A collection of AI Apps you can run with 1 command 🚀
⭐️ for when you need to spin up an AI project quickly.
## ⭐ Features:
- **Powerful:** Get a production-ready LLM App with 1 command.
- **Simple**: Built using a human-like `Conversation` interface to language models.
- **Production Ready:** Your app can be deployed to aws with 1 command.
## 🚀 How it works
- Create your codebase using a template: `phi ws create`
- Run your app locally: `phi ws up dev:docker`
- Run your app on AWS: `phi ws up prd:aws`
## 💻 Example: Build a RAG LLM App
Let's build a **RAG LLM App** with GPT-4. We'll use PgVector for Knowledge Base and Storage and serve the app using Streamlit and FastApi. Read the full tutorial here.
> Install docker desktop to run this app locally.
### Installation
Open the `Terminal` and create an `ai` directory with a python virtual environment.
```bash
mkdir ai && cd ai
python3 -m venv aienv
source aienv/bin/activate
```
Install phidata
```bash
pip install phidata
```
### Create your codebase
Create your codebase using the `llm-app` template pre-configured with FastApi, Streamlit and PgVector. Use this codebase as a starting point for your LLM product.
```bash
phi ws create -t llm-app -n llm-app
```
This will create a folder named `llm-app`
### Serve your LLM App using Streamlit
Streamlit allows us to build micro front-ends for our LLM App and is extremely useful for building basic applications in pure python. Start the `app` group using:
```bash
phi ws up --group app
```
**Press Enter** to confirm and give a few minutes for the image to download (only the first time). Verify container status and view logs on the docker dashboard.
### Example: Chat with PDFs
- Open localhost:8501 to view streamlit apps that you can customize and make your own.
- Click on **Chat with PDFs** in the sidebar
- Enter a username and wait for the knowledge base to load.
- Choose the `RAG` Conversation type.
- Ask "How do I make chicken curry?"
- Upload PDFs and ask questions
### Serve your LLM App using FastApi
Streamlit is great for building micro front-ends but any production application will be built using a front-end framework like `next.js` backed by a RestApi built using a framework like `FastApi`.
Your LLM App comes ready-to-use with FastApi endpoints, start the `api` group using:
```bash
phi ws up --group api
```
**Press Enter** to confirm and give a few minutes for the image to download.
### View API Endpoints
- Open localhost:8000/docs to view the API Endpoints.
- Load the knowledge base using `/v1/pdf/conversation/load-knowledge-base`
- Test the `v1/pdf/conversation/chat` endpoint with `{"message": "How do I make chicken curry?"}`
- The LLM Api comes pre-built with endpoints that you can integrate with your front-end.
### Optional: Run Jupyterlab
A jupyter notebook is a must have for AI development and your `llm-app` comes with a notebook pre-installed with the required dependencies. Enable it by updating the `workspace/settings.py` file:
```python {{ title: 'workspace/settings.py'}}
...
ws_settings = WorkspaceSettings(
...
# Uncomment the following line
dev_jupyter_enabled=True,
...
```
Start `jupyter` using:
```bash
phi ws up --group jupyter
```
**Press Enter** to confirm and give a few minutes for the image to download (only the first time). Verify container status and view logs on the docker dashboard.
### View Jupyterlab UI
- Open localhost:8888 to view the Jupyterlab UI. Password: **admin**
- Play around with cookbooks in the `notebooks` folder.
### Delete local resources
Play around and stop the workspace using:
```bash
phi ws down
```
### Run your LLM App on AWS
Read how to run your LLM App on AWS here.
## 📚 More Information:
- Read the documentation
- Chat with us on Discord
- Email us at help@phidata.com
