Create a corpus

POST 
/v2/corpora

Create a corpus, which is a container to store documents and associated metadata.

Request

application/json

Body

key CorpusKeyrequired

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

A user-provided key for a corpus.

name string

The name for the corpus. This value defaults to the key.

description string

Description for the corpus.

queries_are_answers boolean

Default value: false

Queries made to this corpus are considered answers, and not questions.

documents_are_questions boolean

Default value: false

Documents inside this corpus are considered questions, and not answers.

encoder_id string

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

The encoder used by the corpus. This value defaults to the most recent Vectara encoder.

filter_attributes object[]
The new filter attributes of the corpus. If unset then the corpus will not have filter attributes.
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 a document or document part metadata filter.
description string
Description of the filter. May be omitted.
indexed boolean
Default value: true
Whether an index is 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[]
A custom dimension is an additional numerical field attached to a document part. You can then multiply this numerical field with a query time custom dimension of the same name. This allows boosting (or deboosting) document parts for arbitrary reasons. This feature is only enabled for Scale customers.
Array [
name stringrequired
The name 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.
]

Responses

201

The corpus has been created.

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 amd 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 string

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

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 a document or document part metadata filter.
description string
Description of the filter. May be omitted.
indexed boolean
Default value: true
Whether an index is 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.
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_bytes int64

The number of bytes contained in the corpus.

max_bytes int64

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

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_id": "string",
  "filter_attributes": [
    {
      "name": "Title",
      "level": "document",
      "description": "The title of the document.",
      "indexed": true,
      "type": "text"
    }
  ],
  "custom_dimensions": [
    {
      "name": "importance",
      "indexing_default": 0,
      "querying_default": 0
    }
  ],
  "limits": {
    "used_bytes": 0,
    "max_bytes": 0,
    "max_metadata_bytes": 0,
    "index_rate": 0
  },
  "created_at": "2024-07-11T20:03:46.377Z"
}

400

Invalid request body in the create corpus request.

application/json

Schema

Schema

field_errors object
The errors that relate to specific fields in the request.
property name* string
messages message[]
request_id string

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

Example (from schema)

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

403

Permissions do not allow creating a 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"
}

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