Retrieval

Evaluation modules.

BaseRetrievalEvaluator

Bases: BaseModel

Base Retrieval Evaluator class.

Source code in llama-index-core/llama_index/core/evaluation/retrieval/base.py

class BaseRetrievalEvaluator(BaseModel):
    """Base Retrieval Evaluator class."""

    model_config = ConfigDict(arbitrary_types_allowed=True)
    metrics: List[BaseRetrievalMetric] = Field(
        ..., description="List of metrics to evaluate"
    )

    @classmethod
    def from_metric_names(
        cls, metric_names: List[str], **kwargs: Any
    ) -> "BaseRetrievalEvaluator":
        """Create evaluator from metric names.

        Args:
            metric_names (List[str]): List of metric names
            **kwargs: Additional arguments for the evaluator

        """
        metric_types = resolve_metrics(metric_names)
        return cls(metrics=[metric() for metric in metric_types], **kwargs)

    @abstractmethod
    async def _aget_retrieved_ids_and_texts(
        self, query: str, mode: RetrievalEvalMode = RetrievalEvalMode.TEXT
    ) -> Tuple[List[str], List[str]]:
        """Get retrieved ids and texts."""
        raise NotImplementedError

    def evaluate(
        self,
        query: str,
        expected_ids: List[str],
        expected_texts: Optional[List[str]] = None,
        mode: RetrievalEvalMode = RetrievalEvalMode.TEXT,
        **kwargs: Any,
    ) -> RetrievalEvalResult:
        """Run evaluation results with query string and expected ids.

        Args:
            query (str): Query string
            expected_ids (List[str]): Expected ids

        Returns:
            RetrievalEvalResult: Evaluation result

        """
        return asyncio_run(
            self.aevaluate(
                query=query,
                expected_ids=expected_ids,
                expected_texts=expected_texts,
                mode=mode,
                **kwargs,
            )
        )

    # @abstractmethod
    async def aevaluate(
        self,
        query: str,
        expected_ids: List[str],
        expected_texts: Optional[List[str]] = None,
        mode: RetrievalEvalMode = RetrievalEvalMode.TEXT,
        **kwargs: Any,
    ) -> RetrievalEvalResult:
        """Run evaluation with query string, retrieved contexts,
        and generated response string.

        Subclasses can override this method to provide custom evaluation logic and
        take in additional arguments.
        """
        retrieved_ids, retrieved_texts = await self._aget_retrieved_ids_and_texts(
            query, mode
        )
        metric_dict = {}
        for metric in self.metrics:
            eval_result = metric.compute(
                query, expected_ids, retrieved_ids, expected_texts, retrieved_texts
            )
            metric_dict[metric.metric_name] = eval_result

        return RetrievalEvalResult(
            query=query,
            expected_ids=expected_ids,
            expected_texts=expected_texts,
            retrieved_ids=retrieved_ids,
            retrieved_texts=retrieved_texts,
            mode=mode,
            metric_dict=metric_dict,
        )

    async def aevaluate_dataset(
        self,
        dataset: EmbeddingQAFinetuneDataset,
        workers: int = 2,
        show_progress: bool = False,
        **kwargs: Any,
    ) -> List[RetrievalEvalResult]:
        """Run evaluation with dataset."""
        semaphore = asyncio.Semaphore(workers)

        async def eval_worker(
            query: str, expected_ids: List[str], mode: RetrievalEvalMode
        ) -> RetrievalEvalResult:
            async with semaphore:
                return await self.aevaluate(query, expected_ids=expected_ids, mode=mode)

        response_jobs = []
        mode = RetrievalEvalMode.from_str(dataset.mode)
        for query_id, query in dataset.queries.items():
            expected_ids = dataset.relevant_docs[query_id]
            response_jobs.append(eval_worker(query, expected_ids, mode))
        if show_progress:
            from tqdm.asyncio import tqdm_asyncio

            eval_results = await tqdm_asyncio.gather(*response_jobs)
        else:
            eval_results = await asyncio.gather(*response_jobs)

        return eval_results

from_metric_names classmethod

from_metric_names(metric_names: List[str], **kwargs: Any) -> BaseRetrievalEvaluator

Create evaluator from metric names.

Parameters:

Name	Type	Description	Default
metric_names	List[str]	List of metric names	required
**kwargs	Any	Additional arguments for the evaluator	{}

Source code in llama-index-core/llama_index/core/evaluation/retrieval/base.py

@classmethod
def from_metric_names(
    cls, metric_names: List[str], **kwargs: Any
) -> "BaseRetrievalEvaluator":
    """Create evaluator from metric names.

    Args:
        metric_names (List[str]): List of metric names
        **kwargs: Additional arguments for the evaluator

    """
    metric_types = resolve_metrics(metric_names)
    return cls(metrics=[metric() for metric in metric_types], **kwargs)

evaluate

evaluate(query: str, expected_ids: List[str], expected_texts: Optional[List[str]] = None, mode: RetrievalEvalMode = RetrievalEvalMode.TEXT, **kwargs: Any) -> RetrievalEvalResult

Run evaluation results with query string and expected ids.

Parameters:

Name	Type	Description	Default
query	str	Query string	required
expected_ids	List[str]	Expected ids	required

Returns:

Name	Type	Description
RetrievalEvalResult	RetrievalEvalResult	Evaluation result

Source code in llama-index-core/llama_index/core/evaluation/retrieval/base.py

def evaluate(
    self,
    query: str,
    expected_ids: List[str],
    expected_texts: Optional[List[str]] = None,
    mode: RetrievalEvalMode = RetrievalEvalMode.TEXT,
    **kwargs: Any,
) -> RetrievalEvalResult:
    """Run evaluation results with query string and expected ids.

    Args:
        query (str): Query string
        expected_ids (List[str]): Expected ids

    Returns:
        RetrievalEvalResult: Evaluation result

    """
    return asyncio_run(
        self.aevaluate(
            query=query,
            expected_ids=expected_ids,
            expected_texts=expected_texts,
            mode=mode,
            **kwargs,
        )
    )

aevaluate async

aevaluate(query: str, expected_ids: List[str], expected_texts: Optional[List[str]] = None, mode: RetrievalEvalMode = RetrievalEvalMode.TEXT, **kwargs: Any) -> RetrievalEvalResult

Run evaluation with query string, retrieved contexts, and generated response string.

Subclasses can override this method to provide custom evaluation logic and take in additional arguments.

Source code in llama-index-core/llama_index/core/evaluation/retrieval/base.py

async def aevaluate(
    self,
    query: str,
    expected_ids: List[str],
    expected_texts: Optional[List[str]] = None,
    mode: RetrievalEvalMode = RetrievalEvalMode.TEXT,
    **kwargs: Any,
) -> RetrievalEvalResult:
    """Run evaluation with query string, retrieved contexts,
    and generated response string.

    Subclasses can override this method to provide custom evaluation logic and
    take in additional arguments.
    """
    retrieved_ids, retrieved_texts = await self._aget_retrieved_ids_and_texts(
        query, mode
    )
    metric_dict = {}
    for metric in self.metrics:
        eval_result = metric.compute(
            query, expected_ids, retrieved_ids, expected_texts, retrieved_texts
        )
        metric_dict[metric.metric_name] = eval_result

    return RetrievalEvalResult(
        query=query,
        expected_ids=expected_ids,
        expected_texts=expected_texts,
        retrieved_ids=retrieved_ids,
        retrieved_texts=retrieved_texts,
        mode=mode,
        metric_dict=metric_dict,
    )

aevaluate_dataset async

aevaluate_dataset(dataset: EmbeddingQAFinetuneDataset, workers: int = 2, show_progress: bool = False, **kwargs: Any) -> List[RetrievalEvalResult]

Run evaluation with dataset.

Source code in llama-index-core/llama_index/core/evaluation/retrieval/base.py

async def aevaluate_dataset(
    self,
    dataset: EmbeddingQAFinetuneDataset,
    workers: int = 2,
    show_progress: bool = False,
    **kwargs: Any,
) -> List[RetrievalEvalResult]:
    """Run evaluation with dataset."""
    semaphore = asyncio.Semaphore(workers)

    async def eval_worker(
        query: str, expected_ids: List[str], mode: RetrievalEvalMode
    ) -> RetrievalEvalResult:
        async with semaphore:
            return await self.aevaluate(query, expected_ids=expected_ids, mode=mode)

    response_jobs = []
    mode = RetrievalEvalMode.from_str(dataset.mode)
    for query_id, query in dataset.queries.items():
        expected_ids = dataset.relevant_docs[query_id]
        response_jobs.append(eval_worker(query, expected_ids, mode))
    if show_progress:
        from tqdm.asyncio import tqdm_asyncio

        eval_results = await tqdm_asyncio.gather(*response_jobs)
    else:
        eval_results = await asyncio.gather(*response_jobs)

    return eval_results

RetrieverEvaluator

Bases: BaseRetrievalEvaluator

Retriever evaluator.

This module will evaluate a retriever using a set of metrics.

Parameters:

Name	Type	Description	Default
metrics	List[BaseRetrievalMetric]	Sequence of metrics to evaluate	required
retriever		Retriever to evaluate.	required
node_postprocessors	Optional[List[BaseNodePostprocessor]]	Post-processor to apply after retrieval.	required

Source code in llama-index-core/llama_index/core/evaluation/retrieval/evaluator.py

class RetrieverEvaluator(BaseRetrievalEvaluator):
    """Retriever evaluator.

    This module will evaluate a retriever using a set of metrics.

    Args:
        metrics (List[BaseRetrievalMetric]): Sequence of metrics to evaluate
        retriever: Retriever to evaluate.
        node_postprocessors (Optional[List[BaseNodePostprocessor]]): Post-processor to apply after retrieval.


    """

    retriever: BaseRetriever = Field(..., description="Retriever to evaluate")
    node_postprocessors: Optional[List[SerializeAsAny[BaseNodePostprocessor]]] = Field(
        default=None, description="Optional post-processor"
    )

    async def _aget_retrieved_ids_and_texts(
        self, query: str, mode: RetrievalEvalMode = RetrievalEvalMode.TEXT
    ) -> Tuple[List[str], List[str]]:
        """Get retrieved ids and texts, potentially applying a post-processor."""
        retrieved_nodes = await self.retriever.aretrieve(query)

        if self.node_postprocessors:
            for node_postprocessor in self.node_postprocessors:
                retrieved_nodes = node_postprocessor.postprocess_nodes(
                    retrieved_nodes, query_str=query
                )

        return (
            [node.node.node_id for node in retrieved_nodes],
            [node.node.text for node in retrieved_nodes],
        )

RetrievalEvalResult

Bases: BaseModel

Retrieval eval result.

NOTE: this abstraction might change in the future.

Attributes:

Name	Type	Description
query	str	Query string
expected_ids	List[str]	Expected ids
retrieved_ids	List[str]	Retrieved ids
metric_dict	Dict[str, BaseRetrievalMetric]	Metric dictionary for the evaluation

Source code in llama-index-core/llama_index/core/evaluation/retrieval/base.py

class RetrievalEvalResult(BaseModel):
    """Retrieval eval result.

    NOTE: this abstraction might change in the future.

    Attributes:
        query (str): Query string
        expected_ids (List[str]): Expected ids
        retrieved_ids (List[str]): Retrieved ids
        metric_dict (Dict[str, BaseRetrievalMetric]): \
            Metric dictionary for the evaluation

    """

    model_config = ConfigDict(arbitrary_types_allowed=True)
    query: str = Field(..., description="Query string")
    expected_ids: List[str] = Field(..., description="Expected ids")
    expected_texts: Optional[List[str]] = Field(
        default=None,
        description="Expected texts associated with nodes provided in `expected_ids`",
    )
    retrieved_ids: List[str] = Field(..., description="Retrieved ids")
    retrieved_texts: List[str] = Field(..., description="Retrieved texts")
    mode: "RetrievalEvalMode" = Field(
        default=RetrievalEvalMode.TEXT, description="text or image"
    )
    metric_dict: Dict[str, RetrievalMetricResult] = Field(
        ..., description="Metric dictionary for the evaluation"
    )

    @property
    def metric_vals_dict(self) -> Dict[str, float]:
        """Dictionary of metric values."""
        return {k: v.score for k, v in self.metric_dict.items()}

    def __str__(self) -> str:
        """String representation."""
        return f"Query: {self.query}\n" f"Metrics: {self.metric_vals_dict!s}\n"

metric_vals_dict property

metric_vals_dict: Dict[str, float]

Dictionary of metric values.