Autoenhance Python API Docs | dltHub

Autoenhance is a REST API for automated, high-quality image enhancement (HDR merging, perspective correction, color correction) for integrating professional photo edits into applications. The REST API base URL is https://api.autoenhance.ai/v3 and all requests require a Bearer API key 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 Autoenhance data in under 10 minutes.

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What data can I load from Autoenhance?

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

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How do I authenticate with the Autoenhance API?

Use the API key as a Bearer token in the Authorization header: Authorization: Bearer <API_KEY>. Include Content-Type: application/json for JSON requests and multipart/form-data for file uploads.

1. Get your credentials

1. Create an account at https://app.autoenhance.ai or sign in. 2) In the dashboard go to API / Developers or Account settings. 3) Create or copy your API key/token. 4) Use the key as a Bearer token in requests.

2. Add them to .dlt/secrets.toml

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[sources.autoenhance_source]
api_key = "your_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 Autoenhance 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 autoenhance_pipeline.py

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

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Pipeline autoenhance_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset autoenhance_data
The duckdb destination used duckdb:/autoenhance.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 autoenhance_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 images and orders from the Autoenhance 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 autoenhance_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://api.autoenhance.ai/v3",
            "auth": {
                "type": "bearer",
                "token": api_key,
            },
        },
        "resources": [
            {"name": "images", "endpoint": {"path": "v3/images/"}},
            {"name": "orders", "endpoint": {"path": "v3/orders/"}}
        ],
    }
    yield from rest_api_resources(config)


def get_data() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="autoenhance_pipeline",
        destination="duckdb",
        dataset_name="autoenhance_data",
    )
    load_info = pipeline.run(autoenhance_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("autoenhance_pipeline").dataset()
sessions_df = data.images.df()
print(sessions_df.head())

SQL (DuckDB example):

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SELECT * FROM autoenhance_data.images LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("autoenhance_pipeline").dataset()
data.images.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 Autoenhance 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 you receive 401 Unauthorized, verify the Authorization header is present and the API key is valid. Regenerate the key in the dashboard if needed.

Not found / invalid IDs

GET requests to resource IDs return 404 when the ID does not exist. Verify the ID and that it belongs to your account/organization.

Validation errors

Requests that fail validation return 422 with details in the response body. Ensure required parameters and correct JSON structure are provided.

Rate limits

The API enforces rate limiting; excessive requests may return 429 Too Many Requests. Implement exponential backoff and retry with jitter.

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