Build a RAG API with FastAPI
Create local AI pipelines with retrieval-augmented generation
Difficulty
Beginner
Time to complete
45 minutes
Availability
Free
BUILD
What you'll build
Build an AI-powered API from scratch using Python, FastAPI, Ollama, and ChromaDB. Learn to create semantic search systems that answer questions using your own documents - all running locally.
1. Set Up RAG Project
Install Ollama and Python, create a virtual environment, and pull the AI models.
2. Build Knowledge Base
Create a personal profile document and store embeddings in the ChromaDB vector database.
3. Build FastAPI Application
Create a FastAPI endpoint that retrieves context, augments prompts, and generates grounded answers.
4. Test with Swagger UI
Launch your API server and test the RAG pipeline using the interactive Swagger documentation.
5. Verify RAG Pipeline
Ask personal questions to confirm the API retrieves context and generates accurate, grounded responses.
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
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RAG Architecture
Build retrieval-augmented generation systems combining search with AI generation
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FastAPI Development
Create REST APIs with automatic validation and interactive Swagger documentation
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Vector Databases
Store and query document embeddings with ChromaDB for semantic search
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Local LLM Integration
Run AI models locally with Ollama without cloud dependencies or costs
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API Testing
Test endpoints using Swagger UI with real-time question answering
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Python Environment Management
Create isolated virtual environments and manage dependencies with pip
Tech stack
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FastAPI
Modern Python framework for building APIs with automatic documentation and validation
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ChromaDB
Open-source vector database for storing embeddings and enabling semantic search
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Ollama
Run large language models locally without cloud dependencies or API costs
OUTCOME
Where this leads.
Relevant Jobs
Roles where these skills matter:
- Backend Developer
- API Engineer
- Full Stack Developer
- AI/ML Engineer
- Python Developer
AI Fundamentals
Master the foundations of AI development with hands-on projects in RAG, vector databases, and local AI infrastructure.
AI Fundamentals
Continue the JourneyFAQs
Everything you need to know
No prior experience with APIs, FastAPI, or RAG is required. This 45-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 Notion, Slack, and GitHub.
This project is completely free to complete. You will run everything locally on your own computer using Ollama for the AI models and ChromaDB for the vector database. No cloud services, no API keys, no subscription fees. The only requirement is a computer with enough storage for the qwen2.5:0.5b model (approximately 400MB) and nomic-embed-text model (approximately 274MB).
Traditional APIs search databases for exact keyword matches. Your RAG API understands meaning through semantic search. If you ask "What are my career goals", it will find information about your professional aspirations 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.
Yes! You can add any text files to your knowledge base. The tutorial uses a personal profile as an example, but you can replace it with your own documents like project documentation, study notes, or company knowledge bases. Just create your content in a text file and the knowledge base script will chunk and embed it automatically.
This project uses two Ollama models running locally: qwen2.5:0.5b (a 500 million parameter chat model made by Alibaba for generating answers) and nomic-embed-text (an embedding model for converting text into vectors for semantic search). Both models are free, open-source, and run entirely on your laptop without requiring cloud API keys.
One Project. Real Skills.
45 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.
Beginner-friendly