LLMs, vectors, classifiers and pre-trained models

In the fast-paced world of artificial intelligence, Kubiya is at the forefront of groundbreaking technology, constantly seeking innovative ways to improve user experiences and drive operational efficiency. The extensive use of Large Language Models (LLMs) and an array of cutting-edge AI components that empower our DevOps Assistant like never before is central to our success. In this article, we will be unraveling the intricate web of LLMs and shedding light on the various AI components we use to create a truly conversational and transformative experience.

In this document you can read about:

• Classifiers
• Embeddings
• Langchain and other LLMs
• Trained models
• Summary

Classifiers

At Kubiya, we use LLMs that have been trained for text classification to define labels that represent various actions and queries. These user-defined labels serve as classifications, allowing our DevOps Assistant to perform a wide range of tasks, from executing actions and creating components to modifying system resources. We also use LLMs in question and answer (Q&A) scenarios, where users can interact naturally and receive insightful responses based on the context of their queries.

Embeddings

To efficiently handle a variety of user actions, we use embedding models, which convert unstructured textual data into vectors. These vector representations enable us to store and organize user actions in a vector database. This powerful database allows us to determine the best action to take based on user input. Whether it's a causal or similarity query, our vector database enables the DevOps Assistant to provide precise and contextually relevant responses.

Langchain and other Large Language Models

Kubiya supports industry-leading Large Language frameworks like Langchain and Microsoft Guidance. These frameworks serve as the foundation of our AI applications, providing open interfaces for effectively managing and manipulating large language models. We ensure that our AI applications are scalable, robust, and tailored to our users' specific needs by leveraging these powerful frameworks. It enables us to optimize our development process, allowing us to create a seamless pipeline that perfectly aligns with your specific use cases.

Fine-tuning for Precision and trained models

In our never-ending pursuit of excellence, Kubiya employs fine-tuning techniques to improve existing language models, ensuring they are optimized for specific use cases. When querying cloud resources, for example, we use a custom-built SQL-based query engine. We can refine pre-trained models to understand SQL queries, effectively transforming them into API calls to various cloud vendors. This approach ensures that our DevOps Assistant can execute queries efficiently, streamlining cloud management for our users.

Summary

A sophisticated and dynamic system of Large Language Models (LLMs) and AI components underpins Kubiya's unwavering pursuit of excellence and innovation. We deliver a fully conversational experience that boosts productivity and streamlines operations by leveraging LLMs for text classification and embeddings for transforming text into vectors. Furthermore, by integrating industry-leading LM frameworks and fine-tuning models for specific use cases, we ensure that our DevOps Assistant remains at the forefront of AI innovation. As the AI landscape evolves, we at Kubiya are dedicated to pushing the limits of possibility, providing users with unparalleled efficiency, and driving the future of conversational AI.

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