Load InvenioILS data in Python using dltHub

Build a InvenioILS-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete InvenioILS data pipeline from API credentials to your first data load in just 10 minutes. You'll end up with a fully declarative Python pipeline based on dlt's REST API connector, like in the partial example code below:

Example code
@dlt.source
def invenioils_loan_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://127.0.0.1:5000/api/",
            "auth": {
                "type": "bearer",
                "token": "API-TOKEN",
            },
        },
        "resources": [
            circulation/loans, documents
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='invenioils_loan_pipeline',
        destination='duckdb',
        dataset_name='invenioils_loan_data', 
    )
    # Load the data
    load_info = pipeline.run(invenioils_loan_source())
    print(load_info)

Why use dltHub Workspace with LLM Context to generate Python pipelines?

  • Accelerate pipeline development with AI-native context
  • Debug pipelines, validate schemas and data with the integrated Pipeline Dashboard
  • Build Python notebooks for end users of your data
  • Low maintenance thanks to Schema evolution with type inference, resilience and self documenting REST API connectors. A shallow learning curve makes the pipeline easy to extend by any team member
  • dlt is the tool of choice for Pythonic Iceberg Lakehouses, bringing mature data loading to pythonic Iceberg with or without catalogs

What you’ll do

We’ll show you how to generate a readable and easily maintainable Python script that fetches data from invenioils_loan’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

  • Circulation/Loans: Manage library loans including retrieving loan records, creating new loans, and processing checkouts
  • Documents: Access and manage document records with retrieval and creation capabilities

You will then debug the InvenioILS pipeline using our Pipeline Dashboard tool to ensure it is copying the data correctly, before building a Notebook to explore your data and build reports.

Setup & steps to follow

💡
Before getting started, let's make sure Cursor is set up correctly:

Now you're ready to get started!

  1. ⚙️ Set up dlt Workspace

    Install dlt with duckdb support:

    pip install dlt[workspace]

    Initialize a dlt pipeline with InvenioILS support.

    dlt init dlthub:invenioils_loan duckdb

    The init command will setup the necessary files and folders for the next step.

  2. 🤠 Start LLM-assisted coding

    Here’s a prompt to get you started:

    Prompt
    Please generate a REST API Source for InvenioILS API, as specified in @invenioils_loan-docs.yaml 
Start with endpoint(s) circulation/loans and documents and skip incremental loading for now. 
Place the code in invenioils_loan_pipeline.py and name the pipeline invenioils_loan_pipeline. 
If the file exists, use it as a starting point. 
Do not add or modify any other files. 
Use @dlt rest api as a tutorial. 
After adding the endpoints, allow the user to run the pipeline with python invenioils_loan_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Authentication uses bearer tokens passed in the Authorization header. Include the header Authorization: Bearer API-TOKEN where API-TOKEN is your actual API token value.

    To get the appropriate API keys, please visit the original source at invenioils.docs.cern.ch. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

  4. 🏃‍♀️ Run the pipeline in the Python terminal in Cursor

    python invenioils_loan_pipeline.py

    If your pipeline runs correctly, you’ll see something like the following:

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

  5. 📈 Debug your pipeline and data with the Pipeline Dashboard

    Now that you have a running pipeline, you need to make sure it’s correct, so you do not introduce silent failures like misconfigured pagination or incremental loading errors. By launching the dlt Workspace Pipeline Dashboard, you can see various information about the pipeline to enable you to test it. Here you can see:

    • Pipeline overview: State, load metrics
    • Data’s schema: tables, columns, types, hints
    • You can query the data itself
    dlt pipeline invenioils_loan_pipeline show

  6. 🐍 Build a Notebook with data explorations and reports

    With the pipeline and data partially validated, you can continue with custom data explorations and reports. To get started, paste the snippet below into a new marimo Notebook and ask your LLM to go from there. Jupyter Notebooks and regular Python scripts are supported as well.

    import dlt

data = dlt.pipeline("invenioils_loan_pipeline").dataset()
# get ["circulation/loans"] table as Pandas frame
data.["circulation/loans"].df().head()

Extra resources:

Next steps