Inference

The main entry point for hallucination detection.

HallucinationDetector(method='transformer', **kwargs)

Facade class that delegates to a concrete detector chosen by method.

Parameters:

Name	Type	Description	Default
method	str	"transformer" (token-classifier) or "llm" (OpenAI function-calling).	'transformer'
kwargs		Passed straight through to the chosen detector's constructor.	{}

Initialize the detector.

Parameters:

Name	Type	Description	Default
method	str	Detection method to use.	'transformer'
kwargs		Passed to the detector constructor.	{}

predict(context, answer, question=None, output_format='tokens')

Predict hallucination tokens or spans given passages and an answer.

This is the call most RAG pipelines use.

See the concrete detector docs for the structure of the returned list.

predict_prompt(prompt, answer, output_format='tokens')

Predict hallucinations when you already have a single full prompt string.

Parameters:

Name	Type	Description	Default
prompt	str	The prompt string.	required
answer	str	The answer string.	required
output_format	str	"tokens" to return token-level predictions, or "spans" to return grouped spans.	'tokens'

predict_prompt_batch(prompts, answers, output_format='tokens')

Batch version of :py:meth:predict_prompt.

Length of prompts and answers must match.

Parameters:

Name	Type	Description	Default
prompts	list[str]	List of prompt strings.	required
answers	list[str]	List of answer strings.	required
output_format	str	"tokens" to return token-level predictions, or "spans" to return grouped spans.	'tokens'