AIAgentOntologyGenerationPipeline

What it is

A pipeline that:

• Loads the latest Artificial Analysis LLM JSON dataset from a datastore folder
• Groups models into AI-agent “modules” (e.g., chatgpt, claude, llama) using name/slug/provider heuristics
• Generates TTL ontology files per agent (BFO-structured content as strings)
• Writes outputs into a timestamped datastore directory and also deploys a “current” TTL into module ontologies folders
• Inserts a small summary rdflib.Graph (file count + timestamp) into a configured triple store

Public API

Classes

• AIAgentOntologyGenerationConfiguration(PipelineConfiguration)

  • Configuration for the pipeline.
  • Fields:
    • triple_store: ITripleStoreService (required) — target triple store service used to insert(Graph)
    • datastore_path: str — output root for generated ontologies (timestamped subfolders)
    • source_datastore_path: str — input folder containing *_llms_data.json files
    • max_models_per_agent: int — cap per agent for performance

• AIAgentOntologyGenerationParameters(PipelineParameters)

  • Execution parameters.
  • Fields:
    • force_regenerate: bool — defined but not used in current implementation
    • agent_filter: Optional[List[str]] — restrict generation to specific agent modules (keys like ["chatgpt","claude"])

• AIAgentOntologyGenerationPipeline(Pipeline)

  • Main pipeline implementation.

Methods (intended for external use)

• AIAgentOntologyGenerationPipeline.run(parameters: PipelineParameters) -> rdflib.Graph

  • Runs the pipeline end-to-end.
  • Validates parameter type (AIAgentOntologyGenerationParameters).
  • Loads latest AA dataset, generates/deploys TTL files, writes a JSON summary file, inserts a summary graph into the triple store, and returns that graph.

• AIAgentOntologyGenerationPipeline.as_tools() -> list[BaseTool]

  • Exposes the pipeline as a LangChain StructuredTool named ai_agent_ontology_generation.
  • Tool calls run(AIAgentOntologyGenerationParameters(**kwargs)).

• AIAgentOntologyGenerationPipeline.as_api(...) -> None

  • Present but currently does nothing (returns None).

• AIAgentOntologyGenerationPipeline.get_configuration() -> AIAgentOntologyGenerationConfiguration

  • Returns the pipeline configuration instance.

Configuration/Dependencies

Required dependencies

• rdflib (Graph, Literal, Namespace) — graph returned/inserted, though TTL is generated as plain text files.
• naas_abi_core.pipeline — base Pipeline, PipelineConfiguration, PipelineParameters.
• naas_abi_core.services.triple_store.TripleStorePorts.ITripleStoreService
  • Must provide an insert(graph: Graph) method.
• langchain_core.tools — for as_tools() (BaseTool, StructuredTool).

Filesystem inputs/outputs

• Input: latest file matching *_llms_data.json in source_datastore_path.
• Output: under datastore_path/<UTC_TIMESTAMP>/
  • <AgentTitle>Ontology.ttl (current)
  • <UTC_TIMESTAMP>_<AgentTitle>Ontology.ttl (audit copy)
  • generation_summary_<UTC_TIMESTAMP>.json
• Deployment output: also writes <AgentTitle>Ontology.ttl into:
  • Path(__file__).parent.parent.parent / <agent_module> / "ontologies" / <AgentTitle>Ontology.ttl

Usage

from naas_abi.pipelines.AIAgentOntologyGenerationPipeline import (
    AIAgentOntologyGenerationPipeline,
    AIAgentOntologyGenerationConfiguration,
    AIAgentOntologyGenerationParameters,
)
 
# Minimal triple store stub for demonstration
class TripleStoreStub:
    def insert(self, graph):
        pass
 
pipeline = AIAgentOntologyGenerationPipeline(
    AIAgentOntologyGenerationConfiguration(
        triple_store=TripleStoreStub(),
        source_datastore_path="storage/datastore/core/modules/abi/ArtificialAnalysisWorkflow",
        datastore_path="storage/datastore/core/modules/abi/AIAgentOntologyGenerationPipeline",
        max_models_per_agent=50,
    )
)
 
graph = pipeline.run(
    AIAgentOntologyGenerationParameters(agent_filter=["chatgpt", "claude"])
)
 
print(len(graph))  # summary triples count

Caveats

• force_regenerate parameter is currently unused.
• The LangChain tool description states “datastore only, no module deployment”, but run() does deploy TTL files into module folders.
• Module deployment path is derived from __file__ by going up 3 directories; this assumes a specific repo/layout and may write files into unexpected locations depending on installation.
• Ontology TTL content is written as text and is not parsed/validated before writing.
• If no *_llms_data.json exists (or source directory missing), run() raises ValueError.