ByteChef Python API Docs | dltHub

Ollama's REST API allows interaction with large language models, with endpoints for generating responses, managing models, and authentication. The API supports both local and cloud models. For detailed documentation, refer to the official Ollama API reference. The REST API base URL is http://localhost:11434 and Bearer token (Ollama Cloud) or no auth for local server.

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 ByteChef data in under 10 minutes.

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What data can I load from ByteChef?

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

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How do I authenticate with the ByteChef API?

For Ollama Cloud use an API key presented as a Bearer token in the Authorization header (Authorization: Bearer <API_KEY>). A locally hosted Ollama server typically does not require authentication by default and listens on http://localhost:11434.

1. Get your credentials

1. For Ollama Cloud: sign in at https://ollama.com (or the cloud UI) and create an API key in the account/API keys page. 2) Copy the API key and store it securely; use it as a Bearer token in the Authorization header. For local Ollama: install and run the Ollama daemon, which listens on http://localhost:11434 and does not require credentials by default.

2. Add them to .dlt/secrets.toml

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[sources.bytechef_source]
api_key = "your_ollama_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 ByteChef 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 bytechef_pipeline.py

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

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Pipeline bytechef_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset bytechef_data
The duckdb destination used duckdb:/bytechef.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 bytechef_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 models and generate from the ByteChef 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 bytechef_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://localhost:11434",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "models", "endpoint": {"path": "models"}},
            {"name": "generate", "endpoint": {"path": "api/generate", "data_selector": "response"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="bytechef_pipeline",
        destination="duckdb",
        dataset_name="bytechef_data",
    )
    load_info = pipeline.run(bytechef_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("bytechef_pipeline").dataset()
sessions_df = data.models.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM bytechef_data.models LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("bytechef_pipeline").dataset()
data.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 ByteChef 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