Junction Lab Testing Python API Docs | dltHub

Junction Lab Testing is a REST API platform to order, manage, and retrieve lab tests, appointments and lab report parsing results. The REST API base URL is https://api.us.junction.com/ (production US); https://api.eu.junction.com/ (production EU); https://api.sandbox.us.junction.com/ (sandbox US); https://api.sandbox.eu.junction.com/ (sandbox EU) and All requests require a Team API key sent in a request 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 Junction Lab Testing data in under 10 minutes.

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What data can I load from Junction Lab Testing?

Here are some of the endpoints you can load from Junction Lab Testing:

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How do I authenticate with the Junction Lab Testing API?

Use a Team API Key in header x-vital-api-key for standard requests and a Management Key in header X-Management-Key for management endpoints.

1. Get your credentials

1. Sign into the Junction dashboard (app.junction.com). 2) Navigate to Team or Org settings → API keys. 3) Create or rotate a Team API Key. 4) Copy the key and use it in the x-vital-api-key header. For Management Keys, go to Org Management → Management Keys and copy the key for the X-Management-Key header.

2. Add them to .dlt/secrets.toml

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[sources.junction_lab_testing_source]
api_key = "your_team_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.

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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 Junction Lab Testing 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 junction_lab_testing_pipeline.py

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

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Pipeline junction_lab_testing_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset junction_lab_testing_data
The duckdb destination used duckdb:/junction_lab_testing.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 junction_lab_testing_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 lab_tests and lab_test from the Junction Lab Testing 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 junction_lab_testing_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.us.junction.com/ (production US); https://api.eu.junction.com/ (production EU); https://api.sandbox.us.junction.com/ (sandbox US); https://api.sandbox.eu.junction.com/ (sandbox EU)",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "lab_tests", "endpoint": {"path": "v3/lab_test", "data_selector": "data"}},
            {"name": "lab_test", "endpoint": {"path": "v3/lab_tests/{lab_test_id}", "data_selector": "lab_test"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="junction_lab_testing_pipeline",
        destination="duckdb",
        dataset_name="junction_lab_testing_data",
    )
    load_info = pipeline.run(junction_lab_testing_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("junction_lab_testing_pipeline").dataset()
sessions_df = data.lab_tests.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM junction_lab_testing_data.lab_tests LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("junction_lab_testing_pipeline").dataset()
data.lab_tests.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 Junction Lab Testing 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 failures

Ensure you provide a valid Team API Key in header x-vital-api-key. Management endpoints require X-Management-Key. A 401 response indicates an invalid or missing key.

Rate limits and 429/503

Junction may return 429 Too Many Requests or 503 Service Unavailable under load. Implement exponential backoff retries for idempotent calls.

Pagination quirks

List endpoints are paginated and return total, page, size and an items array inside the top‑level data object. Use the provided parameters to page through results.

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