SemanticRetriever

class ragger_duck.retrieval.SemanticRetriever(*, embedding, top_k=1)

Retrieve the k-nearest neighbors using a semantic embedding.

The index is build using the FAISS library.

Parameters:

embeddingtransformer

An embedding following the scikit-learn transformer API.

top_kint, default=1

Number of documents to retrieve.

Attributes:

X_fit_list of str or dict

The input data.

X_embedded_ndarray of shape (n_sentences, n_features)

The embedded data.

index_faiss index

The index to retrieve the k-nearest neighbors.

Methods

fit(X[, y])	Embed the sentences and create the index.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
query(query)	Retrieve the most relevant documents for the query.
set_params(**params)	Set the parameters of this estimator.

fit(X, y=None)

Embed the sentences and create the index.

Parameters:

Xlist of str or dict

The input data.

yNone

This parameter is ignored.

Returns:

self

The fitted estimator.

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

query(query)

Retrieve the most relevant documents for the query.

The inner product is used to compute the cosine similarity meaning that we expect the embedding to be normalized.

Parameters:

querystr

The input data.

Returns:

list of str or dict

The list of the most relevant document from the training set.

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.