Python Helper Functions

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.

Functions

Insert data

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.

Retrieve data

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.

Retrieve All Data

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

Upload a temporary file

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.

Prompt completion

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.

Run a step

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.

Classes

Integration

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

api_call()

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 or list containing the request body (for POST, PUT, etc.). Supports both JSON objects (dictionaries) and JSON arrays (lists). 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.

Usage Examples

Insert data

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

Retrieve data

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

Retrieve all

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

Upload a temporary file

Note: Make sure to replace the region variable with your actual region.

file_path = "path/to/file.txt"
response = insert_temp_file(file_path)

Prompt completion

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

Run a step

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

Integration

# 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 with object body
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

Integration with Array Body

# Example: Bulk update operation with array body
account_id = params['my_oauth_account']
integration = Integration('confluence', account_id)

# Make API call with array body
response = integration.api_call(
    method='PUT',
    url='https://your-domain.atlassian.net/wiki/api/v2/pages/123/properties',
    body=[
        {"key": "status", "value": "reviewed"},
        {"key": "priority", "value": "high"}
    ],
    headers={'accept': 'application/json'}
)