Completions

Create Completion

POST /completions

Generate text completions using the selected Large Language Model (LLM).

Creates a completion for the provided prompt and parameters.

POSThttps://api.portkey.ai/v1/completions

Body

model*any of

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.

prompt*nullable one of

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.

best_ofnullable integer

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.

echonullable boolean

Echo back the prompt in addition to the completion

frequency_penaltynullable number

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.

logit_biasnullable object

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 (which works for both GPT-2 and GPT-3) 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.

logprobsnullable integer

Include the log probabilities on the logprobs most likely 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.

max_tokensnullable integer

The maximum number of tokens to generate 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.

nnullable integer

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.

presence_penaltynullable number

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.

seednullable integer

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.

stopnullable one of

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

streamnullable boolean

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.

suffixnullable string

The suffix that comes after a completion of inserted text.

temperaturenullable number

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.

top_pnullable number

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.

userstring

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

Response

Body

id*string

A unique identifier for the completion.

choices*array of object

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

created*integer

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

model*string

The model used for completion.

system_fingerprintstring

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.

object*enum

The object type, which is always "text_completion"

text_completion

usageCompletionUsage (object)

Usage statistics for the completion request.

Request

const response = await fetch('https://api.portkey.ai/v1/completions', {
    method: 'POST',
    headers: {
      "Content-Type": "application/json"
    },
    body: JSON.stringify({
      "model": "text",
      "prompt": "This is a test."
    }),
});
const data = await response.json();

Response

{
  "id": "text",
  "choices": [
    {
      "finish_reason": "stop",
      "logprobs": {
        "text_offset": [],
        "token_logprobs": [
          0
        ],
        "tokens": [
          "text"
        ],
        "top_logprobs": []
      },
      "text": "text"
    }
  ],
  "model": "text",
  "system_fingerprint": "text",
  "object": "text_completion",
  "usage": {}
}

The request body for this endpoint is structured to generate text completions based on a given prompt and model selection. The response will be a Completion Object.

Pass the config parameters for the request in the headers as defined here.

Portkey automatically transforms the parameters for LLMs other than OpenAI. If some parameters don't exist in the other LLMs, they will be dropped.

SDK Usage

The completions.create method in the Portkey SDK allows you to generate text completions using various LLMs. This method provides a straightforward interface for requesting text completions similar to the OpenAI API.

Method Signature

portkey.completions.create(requestParams[configParams]);

# with only request params
portkey.completions.create(requestParams);
# with request and config params
portkey.with_options(configParams).completions.create(requestParams);

For REST API examples, scroll here.

Parameters

requestParams (Object): Parameters for the completion request. These parameters should include the prompt and model, and are transformed automatically by Portkey for LLMs other than OpenAI. Unsupported parameters for other LLMs will be dropped.
configParams (Object): Additional configuration options for the request. This is an optional parameter that can include custom config options for this specific request. These will override the configs set in the Portkey Client.

Example Usage

import Portkey from 'portkey-ai';

// Initialize the Portkey client
const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY",  // Replace with your Portkey API key
    virtualKey: "VIRTUAL_KEY"   // Optional: For virtual key management
});

// Generate a text completion
async function getTextCompletion() {
    const completion = await portkey.completions.create({
        prompt: "Say this is a test",
        model: "gpt-3.5-turbo-instruct",
    });

    console.log(completion);
}
await getTextCompletion();

// Generate a streaming text completion
async function getTextCompletionStream(){
    const completionStream = await portkey.completions.create({
        prompt: "Continuously stream this test",
        model: "gpt-3.5-turbo-instruct",
        stream: true
    });

    for await (const chunk of completionStream) {
        console.log(chunk.content);
    }
}
await getTextCompletionStream();

// Generate a text completion with config params
async function getTextCompletionWithConfig() {
    const completion = await portkey.completions.create({
        prompt: "Say this is a test with specific config",
        model: "gpt-3.5-turbo-instruct",
    }, {config: "custom-config-123"});

    console.log(completion);
}
await getTextCompletionWithConfig();

from portkey_ai import Portkey

# Initialize the Portkey client
portkey = Portkey(
    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
    virtual_key="VIRTUAL_KEY"   # Optional: For virtual key management
)

# Generate a text completion
def get_text_completion():
    completion = portkey.completions.create(
        prompt="Say this is a test",
        model="gpt-3.5-turbo-instruct"
    )
    print(completion)

get_text_completion()

# Example with config parameters
def get_chat_completion_with_config():
    completion = portkey.with_options({'config': 'sample-7g5tr4'}).completions.create(
        messages=[{'role': 'user', 'content': 'Say this is a test'}],
        model='gpt-3.5-turbo-instruct'
    )
    print(completion)

get_chat_completion_with_config()

# Generate a streaming chat completion
async def get_chat_completion_stream():
    completion_stream = portkey.completions.create(
        messages=[{'role': 'user', 'content': 'Say this is a test'}],
        model='gpt-3.5-turbo-instruct',
        stream=True
    })

    for chunk in completion:
        print(chunk.choices[0].delta)

await get_chat_completion_stream()

REST API Example

In REST calls, x-portkey-api-key is a compulsory header, it can be paired with the following options for sending provider details:

x-portkey-provider & Authorization (or similar auth headers)
x-portkey-virtual-key
x-portkey-config

Example request using Provider + Auth:

curl "https://api.portkey.ai/v1/completions" \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-provider: openai" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-3.5-turbo-instruct",
    "prompt": "Hello!"
  }'

Example request using Virtual Key:

curl "https://api.portkey.ai/v1/completions" \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-virtual-key: openai-virtual-key" \
  -d '{
    "model": "gpt-3.5-turbo-instruct",
    "prompt": "Hello!"
  }'

Example request using Config:

curl "https://api.portkey.ai/v1/completions" \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-config: config-key" \
  -d '{
    "model": "gpt-3.5-turbo-instruct",
    "prompt": "Hello!"
  }'

You can send 3 other headers in your Portkey requests

x-portkey-trace-id: Send trace id
x-portkey-metadata: Send custom metadata
x-portkey-cache-force-refresh: Force refresh cache for this request

Example request using these 3:

curl "https://api.portkey.ai/v1/completions" \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -H "x-portkey-config: config-key" \
  -H "x-portkey-trace-id: $UNIQUE_TRACE_ID" \
  -H "x-portkey-metadata: {\"_user\":\"john\"}" \
  -H "x-portkey-cache-force-refresh: True" \
  -d '{
    "model": "gpt-3.5-turbo-instruct",
    "prompt": "Hello!"
  }'

Response Format

The response will conform to the Text Completions Object schema from the Portkey API, typically including the generated text based on the prompt and the selected model.

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Last updated 25 days ago