ucbepic / docetl

A system for complex LLM-powered document processing
https://docetl.org
MIT License
689 stars 67 forks source link
data data-pipelines elt etl llm python workflow

DocETL: Powering Complex Document Processing Pipelines

Website (Includes Demo) | Documentation | Discord | NotebookLM Podcast (thanks Shabie from our Discord community!) | Paper (coming soon!)

DocETL Figure

DocETL is a tool for creating and executing data processing pipelines, especially suited for complex document processing tasks. It offers a low-code, declarative YAML interface to define LLM-powered operations on complex data.

When to Use DocETL

DocETL is the ideal choice when you're looking to maximize correctness and output quality for complex tasks over a collection of documents or unstructured datasets. You should consider using DocETL if:

Installation

See the documentation for installing from PyPI.

Prerequisites

Before installing DocETL, ensure you have Python 3.10 or later installed on your system. You can check your Python version by running:

python --version

Installation Steps (from Source)

  1. Clone the DocETL repository:
git clone https://github.com/shreyashankar/docetl.git
cd docetl
  1. Install Poetry (if not already installed):
pip install poetry
  1. Install the project dependencies:
poetry install
  1. Set up your OpenAI API key:

Create a .env file in the project root and add your OpenAI API key:

OPENAI_API_KEY=your_api_key_here

Alternatively, you can set the OPENAI_API_KEY environment variable in your shell.

  1. Run the basic test suite to ensure everything is working (this costs less than $0.01 with OpenAI):
make tests-basic

That's it! You've successfully installed DocETL and are ready to start processing documents.

For more detailed information on usage and configuration, please refer to our documentation.