🧪 Postman Collection to Test Your API – Complete Guide for Developers in 2025

💡 Introduction

APIs are the backbone of modern applications—whether you're building a DeepSeek-powered chatbot, a LangChain agent, or a simple CRUD backend for a web app. Testing and documenting your API properly is crucial to ensure:

• Functionality

• Reliability

• Developer onboarding

• Integration success

Enter Postman, the most popular platform for API development, testing, and collaboration. In this guide, you'll learn everything you need to know about creating, using, and sharing a Postman collection to test your API—whether it's local, deployed, or running on a remote container.

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✅ Table of Contents

1. What is Postman?

2. Why Use Postman Collections?

3. Sample API: Flask + DeepSeek Chatbot

4. Installing Postman

5. Setting Up Your First Collection

6. Creating Environments (Local, Dev, Prod)

7. Defining Variables and Headers

8. Testing POST, GET, PUT, DELETE

9. Advanced: Tests, Scripts, Auth, and Pre-request Scripts

10. Exporting and Importing Collections

11. Version Control with Git + Postman

12. Postman CLI (newman) for CI/CD

13. Sharing with Teams & API Docs

14. Common Errors and How to Fix Them

15. Example Use Case: DeepSeek Chat API

16. Real-World Automation Scenarios

17. Bonus: Collections for LangChain, FastAPI, Node.js

18. Integrating Postman with Swagger/OpenAPI

19. Recommended Postman Practices in 2025

20. Final Thoughts & Downloadable Postman Collection

1. 🧠 What is Postman?

Postman is a platform for building, testing, and collaborating on APIs. It provides:

• Graphical interface for sending requests

• Visual debugging of responses

• Automation of test cases

• API documentation and mock servers

• Multi-environment support

• Collection versioning

2. 📦 Why Use Postman Collections?

Feature	Benefit
Collections	Save and organize all API requests in one place
Reusability	No need to rewrite requests for every test
Automation	Easily run suites of requests with scripts
Documentation	Generate auto docs for sharing APIs
CI/CD Ready	Integrate with pipelines using Newman

3. 🔧 Sample API: Flask + DeepSeek Chatbot

Let’s say you're running this Flask API:

python
from flask import Flask, request, jsonifyfrom llama_cpp import Llama

app = Flask(__name__)
llm = Llama(model_path="./models/deepseek.gguf", 
n_ctx=4096)@app.route('/chat', 
methods=['POST'])def chat():
    prompt = request.json.get('prompt', '')
    response = llm(prompt=prompt, max_tokens=256)    
    return jsonify({'response': response['choices'][0]['text'].strip()})

URL to test:

bash
POST http://localhost:5000/chat

4. 📥 Installing Postman

a. Desktop App (Recommended):

Download from: https://www.postman.com/downloads/

b. Web Version:

https://web.postman.co

Requires login, cloud-based storage.

5. 🧪 Setting Up Your First Collection

Step-by-step:

1. Open Postman → Click New → Collection

2. Name it: DeepSeek Chatbot API

3. Add request: New → Request

• Name: Chat Request

• Method: POST

• URL: http://localhost:5000/chat

• Body → raw → JSON

  json

• {
    "prompt": "What is LangChain?"}

• Click Send

You’ll get a JSON response back. Save the request into the collection.

6. 🌍 Creating Environments (Local, Dev, Prod)

Create Environment:

• Click the gear ⚙️ icon → Manage Environments

• Add variables:

  • host → http://localhost:5000

  • apiKey → your-token (optional)

Now in your request, use:

bash
{{host}}/chat

Postman will automatically resolve it based on the selected environment.

7. 🧩 Defining Variables and Headers

Global / Environment Variables:

json
{
  "host": "http://localhost:5000",
  "auth_token": "Bearer abc123"}

Set Header:

css
Authorization: {{auth_token}}Content-Type: application/json

You can use these dynamically across all your requests.

8. 🔄 Testing POST, GET, PUT, DELETE

Here’s a common layout for API collections:

bash
🧪 DeepSeek Chatbot API (Collection)
├── 🔵 POST /chat
├── 🟢 GET /status
├── 🟡 PUT /config
├── 🔴 DELETE /session

Each request can have:

• Descriptions

• Example responses

• Tests to validate responses

• Documentation tabs

9. 🧠 Advanced: Tests, Scripts, Auth

Pre-request Script (e.g., generate token):

js
pm.environment.set("auth_token", "Bearer xyz123");

Test Script Example:

js
pm.test("Status code is 200", () => {
    pm.response.to.have.status(200);
});

pm.test("Response has 'response' field", () => {    const jsonData = pm.response.json();
    pm.expect(jsonData).to.have.property('response');
});

You can run tests after every request automatically.

10. 📤 Exporting and Importing Collections

Export:

1. Click ... next to Collection → Export

2. Choose JSON v2.1 format

3. Save to deepseek-api.postman_collection.json

Import:

• Click Import → Drag JSON file

• Done!

You can version-control this file with Git.

11. 🔁 Version Control with Git + Postman

Store your Postman collection in your repository:

pgsql
/api
├── postman/
│   ├── deepseek-collection.json
│   └── env-local.json

Update manually or use Postman Sync.

12. 🧪 Postman CLI (Newman) for CI/CD

Install Newman:

bash
npm install -g newman

Run collection from CLI:

bash
newman run deepseek-api.postman_collection.json \
  --environment local.postman_environment.json

Integrate with GitHub Actions, GitLab CI, CircleCI, etc.

13. 👨‍👩‍👧 Sharing with Teams & API Docs

• Invite teammates via workspace

• Auto-generate docs from collections

• Share as public URL or embed in Notion

Docs include:

• Request/response format

• Headers and parameters

• Examples and curl equivalents

14. ❗ Common Errors and Fixes

Error	Fix
CORS issue	Use Postman, not browser
500 Internal Error	Check backend logs
401 Unauthorized	Ensure token is set in headers
JSON parse error	Check syntax and headers
Cannot resolve {{host}}	Select the right environment

15. 🔍 Example Use Case: DeepSeek Chat API

POST /chat

• URL: {{host}}/chat

• Headers: Content-Type: application/json

• Body:

json{
  "prompt": "Explain quantum computing"}

Test Script:

js
pm.test("Response contains 'quantum'", () => {
    pm.expect(pm.response.text()).to.include("quantum");
});

16. ⚙️ Real-World Automation Scenarios

• Smoke testing after deployment

• Regression testing APIs on push

• Stress testing AI endpoints

• Automatically generating API usage logs

• Validating response accuracy over time (diffing)

17. 📚 Bonus: LangChain, FastAPI, Node.js Collections

For LangChain:

bash
POST /agent
{  "query": "Use calculator tool to add 5 and 9"}

For FastAPI backend:

python
@app.post("/ask")def ask(query: QueryRequest):
    ...

For Node.js (Express):

js
router.post('/chat', async (req, res) => {
  ...
});

You can test all with the same Postman workflow.

18. 🔄 Integrating Postman with OpenAPI / Swagger

• Upload openapi.json → Auto-generate collection

• Sync with SwaggerHub

• Import Postman into Swagger UI for documentation

Convert Postman ↔ Swagger via: https://converter.swagger.io/

19. 🧠 Recommended Postman Practices in 2025

Tip	Why
Use environment variables	Avoid hardcoding endpoints
Write tests	Automate validation
Use folders for grouping	Keep collections organized
Export regularly	Version safely
Share with markdown docs	Improve onboarding

20. ✅ Final Thoughts & Downloadable Postman Collection

Postman remains the #1 tool for API quality assurance, collaboration, and automation. In AI-driven apps, where logic is dynamic and responses are fluid, Postman helps ensure your endpoints are:

• Stable

• Functional

• Documented

• Shareable

• Testable

📁 Want the Ready-Made Collection?

Let me know and I’ll send you:

• ✅ deepseek-api.postman_collection.json

• ✅ deepseek-environment-local.json

• ✅ Example test suite

• ✅ Newman CLI setup