Bloomreach Python API Docs | dltHub

Bloomreach is a digital experience platform offering search, content, and engagement REST APIs for e‑commerce and personalization. The REST API base URL is https://core.dxpapi.com/api/v1/core/ and Discovery requires an auth_key query parameter; Content Delivery API 1.0 is unauthenticated, 2.0 uses a Bearer JWT; Engagement requires a project token in the Authorization 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 Bloomreach data in under 10 minutes.

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What data can I load from Bloomreach?

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

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How do I authenticate with the Bloomreach API?

Discovery API requests include an auth_key query parameter. Content Delivery API 2.0 expects an Authorization: Bearer <JWT> header. Engagement API calls require Authorization: Bearer <projectToken>.

1. Get your credentials

1. Log in to the Bloomreach console.
2. Navigate to the project or account settings.
3. For Discovery, locate the "API Keys" section and copy the generated auth_key.
4. For Content Delivery API 2.0, create a JWT secret under "Security > JWT" and copy the generated token.
5. For Engagement, open the "Projects" page, select your project, and copy the projectToken shown in the API credentials tab.

2. Add them to .dlt/secrets.toml

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[sources.bloomreach_product_and_category_search_source]
api_key = "your_api_key_here"
# or for JWT
jwt = "your_jwt_token_here"
# for Engagement project token
project_token = "your_project_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 Bloomreach 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 bloomreach_product_and_category_search_pipeline.py

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

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Pipeline bloomreach_product_and_category_search_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bloomreach_product_and_category_search_data
The duckdb destination used duckdb:/bloomreach_product_and_category_search.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 bloomreach_product_and_category_search_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 product_search and content_fetch from the Bloomreach 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 bloomreach_product_and_category_search_source(auth_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://core.dxpapi.com/api/v1/core/",
            "auth": {
                "type": "api_key",
                "api_key": auth_key,
            },
        },
        "resources": [
            {"name": "product_search", "endpoint": {"path": "search", "data_selector": "docs"}},
            {"name": "content_fetch", "endpoint": {"path": "content"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bloomreach_product_and_category_search_pipeline",
        destination="duckdb",
        dataset_name="bloomreach_product_and_category_search_data",
    )
    load_info = pipeline.run(bloomreach_product_and_category_search_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("bloomreach_product_and_category_search_pipeline").dataset()
sessions_df = data.product_search.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM bloomreach_product_and_category_search_data.product_search LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bloomreach_product_and_category_search_pipeline").dataset()
data.product_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 Bloomreach 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 – occurs when auth_key (Discovery) or JWT/Project token (Content & Engagement) is missing or invalid. Verify that the correct key/token is supplied in the query string or Authorization header.

Rate limiting

• 429 Too Many Requests – Bloomreach enforces request quotas. If you receive this response, implement exponential back‑off and respect the Retry-After header if present.

Pagination quirks

• Discovery uses start (offset) and rows (page size) query parameters. Omit rows to use the default page size. Responses include numFound and start to help calculate next offsets.
• Content Delivery API may return a top‑level array; no pagination parameters are needed.
• Engagement endpoints typically do not paginate large result sets; if pagination is needed, use the provided page and size parameters where documented.

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