PTX Verification Python API Docs | dltHub

PTX Verify is a REST API platform for validating bank accounts, IBAN/BBAN, UK account details, payee/payer identity and addresses. The REST API base URL is https://verify.uk.pt-x.com/ and All requests require an API key provided in request headers for authentication..

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 PTX Verification data in under 10 minutes.

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What data can I load from PTX Verification?

Here are some of the endpoints you can load from PTX Verification:

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How do I authenticate with the PTX Verification API?

The API uses an API key supplied in a request header (e.g., x-api-key: YOUR_KEY). All endpoints require this header.

1. Get your credentials

1. Sign in to your PTX/Bottomline developer account or contact Bottomline support/sales to request access to PTX Verify.
2. Create an API user/application for Verify in the PTX admin/console.
3. Retrieve the provided API key (and note which environment – UAT or production).
4. Use that API key in request headers for all calls.

2. Add them to .dlt/secrets.toml

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[sources.ptx_verification_source]
api_key = "your_ptx_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 PTX Verification 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 ptx_verification_pipeline.py

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

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Pipeline ptx_verification_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset ptx_verification_data
The duckdb destination used duckdb:/ptx_verification.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 ptx_verification_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 validate_uk_bank_account and verify_payee_match from the PTX Verification 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 ptx_verification_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://verify.uk.pt-x.com/",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "validate_uk_bank_account", "endpoint": {"path": "v1/api/ValidateUKBankAccount"}},
            {"name": "verify_payee_match", "endpoint": {"path": "v1/api/VerifyPayeeWithMatch"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="ptx_verification_pipeline",
        destination="duckdb",
        dataset_name="ptx_verification_data",
    )
    load_info = pipeline.run(ptx_verification_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("ptx_verification_pipeline").dataset()
sessions_df = data.validate_uk_bank_account.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM ptx_verification_data.validate_uk_bank_account LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("ptx_verification_pipeline").dataset()
data.validate_uk_bank_account.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 PTX Verification 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: confirm you are using the correct API key for the target environment (UAT vs production), and that the key is sent in the required header. Contact PTX support if your key is not active.

Invalid request / Bad payload (400)

The API returns 400 for malformed or missing required fields. Ensure Content-Type: application/json and the request body matches the endpoint schema in PTX docs.

Server errors (500)

500 indicates an internal server error at PTX. Retry with exponential backoff and contact PTX support with request details if persistent.

Rate limiting / throttling

The public docs reference standard HTTP response codes; the documentation does not publish explicit rate limit headers or quotas. Implement retries with backoff and contact PTX for commercial limits.

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