Get One Document
Get one document
Gets a document using its ID.
GET /indexes/{index_name}/documents/{document_id}
Path parameters
| Name | Type | Description |
|---|---|---|
index_name |
String | name of the index |
document_id |
String | ID of the document |
Query parameters
| Search parameter | Type | Default value | Description |
|---|---|---|---|
expose_facets |
Boolean | False | If true, the document's tensor facets are returned. This is a list of objects. Each facet object contains document data and its associated embedding (found in the facet's _embedding field) |
Example
curl -XGET 'http://localhost:8882/indexes/my-first-index/documents/article_591?expose_facets=true'
mq.index("my-first-index").get_document(
document_id="article_591",
expose_facets=True
)
Response: 200 OK
{'Blurb': 'A rocket car is a car powered by a rocket engine. This treatise '
'proposes that rocket cars are the inevitable future of land-based '
'transport.',
'Title': 'Treatise on the viability of rocket cars',
'_id': 'article_152',
'_tensor_facets': [{'Title': 'Treatise on the viability of rocket cars',
'_embedding': [-0.10393160581588745,
0.0465407557785511,
-0.01760256476700306,
...]},
{'Blurb': 'A rocket car is a car powered by a rocket '
'engine. This treatise proposes that rocket cars '
'are the inevitable future of land-based '
'transport.',
'_embedding': [-0.045681700110435486,
0.056278493255376816,
0.022254955023527145,
...]}]
}
GET document request was sent with the expose_facets parameter set to true.
The _tensor_facets field is returned as a result. Within each facet, there is a key-value pair that
holds the content of the facet, and an _embedding field, which is the content's vector representation.
Get custom vector document
The custom_vector document field is special, as the content structure is not stored exactly the same as how it was input. Instead, the content field of the dictionary is stored as the value for the whole field (for use in lexical search, filtering, and highlights). The GET document request can be used to retrieve the vector field as the _embedding field, when the expose_facets parameter is set to true.
Example
# Random vector for example purposes. replace these with your own.
example_vector_1 = [i for i in range(512)]
# Add custom vector document
mq.index("my-first-index").add_documents(
[
{
"_id": "doc_0",
"my_custom_vector": {
"vector": example_vector_1, # Put your custom generated vector here
"content": "Your content goes here!",
},
}
],
tensor_fields=["my_custom_vector"],
mappings={"my_custom_vector": {"type": "custom_vector"}},
)
# Get the custom vector document
mq.index("my-first-index").get_document(document_id="doc_0", expose_facets=True)
Response: 200 OK
{
'_id': 'doc_0',
'my_custom_vector': 'Your content goes here!',
'_tensor_facets': [{'my_custom_vector': 'Your content goes here!',
'_embedding': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511]
}]
}
content and vector subfields, as they have been restructured in this way.