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GNN file discovery, parsing, and multi-format serialization. Use when reading GNN model files, parsing StateSpaceBlock definitions, extracting connections, validating GNN syntax, or converting between GNN formats.
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基于 SOC 职业分类
| name | gnn-parsing |
| description | GNN file discovery, parsing, and multi-format serialization. Use when reading GNN model files, parsing StateSpaceBlock definitions, extracting connections, validating GNN syntax, or converting between GNN formats. |
Discovers, parses, and validates GNN model files. Extracts structured data from Markdown-based GNN specifications including state spaces, connections, parameterizations, and ontology annotations.
# Parse all GNN files in a directory
python src/3_gnn.py --target-dir input/gnn_files --output-dir output --verbose
# As part of pipeline
python src/main.py --only-steps 3 --verbose
| Section | Purpose | Example |
|---|---|---|
GNNSection | Model type declaration | ActInfPOMDP |
ModelName | Model identifier | T-Maze Agent |
StateSpaceBlock | Matrix/vector definitions | A[3,3,type=float] |
Connections | Directed (>) and undirected (-) edges | D>s, s-A |
InitialParameterization | Default values | A={(0.9,0.05,0.05)} |
ActInfOntologyAnnotation | Semantic labels | A=LikelihoodMatrix |
import logging
from pathlib import Path
from gnn import (
discover_gnn_files,
parse_gnn_file,
process_gnn_directory,
process_gnn_multi_format,
validate_gnn,
)
from gnn import GNNFormalParser
logger = logging.getLogger(__name__)
# Discover GNN files in a directory
files = discover_gnn_files("input/gnn_files/")
# Parse a single file (lightweight dict-style summary)
model = parse_gnn_file("input/gnn_files/my_model.md")
# Process entire directory (used by pipeline)
results = process_gnn_directory("input/gnn_files/", "output/")
# Multi-format processing (requires a Logger — same contract as 3_gnn.py)
process_gnn_multi_format(
Path("input/gnn_files"),
Path("output"),
logger,
verbose=True,
)
# Formal parsing and validation
is_valid, errors = validate_gnn(content_string)
discover_gnn_files — find .md GNN files in a directory treeparse_gnn_file — parse a single GNN file into structured dataprocess_gnn_directory — process all files in a directoryprocess_gnn_multi_format — full multi-format serialization (needs logging.Logger)validate_gnn_structure — structural validation of a parsed modelvalidate_gnn — content-level syntax validationGNNParsingSystem, GNNFormat — registry-backed multi-format I/OGNNFormalParser, ParsedGNN, ParsedGNNFormal — formal parser typesParsed models are consumed by downstream steps:
This module registers tools with the GNN MCP server (see mcp.py):
get_gnn_documentationget_gnn_module_infoget_gnn_schema_infoparse_gnn_contentprocess_gnn_directoryrun_round_trip_testsvalidate_cross_format_consistency_contentvalidate_directory_cross_format_consistencyvalidate_gnn_contentvalidate_schema_definitions_consistencyGeneralized Notation Notation (GNN) processing pipeline for Active Inference generative models. Use when working with GNN files, running the 25-step pipeline, parsing model specifications, generating simulations, or producing visualizations and reports from GNN model definitions.
GNN command-line interface dispatch and health checks. Use when invoking the `gnn` CLI, validating subcommand availability, or checking command-line access to pipeline modules.
GNN LLM-enhanced analysis and model interpretation. Use when generating natural language descriptions of GNN models, getting AI-assisted model explanations, or performing LLM-powered analysis of Active Inference specifications.
GNN Language Server Protocol integration. Use when starting editor diagnostics, checking hover behavior, or validating LSP support for GNN files.
GNN Model Context Protocol processing and tool registration. Use when registering GNN operations as MCP tools, building MCP server configurations, or integrating GNN capabilities with LLM tool-use workflows.
GNN pipeline orchestration and configuration management. Use when configuring pipeline execution, setting step inclusion/exclusion, managing pipeline state, or customizing the 25-step execution flow.