Encode::Encode

Generates a vector representation of text or images using the specified embedding model. If the model does not exist or has not been loaded, an error will be returned for that particular data item.

It is recommended that multiple pieces of content are specified in a single request to take advantage of batching, especially when running with an attached GPU. Recommended sizes can be found at embedds/model-details and batching will be automatically done.
----
Not all models support [text, images, image_uris] a breakdown of present support is outlined below:

ModelClass_INSTRUCTOR - requires: {text and data.instruction}
ModelClass_CLIP - any_of: {text, images}

application/json

Request Body required

batches object[]
repeated data items to generate encodings for
Array [
model_class apiModelClass
Possible values: [ModelClass_Unknown, ModelClass_CLIP, ModelClass_INSTRUCTOR, ModelClass_E5]
Default value: ModelClass_Unknown
model_name string
specific name of the model to apply the encoding transformation
text object
data object[]
Array [
id string
value string
bytes byte
instruction string
]
images object
data object[]
Array [
id string
value string
bytes byte
instruction string
]
image_uris object
data object[]
Array [
id string
value string
bytes byte
instruction string
]
]

Responses

200
default

A successful response.

application/json

Schema
Example (from schema)

Schema

results object[]
list of embedding results corresponding to the ordered content submitted
Array [
err_message string
explanation for why the content could not be encoded
embedding float[]
embedding representation of the the content
]

{
  "results": [
    {
      "err_message": "string",
      "embedding": [
        0
      ]
    }
  ]
}

An unexpected error response.

application/json

Schema
Example (from schema)

Schema

code int32
message string
details object[]
Array [
@type string
]

{
  "code": 0,
  "message": "string",
  "details": [
    {
      "@type": "string"
    }
  ]
}

POST /encode

Request

Body required

{
  "data": [
    {
      "model_class": "ModelClass_INSTRUCTOR",
      "model_name": "INSTRUCTOR_LARGE",
      "text": [
        "3D ActionSLAM: wearable person tracking ...",
        "Inside Gohar World and the Fine, Fantastical Art"
      ],
      "instructions": [
        "Represent the Science title:",
        "Represent the Magazine title:"
      ]
    }
  ]
}

curl / cURL
curl -L -X POST 'https://infrawhispers.github.io/encode' \
-H 'Content-Type: application/json' \
-H 'Accept: application/json' \
--data-raw '{
  "data": [
    {
      "model_class": "ModelClass_INSTRUCTOR",
      "model_name": "INSTRUCTOR_LARGE",
      "text": [
        "3D ActionSLAM: wearable person tracking ...",
        "Inside Gohar World and the Fine, Fantastical Art"
      ],
      "instructions": [
        "Represent the Science title:",
        "Represent the Magazine title:"
      ]
    }
  ]
}'

python / requests
curl -L -X POST 'https://infrawhispers.github.io/encode' \
-H 'Content-Type: application/json' \
-H 'Accept: application/json' \
--data-raw '{
  "data": [
    {
      "model_class": "ModelClass_INSTRUCTOR",
      "model_name": "INSTRUCTOR_LARGE",
      "text": [
        "3D ActionSLAM: wearable person tracking ...",
        "Inside Gohar World and the Fine, Fantastical Art"
      ],
      "instructions": [
        "Represent the Science title:",
        "Represent the Magazine title:"
      ]
    }
  ]
}'

go / native
curl -L -X POST 'https://infrawhispers.github.io/encode' \
-H 'Content-Type: application/json' \
-H 'Accept: application/json' \
--data-raw '{
  "data": [
    {
      "model_class": "ModelClass_INSTRUCTOR",
      "model_name": "INSTRUCTOR_LARGE",
      "text": [
        "3D ActionSLAM: wearable person tracking ...",
        "Inside Gohar World and the Fine, Fantastical Art"
      ],
      "instructions": [
        "Represent the Science title:",
        "Represent the Magazine title:"
      ]
    }
  ]
}'

nodejs / axios
curl -L -X POST 'https://infrawhispers.github.io/encode' \
-H 'Content-Type: application/json' \
-H 'Accept: application/json' \
--data-raw '{
  "data": [
    {
      "model_class": "ModelClass_INSTRUCTOR",
      "model_name": "INSTRUCTOR_LARGE",
      "text": [
        "3D ActionSLAM: wearable person tracking ...",
        "Inside Gohar World and the Fine, Fantastical Art"
      ],
      "instructions": [
        "Represent the Science title:",
        "Represent the Magazine title:"
      ]
    }
  ]
}'

Encode::Encode​

Request

Encode::Encode