phidata 2.1.2

AI Toolkit for Engineers

phidata

Build, ship and monitor AI products

✨ What is phidata?

Phidata is an OSS toolkit for building AI products.

It gives you production-ready AI Apps with 1 command.

Its goal is to provide a paved-path for building AI products, for anyone with basic python skills.

🎖 Use it to build

• AI Apps (RAG, autonomous or multimodal applications)
• AI Assistants (automate data engineering, python or snowflake tasks)
• Rest Apis (with FastApi, PostgreSQL)
• Web Apps (with Django, PostgreSQL)
• Data Platforms (with Airflow, Superset, Jupyter)

💡 What you get

Production ready codebases built with:

• Building blocks like conversations, agents, knowledge bases defined as pydantic objects
• Applications like FastApi, Streamlit, Django, Postgres defined as pydantic objects
• Infrastructure components (docker, AWS) also defined as pydantic objects

Phidata applications run locally using docker and 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

📚 More Information:

• Read the documentation
• Chat with us on Discord
• Email us at [e-mail]

🚀 Quickstart: Build a RAG LLM App

Let's build a RAG LLM App with GPT-4. We'll use:

• Streamlit for the front-end
• FastApi for the back-end
• PgVector for Knowledge Base and Storage
• Read the full tutorial here.

Install docker desktop to run this app locally.

Create a virtual environment

Open the Terminal and create an ai directory with a python virtual environment.

mkdir ai && cd ai

python3 -m venv aienv
source aienv/bin/activate

Install

Install phidata

pip install -U phidata

Create your codebase

Create your codebase using the llm-app template pre-configured with FastApi, Streamlit and PgVector.

phi ws create -t llm-app -n llm-app

This will create a folder llm-app with a pre-built LLM App that you can customize and make your own.

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:

phi ws up --group app

Press Enter to confirm and give a few minutes for the image to download.

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:

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:

...
ws_settings = WorkspaceSettings(
    ...
    # Uncomment the following line
    dev_jupyter_enabled=True,
...

Start jupyter using:

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:

phi ws down

Run your LLM App on AWS

Read how to run your LLM App on AWS.

📚 More Information:

• Read the documentation
• Chat with us on Discord
• Email us at [e-mail]