Skip to main content
DEVOPS × AI SERIES

Build a RAG API with FastAPI

Master production-ready API development with AI-powered question answering

Start Building
Build a RAG API with FastAPI project preview
FastAPI
Chroma

Difficulty

Mildly spicy

Time to complete

60 minutes

Availability

Free

BUILD

What you'll build

Build your very first AI API! Use Python and FastAPI to answer questions with your own files - no experience needed.

1. Set Up Development Environment

Install Python and Ollama, pull the tinyllama model, and verify your infrastructure.

2. Create Python Workspace

Set up a virtual environment and install FastAPI dependencies for isolation.

3. Build Knowledge Base

Create knowledge documents and store their embeddings in Chroma vector database.

4. Create FastAPI Application

Build a RAG endpoint that retrieves context and generates AI-powered answers.

5. Test & Document

Test your API with curl and explore the auto-generated Swagger UI documentation.

Your portfolio builds as you work.

Every project documents itself as you go. Finish the work, and your proof is ready to share.

PROJECT

Real world application

Skills you'll learn

  • RAG Architecture

    Build retrieval-augmented generation systems combining search with AI generation

  • FastAPI Development

    Create production-ready REST APIs with automatic validation and documentation

  • Vector Databases

    Store and query document embeddings with Chroma for semantic search

  • Local LLM Integration

    Run AI models locally with Ollama without cloud costs

  • API Testing

    Test REST APIs using curl commands and interactive Swagger UI

  • Python Environment Management

    Create isolated virtual environments and manage dependencies with pip

Tech stack

  • FastAPI logo

    FastAPI

    Modern Python framework for building APIs with automatic documentation and validation

  • Chroma logo

    Chroma

    Open-source vector database for storing embeddings and semantic search

Huge thanks to NextWork for all the awesome hands on projects. I have done 17 so far and learned so much. Keep up the amazing work!

NextWork Community Member

Active Student

OUTCOME

Where this leads.

Relevant Jobs

Roles where these skills matter:

  • Backend Developer
  • API Engineer
  • DevOps Engineer
  • Full Stack Developer
  • AI/ML Engineer

DevOps × AI Series

Start the series. Learn Docker containerization, Kubernetes orchestration, CI/CD automation, and production monitoring

DevOps × AI Series

Continue the Journey

FAQs

Everything you need to know

No prior experience with APIs, FastAPI, or RAG is required. This 60-minute NextWork project helps complete beginners build from scratch. The guide explains every concept (APIs, embeddings, vector databases) as you build, and includes detailed troubleshooting for common issues. By the end, you will understand how production teams build AI-powered APIs with automatic documentation.

RAG (Retrieval-Augmented Generation) combines document search with AI generation to create accurate, context-aware responses. Instead of the AI guessing from training data, RAG first searches your knowledge base for relevant information, then uses that context to generate answers. This is the same technique used by ChatGPT plugins and enterprise AI assistants at companies like OpenAI and Anthropic.

This project is completely free to complete. You will run everything locally on your own computer using Ollama for the AI model and Chroma for the vector database. No cloud services, no API keys, no subscription fees. The only requirement is a computer with enough storage for the tinyllama model (approximately 600MB).

Traditional APIs search databases for exact keyword matches. Your RAG API understands meaning. If you search for "container management system", it will find information about Kubernetes even if those exact words are not in your knowledge base. This semantic search is powered by embeddings (numerical representations of text meaning) stored in a vector database, making your API much more intelligent than traditional keyword-based systems.

This is Project 1 of a 4-part series that takes you from local development to production deployment. Next, you will learn to containerize your API with Docker (Project 2), deploy to Kubernetes for scalability (Project 3), and automate rebuilds with GitHub Actions (Project 4). Each project builds on the previous one, teaching you how professional teams deploy AI applications at scale.

One Project. Real Skills.

60 minutes from now, you'll have completed Build a RAG API with FastAPI. No prior experience needed. Just step-by-step guidance and a real project for your portfolio.

Let's Build This

Mildly spicy level