In simple English
This tool helps you process large sets of documents using artificial intelligence. You write simple rules in plain English or Python to clean, sort, and summarize messy files. It handles the heavy lifting of sending requests in parallel and checking results. It saves you from writing custom scripts for every new data batch. You pay for the underlying language model API usage while running your tasks.
What it is
DocETL is an open-source Python library and command-line tool for building declarative data pipelines powered by language models (computer programs that understand and generate text). It automates complex data workflows like mapping, filtering, and reducing unstructured documents into clean, structured tables.
What it is good for
Solo builders who need to extract structured data from thousands of messy text files, support tickets, or PDFs without writing complex parallel processing code from scratch.
- Runs data processing tasks in parallel to save time.
- Supports both a Python API and low-code text configuration files.
- Includes a visual web playground to test prompts interactively.
- Requires programming knowledge to set up and write pipeline scripts.
- API usage fees for language models can add up quickly on large datasets.
- Debugging unexpected text parsing errors from model outputs takes manual effort.
Writing custom Python scripts and loops for every LLM data extraction task
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pip install docetl export OPENAI_API_KEY=your_key