You.com Python API Docs | dltHub

You.com is a provider of AI-ready web and news search APIs that return structured, LLM-optimized JSON results. The REST API base URL is https://ydc-index.io and All requests require an X-API-Key header 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 You.com data in under 10 minutes.

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What data can I load from You.com?

Here are some of the endpoints you can load from You.com:

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How do I authenticate with the You.com API?

Authentication is performed with a required X-API-Key HTTP header (string). Include the API key in every request header (X-API-Key: <your_key>).

1. Get your credentials

1. Sign in or create an account at https://you.com/platform. 2) Visit the API Keys / Platform page (https://you.com/platform/api-keys) or the dashboard. 3) Create a new API key and copy it. 4) Use the key as the value of the X-API-Key header in API requests.

2. Add them to .dlt/secrets.toml

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[sources.you_com_search_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 You.com 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 you_com_search_pipeline.py

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

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Pipeline you_com_search_pipeline load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset you_com_search_data
The duckdb destination used duckdb:/you_com_search.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 you_com_search_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 search and search_news from the You.com 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 you_com_search_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://ydc-index.io",
            "auth": {
                "type": "api_key",
                "api_key": api_key,
            },
        },
        "resources": [
            {"name": "search", "endpoint": {"path": "v1/search", "data_selector": "results.web"}},
            {"name": "search_news", "endpoint": {"path": "v1/search", "data_selector": "results.news"}}
        ],
    }
    yield from rest_api_resources(config)


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

SQL (DuckDB example):

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SELECT * FROM you_com_search_data.search LIMIT 10;

In a marimo or Jupyter notebook:

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import dlt

data = dlt.pipeline("you_com_search_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 You.com 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 that the X-API-Key header is present and the key is valid. 403 Forbidden indicates the key lacks the required scope or organization permissions.

Rate limits & pricing

Exceeding the allowed request quota returns 429 Too Many Requests. Rate limits depend on your subscription plan; see the pricing page or contact support for exact limits.

Pagination & large result sets

The search response returns arrays under results.web and results.news. Use query parameters such as count, offset, or page (as documented) to page through results and avoid timeouts when live crawling is enabled.

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