TFIDFRetriever

RetrieverModel

DashAI.back.models.RAG.retrievers.sparse.TFIDFRetriever

Sparse retriever using TF-IDF vectorization for document retrieval.

Fits a :class:sklearn.feature_extraction.text.TfidfVectorizer on the injected chunks and retrieves via pairwise distance.

Parameters

TFIDFVectorizer : object: TF-IDF Vectorizer parameters.
similarity_function : string, default=cosine: Distance metric for comparing TF-IDF vectors.
top_k : integer, default=5: Number of chunks to select.

Methods

get_chunk_vectors(self, chunk_ids: List[int]) -> numpy.ndarray

Defined on TFIDFRetriever

Return the TF-IDF vectors for the given chunk IDs.

init_model(self) -> None

Defined on TFIDFRetriever

Restore saved state or fit the vectorizer from scratch.

load(self) -> bool

Defined on TFIDFRetriever

Load a previously saved TF-IDF state from disk.

retrieve(self, query: str, top_k: int | None = None) -> List[DashAI.back.models.RAG.documents.chunk.Chunk]

Defined on TFIDFRetriever

Retrieve the top-k chunks by TF-IDF similarity.

save(self) -> None

Defined on TFIDFRetriever

Persist the vectorizer, TF-IDF matrix, and chunk map to disk.

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

Defined on TFIDFRetriever

Score a set of chunk IDs against the query.

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

Defined on UnitRetriever

Add a child retriever (not supported for unit retrievers).

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 UnitRetriever

Return the (empty) list of child retrievers.

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

Defined on BaseModel

Get metadata values for the current model.

Returns

Dict[str, Any]: Dictionary containing UI metadata such as the model icon used in the DashAI frontend.

get_schema(cls) -> dict

Defined on ConfigObject

Generates the component related Json Schema.

Returns

dict: Dictionary representing the Json Schema of the component.

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

Defined on UnitRetriever

Inject runtime infrastructure with type-checked persistence.

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 UnitRetriever

Remove a child retriever (not supported for unit retrievers).

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​

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Parameters

Methods