SwiftDil Python API Docs | dltHub

SwiftDil is an AML/KYC and identity verification API platform providing customer onboarding, document and identity verifications, screening and reporting services. The REST API base URL is https://api.swiftdil.com and OAuth2 access token (Bearer) obtained via client_id and client_secret..

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 SwiftDil data in under 10 minutes.

---

What data can I load from SwiftDil?

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

---

How do I authenticate with the SwiftDil API?

OAuth2 client credentials flow. Obtain client_id and client_secret from your SwiftDil dashboard, request an access token at /v1/oauth2/token using HTTP Basic (client_id:client_secret) and include the returned access token in requests as Authorization: Bearer . Access tokens expire after 60 minutes.

1. Get your credentials

1. Register for a SwiftDil account (sandbox or live) on SwiftDil website or contact sales. 2) In the dashboard SwiftDil issues OAuth client credentials (client_id and client_secret) for sandbox and live. 3) Exchange client_id:client_secret at POST https://api.swiftdil.com/v1/oauth2/token (Content-Type: application/x-www-form-urlencoded) to get access token. 4) Use the token in Authorization header for API calls.

2. Add them to .dlt/secrets.toml

Copy
[sources.swift_dil_source]
client_id = "your_client_id_here"
client_secret = "your_client_secret_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.

---

How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

Copy
uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

Copy
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:

Copy
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 SwiftDil 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:

Copy
python swift_dil_pipeline.py

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

Copy
Pipeline swift_dil_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset swift_dil_data
The duckdb destination used duckdb:/swift_dil.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

Inspect your pipeline and data:

Copy
dlt pipeline swift_dil_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.

---

Python pipeline example

This example loads customers and files from the SwiftDil API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

Copy
import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def swift_dil_source(client_credentials=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.swiftdil.com",
            "auth": {
                "type": "bearer",
                "token": client_credentials,
            },
        },
        "resources": [
            {"name": "customers", "endpoint": {"path": "v1/customers", "data_selector": "content"}},
            {"name": "files", "endpoint": {"path": "v1/files", "data_selector": "content"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="swift_dil_pipeline",
        destination="duckdb",
        dataset_name="swift_dil_data",
    )
    load_info = pipeline.run(swift_dil_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.

---

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):

Copy
import dlt

data = dlt.pipeline("swift_dil_pipeline").dataset()
sessions_df = data.customers.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM swift_dil_data.customers LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("swift_dil_pipeline").dataset()
data.customers.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

---

What destinations can I load SwiftDil 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.

---

Troubleshooting

Authentication failures

If you receive 401/invalid_token errors: ensure you exchanged client_id:client_secret at POST /v1/oauth2/token (use HTTP Basic auth) and include the returned access token in Authorization: Bearer . Tokens expire in 60 minutes; refresh and retry.

Rate limits and server errors

SwiftDil uses standard HTTP codes. 5xx indicates server errors — retry with backoff. If you see 429 (rate limit), implement exponential backoff and consult support for limits.

Pagination quirks

List endpoints are paginated (default page size 20). Use page (0-based) and size query params. List responses return the records array under the "content" key when paginated; individual GETs return single objects.

Common error object

Errors return JSON with keys: id, type, message, errors (array). Typical types: invalid_request, invalid_token, access_denied, insufficient_permissions, resource_not_found, duplicate, conflict, internal_error.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

---

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.

Copy
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