Load OpenAI Chat Completions data to DuckDB

OpenAI Chat Completions API provides an endpoint to generate model responses from a conversation history of messages. Everything needed to build a working OpenAI Chat Completions → 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 Chat Completions 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 Chat Completions 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 Chat Completions 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 Chat Completions API at a glance

These values come from the OpenAI Chat Completions API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the OpenAI Chat Completions API?

The OpenAI API requires Bearer authentication, where the API key or short-lived access token is provided in the 'Authorization' header using the 'Bearer' scheme (e.g., 'Authorization: Bearer OPENAI_API_KEY_OR_ACCESS_TOKEN'). Additionally, a 'Content-Type: application/json' header is required for requests containing a body.

1. Get your credentials

1. Navigate to the OpenAI Platform dashboard (platform.openai.com). 2. Sign in with your OpenAI account. 3. Access the 'API keys' section under Settings (typically found at platform.openai.com/settings/organization/api-keys). 4. Click 'Create new secret key'. 5. Copy the key immediately, as it will not be displayed again for security reasons.

2. Add them to .dlt/secrets.toml

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[sources.openai_chat_completions_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 Chat Completions data can I load into DuckDB?

These are the OpenAI Chat Completions endpoints dlt can load into DuckDB:

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How do I load only new OpenAI Chat Completions records?

The OpenAI Chat Completions 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.

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{"name": "chat_completions", "endpoint": {
    "path": "chat/completions",
    # 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.

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What does the generated OpenAI Chat Completions pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /chat/completions and /chat/completions/{completion_id}/messages from the OpenAI Chat Completions API into DuckDB:

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

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


def load_openai_chat_completions_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="openai_chat_completions_pipeline",
        destination="duckdb",
        dataset_name="openai_chat_completions_data",
    )
    load_info = pipeline.run(openai_chat_completions_source())
    print(load_info)


if __name__ == "__main__":
    load_openai_chat_completions_to_duckdb()

Run it with python openai_chat_completions_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 Chat Completions 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_chat_completions_pipeline").dataset()
df = data.chat_completions.df()
print(df.head())

SQL:

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SELECT * FROM openai_chat_completions_data.chat_completions LIMIT 10;

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

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How do I deploy the OpenAI Chat Completions 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 Chat Completions 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 Chat Completions 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