Google Gemini API Python API Docs | dltHub

Google Gemini API is a REST API that provides access to Google's Gemini family of generative models for text, code and multimodal generation. The REST API base URL is https://generativelanguage.googleapis.com and all requests require an API key header or OAuth 2.0 Bearer token for authentication.

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 Google Gemini API data in under 10 minutes.

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What data can I load from Google Gemini API?

Here are some of the endpoints you can load from Google Gemini API:

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How do I authenticate with the Google Gemini API API?

The Gemini REST API accepts an API key passed in the x-goog-api-key header for simple access (recommended for quickstarts). For production use, OAuth 2.0 access tokens (Bearer Authorization header) or Application Default Credentials can be used; when using OAuth, include Authorization: Bearer <ACCESS_TOKEN> and (for some requests) x-goog-user-project: <PROJECT_ID>.

1. Get your credentials

1. For API key: open Google AI Studio (https://aistudio.google.com/app/apikey), create an API key and copy it. 2) For OAuth / Google Cloud: enable the Generative Language API in Google Cloud Console, configure OAuth consent, create OAuth 2.0 credentials (or use service account / gcloud ADC), then obtain an access token via gcloud auth application-default login or OAuth flow.

2. Add them to .dlt/secrets.toml

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[sources.google_gemini_api_source]
api_key = "YOUR_GEMINI_API_KEY"

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 Google Gemini API 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 google_gemini_api_pipeline.py

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

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Pipeline google_gemini_api_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset google_gemini_api_data
The duckdb destination used duckdb:/google_gemini_api.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 google_gemini_api_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 model from the Google Gemini API 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 google_gemini_api_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://generativelanguage.googleapis.com",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "models", "endpoint": {"path": "v1/models", "data_selector": "models"}},
            {"name": "model", "endpoint": {"path": "v1/models/{name}"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM google_gemini_api_data.models LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("google_gemini_api_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 Google Gemini API 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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Troubleshooting

Authentication failures

If using an API key: ensure the x-goog-api-key header is present and the key is valid; 401/403 errors indicate missing/invalid key or insufficient IAM permissions. If using OAuth: ensure Authorization: Bearer <token> is provided and the token includes required scopes (e.g., https://www.googleapis.com/auth/cloud-platform).

Rate limits and quota

The Gemini API enforces per‑project quotas. Exceeding them returns 429 Too Many Requests or quota‑related 403 responses. Check Cloud Console quotas and request increases as needed.

Pagination

List endpoints return a top‑level list field (e.g., models, locations) and a nextPageToken. Use pageSize and pageToken query parameters to retrieve subsequent pages.

Common error responses

• 401 Unauthorized – missing or invalid API key / token.
• 403 Forbidden – insufficient permissions or quota limits.
• 404 Not Found – invalid resource name.
• 429 Too Many Requests – rate limiting.
• 5xx – transient server errors; retry with exponential backoff.

Ensure that the API key is valid to avoid 401 Unauthorized errors. Also, verify endpoint paths and parameters to avoid 404 Not Found errors.

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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