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process_messages

def process_messages(chat_history: List[Dict[str, Any]], prompt: str):
Processes chat history into a structured format for further use. Parameters:
  • chat_history (List[Dict[str, Any]): A list of dictionaries representing the chat history.
  • prompt (str): A prompt to be set as the system message.
Returns: List[Dict[str, str]]: A list of dictionaries representing the processed messages, where each dictionary has:
  • ‘role’: The role of the message (‘system’, ‘user’, or ‘assistant’).
  • ‘content’: The content of the message, including formatted API responses when applicable.

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APIBankBenchmark

class APIBankBenchmark(BaseBenchmark):
API-Bank Benchmark adapted from API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs <https://github.com/AlibabaResearch/DAMO-ConvAI/tree/main/api-bank>. Parameters:
  • save_to (str): The file to save the results.
  • processes (int, optional): The number of processes to use. (default: :obj:1)

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init

def __init__(self, save_to: str, processes: int = 1):
Initialize the APIBank benchmark. Parameters:
  • save_to (str): The file to save the results.
  • processes (int, optional): The number of processes to use for parallel processing. (default: :obj:1)

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download

def download(self):
Download APIBank dataset and code from Github.

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load

def load(self, level: str, force_download: bool = False):
Load the APIBank Benchmark dataset. Parameters:
  • level (str): Level to run benchmark on.
  • force_download (bool, optional): Whether to force download the data.

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run

def run(
    self,
    agent: ChatAgent,
    level: Literal['level-1', 'level-2'],
    api_test_enabled = True,
    randomize: bool = False,
    subset: Optional[int] = None
):
Run the benchmark. Parameters:
  • agent (ChatAgent): The agent to run the benchmark.
  • level (Literal['level-1', 'level-2']): The level to run the benchmark on.
  • randomize (bool, optional): Whether to randomize the data.
  • api_test_enabled (bool): Whether to test API calling (True) or response (False) (default: :obj:False)
  • subset (Optional[int], optional): The subset of data to run. (default: :obj:None)
Returns: Dict[str, Any]: The results of the benchmark.

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agent_call

def agent_call(messages: List[Dict], agent: ChatAgent):
Add messages to agent memory and get response.

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calculate_rouge_l_score

def calculate_rouge_l_score(reference, hypothesis):
Calculate rouge l score between hypothesis and reference.

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get_api_call

def get_api_call(model_output):
Parse api call from model output.

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APIBankSample

class APIBankSample:
APIBank sample used to load the datasets.

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init

def __init__(
    self,
    chat_history,
    apis,
    ground_truth
):

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repr

def __repr__(self):

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from_chat_history

def from_chat_history(cls, chat_history):

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Evaluator

class Evaluator:
Evaluator for APIBank benchmark.

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init

def __init__(self, samples: List[APIBankSample]):

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get_all_sample_ids

def get_all_sample_ids(self):

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get_api_description

def get_api_description(self, api_name):

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get_model_input

def get_model_input(self, sample_id: int):

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evaluate

def evaluate(self, sample_id, model_output):