Container Image Building With NLP-Based Base Image Selection
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Solution Overview
Problem
Existing container image building methods lack an efficient and intuitive way for users to specify their intent and select appropriate base images, often requiring extensive knowledge of container images and engines.
Innovation Solution
A method utilizing natural language processing (NLP) to process user input specifying container image characteristics, select a base image from a repository, and present prompting data to build a new container image based on user intent, employing predictive models trained with supervised learning and techniques like Word2Vec and BERT for accurate intent matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional container image building methods are used, then users can build container images, but users require extensive knowledge of container images and engines
Solution Approach 1:
The patent introduces natural language processing as an intermediary between the user and the container image building system. Users provide intent through natural language text strings, and the NLP system translates this into technical parameters and selects appropriate base images, eliminating the need for users to directly interact with complex container engine configurations
Solution Approach 2:
The system performs automatic base image selection and configuration based on user intent without requiring manual intervention. The NLP processing automatically parses user requirements, matches them with suitable base images from the repository, and configures the building process, allowing the system to serve itself rather than requiring expert user configuration
2Productivity
If users manually select base images, then they can control the container image building, but the process becomes time-consuming and inefficient
Solution Approach 1:
Base images are pre-categorized and stored in a repository with structured metadata before the container image building process begins. The NLP system can quickly query and match user intent against this pre-organized repository, eliminating the need for users to manually search and evaluate multiple base image options during the building process
Solution Approach 2:
The system provides prompting data to users that references the selected base image, allowing users to review and confirm the selection. This feedback mechanism ensures accuracy while maintaining efficiency, as the system handles the complex selection process automatically and only requires user confirmation rather than manual selection
Data Source
AI summary
Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: performing natural language processing to process a text string of a user, wherein the text string specifies characteristics of a container image to be built; processing, with use of natural language processing, instances of text-based data that describe respective ones of a plurality of container images stored within a container image repository; selecting, in dependence on a result of the performing natural language processing, and the processing, a base image from the plurality of container images; and presenting prompting data to the user that prompts building of a new container image, wherein the prompting data references the base image.