Load LivingDocs data to DuckDB

LivingDocs provides a Public API for interacting with project configurations, documents, publications, and site structures. Everything needed to build a working LivingDocs → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

---

Build your LivingDocs to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from LivingDocs to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the LivingDocs API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

---

LivingDocs API at a glance

These values come from the LivingDocs API reference — the authoritative source if anything here looks out of date.

---

How do I authenticate with the LivingDocs API?

All API requests require an Authorization header with the format 'Bearer <your_token>'. The token is created within the Project Settings page of the Livingdocs Editor.

1. Get your credentials

To obtain an API key for the Livingdocs REST API, log in to the Livingdocs Editor and navigate to the project settings. Go to Menu, select Preferences, and then Project Admin. In the sidebar, click on Api Clients, and then use the Add Api Client button to initiate the token creation flow. You can define the token name, expiration, and specific permissions before generating the AccessToken, which should be copied securely upon creation.

2. Add them to .dlt/secrets.toml

Copy
[sources.livingdocs_source]
api_key = "your_access_token_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

---

What LivingDocs data can I load into DuckDB?

These are the LivingDocs endpoints dlt can load into DuckDB:

---

How do I load only new LivingDocs records?

LivingDocs exposes after on api/2026-01/publications/search, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

Copy
{"name": "publications_search", "endpoint": {
    "path": "api/2026-01/publications/search",
    "data_selector": "results",
    "incremental": {"cursor_path": "after", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

---

What does the generated LivingDocs pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /projectConfig and /documents/latestPublications from the LivingDocs API into DuckDB:

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

@dlt.source
def livingdocs_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://server.livingdocs.io/",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "publications_search", "endpoint": {"path": "api/2026-01/publications/search", "data_selector": "results"}},
            {"name": "incoming_references", "endpoint": {"path": "api/2026-01/documents/:documentId/incomingDocumentReferences", "data_selector": "results"}}
        ],
    }
    yield from rest_api_resources(config)


def load_livingdocs_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="livingdocs_pipeline",
        destination="duckdb",
        dataset_name="livingdocs_data",
    )
    load_info = pipeline.run(livingdocs_source())
    print(load_info)


if __name__ == "__main__":
    load_livingdocs_to_duckdb()

Run it with python livingdocs_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

---

How do I query LivingDocs data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

Copy
import dlt

data = dlt.pipeline("livingdocs_pipeline").dataset()
df = data.publications_search.df()
print(df.head())

SQL:

Copy
SELECT * FROM livingdocs_data.publications_search LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

---

How do I deploy the LivingDocs to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw LivingDocs loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

---

What other destinations can I load LivingDocs data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

---

Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack