Searchspring Python API Docs | dltHub

Searchspring is a site search and merchandising platform providing REST APIs to retrieve search results, personalization, recommendations, and indexing functionality. The REST API base URL is https://{siteId}.a.searchspring.io and Bulk Indexing uses standard HTTP Basic Authentication; Search APIs use site ID-based paths and session tracking..

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 Searchspring data in under 10 minutes.

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What data can I load from Searchspring?

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

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How do I authenticate with the Searchspring API?

The Bulk Indexing API utilizes standard HTTP Basic Authentication, where the username is the Site ID and the password is the secret key. The Search API relies on public-facing site ID-based endpoints and session tracking rather than traditional API key headers.

1. Get your credentials

To obtain your credentials for the Searchspring Bulk Indexing API, log in to the Searchspring Management Console and navigate to the My Account page. Your Site ID is available directly on this page. For the secret key, you must contact Searchspring Support at support@searchspring.com, as it is not displayed in the dashboard. Note that standard Search/Search-related APIs generally do not require a secret key and instead use the Site ID as a query parameter.

2. Add them to .dlt/secrets.toml

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[sources.searchspring_source]
api_key = "REPLACE_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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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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:

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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 Searchspring 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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uv run python searchspring_pipeline.py

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

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Pipeline searchspring_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset searchspring_data
The duckdb destination used duckdb:/searchspring.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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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.

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Python pipeline example

This example loads /api/search/search.json and /api/index/feed from the Searchspring 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 searchspring_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://{siteId}.a.searchspring.io",
            "auth": {"type": "http_basic", "username": "REPLACE_ME", "password": api_key},
        },
        "resources": [
            {"name": "search", "endpoint": {"path": "api/search/search.json", "data_selector": "results"}},
            {"name": "category", "endpoint": {"path": "api/search/category.json", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="searchspring_pipeline",
        destination="duckdb",
        dataset_name="searchspring_data",
    )
    load_info = pipeline.run(searchspring_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("searchspring_pipeline").dataset()
sessions_df = data.search.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM searchspring_data.search LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("searchspring_pipeline").dataset()
data.search.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 Searchspring 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 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.

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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