Create Document Index | Vellum

1 from vellum import ( 2 DocumentIndexIndexingConfigRequest, 3 OpenAiVectorizerConfigRequest, 4 OpenAiVectorizerTextEmbedding3SmallRequest, 5 Vellum, 6 ) 7 8 client = Vellum( 9 api_version="YOUR_API_VERSION", 10 api_key="YOUR_API_KEY", 11 ) 12 client.document_indexes.create( 13 label="x", 14 name="x", 15 indexing_config=DocumentIndexIndexingConfigRequest( 16 vectorizer=OpenAiVectorizerTextEmbedding3SmallRequest( 17 config=OpenAiVectorizerConfigRequest(), 18 ), 19 ), 20 )

1 { 2 "id": "string", 3 "created": "2024-01-15T09:30:00Z", 4 "label": "string", 5 "name": "string", 6 "indexing_config": { 7 "vectorizer": { 8 "config": { 9 "add_openai_api_key": true 10 }, 11 "model_name": "text-embedding-3-small" 12 }, 13 "chunking": { 14 "chunker_name": "reducto-chunker", 15 "chunker_config": { 16 "character_limit": 1000 17 } 18 } 19 }, 20 "status": "ACTIVE" 21 }

Creates a new document index.

Authentication

X-API-KEYstring

API Key authentication via header

Request

This endpoint expects an object.

labelstringRequired>=1 character<=150 characters

A human-readable label for the document index

namestringRequired>=1 character<=150 characters

A name that uniquely identifies this index within its workspace

indexing_configobjectRequired

statusenumOptional

The current status of the document index

ACTIVE - Active
ARCHIVED - Archived

The current status of the document index * `ACTIVE` - Active * `ARCHIVED` - Archived

Allowed values:

copy_documents_from_index_idstringOptionalformat: "uuid"

Optionally specify the id of a document index from which you’d like to copy and re-index its documents into this newly created index

Response

idstringformat: "uuid"

createdstringformat: "date-time"

labelstring<=150 characters

A human-readable label for the document index

namestring<=150 characters