abacusai.api_class.document_retriever

Attributes

DocumentRetrieverConfig

Classes

VectorStoreConfig

Config for indexing options of a document retriever. Default values of optional arguments are heuristically selected by the Abacus.AI platform based on the underlying data.

Module Contents

class abacusai.api_class.document_retriever.VectorStoreConfig

Bases: abacusai.api_class.abstract.ApiClass

Config for indexing options of a document retriever. Default values of optional arguments are heuristically selected by the Abacus.AI platform based on the underlying data.

Parameters:

  • chunk_size (int) – The size of text chunks in the vector store.

  • chunk_overlap_fraction (float) – The fraction of overlap between chunks.

  • text_encoder (VectorStoreTextEncoder) – Encoder used to index texts from the documents.

  • chunk_size_factors (list) – Chunking data with multiple sizes. The specified list of factors are used to calculate more sizes, in addition to chunk_size.

  • score_multiplier_column (str) – If provided, will use the values in this metadata column to modify the relevance score of returned chunks for all queries.

  • prune_vectors (bool) – Transform vectors using SVD so that the average component of vectors in the corpus are removed.

  • index_metadata_columns (bool) – If True, metadata columns of the FG will also be used for indexing and querying.

  • use_document_summary (bool) – If True, uses the summary of the document in addition to chunks of the document for indexing and querying.

  • summary_instructions (str) – Instructions for the LLM to generate the document summary.

chunk_size: int
chunk_overlap_fraction: float
text_encoder: abacusai.api_class.enums.VectorStoreTextEncoder
chunk_size_factors: list
score_multiplier_column: str
prune_vectors: bool
index_metadata_columns: bool
use_document_summary: bool
summary_instructions: str
abacusai.api_class.document_retriever.DocumentRetrieverConfig