NVIDIA NIM Ingestion Object Detection Python API Docs | dltHub

NVIDIA NIM for Object Detection provides APIs to perform object detection, table structure analysis, and page element extraction on image data. The REST API base URL is http://localhost:8000 (for local deployment) or https://integrate.api.nvidia.com (for hosted service) and all requests to hosted NIM endpoints require a Bearer token; locally deployed NIM containers generally do not validate specific authentication headers and rely on external proxy security..

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 add "dlt[hub]" and start loading NVIDIA NIM Ingestion Object Detection data in under 10 minutes.

---

What data can I load from NVIDIA NIM Ingestion Object Detection?

Here are some of the endpoints you can load from NVIDIA NIM Ingestion Object Detection:

---

How do I authenticate with the NVIDIA NIM Ingestion Object Detection API?

NVIDIA NIM deployments for object detection do not natively implement a unified authentication layer; developers are responsible for securing endpoints, typically via an application gateway or proxy requiring standard HTTPS/TLS authentication. For services hosted on the NVIDIA API catalog, authentication is handled via a Bearer token in the Authorization header.

1. Get your credentials

To obtain an API key for NVIDIA NIM, go to the NGC Setup page at https://org.ngc.nvidia.com/setup/api-keys. Sign in with your NVIDIA developer account. Click the button to generate a new personal API key. When prompted to select Services Included, ensure you select 'NGC Catalog' at a minimum. Copy the key immediately after generation, as it will not be displayed again. For runtime authentication, you should set this key as an environment variable named NGC_API_KEY.

2. Add them to .dlt/secrets.toml

Copy
[sources.nvidia_nim_ingestion_object_detection_source]
ngc_api_key = "your_ngc_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv init
uv add "dlt[hub]"

1. Install the dlt AI harness:

Copy
uv run dlthub 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:

Copy
uv run dlthub ai toolkit install rest-api-pipeline

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 NVIDIA NIM Ingestion Object Detection 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:

Copy
uv run python nvidia_nim_ingestion_object_detection_pipeline.py

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

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

Inspect your pipeline and data:

Copy
uv run dlthub show

This opens the Pipeline Dashboard where you can verify pipeline state, load metrics, schema (tables, columns, types), and query the loaded data directly.

---

Python pipeline example

This example loads /v1/models and /v1/chat/completions from the NVIDIA NIM Ingestion Object Detection API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def nvidia_nim_ingestion_object_detection_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://localhost:8000 (for local deployment) or https://integrate.api.nvidia.com (for hosted service)",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "models", "endpoint": {"path": "v1/models"}},
            {"name": "version", "endpoint": {"path": "v1/version"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="nvidia_nim_ingestion_object_detection_pipeline",
        destination="duckdb",
        dataset_name="nvidia_nim_ingestion_object_detection_data",
    )
    load_info = pipeline.run(nvidia_nim_ingestion_object_detection_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.

---

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):

Copy
import dlt

data = dlt.pipeline("nvidia_nim_ingestion_object_detection_pipeline").dataset()
sessions_df = data.models.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM nvidia_nim_ingestion_object_detection_data.models LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("nvidia_nim_ingestion_object_detection_pipeline").dataset()
data.models.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load NVIDIA NIM Ingestion Object Detection 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.

---

Next steps

Continue your data engineering journey with the other toolkits of the dltHub AI harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

Copy
uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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