Embeddings

POST 
/embed

Embeds a text using a specific model. Resulting vectors that can be used for downstream tasks (e.g. semantic similarity) and models (e.g. classifiers). To obtain a valid model, use GET /model-settings.

Request

Query Parameters

nice boolean

Setting this to True, will signal to the API that you intend to be nice to other users by de-prioritizing your request below concurrent ones.

application/json

Bodyrequired

modelstringrequired
Name of model to use. A model name refers to a model architecture (number of parameters among others). Always the latest version of model is used. The model output contains information as to the model version.
hostingHosting (string)nullable
Optional parameter that specifies which datacenters may process the request. You can either set the parameter to "aleph-alpha" or omit it (defaulting to null).
Not setting this value, or setting it to null, gives us maximal flexibility in processing your request in our own datacenters and on servers hosted with other providers. Choose this option for maximum availability.
Setting it to "aleph-alpha" allows us to only process the request in our own datacenters. Choose this option for maximal data privacy.
Possible values: [aleph-alpha, null]
prompt objectrequired
This field is used to send prompts to the model. A prompt can either be a text prompt or a multimodal prompt. A text prompt is a string of text. A multimodal prompt is an array of prompt items. It can be a combination of text, images, and token ID arrays.
In the case of a multimodal prompt, the prompt items will be concatenated and a single prompt will be used for the model.
Tokenization:
• Token ID arrays are used as as-is.
• Text prompt items are tokenized using the tokenizers specific to the model.
• Each image is converted into 144 tokens.
oneOf
Text Prompt
string
The text to be completed. Unconditional completion can be started with an empty string (default). The prompt may contain a zero shot or few shot task.
Multimodal
Array [
oneOf
Text
typestringrequired
Possible values: [text]
datastringrequired
controls object[]
Array [
startintegerrequired
Starting character index to apply the factor to.
lengthintegerrequired
The amount of characters to apply the factor to.
factornumberrequired
Factor to apply to the given token in the attention matrix.
• 0 <= factor < 1 => Suppress the given token
• factor == 1 => identity operation, no change to attention
• factor > 1 => Amplify the given token
token_overlapstring
What to do if a control partially overlaps with a text token.
If set to "partial", the factor will be adjusted proportionally with the amount of the token it overlaps. So a factor of 2.0 of a control that only covers 2 of 4 token characters, would be adjusted to 1.5. (It always moves closer to 1, since 1 is an identity operation for control factors.)
If set to "complete", the full factor will be applied as long as the control overlaps with the token at all.
Possible values: [partial, complete]
Default value: partial
]
Image
typestringrequired
Possible values: [image]
datastringrequired
An image send as part of a prompt to a model. The image is represented as base64.
Note: The models operate on square images. All non-square images are center-cropped before going to the model, so portions of the image may not be visible.
You can supply specific cropping parameters if you like, to choose a different area of the image than a center-crop. Or, you can always transform the image yourself to a square before sending it.
xinteger
x-coordinate of top left corner of cropping box in pixels
yinteger
y-coordinate of top left corner of cropping box in pixels
sizeinteger
Size of the cropping square in pixels
controls object[]
Array [
rect objectrequired
Bounding box in logical coordinates. From 0 to 1. With (0,0) being the upper left corner, and relative to the entire image.
Keep in mind, non-square images are center-cropped by default before going to the model. (You can specify a custom cropping if you want.). Since control coordinates are relative to the entire image, all or a portion of your control may be outside the "model visible area".
leftnumberrequired
x-coordinate of top left corner of the control bounding box. Must be a value between 0 and 1, where 0 is the left corner and 1 is the right corner.
topnumberrequired
y-coordinate of top left corner of the control bounding box Must be a value between 0 and 1, where 0 is the top pixel row and 1 is the bottom row.
widthnumberrequired
width of the control bounding box Must be a value between 0 and 1, where 1 means the full width of the image.
heightnumberrequired
height of the control bounding box Must be a value between 0 and 1, where 1 means the full height of the image.
factornumberrequired
Factor to apply to the given token in the attention matrix.
• 0 <= factor < 1 => Suppress the given token
• factor == 1 => identity operation, no change to attention
• factor > 1 => Amplify the given token
token_overlapstring
What to do if a control partially overlaps with an image token.
If set to "partial", the factor will be adjusted proportionally with the amount of the token it overlaps. So a factor of 2.0 of a control that only covers half of the image "tile", would be adjusted to 1.5. (It always moves closer to 1, since 1 is an identity operation for control factors.)
If set to "complete", the full factor will be applied as long as the control overlaps with the token at all.
Possible values: [partial, complete]
Default value: partial
]
Token Ids
typestringrequired
Possible values: [token_ids]
datainteger[]required
controls object[]
Array [
indexintegerrequired
Index of the token, relative to the list of tokens IDs in the current prompt item.
factornumberrequired
Factor to apply to the given token in the attention matrix.
• 0 <= factor < 1 => Suppress the given token
• factor == 1 => identity operation, no change to attention
• factor > 1 => Amplify the given token
]
]
layersinteger[]
A list of layer indices from which to return embeddings.
- Index 0 corresponds to the word embeddings used as input to the first transformer layer - Index 1 corresponds to the hidden state as output by the first transformer layer, index 2 to the output of the second layer etc. - Index -1 corresponds to the last transformer layer (not the language modelling head), index -2 to the second last
tokensbooleannullable
Flag indicating whether the tokenized prompt is to be returned (True) or not (False)
poolingstring[]
Pooling operation to use. Pooling operations include:
- mean: Aggregate token embeddings across the sequence dimension using an average. - weighted_mean: Position weighted mean across sequence dimension with latter tokens having a higher weight. - max: Aggregate token embeddings across the sequence dimension using a maximum. - last_token: Use the last token. - abs_max: Aggregate token embeddings across the sequence dimension using a maximum of absolute values.
typestringnullable
Explicitly set embedding type to be passed to the model. This parameter was created to allow for semantic_embed embeddings and will be deprecated. Please use the semantic_embed-endpoint instead.
normalizeboolean
Return normalized embeddings. This can be used to save on additional compute when applying a cosine similarity metric.
Default value: false
contextual_control_thresholdnumbernullable
If set to null, attention control parameters only apply to those tokens that have explicitly been set in the request. If set to a non-null value, we apply the control parameters to similar tokens as well. Controls that have been applied to one token will then be applied to all other tokens that have at least the similarity score defined by this parameter. The similarity score is the cosine similarity of token embeddings.
Default value: null
control_log_additiveboolean
true: apply controls on prompt items by adding the log(control_factor) to attention scores. false: apply controls on prompt items by (attention_scores - -attention_scores.min(-1)) * control_factor
Default value: true

Responses

200

OK

application/json

Schema

Schema

model_versionstring
model name and version (if any) of the used model for inference
embeddingsobjectnullable
embeddings: - pooling: a dict with layer names as keys and and pooling output as values. A pooling output is a dict with pooling operation as key and a pooled embedding (list of floats) as values
tokensstring[]nullable
num_tokens_prompt_totalinteger
Number of tokens in the prompt.
Tokenization:
• Token ID arrays are used as as-is.
• Text prompt items are tokenized using the tokenizers specific to the model.
• Each image is converted into a fixed amount of tokens that depends on the chosen model.

Example (auto)

{
  "model_version": "2021-12",
  "embeddings": {
    "layer_0": {
      "max": [
        -0.053497314,
        0.0053749084,
        0.06427002,
        0.05316162,
        -0.0044059753,
        "..."
      ]
    },
    "layer_1": {
      "max": [
        0.14086914,
        -0.24780273,
        1.3232422,
        -0.07055664,
        1.2148438,
        "..."
      ]
    }
  },
  "tokens": null,
  "num_tokens_prompt_total": 42
}

Authorization: http

name: tokentype: httpscheme: bearerdescription: Can be generated in your [Aleph Alpha profile](https://app.aleph-alpha.com/profile)

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• HTTPCLIENT
• RESTSHARP

var client = new HttpClient();
var request = new HttpRequestMessage(HttpMethod.Post, "https://docs.aleph-alpha.com/embed");
request.Headers.Add("Accept", "application/json");
request.Headers.Add("Authorization", "Bearer <token>");
var content = new StringContent("{\n  \"model\": \"luminous-base\",\n  \"prompt\": \"An apple a day keeps the doctor away.\",\n  \"layers\": [\n    0,\n    1\n  ],\n  \"tokens\": false,\n  \"pooling\": [\n    \"max\"\n  ],\n  \"type\": \"default\"\n}", null, "application/json");
request.Content = content;
var response = await client.SendAsync(request);
response.EnsureSuccessStatusCode();
Console.WriteLine(await response.Content.ReadAsStringAsync());

Request Collapse all

Auth

Bearer Token

Parameters

nice — query

---truefalse

Body required

{
  "model": "luminous-base",
  "prompt": "An apple a day keeps the doctor away.",
  "layers": [
    0,
    1
  ],
  "tokens": false,
  "pooling": [
    "max"
  ],
  "type": "default"
}