Docling Tools Run

Entry point that runs the basic conversion and OCR Docling examples in sequence.

run.py

from basic_examples import run_basic_examples
from ocr_example import run_ocr_example

if __name__ == "__main__":
    run_basic_examples()
    run_ocr_example()

The example imports these helper modules from the same directory:

basic_examples.py

from agno.agent import Agent
from agno.tools.docling import DoclingTools
from paths import (
    audio_video_path,
    docx_path,
    html_path,
    image_path,
    md_path,
    pdf_path,
    pptx_path,
    xlsx_path,
    xml_path,
)


def run_basic_examples() -> None:
    agent = Agent(
        tools=[DoclingTools(all=True)],
        description="You are an agent that converts documents from all Docling parsers and exports to all supported output formats.",
    )

    agent.print_response(
        "List supported Docling input parsers and active allowed parsers.",
        markdown=True,
    )

    agent.print_response(
        f"Convert to Markdown: {pdf_path}",
        markdown=True,
    )
    agent.print_response(
        f"Convert to JSON and return the full JSON without summarizing: {pdf_path}",
        markdown=True,
    )
    agent.print_response(
        f"Convert to YAML: {pdf_path}",
        markdown=True,
    )
    agent.print_response(
        f"Convert to DocTags: {pdf_path}",
        markdown=True,
    )
    agent.print_response(
        f"Convert to VTT: {pdf_path}",
        markdown=True,
    )
    agent.print_response(
        f"Convert to HTML split page: {pdf_path}",
        markdown=True,
    )

    # Additional parser examples based on static resources.
    agent.print_response(
        f"Convert to Markdown: {docx_path}",
        markdown=True,
    )
    agent.print_response(
        f"Convert to Markdown: {md_path}",
        markdown=True,
    )
    agent.print_response(
        f"Convert to Markdown: {html_path}",
        markdown=True,
    )
    agent.print_response(
        f"Convert to Markdown: {xml_path}",
        markdown=True,
    )
    agent.print_response(
        f"Convert to Markdown: {xlsx_path}",
        markdown=True,
    )
    agent.print_response(
        f"Convert to Markdown: {pptx_path}",
        markdown=True,
    )
    agent.print_response(
        f"Convert to Markdown: {image_path}",
        markdown=True,
    )
    agent.print_response(
        f"Convert to VTT: {audio_video_path}",
        markdown=True,
    )

    # convert_string is limited by Docling to Markdown and HTML source content.
    agent.print_response(
        "Use convert_string_content to convert this markdown string to JSON: # Inline Markdown\n\nThis is a parser test.",
        markdown=True,
    )
    agent.print_response(
        "Use convert_string_content to convert this html string to Markdown: <h1>Inline HTML</h1><p>This is a parser test.</p>",
        markdown=True,
    )

ocr_example.py

from agno.agent import Agent
from agno.tools.docling import DoclingTools
from paths import pdf_path


def run_ocr_example() -> None:
    # pdf_ocr_engine accepts: auto | easyocr | tesseract | tesseract_cli | ocrmac | rapidocr
    # Some engines may require extra runtime dependencies in your environment.
    ocr_tools = DoclingTools(
        pdf_enable_ocr=True,
        pdf_ocr_engine="easyocr",
        pdf_ocr_lang=["pt", "en"],
        pdf_force_full_page_ocr=True,
        pdf_enable_table_structure=True,
        pdf_enable_picture_description=False,
        pdf_document_timeout=120.0,
    )

    ocr_agent = Agent(
        tools=[ocr_tools],
        description="You are an agent that converts PDFs using advanced OCR.",
    )

    ocr_agent.print_response(
        f"Convert to Markdown: {pdf_path}",
        markdown=True,
    )

paths.py

from pathlib import Path

repo_root = Path(__file__).resolve().parents[3]
testing_resources_path = repo_root / "cookbook/07_knowledge/testing_resources"


def get_test_resource_path(filename: str) -> str:
    return str(testing_resources_path / filename)


pdf_path = get_test_resource_path("cv_1.pdf")
docx_path = get_test_resource_path("project_proposal.docx")
md_path = get_test_resource_path("coffee.md")
html_path = get_test_resource_path("company_info.html")
xml_path = get_test_resource_path("patent_sample.xml")
xlsx_path = get_test_resource_path("sample_products.xlsx")
pptx_path = get_test_resource_path("ai_presentation.pptx")
image_path = get_test_resource_path("restaurant_invoice.png")
audio_video_path = get_test_resource_path("agno_description.mp4")

Run the Example

1. Set up your virtual environment

   Mac

   uv venv --python 3.12
   source .venv/bin/activate
   

   uv venv --python 3.12
   .venv\Scripts\activate
   
2. Install dependencies

   uv pip install -U agno docling easyocr openai rapidocr-onnxruntime
   
3. Export your OpenAI API key

   Mac/Linux

   export OPENAI_API_KEY="your_openai_api_key_here"
   

   $Env:OPENAI_API_KEY="your_openai_api_key_here"
   
4. Run the example

   Clone Agno and run the example from the repository root:

   git clone https://github.com/agno-agi/agno.git
   cd agno
   python cookbook/91_tools/docling_tools/run.py
   

Full source: cookbook/91_tools/docling_tools/run.py