Vespa Python API Docs | dltHub

Vespa is an open source big data serving engine providing REST APIs for document storage, retrieval and query. The REST API base URL is http://{host}:{port} (examples: http://localhost:8080 for local, mTLS endpoint for Vespa Cloud) and mTLS client-cert or application keys for authenticated Vespa Cloud endpoints; local endpoints usually unauthenticated..

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 Vespa data in under 10 minutes.

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What data can I load from Vespa?

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

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How do I authenticate with the Vespa API?

Vespa supports TLS/mTLS client certificate authentication for cloud endpoints and can be used with application keys or Vespa CLI for authenticated operations. For local development no auth is required; for Vespa Cloud use client certificate (cert,key) on the requests session or application/deployment keys where applicable.

1. Get your credentials

1. For Vespa Cloud: obtain mTLS client certificate and private key from Vespa Cloud console or CLI (Application Keys/credentials). 2) Download/store certificate (.pem) and key (.pem). 3) In code, pass them to requests.Session().cert = (cert_path, key_path) or configure your HTTP client to use the client certificate pair.

2. Add them to .dlt/secrets.toml

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[sources.vespa_document_v1_source]
client_cert = ("/path/to/cert.pem", "/path/to/key.pem")

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 Vespa 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 vespa_document_v1_pipeline.py

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

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Pipeline vespa_document_v1_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset vespa_document_v1_data
The duckdb destination used duckdb:/vespa_document_v1.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 vespa_document_v1_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 documents and document_by_id from the Vespa 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 vespa_document_v1_source(client_cert=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://{host}:{port} (examples: http://localhost:8080 for local, mTLS endpoint for Vespa Cloud)",
            "auth": {
                "type": "http_client_cert",
                "cert_and_key (use tuple of cert and key file paths)": client_cert,
            },
        },
        "resources": [
            {"name": "documents", "endpoint": {"path": "document/v1/?cluster={cluster}", "data_selector": "documents"}},
            {"name": "document_by_id", "endpoint": {"path": "document/v1/{namespace}/{doctype}/docid/{id}"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="vespa_document_v1_pipeline",
        destination="duckdb",
        dataset_name="vespa_document_v1_data",
    )
    load_info = pipeline.run(vespa_document_v1_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("vespa_document_v1_pipeline").dataset()
sessions_df = data.documents.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM vespa_document_v1_data.documents LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("vespa_document_v1_pipeline").dataset()
data.documents.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 Vespa 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

For Vespa Cloud, ensure your client certificate and key are correct and supplied to the HTTP client (requests.Session().cert=(cert_path,key_path)). A 401/403 indicates missing or invalid credentials; verify application keys or mTLS certificate validity.

Rate limiting and backpressure

The /document/v1 feed APIs can return 429 Too Many Requests when Vespa signals backpressure. Implement retries with exponential backoff, or use Vespa feed clients which handle backpressure.

Large request payloads

POST/PUT requests exceeding Vespa limits return 413 Content Too Large. Split or truncate documents to stay below recommended sizes (<=10MB) or adjust client logic.

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