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RetrieverProtocol

class RetrieverProtocol(Protocol):
Protocol for the retriever class. Any retriever class implementing this protocol can be used in the benchmark class.

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retrieve

def retrieve(
    self,
    query: str,
    contents: List[str],
    **kwargs: Dict[str, Any]
):
Retrieve the relevant content for the query. Parameters:
  • query (str): The query to retrieve the content for.
  • contents (List[str]): The list of contents to search in. **kwargs (Dict[str, Any]): Additional keyword arguments.
Returns: Dict[str, Any]: The relevant content for the query.

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reset

def reset(self, **kwargs):
Reset the retriever. Some benchmarks may require resetting the retriever after each query. Returns: bool: True if the reset was successful, False otherwise.

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DefaultGAIARetriever

class DefaultGAIARetriever(AutoRetriever):
Default retriever for the GAIA benchmark. This retriever uses AutoRetriever in camel to retrieve the content based on the query.

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retrieve

def retrieve(
    self,
    query: str,
    contents: List[str],
    **kwargs: Any
):
Retrieve the content based on the query. Parameters:
  • query (str): The query to search for.
  • contents (List[str]): The list of contents to search from. **kwargs (Any): The keyword arguments to pass to the retriever.
Returns: Dict[str, Any]: The retrieved content.

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reset

def reset(self, **kwargs: Any):
Reset the retriever. Returns: bool: Whether the reset was successful.

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GAIABenchmark

class GAIABenchmark(BaseBenchmark):
GAIA Benchmark adapted from “GAIA: a benchmark for General AI Assistants”. Parameters:
  • data_dir (str): The directory to save the data.
  • save_to (str): The file to save the results.
  • retriever (Optional[RetrieverProtocol]): The retriever to use. (default: :obj:None)
  • processes (int, optional): The number of processes to use. (default: :obj:1)

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init

def __init__(
    self,
    data_dir: str,
    save_to: str,
    retriever: Optional[RetrieverProtocol] = None,
    processes: int = 1
):
Initialize the GAIA benchmark. Parameters:
  • data_dir (str): The directory to save the data.
  • save_to (str): The file to save the results.
  • retriever (Optional[RetrieverProtocol], optional): The retriever to use. (default: :obj:None)
  • processes (int, optional): The number of processes to use for parallel processing. (default: :obj:1)

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download

def download(self):
Download the GAIA dataset.

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load

def load(self, force_download = False):
Load the GAIA dataset. Parameters:
  • force_download (bool, optional): Whether to force download the data.

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train

def train(self):
Get the training set.

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run

def run(
    self,
    agent: ChatAgent,
    on: Literal['train', 'valid', 'test'],
    level: Union[int, List[int], Literal['all']],
    randomize: bool = False,
    subset: Optional[int] = None
):
Run the benchmark. Parameters:
  • agent (ChatAgent): The agent to run the benchmark.
  • on (Literal["valid", "test"]): The set to run the benchmark.
  • level (Union[int, List[int], Literal["all"]]): The level to run the benchmark.
  • randomize (bool, optional): Whether to randomize the data. (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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_prepare_task

def _prepare_task(self, task: Dict[str, Any]):
Prepare the task by validating and enriching its data.

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_create_user_message

def _create_user_message(self, task: Dict[str, Any]):
Create a user message from a task.

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_process_result

def _process_result(
    self,
    agent: ChatAgent,
    task: Dict[str, Any],
    result: Any,
    file_obj: Any
):
Process and store the result of a task.

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_handle_error

def _handle_error(
    self,
    task: Dict[str, Any],
    error: Exception,
    file_obj: Any
):
Handle errors encountered during task processing.

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_generate_summary

def _generate_summary(self):
Generate and return a summary of the benchmark results.

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question_scorer

def question_scorer(self, model_answer: str, ground_truth: str):
Scorer for the GAIA benchmark. https://huggingface.co/spaces/gaia-benchmark/leaderboard/blob/main/ scorer.py Parameters:
  • model_answer (str): The model answer.
  • ground_truth (str): The ground truth answer.
Returns: bool: The score of the model

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normalize_number_str

def normalize_number_str(self, number_str: str):

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split_string

def split_string(self, s: str, char_list: Optional[List[str]] = None):
Split a string based on a list of characters. Parameters:
  • s (str): The string to split.
  • char_list (Optional[List[str]], optional): T he list of characters to split on. (default: :obj:None)

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normalize_str

def normalize_str(self, input_str, remove_punct = True):
Normalize a string. Parameters:
  • input_str: The input string to normalize.
  • remove_punct: Whether to remove punctuation.
Returns: str: The normalized string.

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get_final_answer

def get_final_answer(self, content: str):
Get the final answer from the content. Parameters:
  • content (str): The content to extract the final answer from.
Returns: str: The final answer.