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AI Toolkit for Engineers

Project description

phidata

Build, ship and monitor AI products

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✨ What is phidata?

Phidata is an open source toolkit for building AI products. It provides a paved-path for building AI products using pre-built AI Apps that you can run locally using docker or deploy to AWS.

🎖 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

🚀 Get Started

  • Read the docs for more information.
  • Chat with us on discord for help.

📖 Quickstart: Build a RAG LLM App

Let's build a RAG LLM App using:

  • GPT-4 as the LLM
  • Streamlit as the chat interface
  • FastApi as the backend
  • 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
chat-with-pdf

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:

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