Mindee Python API Docs | dltHub

Mindee is an AI-powered document extraction platform that provides Invoice and Receipt OCR APIs to convert scanned or digital documents into structured JSON data. The REST API base URL is `` and Requests require an API key created in the Mindee dashboard, sent in request headers..

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 Mindee data in under 10 minutes.

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What data can I load from Mindee?

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

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How do I authenticate with the Mindee API?

Mindee uses API keys created in the dashboard; include the key in the request header as Authorization: Token <api_key>.

1. Get your credentials

1. Sign in to your Mindee account at https://app.mindee.com. 2. Open the dashboard and navigate to API Keys under Settings or Integrations. 3. Click Create new API key, give it a name and assign required scopes. 4. Copy the generated key and store it securely; it will be used in request headers.

2. Add them to .dlt/secrets.toml

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[sources.mindee_invoice_ocr_source]
api_key = "your_mindee_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 Mindee 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 mindee_invoice_ocr_pipeline.py

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

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Pipeline mindee_invoice_ocr_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset mindee_invoice_ocr_data
The duckdb destination used duckdb:/mindee_invoice_ocr.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 mindee_invoice_ocr_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 documents_create and documents_get from the Mindee 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 mindee_invoice_ocr_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "documents_create", "endpoint": {"path": "documents"}},
            {"name": "documents_get", "endpoint": {"path": "documents/{id}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="mindee_invoice_ocr_pipeline",
        destination="duckdb",
        dataset_name="mindee_invoice_ocr_data",
    )
    load_info = pipeline.run(mindee_invoice_ocr_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("mindee_invoice_ocr_pipeline").dataset()
sessions_df = data.documents_get.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM mindee_invoice_ocr_data.documents_get LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("mindee_invoice_ocr_pipeline").dataset()
data.documents_get.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 Mindee 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 a request returns 401 or 403, verify that a valid API key from the Mindee dashboard is included in the Authorization header. Rotate the key if it may have been compromised.

Rate limits and throttling

When the API responds with 429, you have exceeded the allocated quota. Implement exponential back‑off and consider contacting Mindee support to raise limits.

Asynchronous/polling quirks

Mindee’s OCR is asynchronous: a POST creates a job, and a GET on /documents/{id} polls for completion. Use webhooks (if configured) to avoid excessive polling in production.

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