SequentialRetriever

RetrieverModel

DashAI.back.models.RAG.retrievers.composite.SequentialRetriever

Queries multiple retrievers in sequence, re-ranking at each step.

Each stage narrows the result set by requiring strictly decreasing top_k values. The last child is the authoritative scorer.

Parameters

children : array, default=[]: Ordered list of child retrievers. The first child retrieves broadly; each subsequent child re-ranks and tightens the results.

Methods

get_metadata(cls) -> Dict[str, Any]

Defined on SequentialRetriever

Return UI metadata including the declarative operation summary.

retrieve(self, query, **kwargs) -> List[DashAI.back.models.RAG.documents.chunk.Chunk]

Defined on SequentialRetriever

Retrieve chunks through the sequential cascade.

score_chunks(self, chunk_ids: List[int], query: str) -> List[Tuple[int, float]]

Defined on SequentialRetriever

Score chunks by delegating to the last child in the cascade.

add(self, child: DashAI.back.models.RAG.retrievers.retriever_model.RetrieverModel) -> None

Defined on CompositeRetriever

Add a child retriever.

calculate_metrics(self, split: DashAI.back.core.enums.metrics.SplitEnum = <SplitEnum.VALIDATION: 'validation'>, level: DashAI.back.core.enums.metrics.LevelEnum = <LevelEnum.LAST: 'last'>, log_index: int = None, x_data: 'DashAIDataset' = None, y_data: 'DashAIDataset' = None, fold_index: int = None, inner_fold_index: int = None)

Defined on BaseModel

Calculate and save metrics for a given data split and level.

Parameters

split : SplitEnum: The data split to evaluate (TRAIN, VALIDATION, or TEST). Defaults to SplitEnum.VALIDATION.
level : LevelEnum: The metric granularity level (LAST, TRIAL, STEP, or BATCH). Defaults to LevelEnum.LAST.
log_index : int, optional: Explicit step index for the metric entry. If None, the next step index is computed automatically. Defaults to None.
x_data : DashAIDataset, optional: Input features. If None, the dataset stored in the model for the given split is used. Defaults to None.
y_data : DashAIDataset, optional: Target labels. If None, the labels stored in the model for the given split are used. Defaults to None.

compute_metrics(self, split: DashAI.back.core.enums.metrics.SplitEnum = <SplitEnum.TEST: 'test'>, x_data: 'DashAIDataset' = None, y_data: 'DashAIDataset' = None) -> Dict[str, float]

Defined on BaseModel

Calculate and return metric scores for a given data split.

Parameters

split : SplitEnum: The data split to evaluate (TRAIN, VALIDATION, or TEST). Defaults to SplitEnum.VALIDATION.
x_data : DashAIDataset, optional: Input features. If None, the dataset stored in the model for the given split is used. Defaults to None.
y_data : DashAIDataset, optional: Target labels. If None, the labels stored in the model for the given split are used. Defaults to None.

Returns

Dict[str, float]: A dictionary mapping metric names to their computed scores.

get_children(self) -> List[DashAI.back.models.RAG.retrievers.retriever_model.RetrieverModel]

Defined on CompositeRetriever

Return a copy of the children list.

get_credential(self, name: str)

Defined on ConfigObject

Resolve a registered credential component by name.

Parameters

name : str: Credential component class name (e.g. "HuggingFaceCredential").

Returns

BaseCredential: An instance of the requested credential component.

get_id(self) -> int | None

Defined on RetrieverModel

Return the database ID of this retriever, or None.

get_schema(cls) -> dict

Defined on ConfigObject

Generates the component related Json Schema.

Returns

dict: Dictionary representing the Json Schema of the component.

init_model(self) -> None

Defined on RetrieverModel

Called by the factory after inject_infra().

inject_infra(self, env_RAG_path: str | os.PathLike, chunks: Dict[int, Dict[int, DashAI.back.models.RAG.documents.chunk.Chunk]], persistence: Any) -> None

Defined on RetrieverModel

Inject runtime infrastructure after schema validation.

load(self, filename: str = '') -> None

Defined on RetrieverModel

Restore the retriever's state from disk.

predict_prepared(self, features: Any) -> Any

Defined on BaseModel

Predict from data that is already in this model's feature space.

Parameters

features : pandas.DataFrame or numpy.ndarray: Feature matrix as returned by prepare_dataset(..., is_fit=False). No further preparation is applied to it.

Returns

Any: The same kind of output as predict: predicted values for regressors, class probabilities for DashAI classifiers.

predict_proba_prepared(self, features: Any) -> Any

Defined on BaseModel

Return class probabilities for data already in the feature space.

Parameters

features : pandas.DataFrame or numpy.ndarray: Feature matrix as returned by prepare_dataset(..., is_fit=False).

Returns

numpy.ndarray: Array of shape (n_samples, n_classes) with class probabilities.

prepare_dataset(self, dataset: 'DashAIDataset', is_fit: bool = False) -> 'DashAIDataset'

Defined on BaseModel

Hook for model specific preprocessing of input features.

Parameters

dataset : DashAIDataset: The input dataset to preprocess.
is_fit : bool: Whether the call is part of a fitting phase. Defaults to False.

Returns

DashAIDataset: The preprocessed dataset ready to be fed into the model.

prepare_output(self, dataset: 'DashAIDataset', is_fit: bool = False) -> 'DashAIDataset'

Defined on BaseModel

Hook for model-specific preprocessing of output targets.

Parameters

dataset : DashAIDataset: The output dataset (target labels) to preprocess.
is_fit : bool: Whether the call is part of a fitting phase. Defaults to False.

Returns

DashAIDataset: The preprocessed output dataset.

remove(self, child: DashAI.back.models.RAG.retrievers.retriever_model.RetrieverModel) -> None

Defined on CompositeRetriever

Remove a child retriever.

save(self, filename: str = '') -> None

Defined on RetrieverModel

Persist the retriever's state to disk.

set_id(self, id: int) -> None

Defined on RetrieverModel

Assign a database ID to this retriever.

train(self, **kwargs)

Defined on RetrieverModel

Train the retriever on the injected chunks.

validate_and_transform(self, raw_data: dict) -> dict

Defined on ConfigObject

It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.

Parameters

raw_data : dict: A dictionary with the data provided by the user to initialize the model.

Returns

dict: A validated dictionary with the necessary objects.

Parameters​

Methods​

Parameters

Methods