Load OpenAI ChatGPT data to DuckDB

OpenAI is an AI platform providing REST API access to models for tasks such as chat completions, audio generation, and administrative management. Everything needed to build a working OpenAI ChatGPT → 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.

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Build your OpenAI ChatGPT 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 OpenAI ChatGPT 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 OpenAI ChatGPT 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.

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OpenAI ChatGPT API at a glance

These values come from the OpenAI ChatGPT API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the OpenAI ChatGPT API?

All API requests must include an Authorization header with the format 'Authorization: Bearer '. The token can be a secret API key (starting with 'sk-') or a short-lived access token from workload identity federation.

1. Get your credentials

To obtain your OpenAI API credentials, navigate to the OpenAI platform at platform.openai.com. Sign in to your account, click on the settings (gear) icon in the top-right corner, and select API keys from the menu. Click the Create new secret key button, provide a descriptive name for your key, select the appropriate project, and configure permissions as needed. Click Create secret key and copy the value immediately, as it will not be displayed again. Store this key securely; do not share it.

2. Add them to .dlt/secrets.toml

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[sources.openai_chatgpt_source]
api_key = "sk-..."

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.

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What OpenAI ChatGPT data can I load into DuckDB?

These are the OpenAI ChatGPT endpoints dlt can load into DuckDB:

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How do I load only new OpenAI ChatGPT records?

OpenAI ChatGPT exposes after on assistants, 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.

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{"name": "assistants", "endpoint": {
    "path": "assistants",
    "data_selector": "data",
    "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.

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What does the generated OpenAI ChatGPT pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /chat/completions and /models from the OpenAI ChatGPT API into DuckDB:

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

@dlt.source
def openai_chatgpt_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.openai.com/v1",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "assistants", "endpoint": {"path": "assistants", "data_selector": "data"}},
            {"name": "evals", "endpoint": {"path": "evals", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def load_openai_chatgpt_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="openai_chatgpt_pipeline",
        destination="duckdb",
        dataset_name="openai_chatgpt_data",
    )
    load_info = pipeline.run(openai_chatgpt_source())
    print(load_info)


if __name__ == "__main__":
    load_openai_chatgpt_to_duckdb()

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

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How do I query OpenAI ChatGPT 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):

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import dlt

data = dlt.pipeline("openai_chatgpt_pipeline").dataset()
df = data.assistants.df()
print(df.head())

SQL:

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SELECT * FROM openai_chatgpt_data.assistants LIMIT 10;

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

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How do I deploy the OpenAI ChatGPT 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 OpenAI ChatGPT loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load OpenAI ChatGPT 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.

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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