MaximApis

ConnectionPool

class ConnectionPool()

Manages HTTP session pooling for efficient network requests. This class provides a reusable session with retry logic for handling transient network errors.

init

def __init__()

Initialize a new session with retry configuration.

get_session

@contextlib.contextmanager
def get_session()

Context manager that yields the session and ensures it’s closed after use. Yields:

requests.Session - The HTTP session object

MaximAPI

class MaximAPI()

Client for interacting with the Maxim API. This class provides methods for all available Maxim API endpoints, handling authentication, request formatting, and error handling.

init

def __init__(base_url: str, api_key: str)

Initialize a new Maxim API client. Arguments:

Name	Description
`base_url`	The base URL for the Maxim API
`api_key`	The API key for authentication

get_prompt

def get_prompt(id: str) -> VersionAndRulesWithPromptId

Get a prompt by ID. Arguments:

Name	Description
`id`	The prompt ID

Returns:

Name	Description
`[VersionAndRulesWithPromptId](/sdk/python/references/models/prompt)`	The prompt details

Raises:

Exception - If the request fails

get_prompts

def get_prompts() -> List[VersionAndRulesWithPromptId]

Get all prompts. Returns:

Name	Description
`List[[VersionAndRulesWithPromptId](/sdk/python/references/models/prompt)]`	List of all prompts

Raises:

Exception - If the request fails

getPromptChain

def getPromptChain(id: str) -> VersionAndRulesWithPromptChainId

Get a prompt chain by ID. Arguments:

Name	Description
`id`	The prompt chain ID

Returns:

Name	Description
`[VersionAndRulesWithPromptChainId](/sdk/python/references/models/prompt_chain)`	The prompt chain details

Raises:

Exception - If the request fails

get_prompt_chains

def get_prompt_chains() -> List[VersionAndRulesWithPromptChainId]

Get all prompt chains. Returns:

Name	Description
`List[[VersionAndRulesWithPromptChainId](/sdk/python/references/models/prompt_chain)]`	List of all prompt chains

Raises:

Exception - If the request fails

run_prompt

def run_prompt(model: str,
               messages: List[ChatCompletionMessage],
               tools: Optional[List[Tool]] = None,
               **kwargs)

Run a custom prompt with the specified model and messages. Arguments:

Name	Description
`model`	The model to use
`messages`	List of chat messages
`tools`	Optional list of tools to use
`**kwargs`	Additional parameters to pass to the API

Returns:

Name	Description
`[PromptResponse](/sdk/python/references/models/prompt)`	The response from the model

Raises:

Exception - If the request fails

run_prompt_version

def run_prompt_version(
        prompt_version_id: str, input: str,
        image_urls: Optional[List[ImageUrls]],
        variables: Optional[dict[str, str]]) -> Optional[PromptResponse]

Run a specific prompt version with the given input. Arguments:

Name	Description
`prompt_version_id`	The ID of the prompt version to run
`input`	The input text for the prompt
`image_urls`	Optional list of image URLs to include
`variables`	Optional dictionary of variables to use

Returns:

Name	Description
`Optional[[PromptResponse](/sdk/python/references/models/prompt)]`	The response from the prompt

Raises:

Exception - If the request fails

run_prompt_chain_version

def run_prompt_chain_version(
        prompt_chain_version_id: str, input: str,
        variables: Optional[dict[str, str]]) -> Optional[AgentResponse]

Run a specific prompt chain version with the given input. Arguments:

Name	Description
`prompt_chain_version_id`	The ID of the prompt chain version to run
`input`	The input text for the prompt chain
`variables`	Optional dictionary of variables to use

Returns:

Name	Description
`Optional[[AgentResponse](/sdk/python/references/models/prompt_chain)]`	The response from the prompt chain

Raises:

Exception - If the request fails

get_folder

def get_folder(id: str) -> Folder

Get a folder by ID. Arguments:

Name	Description
`id`	The folder ID

Returns:

Name	Description
`[Folder](/sdk/python/references/models/folder)`	The folder details

Raises:

Exception - If the request fails

get_folders

def get_folders() -> List[Folder]

Get all folders. Returns:

Name	Description
`List[[Folder](/sdk/python/references/models/folder)]`	List of all folders

Raises:

Exception - If the request fails

add_dataset_entries

def add_dataset_entries(dataset_id: str,
                        dataset_entries: List[DatasetEntry]) -> dict[str, Any]

Add entries to a dataset. Arguments:

Name	Description
`dataset_id`	The ID of the dataset
`dataset_entries`	List of dataset entries to add

Returns: dict[str, Any]: Response from the API Raises:

Exception - If the request fails

get_dataset_total_rows

def get_dataset_total_rows(dataset_id: str) -> int

Get the total number of rows in a dataset. Arguments:

Name	Description
`dataset_id`	The ID of the dataset

Returns:

Name	Description
`int`	The total number of rows

Raises:

Exception - If the request fails

get_dataset_row

def get_dataset_row(dataset_id: str, row_index: int) -> Optional[DatasetRow]

Get a specific row from a dataset. Arguments:

Name	Description
`dataset_id`	The ID of the dataset
`row_index`	The index of the row to retrieve

Returns:

Name	Description
`Optional[[DatasetRow](/sdk/python/references/models/dataset)]`	The dataset row, or None if not found

Raises:

Exception - If the request fails

get_dataset_structure

def get_dataset_structure(dataset_id: str) -> Dict[str, str]

Get the structure of a dataset. Arguments:

Name	Description
`dataset_id`	The ID of the dataset

Returns: Dict[str, str]: The dataset structure Raises:

Exception - If the request fails

does_log_repository_exist

def does_log_repository_exist(logger_id: str) -> bool

Check if a log repository exists. Arguments:

Name	Description
`logger_id`	The ID of the logger

Returns:

Name	Description
`bool`	True if the repository exists, False otherwise

push_logs

def push_logs(repository_id: str, logs: str) -> None

Push logs to a repository. Arguments:

Name	Description
`repository_id`	The ID of the repository
`logs`	The logs to push

Raises:

Exception - If the request fails

fetch_platform_evaluator

def fetch_platform_evaluator(name: str, in_workspace_id: str) -> Evaluator

Fetch a platform evaluator by name. Arguments:

Name	Description
`name`	The name of the evaluator
`in_workspace_id`	The workspace ID

Returns:

Name	Description
`[Evaluator](/sdk/python/references/models/evaluator)`	The evaluator details

Raises:

Exception - If the request fails

create_test_run

def create_test_run(
    name: str,
    workspace_id: str,
    workflow_id: Optional[str],
    prompt_version_id: Optional[str],
    prompt_chain_version_id: Optional[str],
    run_type: RunType,
    evaluator_config: list[Evaluator],
    requires_local_run: bool,
    human_evaluation_config: Optional[HumanEvaluationConfig] = None
) -> TestRun

Create a new test run. Arguments:

Name	Description
`name`	The name of the test run
`workspace_id`	The workspace ID
`workflow_id`	Optional workflow ID
`prompt_version_id`	Optional prompt version ID
`prompt_chain_version_id`	Optional prompt chain version ID
`run_type`	The type of run
`evaluator_config`	List of evaluators to use
`requires_local_run`	Whether the test run requires local execution
`human_evaluation_config`	Optional human evaluation configuration

Returns:

Name	Description
`[TestRun](/sdk/python/references/models/test_run)`	The created test run

Raises:

Exception - If the request fails

attach_dataset_to_test_run

def attach_dataset_to_test_run(test_run_id: str, dataset_id: str) -> None

Attach a dataset to a test run. Arguments:

Name	Description
`test_run_id`	The ID of the test run
`dataset_id`	The ID of the dataset

Raises:

Exception - If the request fails

push_test_run_entry

def push_test_run_entry(test_run: Union[TestRun, TestRunWithDatasetEntry],
                        entry: TestRunEntry,
                        run_config: Optional[Dict[str, Any]] = None) -> None

Push an entry to a test run. Arguments:

Name	Description
`test_run`	The test run
`entry`	The test run entry to push
`run_config`	Optional run configuration

Raises:

Exception - If the request fails

mark_test_run_processed

def mark_test_run_processed(test_run_id: str) -> None

Mark a test run as processed. Arguments:

Name	Description
`test_run_id`	The ID of the test run

Raises:

Exception - If the request fails

mark_test_run_failed

def mark_test_run_failed(test_run_id: str) -> None

Mark a test run as failed. Arguments:

Name	Description
`test_run_id`	The ID of the test run

Raises:

Exception - If the request fails

get_test_run_status

def get_test_run_status(test_run_id: str) -> TestRunStatus

Get the status of a test run. Arguments:

Name	Description
`test_run_id`	The ID of the test run

Returns:

Name	Description
`[TestRunStatus](/sdk/python/references/models/test_run)`	The status of the test run

Raises:

Exception - If the request fails

get_test_run_final_result

def get_test_run_final_result(test_run_id: str) -> TestRunResult

Get the final result of a test run. Arguments:

Name	Description
`test_run_id`	The ID of the test run

Returns:

Name	Description
`[TestRunResult](/sdk/python/references/models/test_run)`	The final result of the test run

Raises:

Exception - If the request fails

get_upload_url

def get_upload_url(key: str, mime_type: str, size: int) -> SignedURLResponse

Get a signed URL for uploading a file. Arguments:

Name	Description
`key`	The key (filename) for the upload
`mime_type`	The MIME type of the file
`size`	The size of the file in bytes

Returns:

Name	Description
`[SignedURLResponse](/sdk/python/references/models/attachment)`	A dictionary containing the signed URL for upload

Raises:

Exception - If the request fails

upload_to_signed_url

def upload_to_signed_url(url: str, data: bytes, mime_type: str) -> bool

Upload data to a signed URL using multipart form data with retry logic. Arguments:

Name	Description
`url`	The signed URL to upload to
`data`	The binary data to upload
`mime_type`	The MIME type of the data

Returns:

Name	Description
`bool`	True if upload was successful, False otherwise

Overview

Python

Typescript

ConnectionPool

init

get_session

MaximAPI

init

get_prompt

get_prompts

getPromptChain

get_prompt_chains

run_prompt

run_prompt_version

run_prompt_chain_version

get_folder

get_folders

add_dataset_entries

get_dataset_total_rows

get_dataset_row

get_dataset_structure

does_log_repository_exist

push_logs

fetch_platform_evaluator

create_test_run

attach_dataset_to_test_run

push_test_run_entry

mark_test_run_processed

mark_test_run_failed

get_test_run_status

get_test_run_final_result

get_upload_url

upload_to_signed_url

Overview

Python

Typescript

​ConnectionPool

​__init__

​get_session

​MaximAPI

​__init__

​get_prompt

​get_prompts

​getPromptChain

​get_prompt_chains

​run_prompt

​run_prompt_version

​run_prompt_chain_version

​get_folder

​get_folders

​add_dataset_entries

​get_dataset_total_rows

​get_dataset_row

​get_dataset_structure

​does_log_repository_exist

​push_logs

​fetch_platform_evaluator

​create_test_run

​attach_dataset_to_test_run

​push_test_run_entry

​mark_test_run_processed

​mark_test_run_failed

​get_test_run_status

​get_test_run_final_result

​get_upload_url

​upload_to_signed_url

ConnectionPool

init

get_session

MaximAPI

init

get_prompt

get_prompts

getPromptChain

get_prompt_chains

run_prompt

run_prompt_version

run_prompt_chain_version

get_folder

get_folders

add_dataset_entries

get_dataset_total_rows

get_dataset_row

get_dataset_structure

does_log_repository_exist

push_logs

fetch_platform_evaluator

create_test_run

attach_dataset_to_test_run

push_test_run_entry

mark_test_run_processed

mark_test_run_failed

get_test_run_status

get_test_run_final_result

get_upload_url

upload_to_signed_url