Nvoip Python API Docs | dltHub

Nvoip is a cloud‑based VoIP service platform providing telephony APIs. The REST API base URL is https://api.nvoip.com.br/v2 and All requests require either an OAuth2 Bearer token or a Napikey query parameter for authentication..

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 Nvoip data in under 10 minutes.

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What data can I load from Nvoip?

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

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How do I authenticate with the Nvoip API?

OAuth2 uses the token endpoint https://api.nvoip.com.br/v2/oauth/token and requires the header "Authorization: Bearer <access_token>". The API‑key flow adds "napikey=<your_napikey>" as a query parameter to every request.

1. Get your credentials

1. Log in to the Nvoip dashboard at https://painel.nvoip.com.br.
2. Navigate to Settings → API.
3. Copy the Napikey for simple API‑key authentication, or copy the numbersip and user‑token for OAuth.
4. To obtain an OAuth access token, POST to https://api.nvoip.com.br/v2/oauth/token with Content‑Type: application/x-www-form-urlencoded and body: username=YOUR-NUMBERSIP&password=USER-TOKEN&grant_type=password. The response returns access_token and refresh_token (valid ~24 hours).

2. Add them to .dlt/secrets.toml

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[sources.nvoip_source]
napikey = "your_napikey_here"
# or for OAuth
access_token = "your_access_token_here"
refresh_token = "your_refresh_token_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 Nvoip 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 nvoip_pipeline.py

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

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Pipeline nvoip_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset nvoip_data
The duckdb destination used duckdb:/nvoip.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 nvoip_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 calls and system_balance from the Nvoip 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 nvoip_source(napikey=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.nvoip.com.br/v2",
            "auth": {
                "type": "api_key",
                "api_key": napikey,
            },
        },
        "resources": [
            {"name": "calls", "endpoint": {"path": "calls"}},
            {"name": "system_balance", "endpoint": {"path": "system/balance", "data_selector": "balance"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="nvoip_pipeline",
        destination="duckdb",
        dataset_name="nvoip_data",
    )
    load_info = pipeline.run(nvoip_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("nvoip_pipeline").dataset()
sessions_df = data.calls.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM nvoip_data.calls LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("nvoip_pipeline").dataset()
data.calls.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 Nvoip 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 Errors

• 401 Unauthorized – Returned when the napikey is missing/invalid or the Bearer token has expired. Verify that you are using a valid napikey or a fresh access_token.
• 403 Forbidden – Indicates the credentials are valid but the user does not have permission for the requested resource.

Rate Limiting

• 429 Too Many Requests – The API enforces a request rate limit. Back‑off for a few seconds before retrying.

Pagination

• Some list endpoints support page and per_page query parameters (not always documented). If a response contains a next_page URL, follow it to retrieve additional records.

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