Typesense Collection

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "$id": "https://typesense.org/schemas/typesense/collection.json",
  "title": "Typesense Collection",
  "description": "Schema for a Typesense collection definition including field schemas, embedding configuration, and indexing options.",
  "type": "object",
  "required": ["name", "fields"],
  "properties": {
    "name": {
      "type": "string",
      "description": "Unique name identifying the collection.",
      "minLength": 1,
      "maxLength": 255,
      "pattern": "^[a-zA-Z0-9_-]+$"
    },
    "fields": {
      "type": "array",
      "description": "Array of field definitions that describe the schema of documents in this collection.",
      "minItems": 1,
      "items": {
        "$ref": "#/$defs/Field"
      }
    },
    "default_sorting_field": {
      "type": "string",
      "description": "Name of the field to sort results by when no sort_by parameter is specified in a search query."
    },
    "token_separators": {
      "type": "array",
      "description": "Characters to use as token separators in addition to whitespace during indexing and search.",
      "items": {
        "type": "string",
        "maxLength": 1
      }
    },
    "symbols_to_index": {
      "type": "array",
      "description": "Characters that should be indexed as part of tokens rather than being stripped.",
      "items": {
        "type": "string",
        "maxLength": 1
      }
    },
    "enable_nested_fields": {
      "type": "boolean",
      "description": "Whether to enable indexing of nested object fields within documents."
    },
    "synonym_sets": {
      "type": "array",
      "description": "Names of synonym sets to attach to this collection for expanding search queries with synonyms.",
      "items": {
        "type": "string"
      }
    },
    "voice_query_model": {
      "type": "object",
      "description": "Configuration for voice query processing model.",
      "properties": {
        "model_name": {
          "type": "string",
          "description": "Name of the voice query model."
        }
      }
    },
    "metadata": {
      "type": "object",
      "description": "Arbitrary metadata to store alongside the collection definition."
    },
    "num_documents": {
      "type": "integer",
      "description": "Number of documents currently indexed in the collection. Read-only.",
      "minimum": 0
    },
    "created_at": {
      "type": "integer",
      "description": "Unix timestamp when the collection was created. Read-only."
    }
  },
  "$defs": {
    "Field": {
      "type": "object",
      "description": "Definition of a field within a collection schema.",
      "required": ["name", "type"],
      "properties": {
        "name": {
          "type": "string",
          "description": "Name of the field. Use .* suffix for dynamic fields matching a pattern."
        },
        "type": {
          "type": "string",
          "description": "Data type of the field.",
          "enum": [
            "string",
            "int32",
            "int64",
            "float",
            "bool",
            "string[]",
            "int32[]",
            "int64[]",
            "float[]",
            "bool[]",
            "auto",
            "object",
            "object[]",
            "geopoint",
            "geopoint[]",
            "image"
          ]
        },
        "optional": {
          "type": "boolean",
          "description": "Whether this field is optional in documents. Defaults to false."
        },
        "facet": {
          "type": "boolean",
          "description": "Whether this field can be used for faceted search. Defaults to false."
        },
        "index": {
          "type": "boolean",
          "description": "Whether this field should be indexed for searching. Defaults to true."
        },
        "sort": {
          "type": "boolean",
          "description": "Whether this field can be used for sorting results."
        },
        "infix": {
          "type": "boolean",
          "description": "Whether infix (substring) search is enabled for this field."
        },
        "locale": {
          "type": "string",
          "description": "Locale code for language-specific tokenization, e.g., ja for Japanese."
        },
        "reference": {
          "type": "string",
          "description": "Reference to a field in another collection for JOINs, in the format collection_name.field_name."
        },
        "num_dim": {
          "type": "integer",
          "description": "Number of dimensions for vector embedding fields. Required when type is float[] and the field is used for vector search.",
          "minimum": 1
        },
        "store": {
          "type": "boolean",
          "description": "Whether to store the field value for retrieval. Set to false to index-only."
        },
        "range_index": {
          "type": "boolean",
          "description": "Whether to build a range index for efficient numeric range filtering."
        },
        "stem": {
          "type": "boolean",
          "description": "Whether stemming is enabled for this field."
        },
        "stem_dictionary": {
          "type": "string",
          "description": "Name of a custom stemming dictionary to use for this field."
        },
        "drop": {
          "type": "boolean",
          "description": "Set to true during a collection update to drop this field from the schema."
        },
        "vec_dist": {
          "type": "string",
          "description": "Distance metric for vector nearest-neighbor search.",
          "enum": ["cosine", "ip"]
        },
        "embed": {
          "$ref": "#/$defs/EmbedConfig"
        }
      }
    },
    "EmbedConfig": {
      "type": "object",
      "description": "Configuration for automatically generating vector embeddings from one or more source fields.",
      "properties": {
        "from": {
          "type": "array",
          "description": "Field names whose values are concatenated and passed to the embedding model.",
          "items": {
            "type": "string"
          }
        },
        "model_config": {
          "type": "object",
          "description": "Configuration for the embedding model.",
          "properties": {
            "model_name": {
              "type": "string",
              "description": "Name of the embedding model. Built-in models include ts/all-MiniLM-L12-v2. External models from OpenAI, Google, and others are also supported."
            },
            "api_key": {
              "type": "string",
              "description": "API key for external embedding service authentication."
            },
            "url": {
              "type": "string",
              "format": "uri",
              "description": "URL of the external embedding service endpoint."
            },
            "access_token": {
              "type": "string",
              "description": "Access token for OAuth-based embedding services."
            },
            "client_id": {
              "type": "string",
              "description": "Client ID for OAuth-based embedding services."
            },
            "client_secret": {
              "type": "string",
              "description": "Client secret for OAuth-based embedding services."
            },
            "project_id": {
              "type": "string",
              "description": "Project ID for cloud-based embedding services."
            }
          }
        }
      }
    }
  }
}