OpenAI Chat completion

POST

openai

{region}

chat

completions

curl --request POST \
  --url https://api.langdock.com/openai/{region}/v1/chat/completions \
  --header 'Authorization: <authorization>' \
  --header 'Content-Type: application/json' \
  --data '{
  "model": "gpt-4o-mini",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user",
      "content": "Write a short poem about cats."
    }
  ]
}'

{
  "choices": [
    {
      "message": {
        "content": "In moonlit shadows soft they prowl,\nWith eyes aglow in night's dark cowl.",
        "role": "assistant"
      },
      "index": 0,
      "finish_reason": "stop",
      "logprobs": null
    }
  ],
  "created": 1721722200,
  "id": "chatcmpl-8o4sq3sSzGVqS0aQyjlXuuEGVZnSj",
  "model": "gpt-4o-2024-05-13",
  "object": "chat.completion",
  "system_fingerprint": "fp_asd28019bf",
  "usage": {
    "completion_tokens": 34,
    "prompt_tokens": 14,
    "total_tokens": 48
  }
}

In dedicated deployments, api.langdock.com maps to <Base URL>/api/public

Creates a model response for the given chat conversation. This endpoint follows the OpenAI API specification and the requests are sent to the Azure OpenAI endpoint.

To use the API you need an API key. Admins can create API keys in the settings.

All parameters from the OpenAI Chat Completion endpoint are supported according to the OpenAI specifications, with the following exceptions:

model: Currently only the o3-mini, o1-preview gpt-4o, gpt-4o-mini, gpt-4 and gpt-35-turbo models are supported.
- The list of available models might differ if you are using your own API keys in Langdock (“Bring-your-own-keys / BYOK”, see here for details). In this case, please reach out to your admin to understand which models are available in the API.
n: Not supported.
service_tier: Not supported.
parallel_tool_calls: Not supported.
stream_options: Not supported.

Rate limits

The rate limit for the Chat Completion endpoint is 500 RPM (requests per minute) and 60.000 TPM (tokens per minute). Rate limits are defined at the workspace level - and not at an API key level. Each model has its own rate limit. If you exceed your rate limit, you will receive a 429 Too Many Requests response.

Please note that the rate limits are subject to change, refer to this documentation for the most up-to-date information. In case you need a higher rate limit, please contact us at support@langdock.com.

Using OpenAI-compatible libraries

As the request and response format is the same as the OpenAI API, you can use popular libraries like the OpenAI Python library or the Vercel AI SDK to use the Langdock API.

Example using the OpenAI Python library

from openai import OpenAI
client = OpenAI(
  base_url="https://api.langdock.com/openai/eu/v1",
  api_key="<YOUR_LANGDOCK_API_KEY>"
)

completion = client.chat.completions.create(
  model="gpt-4o-mini",
  messages=[
    {"role": "user", "content": "Write a short poem about cats."}
  ]
)

print(completion.choices[0].message.content)

Example using the Vercel AI SDK in Node.js

import { streamText } from "ai";
import { createOpenAI } from "@ai-sdk/openai";

const langdockProvider = createOpenAI({
  baseURL: "https://api.langdock.com/openai/eu/v1",
  apiKey: "<YOUR_LANGDOCK_API_KEY>",
});

const result = await streamText({
  model: langdockProvider("gpt-4o-mini"),
  prompt: "Write a short poem about cats",
});

for await (const textPart of result.textStream) {
  process.stdout.write(textPart);
}

Headers

Authorization

string

required

API key as Bearer token. Format "Bearer YOUR_API_KEY"

Path Parameters

region

enum<string>

required

The region of the API to use.

Available options:

eu,

us

Body

application/json

messages

object[]

required

A list of messages comprising the conversation so far. Example Python code.

model

required

ID of the model to use. See the model endpoint compatibility table for details on which models work with the Chat API.

Example:

"gpt-4-turbo"

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

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 tokenizer) to an associated bias value from -100 to 100. 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.

logprobs

boolean | null

default:false

Whether to return log probabilities of the output tokens or not. If true, returns the log probabilities of each output token returned in the content of message.

top_logprobs

integer | null

An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. logprobs must be set to true if this parameter is used.

Required range: 0 <= x <= 20

max_tokens

integer | null

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

The total length of input tokens and generated tokens is limited by the model's context length. Example Python code for counting tokens.

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

response_format

object

An object specifying the format that the model must output. Compatible with GPT-4 Turbo and all GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106.

Setting to { "type": "json_object" } enables JSON mode, which guarantees the message the model generates is valid JSON.

Important: when using JSON mode, you must also instruct the model to produce JSON yourself via a system or user message. Without this, the model may generate an unending stream of whitespace until the generation reaches the token limit, resulting in a long-running and seemingly "stuck" request. Also note that the message content may be partially cut off if finish_reason="length", which indicates the generation exceeded max_tokens or the conversation exceeded the max context length.

seed

integer | null

This feature is in Beta. If specified, OpenAI's 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

stop

Up to 4 sequences where the API will stop generating further tokens.

stream

boolean | null

default:false

If set, partial message deltas will be sent, like in ChatGPT. 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.

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

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

tools

object[]

A list of tools the model may call. Currently, only functions are supported as a tool. Use this to provide a list of functions the model may generate JSON inputs for. A max of 128 functions are supported.

tool_choice

Controls which (if any) tool is called by the model. none means the model will not call any tool and instead generates a message. auto means the model can pick between generating a message or calling one or more tools. required means the model must call one or more tools. Specifying a particular tool via {"type": "function", "function": {"name": "my_function"}} forces the model to call that tool.

none is the default when no tools are present. auto is the default if tools are present.

Available options:

none,

auto,

required

user

string

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

Example:

"user-1234"

function_call

deprecated

Deprecated in favor of tool_choice.

Controls which (if any) function is called by the model. none means the model will not call a function and instead generates a message. auto means the model can pick between generating a message or calling a function. Specifying a particular function via {"name": "my_function"} forces the model to call that function.

none is the default when no functions are present. auto is the default if functions are present.

Available options:

none,

auto

functions

object[]

deprecated

Deprecated in favor of tools.

A list of functions the model may generate JSON inputs for.

Response

200 - application/json

Represents a chat completion response returned by model, based on the provided input.

string

required

A unique identifier for the chat completion.

choices

object[]

required

A list of chat completion choices. Can be more than one if n is greater than 1.

choices.finish_reason

enum<string>

required

The reason the model stopped generating tokens. This will be stop if the model hit a natural stop point or a provided stop sequence, length if the maximum number of tokens specified in the request was reached, content_filter if content was omitted due to a flag from OpenAI's content filters, tool_calls if the model called a tool, or function_call (deprecated) if the model called a function.

Available options:

stop,

length,

tool_calls,

content_filter,

function_call

choices.index

integer

required

The index of the choice in the list of choices.

choices.message

object

required

A chat completion message generated by the model.

choices.logprobs

object | null

required

Log probability information for the choice.

created

integer

required

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

model

string

required

The model used for the chat completion.

object

enum<string>

required

The object type, which is always chat.completion.

Available options:

chat.completion

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.

usage

object

Usage statistics for the completion request.

Anthropic Messages

curl --request POST \
  --url https://api.langdock.com/openai/{region}/v1/chat/completions \
  --header 'Authorization: <authorization>' \
  --header 'Content-Type: application/json' \
  --data '{
  "model": "gpt-4o-mini",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user",
      "content": "Write a short poem about cats."
    }
  ]
}'

{
  "choices": [
    {
      "message": {
        "content": "In moonlit shadows soft they prowl,\nWith eyes aglow in night's dark cowl.",
        "role": "assistant"
      },
      "index": 0,
      "finish_reason": "stop",
      "logprobs": null
    }
  ],
  "created": 1721722200,
  "id": "chatcmpl-8o4sq3sSzGVqS0aQyjlXuuEGVZnSj",
  "model": "gpt-4o-2024-05-13",
  "object": "chat.completion",
  "system_fingerprint": "fp_asd28019bf",
  "usage": {
    "completion_tokens": 34,
    "prompt_tokens": 14,
    "total_tokens": 48
  }
}

Completion API

Embedding API

Assistant API

Knowledge Folder API

Rate limits

Using OpenAI-compatible libraries

Example using the OpenAI Python library

Example using the Vercel AI SDK in Node.js

Headers

Path Parameters

Body

Response

Completion API

Embedding API

Assistant API

Knowledge Folder API

​Rate limits

​Using OpenAI-compatible libraries

​Example using the OpenAI Python library

​Example using the Vercel AI SDK in Node.js

Headers

Path Parameters

Body

Response

Rate limits

Using OpenAI-compatible libraries

Example using the OpenAI Python library

Example using the Vercel AI SDK in Node.js