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Load Read the Docs data to DuckDB

Build a Read the Docs to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the Read the Docs API base URL, auth, endpoints, and incremental loading.

SourceRead the DocsRead the Docs API DocumentationDestinationDuckDBIn-process analytical database. The default local destination for dlt pipelines.

Read the Docs is a documentation hosting platform that provides a REST API for accessing and managing projects, builds, and versions. Everything needed to build a working Read the Docs → 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 Read the Docs to DuckDB pipeline

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

Prompt
Run uvx dlthub-init@latest to build a pipeline from Read the Docs 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 Read the Docs 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.


Read the Docs API at a glance

Base URLhttps://app.readthedocs.com/api/v3/
Example endpointGET api/v3/projects/
Records found atresults
Authenticationrequests require an Authorization header with a Token scheme — sent in the Authorization header, prefixed Token
PaginationNot paginated
Record idid
API referencehttps://docs.readthedocs.com/platform/latest/api/v3.html

These values come from the Read the Docs API reference — the authoritative source if anything here looks out of date.


How do I authenticate with the Read the Docs API?

The API uses Token authentication. Requests must include an 'Authorization' HTTP header with the value 'Token <your_token>'.

1. Get your credentials

To obtain your API token for the Read the Docs REST API, navigate to your account profile settings. For Read the Docs Community, visit https://app.readthedocs.org/accounts/tokens/. For Read the Docs Business, visit https://app.readthedocs.com/accounts/tokens/. From these pages, you can generate and manage your API authentication token.

2. Add them to .dlt/secrets.toml

[sources.read_the_docs_source] read_the_docs_api_token = "your_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 Read the Docs data can I load into DuckDB?

These are the Read the Docs endpoints dlt can load into DuckDB:

ResourceEndpointMethodData selectorDescription
projects/api/v3/projects/GETresultsRetrieve a list of projects.
versions/api/v3/projects/{project_slug}/versions/GETresultsList versions for a project.
builds/api/v3/projects/{project_slug}/builds/GETresultsList builds for a project.
remote_repositories/api/v3/remote/repositories/GETresultsList remote repositories.
remote_organizations/api/v3/remote/organizations/GETresultsList remote organizations.

How do I load only new Read the Docs records?

The Read the Docs API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

{"name": "projects", "endpoint": { "path": "api/v3/projects/", # Replace with a field that increases on every write. "incremental": {"cursor_path": "REPLACE_ME", "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 Read the Docs pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading projects and builds from the Read the Docs API into DuckDB:

import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def read_the_docs_source(api_token=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://app.readthedocs.com/api/v3/", "auth": {"type": "bearer", "token": api_token}, }, "resources": [ {"name": "projects", "endpoint": {"path": "api/v3/projects/", "data_selector": "results"}}, {"name": "builds", "endpoint": {"path": "api/v3/projects/{project_slug}/builds/", "data_selector": "results"}} ], } yield from rest_api_resources(config) def load_read_the_docs_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="read_the_docs_pipeline", destination="duckdb", dataset_name="read_the_docs_data", ) load_info = pipeline.run(read_the_docs_source()) print(load_info) if __name__ == "__main__": load_read_the_docs_to_duckdb()

Run it with python read_the_docs_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 Read the Docs 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):

import dlt data = dlt.pipeline("read_the_docs_pipeline").dataset() df = data.projects.df() print(df.head())

SQL:

SELECT * FROM read_the_docs_data.projects LIMIT 10;

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


How do I deploy the Read the Docs 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 Read the Docs 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 Read the Docs data to?

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

DestinationExample value
PostgreSQL"postgres"
BigQuery"bigquery"
Snowflake"snowflake"
Redshift"redshift"
Databricks"databricks"
Filesystem (S3, GCS, Azure)"filesystem"

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.


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