Method
renameDocumentRetriever POST
Copy POST

Updates an existing document retriever.

Arguments:

REQUIRED KEY TYPE DESCRIPTION
Yes documentRetrieverId str The unique ID associated with the document retriever.
Yes name str The name to update the document retriever with.
Note: The arguments for the API methods follow camelCase but for Python SDK underscore_case is followed.

Response:

KEY TYPE DESCRIPTION
success Boolean true if the call succeeded, false if there was an error
result DocumentRetriever
KEY TYPE Description
name str The name of the document retriever.
documentRetrieverId str The unique identifier of the vector store.
createdAt str When the vector store was created.
featureGroupId str The feature group id associated with the document retriever.
featureGroupName str The feature group name associated with the document retriever.
indexingRequired bool Whether the document retriever is required to be indexed due to changes in underlying data.
latestDocumentRetrieverVersion DocumentRetrieverVersion The latest version of vector store.
KEY TYPE Description
documentRetrieverId str The unique identifier of the Document Retriever.
documentRetrieverVersion str The unique identifier of the Document Retriever version.
createdAt str When the Document Retriever was created.
status str The status of Document Retriever version. It represents indexing status until indexing isn't complete, and deployment status after indexing is complete.
deploymentStatus str The status of deploying the Document Retriever version.
featureGroupId str The feature group id associated with the document retriever.
featureGroupVersion str The unique identifier of the feature group version at which the Document Retriever version is created.
error str The error message when it failed to create the document retriever version.
numberOfChunks int The number of chunks for the document retriever.
embeddingFileSize int The size of embedding file for the document retriever.
warnings list The warning messages when creating the document retriever.
resolvedConfig VectorStoreConfig The resolved configurations, such as default settings, for indexing documents.
KEY TYPE Description
chunkOverlapFraction float The fraction of overlap between chunks.
scoreMultiplierColumn str If provided, will use the values in this metadata column to modify the relevance score of returned chunks for all queries.
textEncoder VectorStoreTextEncoder Encoder used to index texts from the documents.
indexMetadataColumns bool If True, metadata columns of the FG will also be used for indexing and querying.
pruneVectors bool Transform vectors using SVD so that the average component of vectors in the corpus are removed.
useDocumentSummary bool If True, uses the summary of the document in addition to chunks of the document for indexing and querying.
chunkSizeFactors list Chunking data with multiple sizes. The specified list of factors are used to calculate more sizes, in addition to `chunk_size`.
summaryInstructions str Instructions for the LLM to generate the document summary.
chunkSize int The size of text chunks in the vector store.
standaloneDeployment bool If True, the document retriever will be deployed as a standalone deployment.
documentRetrieverConfig VectorStoreConfig The config used to create the document retriever version.
KEY TYPE Description
chunkOverlapFraction float The fraction of overlap between chunks.
scoreMultiplierColumn str If provided, will use the values in this metadata column to modify the relevance score of returned chunks for all queries.
textEncoder VectorStoreTextEncoder Encoder used to index texts from the documents.
indexMetadataColumns bool If True, metadata columns of the FG will also be used for indexing and querying.
pruneVectors bool Transform vectors using SVD so that the average component of vectors in the corpus are removed.
useDocumentSummary bool If True, uses the summary of the document in addition to chunks of the document for indexing and querying.
chunkSizeFactors list Chunking data with multiple sizes. The specified list of factors are used to calculate more sizes, in addition to `chunk_size`.
summaryInstructions str Instructions for the LLM to generate the document summary.
chunkSize int The size of text chunks in the vector store.
standaloneDeployment bool If True, the document retriever will be deployed as a standalone deployment.
documentRetrieverConfig VectorStoreConfig The config for vector store creation.
KEY TYPE Description
chunkOverlapFraction float The fraction of overlap between chunks.
scoreMultiplierColumn str If provided, will use the values in this metadata column to modify the relevance score of returned chunks for all queries.
textEncoder VectorStoreTextEncoder Encoder used to index texts from the documents.
indexMetadataColumns bool If True, metadata columns of the FG will also be used for indexing and querying.
pruneVectors bool Transform vectors using SVD so that the average component of vectors in the corpus are removed.
useDocumentSummary bool If True, uses the summary of the document in addition to chunks of the document for indexing and querying.
chunkSizeFactors list Chunking data with multiple sizes. The specified list of factors are used to calculate more sizes, in addition to `chunk_size`.
summaryInstructions str Instructions for the LLM to generate the document summary.
chunkSize int The size of text chunks in the vector store.
standaloneDeployment bool If True, the document retriever will be deployed as a standalone deployment.

Exceptions:

TYPE WHEN
DataNotFoundError

documentRetrieverId is not found.
Language:

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