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---
title: Backend
emoji: 🐢
colorFrom: pink
colorTo: blue
sdk: docker
pinned: false
license: mit
app_port: 8000
---
This is a [LlamaIndex](https://www.llamaindex.ai/) project using [FastAPI](https://fastapi.tiangolo.com/) bootstrapped with [`create-llama`](https://github.com/run-llama/LlamaIndexTS/tree/main/packages/create-llama).
## Getting Started
First, setup the environment with poetry:
> **_Note:_** This step is not needed if you are using the dev-container.
```
poetry install
poetry shell
```
Then check the parameters that have been pre-configured in the `.env` file in this directory. (E.g. you might need to configure an `OPENAI_API_KEY` if you're using OpenAI as model provider).
If you are using any tools or data sources, you can update their config files in the `config` folder.
Second, generate the embeddings of the documents in the `./data` directory (if this folder exists - otherwise, skip this step):
```
poetry run generate
```
Third, run the development server:
```
python main.py
```
The example provides two different API endpoints:
1. `/api/chat` - a streaming chat endpoint
2. `/api/chat/request` - a non-streaming chat endpoint
You can test the streaming endpoint with the following curl request:
```
curl --location 'localhost:8000/api/chat' \
--header 'Content-Type: application/json' \
--data '{ "messages": [{ "role": "user", "content": "Hello" }] }'
```
And for the non-streaming endpoint run:
```
curl --location 'localhost:8000/api/chat/request' \
--header 'Content-Type: application/json' \
--data '{ "messages": [{ "role": "user", "content": "Hello" }] }'
```
You can start editing the API endpoints by modifying `app/api/routers/chat.py`. The endpoints auto-update as you save the file. You can delete the endpoint you're not using.
Open [http://localhost:8000/docs](http://localhost:8000/docs) with your browser to see the Swagger UI of the API.
The API allows CORS for all origins to simplify development. You can change this behavior by setting the `ENVIRONMENT` environment variable to `prod`:
```
ENVIRONMENT=prod python main.py
```
## Using Docker
1. Build an image for the FastAPI app:
```
docker build -t <your_backend_image_name> .
```
2. Generate embeddings:
Parse the data and generate the vector embeddings if the `./data` folder exists - otherwise, skip this step:
```
docker run \
--rm \
-v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
-v $(pwd)/config:/app/config \
-v $(pwd)/data:/app/data \ # Use your local folder to read the data
-v $(pwd)/storage:/app/storage \ # Use your file system to store the vector database
<your_backend_image_name> \
poetry run generate
```
3. Start the API:
```
docker run \
-v $(pwd)/.env:/app/.env \ # Use ENV variables and configuration from your file-system
-v $(pwd)/config:/app/config \
-v $(pwd)/storage:/app/storage \ # Use your file system to store gea vector database
-p 8000:8000 \
<your_backend_image_name>
```
## Learn More
To learn more about LlamaIndex, take a look at the following resources:
- [LlamaIndex Documentation](https://docs.llamaindex.ai) - learn about LlamaIndex.
You can check out [the LlamaIndex GitHub repository](https://github.com/run-llama/llama_index) - your feedback and contributions are welcome!
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