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ontocast.onto.run_manifest

Per-document record of what a batch run cost and how it was configured.

The pipeline already computes all of this -- BudgetTracker accumulates it and the HTTP path returns it in ProcessResultMetadata -- but a ontocast process run logged it at INFO and then dropped it, so a finished batch left its TTL output with no record of the model, the settings, or the tokens that produced it. Written beside the facts dump, one file per document.

RunManifest

Bases: BaseModel

What produced one document's dump, and what it cost.

Source code in ontocast/onto/run_manifest.py 
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class RunManifest(BaseModel):
    """What produced one document's dump, and what it cost."""

    source: str = Field(description="Input file name.")
    line_number: int | None = Field(
        default=None, description="1-based line, for JSONL inputs."
    )
    ontocast_version: str
    render_mode: str
    current_domain: str
    doc_iri: str | None = None
    tenant: str | None = None
    project: str | None = None
    llm: RunManifestLLM
    budget: BudgetTracker
    ontology_triples: int = 0
    facts_triples: int = 0
    retrieval_metrics: dict[str, Any] = Field(
        default_factory=dict,
        description=(
            "``AgentState.retrieval_metrics`` for this document -- the same "
            "payload ``/process`` returns in ``ProcessResultMetadata``. Without "
            "it a batch run carried no retrieval telemetry at all, which also "
            "left ONTOLOGY_PATCH_DUMP_ONTOLOGY_RANKS with no reader outside the "
            "HTTP path. Keys are enumerated by "
            ":class:`~ontocast.onto.enum.RetrievalMetric`."
        ),
    )

RunManifestLLM

Bases: BaseModel

The provider settings that shaped the output.

Mirrors the discriminators :func:ontocast.tool.llm.llm_cache_config puts in the cache key, so two dumps whose manifests agree here were produced by the same model under the same generation settings.

Source code in ontocast/onto/run_manifest.py 
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class RunManifestLLM(BaseModel):
    """The provider settings that shaped the output.

    Mirrors the discriminators :func:`ontocast.tool.llm.llm_cache_config` puts
    in the cache key, so two dumps whose manifests agree here were produced by
    the same model under the same generation settings.
    """

    provider: str
    model_name: str
    temperature: float | None = None
    think: bool | None = None
    num_ctx: int | None = None
    num_predict: int | None = None