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POST
AI Completions Proxy Endpoint

Authorizations

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apikey
string
header
required

Headers

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X-Enkrypt-Deployment
string
required

The deployment saved name. E.g. test-deployment-1

Example:

"test-deployment"

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X-Enkrypt-Tags
string

Comma separated tags for the deployment if any

Example:

"openai-testing,unit-testing"

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X-Enkrypt-Refresh-Cache
boolean

Refresh the cache if deployment, policy or model data is stale

Example:

false

Body

application/json
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prompt
default:<|endoftext|>
required

The prompt(s) to generate completions for, encoded as a string, array of strings, array of tokens, or array of token arrays.

Note that <|endoftext|> is the document separator that the model sees during training, so if a prompt is not specified the model will generate as if from the beginning of a new document.

Example:

"This is a test."

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enkrypt_context
string

The context for the request if using relevancy or hallucion detection in output guardrails

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model

ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.

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best_of
integer | null
default:1

Generates best_of completions server-side and returns the "best" (the one with the highest log probability per token). Results cannot be streamed.

When used with n, best_of controls the number of candidate completions and n specifies how many to return – best_of must be greater than n.

Note: Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for max_tokens and stop.

Required range: 0 <= x <= 20
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echo
boolean | null
default:false

Echo back the prompt in addition to the completion

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frequency_penalty
number | null
default:0

Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.

See more information about frequency and presence penalties.

Required range: -2 <= x <= 2
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logit_bias
object | null

Modify the likelihood of specified tokens appearing in the completion.

Accepts a JSON object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100. You can use this tokenizer tool to convert text to token IDs. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.

As an example, you can pass {"50256": -100} to prevent the <|endoftext|> token from being generated.

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logprobs
integer | null

Include the log probabilities on the logprobs most likely output tokens, as well the chosen tokens. For example, if logprobs is 5, the API will return a list of the 5 most likely tokens. The API will always return the logprob of the sampled token, so there may be up to logprobs+1 elements in the response.

The maximum value for logprobs is 5.

Required range: 0 <= x <= 5
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max_tokens
integer | null
default:16

The maximum number of tokens that can be generated in the completion.

The token count of your prompt plus max_tokens cannot exceed the model's context length. Example Python code for counting tokens.

Required range: x >= 0
Example:

16

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n
integer | null
default:1

How many completions to generate for each prompt.

Note: Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for max_tokens and stop.

Required range: 1 <= x <= 128
Example:

1

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presence_penalty
number | null
default:0

Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.

See more information about frequency and presence penalties.

Required range: -2 <= x <= 2
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seed
integer | null

If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result.

Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.

Required range: -9223372036854776000 <= x <= 9223372036854776000
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stop
default:<|endoftext|>

Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.

Example:

"\n"

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stream
boolean | null
default:false

Whether to stream back partial progress. If set, tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message. Example Python code.

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stream_options
object | null

Options for streaming response. Only set this when you set stream: true.

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suffix
string | null

The suffix that comes after a completion of inserted text.

This parameter is only supported for gpt-3.5-turbo-instruct.

Example:

"test."

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temperature
number | null
default:1

What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.

We generally recommend altering this or top_p but not both.

Required range: 0 <= x <= 2
Example:

1

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top_p
number | null
default:1

An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.

We generally recommend altering this or temperature but not both.

Required range: 0 <= x <= 1
Example:

1

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user
string

A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.

Example:

"user-1234"

Response

200 - application/json

Successful Response

Represents a completion response from the API. Note: both the streamed and non-streamed response objects share the same shape (unlike the chat endpoint).

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id
string
required

A unique identifier for the completion.

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choices
object[]
required

The list of completion choices the model generated for the input prompt.

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created
integer
required

The Unix timestamp (in seconds) of when the completion was created.

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model
string
required

The model used for completion.

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object
enum<string>
required

The object type, which is always "text_completion"

Available options:
text_completion
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system_fingerprint
string

This fingerprint represents the backend configuration that the model runs with.

Can be used in conjunction with the seed request parameter to understand when backend changes have been made that might impact determinism.

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usage
object

Usage statistics for the completion request.

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enkrypt_policy_detections
object

The policy detections for the completion.