Introduction

Phidata is an AI toolkit designed to run LLM Apps with 1 command 🚀

It provides production-ready templates for common AI Apps that you can run locally or on AWS.

🚀 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

⭐ Features:

  • Powerful: Get a production-ready AI 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.

For example, run a RAG LLM App built with FastApi, Streamlit and PgVector:

Create LLM App

Quickstart your AI App using one of these examples:

Examples

RAG LLM App

Build a RAG LLM App using FastApi, Streamlit and PgVector.

Read more

Autonomous LLM App

Build an Autonomous LLM App using FastApi, Streamlit and PgVector.

Read more

Multimodal LLM App

Build a Multimodal LLM App that can understand images and text.

Read more

Junior Data Engineer

Build a Junior DE to automate data analysis using DuckDb and Python.

Read more

Under the hood, phidata solves the problem of building LLM products by providing:

Software layer

  • Access to LLMs using a human-like Conversation interface
  • Strategies for improving LLM responses: RAG, Tasks, Structured Outputs, Validators
  • Building blocks for LLM apps: Agents, Assistants, Knowledge Bases, Storage, Memory
  • Monitoring for LLM apps: Model Inputs/Outputs, Quality, Cost, Evals

Application layer

  • Tools for serving LLM apps: FastApi, Django, Streamlit
  • Tools for serving LLM components: PgVector, Postgres, Redis

Infrastructure layer

  • Infrastructure for running LLM apps locally: Docker
  • Infrastructure for running LLM apps in production: AWS
  • Best practices like testing, formatting, CI/CD, security and secret management.

Most LLM apps are built as a house of cards because engineers have to stitch each layer manually. Phidata bridges the 3 layers of software development to deliver production-grade LLM Apps.

Continue reading to learn more or get started with a production-ready template.

Templates

LLM App

Build an LLM App using FastApi, Streamlit and PgVector

LLM Api

Build an LLM Api using FastApi and PgVector

Streamlit App

Build a Micro-LLM App using Streamlit and PgVector

Django App

Build an LLM Web App using Django and PgVector

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