Load Yandex Cloud AI Studio Text Classification data to DuckDB

Yandex Cloud AI Studio provides an API for classifying text using YandexGPT-based models. Everything needed to build a working Yandex Cloud AI Studio Text Classification → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.

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Build your Yandex Cloud AI Studio Text Classification to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

PromptCopy prompt
Run uvx dlthub-init@latest to build a pipeline from Yandex Cloud AI Studio Text Classification to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Yandex Cloud AI Studio Text Classification API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.

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Yandex Cloud AI Studio Text Classification API at a glance

These values come from the Yandex Cloud AI Studio Text Classification API reference — the authoritative source if anything here looks out of date.

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How do I authenticate with the Yandex Cloud AI Studio Text Classification API?

Authentication is performed by passing an API key in the 'Authorization' header using the 'Api-Key' format.

1. Get your credentials

1. Navigate to the Yandex Cloud management console and select your folder. 2. Go to Identity and Access Management and select the target Service account. 3. In the top panel, click Create new key and select Create API key. 4. Provide a description and select the appropriate scopes (e.g., yc.ai.languageModels.execute or similar required scopes for classification). 5. Click Create and save the secret key immediately, as it cannot be retrieved again.

2. Add them to .dlt/secrets.toml

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[sources.yandex_cloud_ai_studio_text_classification_source]
yandex_cloud_api_key = "your_secret_api_key_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.

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What Yandex Cloud AI Studio Text Classification data can I load into DuckDB?

These are the Yandex Cloud AI Studio Text Classification endpoints dlt can load into DuckDB:

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How do I load only new Yandex Cloud AI Studio Text Classification records?

Yandex Cloud AI Studio Text Classification exposes next_page_token on foundation_models/v1/classification/models, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

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{"name": "classification_models", "endpoint": {
    "path": "foundation_models/v1/classification/models",
    "data_selector": "next_page_token",
    "incremental": {"cursor_path": "next_page_token", "initial_value": "2024-01-01T00:00:00Z"},
}}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.

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What does the generated Yandex Cloud AI Studio Text Classification pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading Classify and FewShotClassify from the Yandex Cloud AI Studio Text Classification API into DuckDB:

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import dlt
from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources

@dlt.source
def yandex_cloud_ai_studio_text_classification_source(api_key=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://llm.api.cloud.yandex.net",
            "auth": {"type": "bearer", "token": api_key},
        },
        "resources": [
            {"name": "classification_models", "endpoint": {"path": "foundation_models/v1/classification/models", "data_selector": "next_page_token"}},
            {"name": "classification_tasks", "endpoint": {"path": "foundation_models/v1/classification/tasks", "data_selector": "next_page_token"}}
        ],
    }
    yield from rest_api_resources(config)


def load_yandex_cloud_ai_studio_text_classification_to_duckdb() -> None:
    pipeline = dlt.pipeline(
        pipeline_name="yandex_cloud_ai_studio_text_classification_pipeline",
        destination="duckdb",
        dataset_name="yandex_cloud_ai_studio_text_classification_data",
    )
    load_info = pipeline.run(yandex_cloud_ai_studio_text_classification_source())
    print(load_info)


if __name__ == "__main__":
    load_yandex_cloud_ai_studio_text_classification_to_duckdb()

Run it with python yandex_cloud_ai_studio_text_classification_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.

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How do I query Yandex Cloud AI Studio Text Classification data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

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import dlt

data = dlt.pipeline("yandex_cloud_ai_studio_text_classification_pipeline").dataset()
df = data.classification_models.df()
print(df.head())

SQL:

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SELECT * FROM yandex_cloud_ai_studio_text_classification_data.classification_models LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.

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How do I deploy the Yandex Cloud AI Studio Text Classification to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

• Deploy & schedule — run the pipeline as a managed job with automatic retries.
• Monitor — observable job queues, alerting, and load metrics for every run.
• Transform — promote raw Yandex Cloud AI Studio Text Classification loads into governed, documented models.
• Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →

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What other destinations can I load Yandex Cloud AI Studio Text Classification data to?

dlt loads into any of these — only the destination argument changes:

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.

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Next steps

• Ready for production? Book a demo of the dltHub platform →
• dlt LLM-native workflow docs
• REST API source reference
• Join the dlt community on Slack