validators

module wittgenstein_agent_guardrails.validators

Generic pydantic-ai output_validator factory for grounding checks.

Functions

• make_grounding_validator — Build a pydantic-ai @agent.output_validator that rejects answers produced without retrieving any grounding facts this turn.

wittgenstein_agent_guardrails.validators.make_grounding_validator

make_grounding_validator(facts_accessor: Callable[[DepsT], list], ungrounded_message: str = DEFAULT_UNGROUNDED_MESSAGE) → Callable[[Any, str], Any]

Build a pydantic-ai @agent.output_validator that rejects answers produced without retrieving any grounding facts this turn.

facts_accessor(ctx.deps) should return whatever the agent's tools accumulated during the run (e.g. knowledge-graph triplets fetched by a query tool). This is a cheap circuit breaker for "answered without looking anything up" — it does not perform entailment/NLI between the answer text and the facts, mirroring the pragmatic, narrow checks already proven in production (e.g. an NLQ chat agent's check that only verifies that a query actually ran). Raising pydantic_ai.ModelRetry is pydantic-ai's retry signal — it tells the model to try again with corrected output instead of crashing the whole agent run.

Raises

• ModelRetry