Shipium Python API Docs | dltHub

Shipium is a shipping platform that provides an Address Validation REST API to validate and normalize shipping addresses before shipment and label generation. The REST API base URL is https://api.shipium.com/api/v1 and All requests require authorization via an API key (Bearer token) or OAuth access token in the Authorization 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 Shipium data in under 10 minutes.

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What data can I load from Shipium?

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

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How do I authenticate with the Shipium API?

Shipium accepts API Key or OAuth‑based authentication. Requests include an Authorization header, e.g., 'Authorization: Bearer <API_KEY>' or an OAuth access token.

1. Get your credentials

1. Log in to the Shipium Console. 2) Navigate to the API / Integrations or Developer settings. 3) Create a new API Key or register an OAuth client to obtain a client_id and client_secret. 4) Copy the API Key (or OAuth token after performing the token exchange) and store it securely for use in the Authorization header.

2. Add them to .dlt/secrets.toml

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[sources.shipium_address_validation_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 Shipium 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 shipium_address_validation_pipeline.py

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

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Pipeline shipium_address_validation_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset shipium_address_validation_data
The duckdb destination used duckdb:/shipium_address_validation.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 shipium_address_validation_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 address_validation and address from the Shipium 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 shipium_address_validation_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.shipium.com/api/v1",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "address_validation", "endpoint": {"path": "address/validate"}},
            {"name": "address", "endpoint": {"path": "address/validate"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="shipium_address_validation_pipeline",
        destination="duckdb",
        dataset_name="shipium_address_validation_data",
    )
    load_info = pipeline.run(shipium_address_validation_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("shipium_address_validation_pipeline").dataset()
sessions_df = data.address_validation.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM shipium_address_validation_data.address_validation LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("shipium_address_validation_pipeline").dataset()
data.address_validation.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 Shipium 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

Authentication failures

If you receive 401 Unauthorized, verify that the Authorization header contains a valid Bearer token or that your OAuth access token has not expired. Ensure the environment variable used for the token is set correctly.

Bad requests (400)

A 400 response indicates a malformed request body or missing required fields such as street1, city, state, countryCode, or postalCode. Check that the JSON payload matches the required schema.

Validation responses (200)

Both valid and invalid addresses return 200. Inspect the valid boolean; for invalid addresses, review the details array for errorCode values like MISMATCH_STREETLINES or MALFORMED_ADDRESSTYPE to understand the issue.

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