SGPVectorStoreCreateRequestBase

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://artifacts.apis.io/scale-ai/json-schema/scale-ai-sgpvector-store-create-request-base-schema.json",
  "title": "SGPVectorStoreCreateRequestBase",
  "x-generated": "2026-10-27",
  "x-method": "derived",
  "x-generator": "derive-json-schema.py",
  "x-source": "openapi/scale-ai-document-understanding-openapi.yml#/components/schemas/SGPVectorStoreCreateRequestBase",
  "properties": {
    "engine": {
      "type": "string",
      "const": "sgp_vector_store",
      "title": "Engine",
      "default": "sgp_vector_store"
    },
    "vector_store_metadata_schema": {
      "anyOf": [
        {
          "additionalProperties": {
            "type": "string",
            "enum": [
              "string",
              "int",
              "double",
              "boolean"
            ]
          },
          "type": "object"
        },
        {
          "type": "null"
        }
      ],
      "title": "Vector Store Metadata Schema",
      "description": "Schema of the vector store metadata. You can set metadata for parsed documents and they will be indexed as extra metadata in the vector store to filter on when searching."
    },
    "name": {
      "type": "string",
      "title": "Name",
      "description": "Name of the vector store"
    },
    "embedding_type": {
      "type": "string",
      "const": "base",
      "title": "Embedding Type",
      "description": "Type of embedding configuration for standard models",
      "default": "base"
    },
    "embedding_model": {
      "anyOf": [
        {
          "type": "string",
          "enum": [
            "sentence-transformers/all-MiniLM-L12-v2",
            "sentence-transformers/all-mpnet-base-v2",
            "sentence-transformers/multi-qa-distilbert-cos-v1",
            "sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
            "openai/text-embedding-ada-002",
            "openai/text-embedding-3-small",
            "openai/text-embedding-3-large",
            "embed-english-v3.0",
            "embed-english-light-v3.0",
            "embed-multilingual-v3.0",
            "gemini/text-embedding-005",
            "gemini/text-multilingual-embedding-002",
            "gemini/gemini-embedding-001"
          ]
        },
        {
          "type": "string"
        }
      ],
      "title": "Embedding Model",
      "description": "Embedding model to use for SGP Vector Store. e.g. openai/text-embedding-3-large"
    }
  },
  "type": "object",
  "required": [
    "name",
    "embedding_model"
  ]
}