PassportPDF Python API Docs | dltHub

PassportPDF is a REST API that provides various functionalities for document processing, including PDF and image manipulation. The REST API base URL is https://passportpdfapi.com and All requests require an API key for authentication, which is included in the HTTP header..

dlt is an open-source Python library that handles authentication, pagination, and schema evolution automatically. dlthub provides AI context files that enable code assistants to generate production-ready pipelines. Install with uv pip install "dlt[workspace]" and start loading PassportPDF data in under 10 minutes.

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What data can I load from PassportPDF?

Here are some of the endpoints you can load from PassportPDF:

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How do I authenticate with the PassportPDF API?

Authentication is performed by including your unique API key in the 'X-PassportPdf-API-Key' HTTP header for each request.

1. Get your credentials

To obtain API credentials, your unique 'passport' identifier acts as an API key. The specific steps to generate or retrieve this key from a dashboard are not detailed in the provided documentation.

2. Add them to .dlt/secrets.toml

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[sources.passport_pdf_source]
api_key = "your_api_key_here"

dlt reads this automatically at runtime — never hardcode tokens in your pipeline script. For production environments, see setting up credentials with dlt for environment variable and vault-based options.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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dlt ai init --agent <your-agent> # <agent>: claude | cursor | codex

This installs project rules, a secrets management skill, appropriate ignore files, and configures the dlt MCP server for your agent. Learn more →

2. Install the rest-api-pipeline toolkit:

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dlt ai toolkit rest-api-pipeline install

This loads the skills and context about dlt the agent uses to build the pipeline iteratively, efficiently, and safely. The agent uses MCP tools to inspect credentials — it never needs to read your secrets.toml directly. Learn more →

3. Start LLM-assisted coding:

Use /find-source to load data from the PassportPDF API into DuckDB.

The rest-api-pipeline toolkit takes over from here — it reads relevant API documentation, presents you with options for which endpoints to load, and follows a structured workflow to scaffold, debug, and validate the pipeline step by step.

4. Run the pipeline:

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python passport_pdf_pipeline.py

If everything is configured correctly, you'll see output like this:

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Pipeline passport_pdf_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset passport_pdf_data
The duckdb destination used duckdb:/passport_pdf.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

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dlt pipeline passport_pdf_pipeline show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

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Python pipeline example

This example loads config_get_api_version and passport_manager_get_passport_info from the PassportPDF API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def passport_pdf_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://passportpdfapi.com",
            "auth": {
                "type": "api_key",
                "X-PassportPDF-API-Key": api_key,
            },
        },
        "resources": [
            {"name": "config_get_api_version", "endpoint": {"path": "api/config/ConfigGetAPIVersion"}},
            {"name": "passport_manager_get_passport_info", "endpoint": {"path": "api/passportmanager/PassportManagerGetPassportInfo"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="passport_pdf_pipeline",
        destination="duckdb",
        dataset_name="passport_pdf_data",
    )
    load_info = pipeline.run(passport_pdf_source())
    print(load_info)

To add more endpoints, append entries from the resource table to the "resources" list using the same name, path, and data_selector pattern.

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How do I query the loaded data?

Once the pipeline runs, dlt creates one table per resource. You can query with Python or SQL.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("passport_pdf_pipeline").dataset()
sessions_df = data.config_get_api_version.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM passport_pdf_data.config_get_api_version LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("passport_pdf_pipeline").dataset()
data.config_get_api_version.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load PassportPDF data to?

dlt supports loading into any of these destinations — only the destination parameter changes:

Change the destination in dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. See the full destinations list.

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Troubleshooting

API Errors

Conventional HTTP response codes are used to indicate the success or failure of an API request. In case of an error, the response will include an 'Error' data model, which provides details about the issue. The presence of this 'Error' model in the serialized response signifies that something went wrong during the API call.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI Workbench:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-runtime — Deploy, schedule, and monitor your pipeline in production.

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dlt ai toolkit data-exploration install
dlt ai toolkit dlthub-runtime install

• How to deploy a pipeline
• How to explore your data in marimo Notebooks
• How to query your data in Python with dataset
• Join the dlt community on Slack