DocETL: عزّز معالجة المستندات الخاصة بك باستخدام مسارات LLM العاملة

DocETL is an open-source system designed to streamline and enhance data processing, particularly for complex document-related tasks. Leveraging the power of Large Language Models (LLMs), DocETL allows users to create agentic pipelines for tasks such as information extraction, transformation, and loading, simplifying traditionally cumbersome ETL processes. This blog post provides a comprehensive overview of DocETL, its features, setup, and how it can revolutionize your document processing workflows.

Key Features of DocETL

• Interactive UI Playground (DocWrangler): DocETL offers an intuitive UI called DocWrangler that allows for iterative prompt engineering and pipeline development. This feature is particularly useful for experimenting with different prompts, building pipelines step-by-step, and visualizing the results in real-time. Once finalized, the pipeline configuration can be exported for production use.

• Python Package for Production: Beyond the interactive UI, DocETL also provides a Python package that enables running pipelines from the command line or directly within Python code. This makes it easy to integrate DocETL into existing workflows and automate document processing tasks.

• LLM Integration: DocETL seamlessly integrates with LLMs like ChatGPT and Claude, providing users with tools to leverage their capabilities for pipeline creation and execution. A “big prompt” is available on the DocETL website to guide LLMs in generating pipelines based on task descriptions.

• Community Projects and Resources: The DocETL community has created several projects and resources demonstrating the tool’s versatility, including conversation generators, text-to-speech applications, and YouTube transcript topic extraction tools. Educational resources and deep dives into specific operators like the Resolve Operator are also available.

Getting Started with DocETL

DocETL provides two primary ways to get started: using the interactive UI playground (DocWrangler) and using the Python package.

• DocWrangler (Recommended for Development): DocWrangler is ideal for iteratively developing pipelines. It allows users to:

  • Experiment with different prompts and view results in real-time.
  • Build pipelines incrementally.
  • Export finalized pipeline configurations for production deployment.

  DocWrangler can be accessed at docetl.org/playground or run locally using Docker or manual setup.

• Python Package (For Production Use): To use DocETL as a Python package, follow these steps:

  • Prerequisites: Python 3.10+ and an OpenAI API key (or equivalent for your chosen LLM).
  • Installation: pip install docetl
  • Configuration: Create a .env file with your OpenAI API key:

    OPENAI_API_KEY=your_api_key_here
    

  Refer to the documentation and tutorials for detailed examples of using DocETL’s Python package.

Local Setup Options

DocETL offers two options for setting up the DocWrangler locally:

• Option A: Using Docker (Recommended for Quick Start): This is the quickest way to get the DocWrangler playground running. Follow these steps:

  1. Create .env and .env.local files with the required configurations (as outlined in the documentation).
  2. Run make docker to build the image and start the container.
  3. The UI will be accessible at http://localhost:3000. To clean up, use make docker-clean.

• Option B: Manual Setup (Development): This option is preferred for development or if Docker is not desired. The steps are:

  1. Clone the repository: git clone https://github.com/ucbepic/docetl.git
  2. Set up environment variables in .env and .env.local files.
  3. Install dependencies: make install and make install-ui.
  4. Start the development server: make run-ui-dev.
  5. Visit http://localhost:3000/playground to access the UI.

AWS Bedrock Integration

DocETL supports integration with AWS Bedrock. To enable it:

1. Configure AWS credentials using aws configure.
2. Test your credentials with make test-aws.
3. Run with AWS support using:

   AWS_PROFILE=your-profile AWS_REGION=your-region make docker
   

   Or with Docker Compose:

   AWS_PROFILE=your-profile AWS_REGION=your-region docker compose --profile aws up
   

   Remember that Bedrock models are prefixed with bedrock. in the configuration.

Conclusion

DocETL is a powerful tool for building and executing complex document processing pipelines using agentic LLMs. Its interactive UI and Python package provide flexibility for both development and production environments. With its growing community, extensive documentation, and support for various LLMs and cloud platforms like AWS Bedrock, DocETL is poised to become a cornerstone for anyone seeking to automate and enhance their document processing workflows.

Source: ucbepic