AI Backend Internship Project

I built this project to demonstrate three practical backend skills often tested in internships:

1. Building a recommendation endpoint using machine learning basics
2. Exposing webhook-friendly APIs for workflow tools such as n8n
3. Creating domain-specific text embeddings for LLM and SLM workflows

What This Project Does

• Accepts a user query and returns relevant catalog items using TF-IDF + cosine similarity
• Accepts text and returns a vector representation suitable for downstream retrieval experiments
• Accepts webhook event payloads for event-driven integration patterns

Tech Stack

• Python
• FastAPI
• Pandas, NumPy
• Scikit-learn
• n8n compatible webhook flow

Structure

• app/main.py: FastAPI routes and app setup
• app/services.py: recommendation and embedding logic
• app/schemas.py: request models
• data/products.csv: sample domain dataset
• workflows/n8n_webhook_recommendation.json: example n8n flow

Run Locally

1. Create and activate a virtual environment
2. Install dependencies from requirements.txt
3. Start the API with uvicorn

Commands:

python -m venv .venv ..venv\Scripts\Activate.ps1 pip install -r requirements.txt uvicorn app.main:app --reload --port 8000

Docs URL after startup (local machine only): http://127.0.0.1:8000/docs

Endpoints

• POST /recommend
• POST /embed
• POST /webhooks/events

For Recruiters

• Local API docs: http://127.0.0.1:8000/docs
• Live deployment URL: add after deployment
• Suggested screenshots:
  • docs_home.png
  • recommend_response.png

Sample Payloads

POST /recommend { "query": "python api backend", "top_k": 3 }

POST /embed { "text": "agentic workflow with embeddings" }

Sample response from POST /recommend { "query": "python api backend", "results": [ { "product_id": 1, "title": "FastAPI Masterclass", "category": "backend", "score": 0.73 } ] }

Future Improvements

• Add authentication and rate limiting
• Add persistent query analytics
• Add automated tests and CI checks