AI Specification Language Parsing

This document outlines the approach used for parsing and processing the AI Specification Language.

Motivation: The AI Specification Language aims to provide a standardized way of describing AI systems. This section describes how the language is interpreted and processed by the codebase.

Language Structure

The AI Specification Language is based on a simple, human-readable syntax that utilizes a combination of keywords, identifiers, and values to define the different elements of an AI system. This syntax allows for easy comprehension and allows for straightforward parsing.

Parsing Process

The parsing process takes the input specification file and transforms it into a structured representation suitable for further processing. This representation can be a tree-like structure or a graph, depending on the specific implementation.

Parsing Tools and Libraries

The parsing process is implemented using various tools and libraries. Some commonly used tools include:

• ANTLR: ANTLR (ANother Tool for Language Recognition) is a powerful parser generator that allows users to define the grammar of a language and automatically generate a parser for that language.
  • https://github.com/antlr/antlr4
• PLY (Python Lex-Yacc): PLY is a Python implementation of the Lex and Yacc parsing tools. It provides a convenient way to define and build parsers for Python applications.
  • https://www.dabeaz.com/ply/
• Jison (JavaScript Interpreter of Subsets of JSON): Jison is a parser generator for JavaScript, based on the Bison parser generator. It provides a way to define and implement grammars for JavaScript applications.
  • https://zaach.github.io/jison/

Example:

# Example Specification
          model: "my_model"
          type: "classification"
          data:
              training:
                  source: "my_data.csv"
                  format: "csv"
              evaluation:
                  source: "my_test.csv"
                  format: "csv"
          

This specification defines a classification model named “my_model” that uses the “my_data.csv” dataset for training and “my_test.csv” for evaluation.

Implementation Details

Specific implementation details might vary based on the chosen tools and the desired level of abstraction. However, the core concepts of parsing and representation remain consistent across implementations.

Note: This is a general overview of the parsing process. Specific implementations may have additional steps or functionalities. For detailed information on the implementation of the parser for the AI Specification Language, refer to the source code and documentation of the specific implementation.