Wiert Archives Python API Docs | dltHub

Wiert Archives is a WordPress-based blog that lists archived posts, and while it does not expose a documented REST API for an archives service, its underlying WordPress site may implicitly expose WordPress REST API endpoints. The REST API base URL is https://wiert.me/wp-json/ and Authentication details for a specific 'Wiert Archives REST API' are not available in the provided documentation. If using the WordPress REST API, authentication methods typically include application passwords or cookie authentication for certain endpoints..

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 Wiert Archives data in under 10 minutes.

---

What data can I load from Wiert Archives?

Here are some of the endpoints you can load from Wiert Archives:

---

How do I authenticate with the Wiert Archives API?

There is no specific authentication information available for a 'Wiert Archives REST API'. If interacting with the WordPress REST API, common authentication methods include application passwords or cookie authentication, often requiring specific headers for secure requests.

1. Get your credentials

There are no instructions for obtaining API credentials specifically for a 'Wiert Archives REST API'. If using the WordPress REST API, credentials like application passwords can typically be generated from the WordPress admin dashboard under 'Users' -> 'Profile' -> 'Application Passwords'.

2. Add them to .dlt/secrets.toml

Copy
[sources.wiert_archives_source]
# No specific authentication method or credentials found for 'Wiert Archives REST API'.
# If using WordPress REST API with application passwords, an example might be:
# wordpress_app_password = "your_application_password_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 Wiert Archives 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 wiert_archives_pipeline.py

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

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

Inspect your pipeline and data:

Copy
dlt pipeline wiert_archives_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 posts and categories from the Wiert Archives 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 wiert_archives_source(No specific authentication parameter name is available for a 'Wiert Archives REST API'. If using WordPress REST API with application passwords, a possible parameter name could be 'wordpress_app_password'.=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://wiert.me/wp-json/",
            "auth": {
                "type": "No specific dlt auth type string is available for a 'Wiert Archives REST API'. If using WordPress REST API with application passwords, it might be 'api_key' or a custom type depending on implementation.",
                "No specific authentication token key is available for a 'Wiert Archives REST API'. If using WordPress REST API with application passwords, the key might be 'password' or 'app_password'.": No specific authentication parameter name is available for a 'Wiert Archives REST API'. If using WordPress REST API with application passwords, a possible parameter name could be 'wordpress_app_password'.,
            },
        },
        "resources": [
            {"name": "posts", "endpoint": {"path": "wp/v2/posts"}},
            {"name": "categories", "endpoint": {"path": "wp/v2/categories"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="wiert_archives_pipeline",
        destination="duckdb",
        dataset_name="wiert_archives_data",
    )
    load_info = pipeline.run(wiert_archives_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("wiert_archives_pipeline").dataset()
sessions_df = data.posts.df()
print(sessions_df.head())

SQL (DuckDB example):

Copy
SELECT * FROM wiert_archives_data.posts LIMIT 10;

In a marimo or Jupyter notebook:

Copy
import dlt

data = dlt.pipeline("wiert_archives_pipeline").dataset()
data.posts.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 Wiert Archives 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

No Specific Wiert Archives API Troubleshooting

There is no specific documentation available for troubleshooting a 'Wiert Archives REST API'. If you are interacting with the WordPress REST API, common issues might include:

• Authentication Failures: Ensure that application passwords (if used) are correctly generated and included in the request headers. Incorrect credentials will result in 401 Unauthorized errors.
• Rate Limiting: While WordPress REST API generally doesn't have strict rate limits by default, excessive requests might be blocked by server-level configurations or security plugins. Check server logs for 429 Too Many Requests errors.
• Endpoint Not Found: Verify the exact endpoint paths. WordPress REST API endpoints are typically prefixed with /wp-json/ followed by the namespace and version (e.g., /wp/v2/posts). Incorrect paths will lead to 404 Not Found errors.
• Missing Permissions: Some endpoints or data might require specific user roles or permissions. Ensure the authenticated user has the necessary capabilities to access the requested resources.

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