Last Update: 2025/3/26

Deepseek Chat Completion API

The Deepseek Chat Completion API allows you to generate conversational responses using Deepseek's language models. This document provides an overview of the API endpoints, request parameters, and response structure.

Endpoint

POST https://platform.llmprovider.ai/v1/chat/completions

Request Headers

Header	Value
Authorization	Bearer YOUR_API_KEY
Content-Type	application/json

Request Body

The request body should be a JSON object with the following parameters:

Parameter	Type	Description
model	string	The model to use (e.g., deepseek-chat).
messages	array	A list of message objects representing the conversation history.
frequency_penalty	number	(Optional) Penalty for new tokens based on their frequency in the text so far. Required: -2 <= x <= 2
max_tokens	integer	(Optional) The maximum number of tokens to generate.
presence_penalty	number	(Optional) Penalty for new tokens based on their presence in the text so far. Required: -2 <= x <= 2
response_format	object	(Optional) An object specifying the format that the model must output.
stop	array	(Optional) Up to 4 sequences where the API will stop generating further tokens.
stream	boolean	(Optional) Whether to stream the response as it is generated.
temperature	number	(Optional) What sampling temperature to use, Required: 0 <= x <= 2.
top_p	number	(Optional) Top-p sampling probability. Required: 0 <= x <= 1
tools	array	(Optional) A list of tools the model may call.
tools_choice	object	(Optional)Controls which (if any) tool is called by the model.
logprobs	bool	(Optional) Whether to return log probabilities of the output tokens or not.
top_logprobs	integer	(Optional) 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. Required: 0 <= x <= 20

Example Request

{
  "messages": [
    {
      "content": "You are a helpful assistant",
      "role": "system"
    },
    {
      "content": "Hi",
      "role": "user"
    }
  ],
  "model": "deepseek-chat",
  "frequency_penalty": 0,
  "max_tokens": 2048,
  "presence_penalty": 0,
  "response_format": {
    "type": "text"
  },
  "stop": null,
  "stream": false,
  "stream_options": null,
  "temperature": 1,
  "top_p": 1,
  "tools": null,
  "tool_choice": "none",
  "logprobs": false,
  "top_logprobs": null
}

Response Body

The response body will be a JSON object containing the generated completions and other metadata.

Field	Type	Description
id	string	Unique identifier for the completion.
object	string	The type of object returned, usually chat.completion.
created	integer	Timestamp of when the completion was created.
model	string	The model used for the completion.
choices	array	A list of generated completion choices.
usage	object	Token usage statistics for the request.
system_fingerprint	string	This fingerprint represents the backend configuration that the model runs with.

Example Response

{
  "id": "0aa5ca3a-2fc0-42c3-a247-fe5897a45d46",
  "object": "chat.completion",
  "created": 1738923553,
  "model": "deepseek-chat",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Hello! How can I assist you today? 😊"
      },
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 9,
    "completion_tokens": 11,
    "total_tokens": 20,
    "prompt_tokens_details": {
      "cached_tokens": 0
    },
    "prompt_cache_hit_tokens": 0,
    "prompt_cache_miss_tokens": 9
  },
  "system_fingerprint": "fp_3a5770e1b4"
}

Example Request

Shell

curl -X POST https://platform.llmprovider.ai/v1/chat/completions \
-H "Authorization: Bearer $YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "deepseek-chat",
"messages": [
  {
    "role": "user",
    "content": "Hello!"
  }
]
}'

nodejs

const axios = require('axios');
const apiKey = 'YOUR_API_KEY';
const url = 'https://platform.llmprovider.ai/v1/chat/completions';

const data = {
    conversation_id: '',
    model: 'deepseek-chat',
    messages: [
        {
            role: 'user',
            content: 'Hello!'
        }
    ]
};

const headers = {
    'Authorization': `Bearer ${apiKey}`,
    'Content-Type': 'application/json'
};

axios.post(url, data, { headers })
.then(response => {
    console.log('Response:', response.data);
})
.catch(error => {
    console.error('Error:', error);
});

python

import requests
import json

api_key = 'YOUR_API_KEY'
url = 'https://platform.llmprovider.ai/v1/chat/completions'

headers = {
    'Authorization': f'Bearer {api_key}',
    'Content-Type': 'application/json'
}

data = {
    'model': 'deepseek-chat',
    'messages': [
        {
            'role': 'user',
            'content': 'Hello!'
        }
    ]
}

response = requests.post(url, headers=headers, data=json.dumps(data))

if response.status_code == 200:
    print('Response:', response.json())
else:
    print('Error:', response.status_code, response.text)

For more details, refer to the Deepseek API documentation.