karenina.benchmark.verification.evaluators.trace

trace

Trace analysis components for detecting abstention and sufficiency.

This package provides: - detect_abstention: Detect when models refuse to answer questions - detect_sufficiency: Detect if responses have sufficient information for templates

Functions

detect_abstention

detect_abstention(
    raw_llm_response: str,
    parsing_model: ModelConfig,
    question_text: str,
    prompt_config: PromptConfig | None = None,
) -> tuple[bool, bool, str | None, dict[str, Any]]

Detect if the model refused to answer or abstained from answering.

This function uses an LLM to analyze the response and determine if it contains patterns indicating refusal, abstention, or evasion. The adapter handles retries for transient errors internally via RetryExecutor.

Parameters:

Name	Type	Description	Default
raw_llm_response	str	The raw response text from the answering model	required
parsing_model	ModelConfig	Configuration for the model to use for abstention detection	required
question_text	str	The original question that was asked	required

Returns:

Type	Description
bool	Tuple of (abstention_detected, check_performed, reasoning, usage_metadata):
bool	abstention_detected: True if model refused/abstained, False if genuine attempt
str | None	check_performed: True if check completed successfully, False if check failed
dict[str, Any]	reasoning: The LLM's explanation for its determination (None if check failed)
tuple[bool, bool, str | None, dict[str, Any]]	usage_metadata: Token usage metadata from the LLM invocation

Examples:

>>> config = ModelConfig(id="parser", model_provider="openai", ...)
>>> detected, performed, reasoning, metadata = detect_abstention("I cannot answer this", config, "What is X?")
>>> print(detected, performed, reasoning)
True, True, "Response contains explicit refusal pattern"

Source code in src/karenina/benchmark/verification/evaluators/trace/abstention.py

def detect_abstention(
    raw_llm_response: str,
    parsing_model: ModelConfig,
    question_text: str,
    prompt_config: PromptConfig | None = None,
) -> tuple[bool, bool, str | None, dict[str, Any]]:
    """
    Detect if the model refused to answer or abstained from answering.

    This function uses an LLM to analyze the response and determine if it contains
    patterns indicating refusal, abstention, or evasion. The adapter handles retries
    for transient errors internally via RetryExecutor.

    Args:
        raw_llm_response: The raw response text from the answering model
        parsing_model: Configuration for the model to use for abstention detection
        question_text: The original question that was asked

    Returns:
        Tuple of (abstention_detected, check_performed, reasoning, usage_metadata):
        - abstention_detected: True if model refused/abstained, False if genuine attempt
        - check_performed: True if check completed successfully, False if check failed
        - reasoning: The LLM's explanation for its determination (None if check failed)
        - usage_metadata: Token usage metadata from the LLM invocation

    Examples:
        >>> config = ModelConfig(id="parser", model_provider="openai", ...)
        >>> detected, performed, reasoning, metadata = detect_abstention("I cannot answer this", config, "What is X?")
        >>> print(detected, performed, reasoning)
        True, True, "Response contains explicit refusal pattern"
    """

    usage_metadata: dict[str, Any] = {}
    llm: Any | None = None
    structured_llm: Any | None = None

    try:
        # Create config copy with temperature=0 for consistent detection
        # Note: LangChain adapter respects temperature; Claude SDK adapter ignores it
        detection_config = parsing_model.model_copy(update={"temperature": 0.0})

        # Get LLM via adapter factory (adapter handles retries internally)
        llm = get_llm(detection_config)

        # Configure for structured output
        structured_llm = llm.with_structured_output(AbstentionResult)

        # Build messages using PromptAssembler (tri-section pattern)
        user_prompt = ABSTENTION_DETECTION_USER.format(question=question_text, response=raw_llm_response)
        assembler = PromptAssembler(
            task=PromptTask.ABSTENTION_DETECTION,
            interface=parsing_model.interface,
            capabilities=PortCapabilities(),
        )
        user_instructions = prompt_config.get_for_task(PromptTask.ABSTENTION_DETECTION.value) if prompt_config else None
        messages = assembler.assemble(
            system_text=ABSTENTION_DETECTION_SYS,
            user_text=user_prompt,
            user_instructions=user_instructions,
        )

        # Invoke with structured output
        response: LLMResponse = structured_llm.invoke(messages)
        usage_metadata = response.usage.to_dict()

        # Extract result from structured output or fall back to manual parsing
        result = extract_judge_result(response, AbstentionResult, "abstention_detected")
        if result is not None:
            logger.debug("Abstention check: %s - Reasoning: %s", result.abstention_detected, result.reasoning)
            return result.abstention_detected, True, result.reasoning, usage_metadata

        # Fallback: manual JSON parsing from content
        return fallback_json_parse(
            response.content, usage_metadata, "abstention_detected", False, "Abstention check (fallback)"
        )

    except json.JSONDecodeError as e:
        # JSON parsing failed: log and treat as check failure
        logger.warning("Failed to parse abstention detection response as JSON: %s", e)
        return False, False, None, usage_metadata

    except Exception as e:
        # Non-recoverable error: log and treat as check failure
        logger.warning("Abstention detection failed: %s", e)
        return False, False, None, usage_metadata
    finally:
        if structured_llm is not None:
            close_adapter(structured_llm)
        if llm is not None and llm is not structured_llm:
            close_adapter(llm)

detect_sufficiency

detect_sufficiency(
    raw_llm_response: str,
    parsing_model: ModelConfig,
    question_text: str,
    template_schema: dict[str, Any],
    prompt_config: PromptConfig | None = None,
) -> tuple[bool, bool, str | None, dict[str, Any]]

Detect if the response contains sufficient information to populate the template schema.

This function uses an LLM to analyze the response against the template schema and determine if all required fields can be populated. The adapter handles retries for transient errors internally via RetryExecutor.

Parameters:

Name	Type	Description	Default
raw_llm_response	str	The raw response text from the answering model	required
parsing_model	ModelConfig	Configuration for the model to use for sufficiency detection	required
question_text	str	The original question that was asked	required
template_schema	dict[str, Any]	The JSON schema of the answer template to populate	required

Returns:

Type	Description
bool	Tuple of (sufficient, check_performed, reasoning, usage_metadata):
bool	sufficient: True if response has info for all fields, False if information missing
str | None	check_performed: True if check completed successfully, False if check failed
dict[str, Any]	reasoning: The LLM's explanation for its determination (None if check failed)
tuple[bool, bool, str | None, dict[str, Any]]	usage_metadata: Token usage metadata from the LLM invocation

Examples:

>>> config = ModelConfig(id="parser", model_provider="openai", ...)
>>> schema = {"properties": {"answer": {"type": "string"}}}
>>> sufficient, performed, reasoning, metadata = detect_sufficiency(
...     "The answer is 42", config, "What is X?", schema
... )
>>> print(sufficient, performed)
True, True

Source code in src/karenina/benchmark/verification/evaluators/trace/sufficiency.py

def detect_sufficiency(
    raw_llm_response: str,
    parsing_model: ModelConfig,
    question_text: str,
    template_schema: dict[str, Any],
    prompt_config: PromptConfig | None = None,
) -> tuple[bool, bool, str | None, dict[str, Any]]:
    """
    Detect if the response contains sufficient information to populate the template schema.

    This function uses an LLM to analyze the response against the template schema and
    determine if all required fields can be populated. The adapter handles retries
    for transient errors internally via RetryExecutor.

    Args:
        raw_llm_response: The raw response text from the answering model
        parsing_model: Configuration for the model to use for sufficiency detection
        question_text: The original question that was asked
        template_schema: The JSON schema of the answer template to populate

    Returns:
        Tuple of (sufficient, check_performed, reasoning, usage_metadata):
        - sufficient: True if response has info for all fields, False if information missing
        - check_performed: True if check completed successfully, False if check failed
        - reasoning: The LLM's explanation for its determination (None if check failed)
        - usage_metadata: Token usage metadata from the LLM invocation

    Examples:
        >>> config = ModelConfig(id="parser", model_provider="openai", ...)
        >>> schema = {"properties": {"answer": {"type": "string"}}}
        >>> sufficient, performed, reasoning, metadata = detect_sufficiency(
        ...     "The answer is 42", config, "What is X?", schema
        ... )
        >>> print(sufficient, performed)
        True, True
    """

    usage_metadata: dict[str, Any] = {}
    llm: Any | None = None
    structured_llm: Any | None = None

    try:
        # Create config copy with temperature=0 for consistent detection
        # Note: LangChain adapter respects temperature; Claude SDK adapter ignores it
        detection_config = parsing_model.model_copy(update={"temperature": 0.0})

        # Get LLM via adapter factory (adapter handles retries internally)
        llm = get_llm(detection_config)

        # Configure for structured output
        structured_llm = llm.with_structured_output(SufficiencyResult)

        # Convert schema to string for prompt
        schema_str = json.dumps(template_schema, indent=2)

        # Build messages using PromptAssembler (tri-section pattern)
        user_prompt = SUFFICIENCY_DETECTION_USER.format(
            question=question_text,
            response=raw_llm_response,
            schema=schema_str,
        )
        assembler = PromptAssembler(
            task=PromptTask.SUFFICIENCY_DETECTION,
            interface=parsing_model.interface,
            capabilities=PortCapabilities(),
        )
        user_instructions = (
            prompt_config.get_for_task(PromptTask.SUFFICIENCY_DETECTION.value) if prompt_config else None
        )
        messages = assembler.assemble(
            system_text=SUFFICIENCY_DETECTION_SYS,
            user_text=user_prompt,
            user_instructions=user_instructions,
        )

        # Invoke with structured output
        response: LLMResponse = structured_llm.invoke(messages)
        usage_metadata = response.usage.to_dict()

        # Extract result from structured output or fall back to manual parsing
        result = extract_judge_result(response, SufficiencyResult, "sufficient")
        if result is not None:
            logger.debug("Sufficiency check: %s - Reasoning: %s", result.sufficient, result.reasoning)
            return result.sufficient, True, result.reasoning, usage_metadata

        # Fallback: manual JSON parsing from content
        return fallback_json_parse(response.content, usage_metadata, "sufficient", True, "Sufficiency check (fallback)")

    except json.JSONDecodeError as e:
        # JSON parsing failed: log and treat as check failure
        logger.warning("Failed to parse sufficiency detection response as JSON: %s", e)
        return True, False, None, usage_metadata  # Default to sufficient on failure

    except Exception as e:
        # Non-recoverable error: log and treat as check failure
        logger.warning("Sufficiency detection failed: %s", e)
        return True, False, None, usage_metadata  # Default to sufficient on failure
    finally:
        if structured_llm is not None:
            close_adapter(structured_llm)
        if llm is not None and llm is not structured_llm:
            close_adapter(llm)