Playbook Generation via Schema-Constrained Causal Language Modeling
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Solution Overview
Problem
Existing automatic code generation models for ANSIBLE® playbooks are inadequate due to difficulties in adapting to changes in modules from one version to the next and the release of new modules, leading to repetitive and time-consuming playbook writing processes.
Innovation Solution
A method and system for generating playbooks that involve parsing a prompt and playbook context to identify modules, adding module names to the prompt for a modified search, identifying relevant documents through semantic search, determining a schema, and using a trained model for automatic completion based on the schema.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing automatic code generation models are used for playbook generation, then code generation speed is improved, but the models fail to adapt to changes in modules between versions and release of new modules
Solution Approach 1:
The system performs preliminary actions by retrieving and analyzing documentation about modules before generating the playbook. The documentation retrieval component fetches relevant module documentation in advance, and the schema determination component analyzes this documentation to establish the correct schema before code generation occurs. This ensures the generation model works with up-to-date module information rather than relying on outdated training data.
Solution Approach 2:
The patent introduces intermediary components between the code generation model and the module documentation. Specifically, a documentation retrieval component acts as an intermediary to fetch relevant module documentation, and a schema determination component serves as another intermediary that processes this documentation into a usable schema. These intermediaries enable the generation model to adapt to new modules without requiring retraining.
2Reliability
If manual playbook writing is performed, then accuracy and compliance with current modules is improved, but the process is time-consuming and repetitive
Solution Approach 1:
The system enables self-service by automatically retrieving relevant module documentation and determining the appropriate schema without requiring manual intervention. The documentation retrieval component autonomously searches for and retrieves necessary documentation, while the schema determination component automatically analyzes this documentation to establish the correct schema. This eliminates the need for manual playbook writing while maintaining high accuracy and compliance with current modules.
Solution Approach 2:
The patent replaces the mechanical manual writing process with an automated system consisting of multiple components. The documentation retrieval component automatically fetches module documentation, the schema determination component automatically analyzes and determines the correct schema, and the generation model automatically produces the playbook. This substitution of manual mechanical work with automated systems dramatically reduces time while maintaining or improving accuracy.
3Productivity
If automatic completion is generated without schema constraints, then generation speed is improved, but hallucination errors increase
Solution Approach 1:
The system changes the parameters of the generation process by introducing schema constraints that guide the generation model. The schema determination component analyzes module documentation to establish a schema with specific parameters and constraints. The generation model then uses this schema to constrain its output, ensuring that generated playbooks comply with the required format and module specifications. This parameter change from unconstrained generation to schema-constrained generation reduces hallucination errors while maintaining efficient generation speed.
Data Source
AI summary
Methods and systems for generating a playbook include parsing a prompt and playbook context to identify modules. Names of identified modules are added to the prompt to create a modified prompt. A document relevant to the playbook context and the modified prompt is identified based on a semantic search. A schema is determined based on the document. An automatic completion of the prompt is generated in accordance with the schema using a trained model.