Refiner Python API Docs | dltHub

Refiner is a customer feedback and survey platform that exposes survey, contact, event and reporting data via a REST API. The REST API base URL is https://api.refiner.io/v1 and all requests require an API key (recommended as Bearer token).

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 Refiner data in under 10 minutes.

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What data can I load from Refiner?

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

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How do I authenticate with the Refiner API?

Refiner uses API keys. The recommended header is Authorization: Bearer YOUR_API_KEY. Alternatively you may send api_key as a request parameter or use HTTP Basic auth with the API key as username and empty password.

1. Get your credentials

1. Log in to your Refiner account. 2) Go to Integrations > Rest API (or Integrations). 3) Copy the displayed API key. 4) Use it as the Bearer token in API requests.

2. Add them to .dlt/secrets.toml

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[sources.refiner_source]
api_key = "your_refiner_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 Refiner 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 refiner_pipeline.py

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

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Pipeline refiner_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset refiner_data
The duckdb destination used duckdb:/refiner.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 refiner_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 responses and contacts from the Refiner 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 refiner_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.refiner.io/v1",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "responses", "endpoint": {"path": "responses", "data_selector": "items"}},
            {"name": "contacts", "endpoint": {"path": "contacts", "data_selector": "items"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="refiner_pipeline",
        destination="duckdb",
        dataset_name="refiner_data",
    )
    load_info = pipeline.run(refiner_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("refiner_pipeline").dataset()
sessions_df = data.responses.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM refiner_data.responses LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("refiner_pipeline").dataset()
data.responses.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 Refiner 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

If you receive 401 Unauthorized, verify your API key value and header formatting. Use header: Authorization: Bearer YOUR_API_KEY. Alternatively ensure api_key is provided as request parameter or use basic auth with API key as username and a trailing colon. Error responses include {"error": "message"}.

Rate limits

Refiner documents rate limits: 4,000 requests/min per API key and 80 requests/min per user ID. If you receive 429 Too Many Requests, back off and retry respecting these limits.

Pagination and large result sets

List endpoints return an items array and a pagination object with page_cursor and next_page_cursor for cursor-based pagination. Use page_cursor for large datasets rather than numeric page to iterate efficiently. page_length can be up to 1000.

Common HTTP errors

Refiner returns JSON error messages with an "error" field and uses standard HTTP codes: 400, 401, 403, 404, 429, 500, 503. The error body includes a human readable message.

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