Large Language Models (LLMs) are transforming how we build applications. One of the most exciting open-source models today is Llama 3, developed by Meta and made freely available for research and commercial use. Unlike proprietary models, Llama 3 is open-source, meaning you can experiment, deploy, and integrate it without licensing barriers. In this FastAPI Ollama Llama 3 streaming API tutorial, we’ll build a Python FastAPI backend that streams responses from Ollama (a lightweight local LLM runner) and sends them to a simple HTML frontend using Server-Sent Events (SSE). This setup allows you to see responses token-by-token, just like chatting with modern AI assistants.
If you’re new to APIs or FastAPI, don’t worry — we’ll keep things beginner-friendly and walk through each step.
FastAPI Ollama Llama 3 streaming API tutorial – Prerequisites
- Python 3.10+ installed
- Ollama installed locally
- Basic knowledge of Python and HTML
What You Need to Install
Before we dive into coding, let’s make sure your environment is ready. You’ll need to install a few tools and libraries to get everything working smoothly:
1. Python 3.10+
- Make sure you have Python installed.
- You can check your version by running:bash
python --version - If you don’t have it, download it from Python.org.
2. FastAPI and Uvicorn
FastAPI is the web framework we’ll use, and Uvicorn is the ASGI server that runs it.
pip install fastapi uvicorn
3. SSE Support (Server-Sent Events)
We’ll use sse-starlette to handle streaming responses via SSE.
pip install sse-starlette
4. Requests Library
This lets our backend talk to Ollama’s local API.
pip install requests
5. Ollama
Ollama is the local runner for LLMs like Llama 3.
- Download and install Ollama from ollama.ai.
- Once installed, pull the Llama 3 model:bash
ollama pull llama3
✅ Quick Recap
You’ll need:
- Python 3.10+
- FastAPI + Uvicorn
- sse-starlette
- requests
- Ollama (with Llama 3 model pulled)
With these installed, you’re ready to build your streaming API!
Step 2: Create the FastAPI Backend
Here’s a simple FastAPI app that streams responses from Ollama’s Llama 3 model:
python
from fastapi import FastAPI
from fastapi.responses import StreamingResponse
import requests
app = FastAPI()
def ollama_stream(prompt: str):
url = "http://localhost:11434/api/generate"
payload = {"model": "llama3", "prompt": prompt, "stream": True}
response = requests.post(url, json=payload, stream=True)
for line in response.iter_lines():
if line:
yield f"data: {line.decode('utf-8')}\n\n"
@app.get("/stream")
async def stream(prompt: str):
return StreamingResponse(ollama_stream(prompt), media_type="text/event-stream")
What’s happening here?
- We send the user’s prompt to Ollama (
llama3model). - Ollama streams back the response token-by-token.
- FastAPI wraps this into Server-Sent Events (SSE) so the frontend can consume it live.
Step 3: Create the HTML Frontend
Here’s a minimal HTML page that connects to the FastAPI stream:
html
<!DOCTYPE html>
<html>
<head>
<title>Llama 3 FastAPI Demo</title>
</head>
<body>
<h1>Chat with Llama 3</h1>
<input id="prompt" type="text" placeholder="Ask me anything..." />
<button onclick="sendPrompt()">Send</button>
<div id="response"></div>
<script>
function sendPrompt() {
const prompt = document.getElementById("prompt").value;
const eventSource = new EventSource(`/stream?prompt=${encodeURIComponent(prompt)}`);
document.getElementById("response").innerHTML = "";
eventSource.onmessage = function(event) {
const data = JSON.parse(event.data);
document.getElementById("response").innerHTML += data.response || "";
};
}
</script>
</body>
</html>
How it works:
- User enters a prompt.
- The frontend opens an EventSource connection to
/stream. - Tokens arrive one by one and are appended to the response div.
Step 4: Run the Server
Start FastAPI with Uvicorn:
uvicorn main:app --reload
Open your HTML file in a browser, type a question, and watch Llama 3 stream its response live!
Troubleshooting
Even with everything installed, you might run into a few common issues. Here’s how to fix them:
1. Ollama Not Running
- If you see errors like
Connection refusedorFailed to connect to localhost:11434, it usually means Ollama isn’t running. - Start Ollama manually:bash
ollama run llama3 - Keep this terminal open while testing your API.
2. Model Not Found
- If you get an error saying
"model not found: llama3", make sure you’ve pulled the model:bashollama pull llama3
3. Port Conflicts
- By default, Ollama runs on port
11434and FastAPI (via Uvicorn) runs on port8000. - If another service is already using these ports, you’ll see errors.
- Fix by running FastAPI on a different port:bash
uvicorn main:app --reload --port 8080
4. Missing Python Packages
- If you see
ModuleNotFoundError, double-check that you installed all dependencies:bashpip install fastapi uvicorn sse-starlette requests
5. Frontend Not Receiving Stream
- If your HTML page doesn’t show responses, check:
- The EventSource URL matches your FastAPI endpoint (
/stream). - You’re running the HTML file from a server (not just double-clicking). Try a simple Python server:bash
python -m http.server 5500Then openhttp://localhost:5500/index.html.
- The EventSource URL matches your FastAPI endpoint (
Why Llama 3 + Ollama?
- Open-source freedom: Llama 3 is free to use, modify, and deploy.
- Local-first: Ollama runs models on your machine, keeping data private.
- Beginner-friendly: FastAPI makes building APIs simple and intuitive.
Next Steps
- Add authentication for secure APIs.
- Build a chat UI with frameworks like React or Vue.
- Explore other models available in Ollama.
Conclusion
By now, you’ve seen how easy it is to set up a Python FastAPI backend that streams responses from Ollama’s Llama 3 model directly into a simple HTML frontend using Server-Sent Events. What makes this approach so powerful is its combination of accessibility and openness: FastAPI keeps the backend lightweight and beginner-friendly, while Ollama ensures you can run cutting-edge models locally without needing massive infrastructure or cloud costs.
The Llama 3 model itself is a milestone in open-source AI. Unlike closed systems, it gives developers, researchers, and hobbyists the freedom to experiment, customize, and deploy without licensing restrictions. This democratization of AI means you can build real-world applications — from chatbots to knowledge assistants — with complete control over your data and workflows.
Streaming responses token-by-token also mirrors the experience of modern AI assistants, making your applications feel responsive and interactive. With just a few lines of Python and HTML, you’ve unlocked a workflow that scales from personal projects to production-ready systems.
This tutorial is only the beginning. You can extend it with authentication, richer frontends, or even integrate multiple models for specialized tasks. The open-source ecosystem around Ollama and Llama 3 is growing rapidly, and by experimenting now, you’re positioning yourself at the forefront of AI innovation.
While you are here, maybe try one of my apps for the iPhone.
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If you enjoyed this guide, don’t stop here — check out more posts on AI and APIs on my blog (From https://mydaytodo.com/blog);
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