Update a corpus

PATCH 
/v2/corpora/:corpus_key

Enable, disable, or update the name and description of a corpus. This lets you manage data availability without deleting the corpus, which is useful for maintenance and security purposes. The corpus_key uniquely identifies the corpus. For more information, see Corpus Key Definition. Consider updating the name and description of a corpus dynamically to help keep your data aligned with changing business needs.

Request

Path Parameters

corpus_key CorpusKeyrequired

Possible values: <= 50 characters, Value must match regular expression [a-zA-Z0-9_\=\-]+$

The unique key identifying the corpus to update.

Header Parameters

Request-Timeout integer

Possible values: >= 1

The API will make a best effort to complete the request in the specified seconds or time out.

Request-Timeout-Millis integer

Possible values: >= 1

The API will make a best effort to complete the request in the specified milliseconds or time out.

application/json

Body

enabled boolean

Set whether or not the corpus is enabled. If unset then the corpus will remain in the same state.

name string

The name for the corpus. If unset or null, then the corpus will remain in the same state.

description string

Description of the corpus. If unset or null, then the corpus will remain in the same state.

Responses

200

Successfully modified the corpus.

application/json

Schema

Schema

id string

Possible values: Value must match regular expression crp_[0-9]+$

Vectara ID of the corpus.

key CorpusKey

Possible values: <= 50 characters, Value must match regular expression [a-zA-Z0-9_\=\-]+$

A user-provided key for a corpus.

name string

Name for the corpus. This value defaults to the key.

description string

Corpus description.

enabled boolean

Specifies whether the corpus is enabled or not.

chat_history_corpus boolean

Indicates that this corpus does not store documents and stores chats instead.

queries_are_answers boolean

Default value: false

Queries made to this corpus are considered answers, and not questions. This swaps the semantics of the encoder used at query time.

documents_are_questions boolean

Default value: false

Documents inside this corpus are considered questions, and not answers. This swaps the semantics of the encoder used at indexing.

encoder_id stringdeprecated

Possible values: Value must match regular expression enc_[0-9]+$

The encoder used by the corpus. Deprecated: use encoder_name instead

encoder_name string

The encoder used by the corpus.

filter_attributes object[]
The new filter attributes of the corpus.
Array [
name stringrequired
The JSON path of the filter attribute in a document or document part metadata.
level stringrequired
Possible values: [document, part]
Indicates whether this is a document or document part metadata filter.
description string
Description of the filter. May be omitted.
indexed boolean
Default value: true
Indicates whether an index should be created for the filter. Creating an index will improve query latency when using the filter.
type stringrequired
Possible values: [integer, real_number, text, boolean, list[integer], list[real_number], list[text]]
The value type of the filter.
]
custom_dimensions object[]
The custom dimensions of all document parts inside the corpus.
Array [
name stringrequired
The name of the custom dimension.
description string
Description of the custom dimension.
indexing_default double
Default value of a custom dimension on a document part if the custom dimension value is not specified when the document part is indexed.
A value of 0 means that custom dimension is not considered.
querying_default double
Default value of a custom dimension for a query if the value of the custom dimension is not specified when querying the corpus.
A value of 0 means that custom dimension is not considered.
]
limits object

used_docs int64

The number of documents contained in the corpus.

used_parts int64

The number of document parts contained in the corpus.

used_bytes int64

NOTE: This field is currently not populated by the system. The number of bytes contained in the corpus. This includes the document metadata, document part metadata, and document contents.

used_characters int64

The number of characters contained in the corpus. This includes the document metadata, document part metadata, and document contents.

max_bytes int64

NOTE: This field is currently not populated by the system. The maximum number of bytes the corpus can be.

max_metadata_bytes int64

The maximum size that metadata can be on documents.

index_rate int64

NOTE: This field is currently not populated by the system. The maximum per-second addition of new documents to corpus.

created_at date-time

Indicates when the corpus was created.

Example (from schema)

{
  "id": "string",
  "key": "my-corpus",
  "name": "string",
  "description": "string",
  "enabled": true,
  "chat_history_corpus": true,
  "queries_are_answers": false,
  "documents_are_questions": false,
  "encoder_name": "boomerang",
  "filter_attributes": [
    {
      "name": "Title",
      "level": "document",
      "description": "The title of the document.",
      "indexed": true,
      "type": "text"
    }
  ],
  "custom_dimensions": [
    {
      "name": "importance",
      "description": "Product importance.",
      "indexing_default": 0,
      "querying_default": 0
    }
  ],
  "limits": {
    "used_docs": 0,
    "used_parts": 0,
    "used_bytes": 0,
    "used_characters": 0,
    "max_bytes": 0,
    "max_metadata_bytes": 0,
    "index_rate": 0
  },
  "created_at": "2024-10-29T01:49:06.202Z"
}

403

Permissions do not allow updating the corpus.

application/json

Schema

Schema

messages message[]

The messages describing why the error occurred.

request_id string

The ID of the request that can be used to help Vectara support debug what went wrong.

Example (from schema)

{
  "messages": [
    "Internal server error."
  ],
  "request_id": "string"
}

404

Corpus not found.

application/json

Schema

Schema

id string

The ID cannot be found.

messages message[]
request_id string

ID of the request that can be used to help Vectara support debug what went wrong.

Example (from schema)

{
  "id": "string",
  "messages": [
    "string"
  ],
  "request_id": "string"
}

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