DeepSeek API Beginner Tutorial

In this tutorial, you will learn the DeepSeek API to interact with the AI model programmatically. An API (Application Programming Interface) is like a messenger that lets your code communicate with external services like DeepSeek. You send requests and the API returns responses.

Normal Workflow of DeepSeek

The user opens a web browser and goes to the DeepSeek website. The user types a prompt into a text box and hits enter or a send button. The browser then sends that prompt to a server. The server processes the request, checks authentication, and then sends the prompt to the DeepSeek AI model. The model generates a response, which goes back to the server, and then the server sends the response back to the user’s browser, which displays it to the user.

 

DeepSeek API

The DeepSeek API provides access to DeepSeek’s Large Language Models (LLMs), which are AI systems designed to understand and generate human-like text. The API is the interface used to interact with DeepSeek AI models.

An API is an interface to a service. It provides programmatic access to the LLMs without any interface like a web browser client. The DeepSeek API acts as a gateway to access and interact with DeepSeek’s Large Language Models (LLMs), like DeepSeek R1/ V3, allowing developers to utilize their capabilities for tasks like text generation, translation, and question answering through a simple interface.

 

 

What You’ll Need

To get started with the API, you need the following pre-requisites:

• Sign up for the DeepSeek Account (  https://deepseek.com/signup ) 
• API Key (from account dashboard)
• Basic programming knowledge

DeepSeek API Platform

The DeepSeek API Platform is a comprehensive AI service platform that provides advanced artificial intelligence capabilities through APIs (Application Programming Interfaces). It enables developers and businesses to integrate AI technologies into their application workflows without requiring extensive expertise in Machine Learning.

 

 

The DeepSeek API uses an API format compatible with OpenAI. You can use the OpenAI SDK to access the DeepSeek API. You can change the parameters like base_url and api_key and start using DeepSeek APIs.

To get the DeepSeek API key, follow the instructions outlined below:

• https://www.testingdocs.com/deepseek-api-platform/

 

The two configuration paramters are:

base_url ->  https://api.deepseek.com
api_key ->   DeepSeek API key

For example, OpenAI API

• https://www.testingdocs.com/openai-completions-api/

Steps to install OpenAI Python SDK are outlined here:

• https://www.testingdocs.com/install-the-openai-python-library/

Basic API Request (Python)

import requests
import os

API_KEY = os.getenv("DEEPSEEK_API_KEY")
ENDPOINT = "https://api.deepseek.com/v1/chat/completions"

response = requests.post(
 ENDPOINT,
 headers={"Authorization": f"Bearer {API_KEY}"},
 json={
 "model": "deepseek-chat",
 "messages": [{"role": "user", "content": "Hello DeepSeek!"}]
 }
)
print(response.json())
 

API Parameters

Authentication: Use your API key in the Authorization header

Endpoints: Different URLs for different tasks (e.g., /chat, /models)

 

Parameter	Description
model	AI model version (e.g., deepseek-chat)
temperature	Creativity control (0=factual, 2=creative)
max_tokens	Maximum response length

Example

import requests

# Replace with your DeepSeek API key
api_key = "<your api key goes here>"

# Example API endpoint
url = "https://api.deepseek.com/chat/completions"

# Headers with your API key for authentication
headers = {
 "Authorization": f"Bearer {api_key}",
 "Content-Type": "application/json"
}

# Sample API data you want to send -This will depend on the
# API endpoint you are using.

request = {
 "model": "deepseek-chat",
 "messages": [
 {"role": "system", "content": "You are an AI assistant."},
 {"role": "user", "content": "What is TestingDocs?"}
 ],
 "stream": False
}


# Make the GET or POST request depending on the API
response = requests.post(url, json=request, headers=headers)


# Check if the request was successful
if response.status_code == 200:
 print("Request was successful!")
 print(response.json()) # Print the JSON response from the API
else:
 print("Failed to get response")
 print(response.status_code)
 print(response.text)

Best Practices

Some of the best practices are as follows:

• 🔒 Store API keys in environment variables
• ⚠️ Handle API errors gracefully
• 📈 Start with simple requests first

Next Steps

Once you are familiar with different API endpoints and interact with them, you can start building AI applications.

1. Explore different API endpoints
2. Implement error handling
3. Build a simple chatbot interface

DeepSeek Limitations

Some of the DeepSeek limitations are outlined here:

• https://www.testingdocs.com/deepseeks-limitations/

Official DeepSeek Documentation

• https://docs.deepseek.com/api