Nuage Networks VSD API Python API Docs | dltHub

The Nuage Networks VSD API documentation provides RESTful endpoints for managing container and VM interfaces, requiring specific proxy headers for authentication. The latest version is 6, with detailed API references available. The documentation includes system monitoring details and metadata information. The REST API base URL is https://<VSD_HOST>:<PORT>/nuage/api/v6_0 and Initial HTTP Basic authentication to /me to obtain an APIKey; subsequent requests use the APIKey + X-Nuage-Organization 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 Nuage Networks VSD API data in under 10 minutes.

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What data can I load from Nuage Networks VSD API?

Here are some of the endpoints you can load from Nuage Networks VSD API:

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How do I authenticate with the Nuage Networks VSD API API?

Authenticate with HTTP Basic (username:password) against the /me endpoint to receive an APIKey in the JSON response. Include the enterprise/org in header X-Nuage-Organization and present the APIKey on subsequent requests via the API key header (APIKey returned by /me).

1. Get your credentials

1. Log into your VSD appliance web UI or have admin credentials for an enterprise user. 2) Use HTTP Basic auth to call GET https://<VSD_HOST>:/nuage/api/v6_0/me with header X-Nuage-Organization: (example: C SP). 3) Read the returned JSON field APIKey from the /me response; that APIKey is the token used for subsequent API calls. (The docs show same flow for v4_0; replace version in path with v6_0.)

2. Add them to .dlt/secrets.toml

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[sources.nuage_networks_vsd_api_source]
username = "your_username"
password = "your_password"
organization = "your_enterprise_name"
api_key = "your_apikey_obtained_from_me"

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 Nuage Networks VSD API 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 nuage_networks_vsd_api_pipeline.py

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

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Pipeline nuage_networks_vsd_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset nuage_networks_vsd_api_data
The duckdb destination used duckdb:/nuage_networks_vsd_api.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 nuage_networks_vsd_api_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 enterprises and domains from the Nuage Networks VSD API 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 nuage_networks_vsd_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://<VSD_HOST>:<PORT>/nuage/api/v6_0",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "enterprises", "endpoint": {"path": "enterprises/", "data_selector": "enterprises"}},
            {"name": "domains", "endpoint": {"path": "domains/", "data_selector": "domains"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="nuage_networks_vsd_api_pipeline",
        destination="duckdb",
        dataset_name="nuage_networks_vsd_api_data",
    )
    load_info = pipeline.run(nuage_networks_vsd_api_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("nuage_networks_vsd_api_pipeline").dataset()
sessions_df = data.enterprises.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM nuage_networks_vsd_api_data.enterprises LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("nuage_networks_vsd_api_pipeline").dataset()
data.enterprises.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 Nuage Networks VSD API 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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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