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Revolutionary Tool Launches to Make AI Language Models Safe – Meet LangKit by WhyLabs!

Revolutionary Tool Launches to Make AI Language Models Safe – Meet LangKit by WhyLabs!

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Introducing LangKit: An Open-Source Technology for Monitoring Large Language Models

WhyLabs, a startup that provides monitoring tools for AI and data applications, has released LangKit, an open source technology that helps enterprises monitor and safeguard their large language models (LLMs). Large language models, such as GPT-3, are AI models that have been trained on extremely large quantities of text data to generate human-like text. LangKit enables users to detect and prevent risks and issues in LLMs such as toxic language, data leakage, hallucinations, and jailbreaks.

WhyLabs cofounder and CEO Alessya Visnjic shares with VentureBeat that the product is designed to help enterprises monitor how their AI systems are functioning and catch problems before they affect customers or users. “LangKit is a culmination of the types of metrics that are critical to monitor for LLM models,” she said. “Essentially, what we have done is we’ve taken this wide range of popular metrics that our customers have been using to monitor LLMs, and we packaged them into LangKit.”

Meeting Rapidly Evolving LLM Standards

LangKit is built on the foundation of two core principles: open sourcing and extensibility. Visnjic believes that by leveraging the open-source community and creating a highly extensible platform, WhyLabs can keep pace with the evolving AI landscape and accommodate diverse customer needs, particularly in industries such as healthcare and fintech, which have higher safety standards.

Features and Metrics Provided by LangKit

LangKit provides a range of metrics, including sentiment analysis, toxicity detection, topic extraction, text quality assessment, personally identifiable information (PII) detection, and jailbreak detection. These metrics can help users:

  • Validate and safeguard individual prompts and responses
  • Evaluate the compliance of the LLM behavior with policy
  • Monitor user interactions inside an LLM-powered application
  • A/B test across different LLM and prompt versions

Ease of Use and Integration

LangKit is relatively easy to use and integrates with several popular platforms and frameworks, such as OpenAI GPT-4, Hugging Face Transformers, AWS Boto3, and more. Users can get started with just a few lines of Python code and leverage the platform to track the metrics over time and set up alerts and guardrails. Users can also customize and extend LangKit with their own models and metrics to suit their specific use cases.

An Emerging Market for AI Monitoring

Visnjic said that LangKit is based on the feedback and collaboration of WhyLabs’ customers, who range from Fortune 100 companies to AI-first startups in various industries. She said that LangKit helps them gain visibility and control over their LLMs in production.

Early adopters of LangKit include Symbl.AI and a company focused on helping enterprises adopt large language models, Tryolabs, both of whom have provided valuable feedback to refine the product. 

Model Monitoring Built for Enterprises

LangKit is specifically designed to handle high-throughput, real-time, and automated systems that require a wide range of metrics and alerts to track LLM behavior and performance. Unlike the embedding-based approach that is commonly used for LLM monitoring and evaluation, LangKit uses a metrics-based approach that is more suitable for scalable and operational use cases. LangKit will be integrated into WhyLabs’ AI observability platform, which also offers solutions for monitoring other types of AI applications, such as embeddings, model performance, and unstructured data drift.

Availability and Access

LangKit is available today as an open-source library on GitHub and as a SaaS solution on WhyLabs’ website. Users can also check out a demo notebook and an overview video to learn more about LangKit’s features and capabilities.

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FAQ

What is LangKit?

LangKit is an open-source technology that helps enterprises monitor and safeguard their large language models (LLMs).

What kind of metrics and features does LangKit provide?

LangKit provides a range of metrics, including sentiment analysis, toxicity detection, topic extraction, text quality assessment, personally identifiable information (PII) detection, and jailbreak detection. These metrics can help users validate and safeguard individual prompts and responses, evaluate the compliance of the LLM behavior with policy, monitor user interactions inside an LLM-powered application, and A/B test across different LLM and prompt versions. LangKit is relatively easy to use and integrates with several popular platforms and frameworks, such as OpenAI GPT-4, Hugging Face Transformers, AWS Boto3, and more. Users can also customize and extend LangKit with their own models and metrics to suit their specific use cases.

Why was LangKit created?

LangKit was created to help enterprises monitor how their AI systems are functioning and catch problems before they affect customers or users. Large language models, such as GPT-3, are AI models that have been trained on extremely large quantities of text data to generate human-like text. With LangKit, users can detect and prevent risks and issues in LLMs such as toxic language, data leakage, hallucinations, and jailbreaks.

What industries can benefit from LangKit?

Industries such as healthcare and fintech, which have higher safety standards, can benefit from LangKit. However, WhyLabs’ customers range from Fortune 100 companies to AI-first startups in various industries.

How does LangKit keep pace with the evolving AI landscape?

LangKit is built on the foundation of two core principles: open sourcing and extensibility. By leveraging the open-source community and creating a highly extensible platform, WhyLabs can keep pace with the evolving AI landscape and accommodate diverse customer needs.

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