Quickwit Python API Docs | dltHub

Quickwit is a high-performance search engine for log management and observability that provides a REST API for indexing, searching, and cluster management. The REST API base URL is http://<quickwit_node_host>:<port>/api/v1 and no native authentication provided by the service; implemented at the infrastructure layer.

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 add "dlt[hub]" and start loading Quickwit data in under 10 minutes.

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What data can I load from Quickwit?

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

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How do I authenticate with the Quickwit API?

Quickwit does not provide built-in authentication for its REST API. Users are typically expected to secure access via network/firewall configurations or by placing the service behind a reverse proxy that handles authentication headers.

1. Get your credentials

Quickwit does not natively manage API keys or user credentials at the application level. To secure your Quickwit REST API, you must deploy the Quickwit node behind a reverse proxy (such as Nginx, Traefik, or an AWS Application Load Balancer). Follow these steps: 1. Deploy your Quickwit instance in a private network or behind a reverse proxy. 2. Configure the proxy to enforce authentication (e.g., Basic Authentication, Bearer token, or mTLS). 3. Generate your credentials (username/password or token) within the proxy's configuration management system. 4. Use these proxy-managed credentials when configuring your dlt source to include them in the HTTP headers.

2. Add them to .dlt/secrets.toml

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[sources.quickwit_source]
api_base = "https://your-proxy-domain.com/api/v1"
auth_username = "your_username"
auth_password = "your_password"
# If using a Bearer token:
# auth_token = "Bearer your_token_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 init
uv add "dlt[hub]"

1. Install the dlt AI harness:

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uv run dlthub 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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uv run dlthub ai toolkit install rest-api-pipeline

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 Quickwit 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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uv run python quickwit_pipeline.py

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

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Pipeline quickwit_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset quickwit_data
The duckdb destination used duckdb:/quickwit.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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uv run dlthub 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 /indexes and /{index_id}/search from the Quickwit 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 quickwit_source(api_base=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "http://<quickwit_node_host>:<port>/api/v1",
            "auth": {"type": "api_key", "api_key": api_base},
        },
        "resources": [
            {"name": "search", "endpoint": {"path": "api/v1/{index}/search", "data_selector": "hits"}},
            {"name": "indexes", "endpoint": {"path": "api/v1/indexes"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="quickwit_pipeline",
        destination="duckdb",
        dataset_name="quickwit_data",
    )
    load_info = pipeline.run(quickwit_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("quickwit_pipeline").dataset()
sessions_df = data.search.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM quickwit_data.search LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("quickwit_pipeline").dataset()
data.search.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 Quickwit 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 harness:

• data-exploration — Build custom notebooks, charts, and dashboards for deeper analysis with marimo notebooks.
• dlthub-platform — Deploy, schedule, and monitor your pipeline in production.

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uv run dlthub ai toolkit install data-exploration
uv run dlthub ai toolkit install dlthub-platform

• 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