garden.engram.defs

{
  "description": "A UTF-8 byte range in a snippet's text, like a rich-text facet's index.",
  "properties": {
    "byteEnd": {
      "minimum": 0,
      "type": "integer"
    },
    "byteStart": {
      "minimum": 0,
      "type": "integer"
    }
  },
  "required": [
    "byteStart",
    "byteEnd"
  ],
  "type": "object"
}

{
  "description": "A vector for a memory, computed by its author from the memory's text and tags (with the space's document prefix).",
  "properties": {
    "dims": {
      "maximum": 16000,
      "minimum": 1,
      "type": "integer"
    },
    "encoding": {
      "description": "f16le: little-endian IEEE half precision, 2 bytes per dimension.",
      "knownValues": [
        "f16le"
      ],
      "type": "string"
    },
    "model": {
      "maxLength": 256,
      "type": "string"
    },
    "modelDigest": {
      "maxLength": 256,
      "type": "string"
    },
    "vector": {
      "maxLength": 32000,
      "type": "bytes"
    }
  },
  "required": [
    "model",
    "modelDigest",
    "dims",
    "encoding",
    "vector"
  ],
  "type": "object"
}

{
  "properties": {
    "rank": {
      "description": "The result's rank by keyword score among the results.",
      "minimum": 1,
      "type": "integer"
    },
    "score": {
      "description": "The BM25 keyword score × 100.",
      "minimum": 0,
      "type": "integer"
    },
    "terms": {
      "description": "The query terms the memory contains: per query word, its exact form if present, else its stem, else an identifier's parts.",
      "items": {
        "ref": "#termMatch",
        "type": "ref"
      },
      "type": "array"
    }
  },
  "required": [
    "rank",
    "score",
    "terms"
  ],
  "type": "object"
}

{
  "description": "Why a search result matched.",
  "properties": {
    "fusion": {
      "description": "Hybrid results: how the vector and keyword scores were combined.",
      "knownValues": [
        "convex",
        "rrf"
      ],
      "type": "string"
    },
    "keyword": {
      "description": "Present when any query term occurs in the memory.",
      "ref": "#keywordMatch",
      "type": "ref"
    },
    "snippet": {
      "ref": "#snippetView",
      "type": "ref"
    },
    "vector": {
      "ref": "#vectorMatch",
      "type": "ref"
    }
  },
  "type": "object"
}

{
  "properties": {
    "author": {
      "format": "did",
      "type": "string"
    },
    "cid": {
      "format": "cid",
      "type": "string"
    },
    "createdAt": {
      "format": "datetime",
      "type": "string"
    },
    "indexedAt": {
      "format": "datetime",
      "type": "string"
    },
    "match": {
      "description": "Hybrid and keyword search results only: why the memory matched.",
      "ref": "#matchView",
      "type": "ref"
    },
    "similarity": {
      "description": "Search results only: cosine similarity to the query, scaled to 0-1000. Absent from keyword-only results.",
      "maximum": 1000,
      "minimum": 0,
      "type": "integer"
    },
    "source": {
      "type": "string"
    },
    "tags": {
      "items": {
        "type": "string"
      },
      "type": "array"
    },
    "text": {
      "type": "string"
    },
    "uri": {
      "description": "The memory's space record URI: at://{authority}/space/{spaceType}/{skey}/{author}/garden.engram.memory/{rkey}.",
      "type": "string"
    }
  },
  "required": [
    "uri",
    "cid",
    "author",
    "text",
    "tags",
    "createdAt",
    "indexedAt"
  ],
  "type": "object"
}

{
  "description": "An embedding model, identified exactly. Vectors are comparable only when all three fields match.",
  "properties": {
    "dims": {
      "maximum": 16000,
      "minimum": 1,
      "type": "integer"
    },
    "model": {
      "description": "The model's name, such as nomic-embed-text.",
      "maxLength": 256,
      "type": "string"
    },
    "modelDigest": {
      "description": "Pins the exact model build, such as Ollama's sha256 digest of the model. A name can point to different weights over time.",
      "maxLength": 256,
      "type": "string"
    }
  },
  "required": [
    "model",
    "modelDigest",
    "dims"
  ],
  "type": "object"
}

{
  "properties": {
    "field": {
      "knownValues": [
        "text",
        "tags",
        "source"
      ],
      "type": "string"
    },
    "highlights": {
      "items": {
        "ref": "#byteSlice",
        "type": "ref"
      },
      "type": "array"
    },
    "text": {
      "description": "Up to about 200 characters of the field around the matches, with … where cut.",
      "type": "string"
    }
  },
  "required": [
    "field",
    "text",
    "highlights"
  ],
  "type": "object"
}

{
  "properties": {
    "field": {
      "knownValues": [
        "text",
        "tags",
        "source"
      ],
      "type": "string"
    },
    "kind": {
      "knownValues": [
        "exact",
        "stem",
        "part"
      ],
      "type": "string"
    },
    "term": {
      "description": "The normalized term; stems start with ~.",
      "type": "string"
    }
  },
  "required": [
    "term",
    "kind",
    "field"
  ],
  "type": "object"
}

{
  "properties": {
    "rank": {
      "description": "The result's rank by similarity among the results.",
      "minimum": 1,
      "type": "integer"
    },
    "similarity": {
      "description": "Cosine similarity, scaled to 0-1000.",
      "maximum": 1000,
      "minimum": 0,
      "type": "integer"
    }
  },
  "required": [
    "rank",
    "similarity"
  ],
  "type": "object"
}