Load QingQue data in Python using dltHub

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

In this guide, we'll set up a complete Kling 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 kling_migration_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.kling.com/kling/v1/",
            "auth": {
                "type": "bearer",
                "token": access_token,
            },
        },
        "resources": [
            文生视频,生成视频,图生视频
            ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='kling_migration_pipeline',
        destination='duckdb',
        dataset_name='kling_migration_data', 
    )
    # Load the data
    load_info = pipeline.run(kling_migration_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 kling_migration’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

The Kling source features various endpoint categories including video generation, user management, and task handling. Key categories include Video Generation for creating and editing videos, User Management for handling user data and tasks, and Feed for retrieving generated content.

You will then debug the Kling 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 Kling support.

    dlt init dlthub:kling_migration 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 Kling API, as specified in @kling_migration-docs.yaml 
Start with endpoints 文生视频 and 生成视频 and skip incremental loading for now. 
Place the code in kling_migration_pipeline.py and name the pipeline kling_migration_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 kling_migration_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    Authentication is handled via an API key, which must be included in the request header under the name 'Authorization'. The format requires a Bearer token.

    To get the appropriate API keys, please visit the original source at https://docs.kling.com/. 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 kling_migration_pipeline.py

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

    Pipeline kling_migration load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset kling_migration_data
The duckdb destination used duckdb:/kling_migration.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 kling_migration_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("kling_migration_pipeline").dataset()
# get 生视 table as Pandas frame
data.生视.df().head()

Running into errors?

When using this source, it is essential to note that the bearer token must be valid and included in the headers for every request. Additionally, requests may be rate limited, and certain endpoints may require specific parameters or tasks to be completed prior to access. Users should also ensure that the resources being processed meet the specified formats and constraints.

Extra resources:

Next steps