indexers.dataclasses

indexers.dataclasses

Classes

Name	Description
VectorStoreEmbedInput	DataFrame-like object for forming and validating text data to be embedded.
VectorStoreEmbedOutput	DataFrame-like object for storing and validating embedded vectors and associated
VectorStoreReverseSearchInput	DataFrame-like object for forming and validating reverse search query
VectorStoreReverseSearchOutput	DataFrame-like object for storing reverse search results.
VectorStoreSearchInput	DataFrame-like object for forming and validating search query input data.
VectorStoreSearchOutput	DataFrame-like object for storing and validating search results with rankings

VectorStoreEmbedInput

indexers.dataclasses.VectorStoreEmbedInput(data)

DataFrame-like object for forming and validating text data to be embedded.

This class validates and represents input texts that will be converted to vector embeddings by the vector store.

Attributes

Name	Type	Description
id	pd.Series	Unique identifier for each text item.
text	pd.Series	The text content to be embedded.

Methods

Name	Description
from_data	Create a validated VectorStoreEmbedInput from a dictionary or DataFrame.
validate	Validate an existing instance against the schema and return a VectorStoreEmbedInput.

from_data

indexers.dataclasses.VectorStoreEmbedInput.from_data(data)

Create a validated VectorStoreEmbedInput from a dictionary or DataFrame.

validate

indexers.dataclasses.VectorStoreEmbedInput.validate(df)

Validate an existing instance against the schema and return a VectorStoreEmbedInput.

VectorStoreEmbedOutput

indexers.dataclasses.VectorStoreEmbedOutput(data)

DataFrame-like object for storing and validating embedded vectors and associated metadata.

This class represents the output of embedding operations, containing the original text data along with their computed vector embeddings.

Attributes

Name	Type	Description
id	pd.Series	Unique identifier for each embedded item.
text	pd.Series	The original text that was embedded.
embedding	pd.Series	The computed vector embedding (numpy array).

Methods

Name	Description
from_data	Create a validated VectorStoreEmbedOutput from a dictionary or DataFrame.
validate	Validate an existing instance against the schema and return a VectorStoreEmbedOutput.

from_data

indexers.dataclasses.VectorStoreEmbedOutput.from_data(data)

Create a validated VectorStoreEmbedOutput from a dictionary or DataFrame.

validate

indexers.dataclasses.VectorStoreEmbedOutput.validate(df)

Validate an existing instance against the schema and return a VectorStoreEmbedOutput.

VectorStoreReverseSearchInput

indexers.dataclasses.VectorStoreReverseSearchInput(data)

DataFrame-like object for forming and validating reverse search query input data.

This class validates and represents input for reverse searches, which find similar documents to a given document in the vector store.

Attributes

Name	Type	Description
id	pd.Series	Unique identifier for the reverse search query.
doc_id	pd.Series	The document ID to find similar documents for.

Methods

Name	Description
from_data	Create a validated VectorStoreReverseSearchInput from a dictionary or DataFrame.
validate	Validate an existing instance against the schema and return a VectorStoreReverseSearchInput.

from_data

indexers.dataclasses.VectorStoreReverseSearchInput.from_data(data)

Create a validated VectorStoreReverseSearchInput from a dictionary or DataFrame.

validate

indexers.dataclasses.VectorStoreReverseSearchInput.validate(df)

Validate an existing instance against the schema and return a VectorStoreReverseSearchInput.

VectorStoreReverseSearchOutput

indexers.dataclasses.VectorStoreReverseSearchOutput(data)

DataFrame-like object for storing reverse search results.

This class represents the output of vector store reverse search operations, containing knowledgebase examples with the same label as in the query.

Attributes

Name	Type	Description
query_id	pd.Series	Identifier for the input label for lookup in the knowledgebase.
doc_id	pd.Series	Identifier for the knowledgebase example retrieved.
doc_text	pd.Series	The text content of the retrieved example.

Methods

Name	Description
from_data	Create a validated VectorStoreReverseSearchOutput from a dictionary or DataFrame.
validate	Validate an existing instance against the schema and return a VectorStoreReverseSearchOutputs.

from_data

indexers.dataclasses.VectorStoreReverseSearchOutput.from_data(data)

Create a validated VectorStoreReverseSearchOutput from a dictionary or DataFrame.

validate

indexers.dataclasses.VectorStoreReverseSearchOutput.validate(df)

Validate an existing instance against the schema and return a VectorStoreReverseSearchOutputs.

VectorStoreSearchInput

indexers.dataclasses.VectorStoreSearchInput(data)

DataFrame-like object for forming and validating search query input data.

This class validates and represents input queries for vector store searches. Each row contains a unique query identifier and the associated query text.

Attributes

Name	Type	Description
id	pd.Series	Unique identifier for each query.
query	pd.Series	The query text to search for.

Methods

Name	Description
from_data	Create a validated VectorStoreSearchInput from a dictionary or DataFrame.
validate	Validate an existing DataFrame against the schema and return a VectorStoreSearchInput.

from_data

indexers.dataclasses.VectorStoreSearchInput.from_data(data)

Create a validated VectorStoreSearchInput from a dictionary or DataFrame.

validate

indexers.dataclasses.VectorStoreSearchInput.validate(df)

Validate an existing DataFrame against the schema and return a VectorStoreSearchInput.

VectorStoreSearchOutput

indexers.dataclasses.VectorStoreSearchOutput(data)

DataFrame-like object for storing and validating search results with rankings and similarity scores.

This class represents the output of vector store search operations, containing query information, matched documents, scores, and similarity rankings.

Attributes

Name	Type	Description
query_id	pd.Series	Identifier for the source query.
query_text	pd.Series	The original query text.
doc_id	pd.Series	Identifier for the retrieved document.
doc_text	pd.Series	The text content of the retrieved document.
rank	pd.Series	The ranking position of the result (0-indexed, non-negative).
score	pd.Series	The similarity score or relevance metric.

Methods

Name	Description
from_data	Create a validated VectorStoreSearchOutput from a dictionary or DataFrame.
validate	Validate an existing instance against the schema and return a VectorStoreSearchOutput.

from_data

indexers.dataclasses.VectorStoreSearchOutput.from_data(data)

Create a validated VectorStoreSearchOutput from a dictionary or DataFrame.

validate

indexers.dataclasses.VectorStoreSearchOutput.validate(df)

Validate an existing instance against the schema and return a VectorStoreSearchOutput.