BoltAI Python API Docs | dltHub

To generate an Azure OpenAI API key for BoltAI, create a new deployment and endpoint in the Azure OpenAI Portal, then obtain the API key. Use this key in BoltAI's settings to connect to Azure's services. The REST API base URL is `` and BoltAI has no public REST API; clients configure external AI provider API keys (OpenAI or Azure OpenAI) instead..

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 BoltAI data in under 10 minutes.

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What data can I load from BoltAI?

Here are some of the endpoints you can load from BoltAI:

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How do I authenticate with the BoltAI API?

BoltAI does not require its own API credentials. To use AI services you must provide the provider's API key (OpenAI) or endpoint and key (Azure OpenAI) in BoltAI Settings.

1. Get your credentials

1. OpenAI: sign in at https://platform.openai.com/, go to "API keys" and create a new key (format sk‑…). 2) Azure OpenAI: create a deployment in the Azure portal, copy the full endpoint URL (including api‑version) and generate an API key under "Keys and Endpoint". 3) For a local OpenAI‑compatible server, start the server (e.g., LM Studio) and note the chat completions URL such as http://localhost:1234/v1/chat/completions.

2. Add them to .dlt/secrets.toml

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[sources.boltai_source]
openai_api_key = "your_openai_api_key_here"
azure_openai_endpoint = "https://<your-azure>.openai.azure.com/openai/deployments/<deployment>/chat/completions?api-version=2023-03-15-preview"
azure_api_key = "your_azure_api_key_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.

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How do I set up and run the pipeline?

Set up a virtual environment and install dlt:

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uv venv && source .venv/bin/activate
uv pip install "dlt[workspace]"

1. Install the dlt AI Workbench:

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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:

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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 BoltAI 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:

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python boltai_pipeline.py

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

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

Inspect your pipeline and data:

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dlt pipeline boltai_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.

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Python pipeline example

This example loads chat.completions and v1/chat/completions from the BoltAI API into DuckDB. It mirrors the endpoint and data selector configuration from the table above:

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

@dlt.source
def boltai_source(openai_api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "",
            "auth": {
                "type": "api_key",
                "api_key": openai_api_key,
            },
        },
        "resources": [
            {"name": "openai_chat_completions", "endpoint": {"path": "v1/chat/completions"}},
            {"name": "openai_models", "endpoint": {"path": "v1/models", "data_selector": "data"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="boltai_pipeline",
        destination="duckdb",
        dataset_name="boltai_data",
    )
    load_info = pipeline.run(boltai_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.

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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):

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import dlt

data = dlt.pipeline("boltai_pipeline").dataset()
sessions_df = data.openai_models.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM boltai_data.openai_models LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("boltai_pipeline").dataset()
data.openai_models.df().head()

See how to explore your data in marimo Notebooks and how to query your data in Python with dataset.

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What destinations can I load BoltAI 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.

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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.

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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