DeepSign Python API Docs | dltHub

DeepSign API offers three types of electronic signatures: simple, advanced, and qualified, with qualified being legally equivalent to handwritten signatures. To use the API, complete an onboarding process. For documentation, visit https://apidocs.deepcloud.swiss/deepsign-api-docs/index.html. The REST API base URL is https://api.sign.deepbox.swiss/api/v1 and All requests require an OAuth2 access token sent as a Bearer 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 DeepSign data in under 10 minutes.

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What data can I load from DeepSign?

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

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How do I authenticate with the DeepSign API?

DeepSign uses OAuth2 (Keycloak) to obtain an access_token (via token endpoint https://deepcloud.swiss/auth/realms/sso/protocol/openid-connect/token). Include the token in each request header: Authorization: Bearer <access_token>.

1. Get your credentials

1. Complete DeepCloud/DeepSign onboarding (partner setup). 2) Create a service account / client in the DeepCloud Keycloak realm (Partner-Service-Client-ID and secret). 3) Use the token endpoint POST https://deepcloud.swiss/auth/realms/sso/protocol/openid-connect/token with grant_type=password or client_credentials (as provided by DeepCloud) and your client_id/client_secret (and service account credentials) to receive an access_token. 4) Use the access_token in Authorization: Bearer for API requests.

2. Add them to .dlt/secrets.toml

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[sources.deepsign_source]
access_token = "your_oauth2_access_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 DeepSign 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 deepsign_pipeline.py

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

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Pipeline deepsign_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset deepsign_data
The duckdb destination used duckdb:/deepsign.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 deepsign_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 and users_me_seals from the DeepSign 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 deepsign_source(token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.sign.deepbox.swiss/api/v1",
            "auth": {
                "type": "bearer",
                "token": token,
            },
        },
        "resources": [
            {"name": "documents", "endpoint": {"path": "documents", "data_selector": "documents"}},
            {"name": "users_me_seals", "endpoint": {"path": "users/me/seals"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="deepsign_pipeline",
        destination="duckdb",
        dataset_name="deepsign_data",
    )
    load_info = pipeline.run(deepsign_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("deepsign_pipeline").dataset()
sessions_df = data.documents.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM deepsign_data.documents LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("deepsign_pipeline").dataset()
data.documents.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 DeepSign 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, your access_token is missing, expired or invalid. Obtain a fresh token from the token endpoint and retry with Authorization: Bearer .

Rate limits and 429

The API may return 429 Too Many Requests. Implement exponential backoff and retries for 429, 500, 503 and 504 responses.

Pagination and data selectors

Overview/list endpoints use offset and limit parameters and return JSON with a "documents" array plus "pagination" and "size". Use the "documents" key as the data selector. Some endpoints (e.g. /users/me/seals) return a top-level JSON array.

Common error response format

Errors typically include timestamp, status, error, message and may include messageId or detail. Example patterns: {"timestamp":"...","status":403,"error":"Forbidden","message":"..."} or {"timestamp":"...","status":400,"error":"Bad Request","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