Load Kairos Face Recognition data to DuckDB

Kairos Face Recognition is a REST API platform for face detection, recognition, verification, enrollment and gallery management. Everything needed to build a working Kairos Face Recognition → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Kairos Face Recognition to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Kairos Face Recognition to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Kairos Face Recognition API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Kairos Face Recognition API at a glance

These values come from the Kairos Face Recognition API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Kairos Face Recognition API?

Requests are authenticated by including the 'app_id' and 'app_key' as HTTP headers. These credentials should be managed securely on the server side.

1. Get your credentials

To obtain your credentials, navigate to the Kairos dashboard at https://developer.kairos.com/login and sign in. If you do not have an account, you will first need to sign up. Once logged in, navigate to the 'Applications' section. Here, you can either create a new application or view your existing default application details. Upon selecting or creating an application, your 'App ID' and 'App Key' will be displayed. Ensure these are stored securely, as they are required for all API authentication.

2. Add them to .dlt/secrets.toml

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[sources.kairos_face_recognition_source]
app_key_pair = "REPLACE_ME"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Kairos Face Recognition data can I load into DuckDB?

These are the Kairos Face Recognition endpoints dlt can load into DuckDB:

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How do I load only new Kairos Face Recognition records?

Kairos Face Recognition exposes updated_at on tasks, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "tasks", "endpoint": {
    "path": "tasks",
    "data_selector": "data",
    "incremental": {"cursor_path": "updated_at", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Kairos Face Recognition pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading detect and recognize from the Kairos Face Recognition API into DuckDB:

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

@dlt.source
def kairos_face_recognition_source(app_key_pair=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.kairos.com",
            "auth": {"type": "api_key", "api_key": app_key_pair, "name": "app_key"},
        },
        "resources": [
            {"name": "tasks", "endpoint": {"path": "tasks", "data_selector": "data"}},
            {"name": "documents", "endpoint": {"path": "documents", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_kairos_face_recognition_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="kairos_face_recognition_pipeline",
        destination="duckdb",
        dataset_name="kairos_face_recognition_data",
    )
    load_info = pipeline.run(kairos_face_recognition_source())
    print(load_info)


if __name__ == "__main__":
    load_kairos_face_recognition_to_duckdb()

Run it with python kairos_face_recognition_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Kairos Face Recognition data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("kairos_face_recognition_pipeline").dataset()
df = data.tasks.df()
print(df.head())

SQL:

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SELECT * FROM kairos_face_recognition_data.tasks LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Kairos Face Recognition to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Kairos Face Recognition loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Kairos Face Recognition data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack