How to Test DeepSeek Chat API in Postman (Based on Your Python Code)

#openai #deepseek #ai #beginners

When working with language models like DeepSeek or OpenAI-compatible APIs in your Python code, it’s often useful to test requests manually using Postman. This guide shows you how to replicate your Python OpenAI SDK call using raw HTTP requests in Postman.

🧠 The Goal

Your Python code does the following:

import os
from dotenv import load_dotenv
from openai import OpenAI

# Load .env variables
load_dotenv()

# Read values from environment
api_key = os.getenv("OPENAI_API_KEY")
base_url = os.getenv("OPENAI_BASE_URL")

# Create OpenAI client
client = OpenAI(api_key=api_key, base_url=base_url)

# Send chat completion request
response = client.chat.completions.create(
    model="deepseek-chat",
    messages=[
        {"role": "system", "content": "You are a helpful assistant"},
        {"role": "user", "content": "Hello"},
    ],
    stream=False
)

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

Now let’s test the same call in Postman.

✅ Step 1: Set the URL and HTTP Method

Method: POST
URL:

  https://api.deepseek.com/v1/chat/completions

This matches the base_url + /v1/chat/completions endpoint used by the OpenAI-compatible API.

✅ Step 2: Set Headers

In Postman's Headers tab, add the following key-value pairs:

Key	Value
`Authorization`	`Bearer YOUR_API_KEY` (e.g. `sk-abc123...`)
`Content-Type`	`application/json`

🔐 Replace YOUR_API_KEY with your actual API key from your .env file.

✅ Step 3: Configure the JSON Body

Go to the Body tab → Select raw → Set type to JSON.

Paste this JSON body:

{
  "model": "deepseek-chat",
  "messages": [
    { "role": "system", "content": "You are a helpful assistant" },
    { "role": "user", "content": "Hello" }
  ],
  "stream": false
}

This mirrors your Python code’s messages and model.

✅ Step 4: Send the Request

Click the Send button. You should get a response like:

{
    "id": "7e1894a2-12d7-4649-864a-da8984118be3",
    "object": "chat.completion",
    "created": 1752505878,
    "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_8802369eaa_prod0623_fp8_kvcache"
}

🎉 That means it’s working just like your Python script!

🛠 Bonus: Use Postman Environment Variables

To avoid hardcoding sensitive values:

In Postman → Environments, create a new environment.
Add:

API_BASE_URL: https://api.deepseek.com
API_KEY: sk-your-key

In your request:

URL: {{API_BASE_URL}}/v1/chat/completions
Header: Authorization: Bearer {{API_KEY}}

Now your requests are portable and secure.

📌 Summary

Task	Equivalent in Postman
`api_key`	`Authorization: Bearer YOUR_API_KEY` header
`base_url`	Postman request URL (`https://api.deepseek.com`)
JSON `messages` list	Raw request body in JSON format
`.env` variables	Postman environment variables