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This client provides an interface to interact with the Relevance API. It includes functions to run various steps, insert and retrieve data, and upload temporary files.

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Functions

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Insert data

Python
insert_data(dataset_id: str, data: List[Dict[str, Any]])
Inserts data into a Relevance dataset. Arguments
  • dataset_id: The ID of the dataset to insert into.
  • data: A list of dictionaries containing the data to insert.
Returns
  • The response from the API as a JSON object.

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Retrieve data

Python
retrieve_data(dataset_id: str, page_size: int = None, include_fields: List[str] = None)
Retrieves data from a Relevance dataset. Arguments
  • dataset_id: The ID of the dataset to retrieve from.
  • page_size: The number of results to return per page (optional).
  • include_fields: A list of fields to include in the response (optional).
Returns
  • The response from the API as a JSON object.

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Retrieve All Data

Python
retrieve_all(dataset_id: str, page_size: int = 1000, include_fields: List[str] = None) -> List[Dict[str, Any]]:
Retrieves all data from a Relevance dataset, paginated to handle large datasets. Arguments
  • dataset_id: The ID of the dataset to retrieve from.
  • page_size: The number of results to return per page. Defaults to 1000 (optional).
  • include_fields: A list of fields to include in the response. Defaults to None (optional).
Returns
  • A list of dictionaries containing the retrieved data. Each dictionary represents a document from the dataset.
Example

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Upload a temporary file

Python
insert_temp_file(file_path_or_bytes: str, ext: str = None)
Uploads a temporary file to Relevance. Arguments
  • file_path_or_bytes: The path to the file or the file contents as bytes.
  • ext: The file extension (optional).
Returns
  • A dictionary containing the download URL of the uploaded file.

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Prompt completion

Python
prompt_completion(prompt: str, model: int = None)
Runs the prompt_completion step with the given prompt and model. Arguments
  • prompt: The prompt to complete.
  • model: The model to use for completion (optional).
Returns
  • The response from the API as a JSON object.

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Run a step

Python
run_step(step_name: str, params: Dict[str, Any])
Runs a Relevance step with the given name and parameters. Arguments
  • step_name: The name of the step to run.
  • params: A dictionary of parameters to pass to the step.
Returns
  • The response from the API as a JSON object.

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Classes

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Integration

Python
Integration(provider_name: str, account_id: str)
The Integration class provides a convenient way to make authenticated API calls to OAuth-connected services in your Python code. It automatically handles OAuth token management and makes authenticated requests to third-party APIs. Constructor Arguments
  • provider_name: The name of the OAuth provider/integration (e.g., 'dataforseo', 'hubspot', 'slack').
  • account_id: The account ID from your OAuth account input. This is accessed via params['your_oauth_input_name'] where your_oauth_input_name is the variable name of your OAuth account input.
Methods

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api_call()

Python
integration.api_call(method: str, url: str, body: Dict[str, Any] = None, headers: Dict[str, str] = None, params: Dict[str, Any] = None)
Makes an authenticated HTTP request to the specified URL using the OAuth credentials associated with the integration. Arguments
  • method: The HTTP method to use (e.g., 'GET', 'POST', 'PUT', 'DELETE').
  • url: The full URL endpoint to make the request to.
  • body: Optional dictionary containing the request body (for POST, PUT, etc.). Will be automatically serialized to JSON.
  • headers: Optional dictionary of additional HTTP headers to include in the request.
  • params: Optional dictionary of query parameters to append to the URL.
Returns
  • The API response as a parsed JSON object (dictionary). The response is automatically parsed, so you can directly access the data without additional JSON parsing.

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Usage Examples

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Insert data

Python
data = [{"field1": "value1", "field2": "value2"}, {"field1": "value3", "field2": "value4"}]
response = insert_data("my_dataset", data)

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Retrieve data

Python
response = retrieve_data("my_dataset", page_size=10, include_fields=["field1", "field2"])

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Retrieve all

Python
response = retrieve_all("my_dataset", page_size=500, include_fields=["field1", "field2"])

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Upload a temporary file

Note: Make sure to replace the region variable with your actual region.
Python
file_path = "path/to/file.txt"
response = insert_temp_file(file_path)

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Prompt completion

Python
response = prompt_completion("My prompt", model="openai-gpt35")

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Run a step

Python
response = run_step("my_step", {"param1": "value1", "param2": "value2"})

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Integration

Python
# Get OAuth account ID from input parameter
account_id = params['my_oauth_account']

# Create Integration instance
integration = Integration('google', account_id)

# Make authenticated API call
response = integration.api_call(
    method='GET',
    url='https://www.googleapis.com/oauth2/v2/userinfo',
    params={'limit': 10}
)

# Access the parsed JSON response
user_info = response