---
title: FastAPI + LlamaIndex RAG Example (Ollama)
 | Developer Documentation
---

This example demonstrates how to build a simple Retrieval-Augmented Generation (RAG) API using LlamaIndex and FastAPI, powered by a **local LLM via Ollama**.

## Features

- Uses `llama3` for text generation and `nomic-embed-text` for embeddings via Ollama
- Local, free LLM (no API keys required)
- Document ingestion and indexing
- Simple query API endpoint
- Clean, production-style structure

## Prerequisites

- Python 3.9+
- Ollama installed locally

## Setup

Pull a model using Ollama:

Terminal window

```
ollama pull llama3
```

Install dependencies:

Terminal window

```
pip install -r requirements.txt
```

## Run

Terminal window

```
uvicorn app:app --reload
```

Alternatively, you can test the API using FastAPI’s built-in Swagger UI:

Open your browser and visit: <http://localhost:8000/docs>

## Example Request

Terminal window

```
curl -X POST "http://localhost:8000/query" \
-H "Content-Type: application/json" \
-d '{"query": "What is this example about?"}'
```

## Notes

- This example uses a local LLM by default.
- It is intended as a minimal, beginner-friendly demonstration of integrating LlamaIndex with FastAPI.

```
! pip install fastapi uvicorn llama-index-llms-ollama llama-index-embeddings-ollama ollama
```

```
from fastapi import FastAPI
from pydantic import BaseModel


from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.settings import Settings
from llama_index.llms.ollama import Ollama
from llama_index.embeddings.ollama import OllamaEmbedding


app = FastAPI(title="LlamaIndex FastAPI RAG (Ollama)")


# Configure local LLM and embedding model via Ollama
Settings.llm = Ollama(model="llama3")
Settings.embed_model = OllamaEmbedding(model_name="nomic-embed-text")


# Load documents and build index at startup
documents = SimpleDirectoryReader("data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()




class QueryRequest(BaseModel):
    query: str




@app.post("/query")
def query_documents(request: QueryRequest):
    # Query indexed documents using a local LLM via Ollama.
    response = query_engine.query(request.query)
    return {"response": str(response)}
```