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This is an API for accessing data and analysis generated by the comp-syn python package and the comp-syn group.
| query required | string Examples:
Text to match |
| include_in_response | string Default: "colorgram" Enum: "all" "colorgram" "vectors" Which attributes to include in the returned data |
| filter | string (internal) Extra filter to include in the query in lucene query syntax |
| size | number [ 1 .. 100 ] Default: 12 Maximum number of hits to return |
| format | string Default: "json" Enum: "json" "html" Format of response, facilitates a nice browser experience with the API |
{- "hits": [
- {
- "query": "string",
- "language": "string",
- "experiment_name": "string",
- "trial_id": "string",
- "hostname": "string",
- "trial_timestamp": "2019-08-24T14:15:22Z",
- "jzazbz_dist": [
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0
], - "jzazbz_dist_std": [
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0
], - "rgb_dist": [
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0
], - "rgb_dist_std": [
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0
], - "rgb_ratio": [
- 0,
- 0,
- 0
], - "rgb_hex": "string",
- "jzazbz_wavelet_embedding": [
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
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- 0,
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- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0
], - "rgb_wavelet_embedding": [
- 0,
- 0,
- 0,
- 0,
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- 0,
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], - "grey_wavelet_embedding": [
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
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], - "presigned_colorgram_url": "string"
}
]
}| query required | string Text to match |
| include_in_response | string Default: "colorgram" Enum: "all" "colorgram" "vectors" Which attributes to include in the returned data |
| filter | string (internal) Extra filter to include in the query in lucene query syntax |
| size | number [ 1 .. 100 ] Default: 12 Maximum number of hits to return |
| format | string Default: "json" Enum: "json" "html" Format of response, facilitates a nice browser experience with the API |
| use_field | string Default: "jzazbz_dist" Enum: "jzazbz_dist" "jzazbz_dist_std" "rgb_dist" Which dense_vector field to use for finding similar hits |
| score_function | string Default: "min_js_divergence" Enum: "l1norm" "l2norm" "cosineSimilarity" "min_js_divergence" "max_js_divergence" Which scoring function to use for comparing embeddings. The js_divergence scoring methods use a similar implementation as comp-syn. For the other scoring functions, see the elasticsearch documentation |
{- "hits": [
- {
- "query": "string",
- "language": "string",
- "experiment_name": "string",
- "trial_id": "string",
- "hostname": "string",
- "trial_timestamp": "2019-08-24T14:15:22Z",
- "jzazbz_dist": [
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0
], - "jzazbz_dist_std": [
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0
], - "rgb_dist": [
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0
], - "rgb_dist_std": [
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0
], - "rgb_ratio": [
- 0,
- 0,
- 0
], - "rgb_hex": "string",
- "jzazbz_wavelet_embedding": [
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
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], - "rgb_wavelet_embedding": [
- 0,
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], - "grey_wavelet_embedding": [
- 0,
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- 0,
- 0,
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- 0,
- 0,
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], - "presigned_colorgram_url": "string"
}
]
}