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What is RagMetrics?

RagMetrics is a comprehensive platform designed to evaluate and instill trust in conversational GenAI, specifically focusing on assessing the capabilities of AI chatbots, agents, and retrieval-augmented generation (RAG) systems before and after deployment. By providing continuous evaluations of AI-generated interactions, it emphasizes critical aspects such as precision, relevance, the frequency of hallucinations, the quality of reasoning, and the performance of tools used in genuine conversations. The system integrates effortlessly with existing AI frameworks, allowing for the monitoring of live dialogues while maintaining a seamless user experience. Equipped with features like automated scoring, customizable evaluation criteria, and thorough diagnostics, it elucidates the underlying causes of any shortcomings in AI responses and offers pathways for enhancement. Users can also perform offline assessments, conduct A/B testing, and engage in regression testing, all while tracking performance trends in real-time via detailed dashboards and alerts. RagMetrics is adaptable, functioning independently of specific models or deployment methods, which enables it to work with various language models, retrieval systems, and agent architectures. This flexibility guarantees that teams can depend on RagMetrics to improve the efficacy of their conversational AI applications in a multitude of settings, ultimately fostering greater trust and reliance on AI technologies. Furthermore, it empowers organizations to make informed decisions based on accurate data about their AI systems' performance.

What is Arize Phoenix?

Phoenix is an open-source library designed to improve observability for experimentation, evaluation, and troubleshooting. It enables AI engineers and data scientists to quickly visualize information, evaluate performance, pinpoint problems, and export data for further development. Created by Arize AI, the team behind a prominent AI observability platform, along with a committed group of core contributors, Phoenix integrates effortlessly with OpenTelemetry and OpenInference instrumentation. The main package for Phoenix is called arize-phoenix, which includes a variety of helper packages customized for different requirements. Our semantic layer is crafted to incorporate LLM telemetry within OpenTelemetry, enabling the automatic instrumentation of commonly used packages. This versatile library facilitates tracing for AI applications, providing options for both manual instrumentation and seamless integration with platforms like LlamaIndex, Langchain, and OpenAI. LLM tracing offers a detailed overview of the pathways traversed by requests as they move through the various stages or components of an LLM application, ensuring thorough observability. This functionality is vital for refining AI workflows, boosting efficiency, and ultimately elevating overall system performance while empowering teams to make data-driven decisions.

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Integrations Supported

Integrations Supported

APIFuzzer
Amazon Bedrock
Arize AI
Codestral
Conda
CrewAI
Databricks
Guardrails AI
JavaScript
JupyterLab
LangChain
LlamaIndex
Mathstral
Ministral 8B
Mistral AI
Mistral Large
Mixtral 8x7B
OpenAI
OpenTelemetry
Slack

API Availability

API Availability

Has API

Pricing Information

$20/month
Free Trial Offered?

Pricing Information

Free
Free Version

Supported Platforms

SaaS
On-Prem

Supported Platforms

SaaS

Customer Service / Support

Standard Support
Web-Based Support

Customer Service / Support

24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars

Training Options

Documentation Hub

Company Facts

Organization Name

RagMetrics

Date Founded

2024

Company Location

United States

Company Website

ragmetrics.ai/

Company Facts

Organization Name

Arize AI

Company Location

United States

Company Website

docs.arize.com/phoenix

Categories and Features

AI Observability

Not specified

LLM Evaluation

Not specified

Categories and Features

AI Observability

Not specified

LLM Evaluation

Not specified

Semantic Layer

Not specified

Telemetry

Not specified

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