telemetry within OpenTelemetry, facilitating the automatic instrumentation of widely-used packages. This versatile library supports tracing for AI applications, allowing for both manual instrumentation and seamless integrations with tools like LlamaIndex, Langchain, and OpenAI. By employing LLM tracing, Phoenix meticulously logs the routes taken by requests as they navigate through various stages or components of an LLM application, thus providing a clearer understanding of system performance and potential bottlenecks. Ultimately, Phoenix aims to streamline the development process, enabling users to maximize the efficiency and reliability of their AI solutions.
Description
Ragas is a comprehensive open-source framework aimed at testing and evaluating applications that utilize Large Language Models (LLMs). It provides automated metrics to gauge performance and resilience, along with the capability to generate synthetic test data that meets specific needs, ensuring quality during both development and production phases. Furthermore, Ragas is designed to integrate smoothly with existing technology stacks, offering valuable insights to enhance the effectiveness of LLM applications. The project is driven by a dedicated team that combines advanced research with practical engineering strategies to support innovators in transforming the landscape of LLM applications. Users can create high-quality, diverse evaluation datasets that are tailored to their specific requirements, allowing for an effective assessment of their LLM applications in real-world scenarios. This approach not only fosters quality assurance but also enables the continuous improvement of applications through insightful feedback and automatic performance metrics that clarify the robustness and efficiency of the models. Additionally, Ragas stands as a vital resource for developers seeking to elevate their LLM projects to new heights.
API Access
Has API
API Access
Has API
Integrations
Codestral
Codestral Mamba
LangChain
Le Chat
Mathstral
Ministral 3B
Ministral 8B
Mistral 7B
Mistral AI
Mistral Large
Integrations
Codestral
Codestral Mamba
LangChain
Le Chat
Mathstral
Ministral 3B
Ministral 8B
Mistral 7B
Mistral AI
Mistral Large
Pricing Details
Free
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Arize AI
Country
United States
Website
docs.arize.com/phoenix
Vendor Details
Company Name
Ragas
Country
United States
Website
www.ragas.io