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Create Vector Database Collection

POST
/api/v3/organizations/{organisation}/ai/vector-db/collections

Creates a new vector database collection (knowledge base category) for semantic search. Collections store documents with embeddings for RAG (Retrieval Augmented Generation). * * Use Cases: * - Product documentation (‘docs’) * - Company policies (‘policies’) * - Support knowledge base (‘support’) * - Technical specifications (‘specs’)

Authorizations

Parameters

Path Parameters

organisation
required
string

The organisation ID

Request Body required

object
name
required

Collection name (used for reference)

string
product-documentation
description
string
Product user guides and API documentation
embeddingModel

Embedding model to use (default: amazon.titan-embed-text-v2:0)

string
amazon.titan-embed-text-v2:0
dimensions

Embedding dimensions (default: 1024)

integer
1024

Responses

201

Collection created successfully

object
success
boolean
true
collection
object
collectionId
string format: uuid
name
string
description
string
embeddingModel
string
dimensions
integer
message
string
Collection created successfully

400

Invalid request parameters

403

Access denied

409

Collection with this name already exists

500

Failed to create collection