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ModelBackendMeta

class ModelBackendMeta(ABCMeta):
Metaclass that automatically preprocesses messages in run method. Automatically wraps the run method of any class inheriting from BaseModelBackend to preprocess messages (remove <think> tags) before they are sent to the model.

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new

def __new__(
    mcs,
    name,
    bases,
    namespace
):
Wraps run method with preprocessing if it exists in the class.

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BaseModelBackend

class BaseModelBackend(ABC):
Base class for different model backends. It may be OpenAI API, a local LLM, a stub for unit tests, etc. Parameters:
  • model_type (Union[ModelType, str]): Model for which a backend is created.
  • model_config_dict (Optional[Dict[str, Any]], optional): A config dictionary. (default: :obj:{})
  • api_key (Optional[str], optional): The API key for authenticating with the model service. (default: :obj:None)
  • url (Optional[str], optional): The url to the model service. (default: :obj:None)
  • token_counter (Optional[BaseTokenCounter], optional): Token counter to use for the model. If not provided, :obj:OpenAITokenCounter will be used. (default: :obj:None)
  • timeout (Optional[float], optional): The timeout value in seconds for API calls. (default: :obj:None)
  • max_retries (int, optional): Maximum number of retries for API calls. (default: :obj:3)

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init

def __init__(
    self,
    model_type: Union[ModelType, str],
    model_config_dict: Optional[Dict[str, Any]] = None,
    api_key: Optional[str] = None,
    url: Optional[str] = None,
    token_counter: Optional[BaseTokenCounter] = None,
    timeout: Optional[float] = None,
    max_retries: int = 3
):

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token_counter

def token_counter(self):
Returns: BaseTokenCounter: The token counter following the model’s tokenization style.

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preprocess_messages

def preprocess_messages(self, messages: List[OpenAIMessage]):
Preprocess messages before sending to model API. Removes thinking content from assistant and user messages. Automatically formats messages for parallel tool calls if tools are detected. Parameters:
  • messages (List[OpenAIMessage]): Original messages.
Returns: List[OpenAIMessage]: Preprocessed messages

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_log_request

def _log_request(self, messages: List[OpenAIMessage]):
Log the request messages to a JSON file if logging is enabled. Parameters:
  • messages (List[OpenAIMessage]): The messages to log.
Returns: Optional[str]: The path to the log file if logging is enabled, None otherwise.

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_log_response

def _log_response(self, log_path: str, response: Any):
Log the response to the existing log file. Parameters:
  • log_path (str): The path to the log file.
  • response (Any): The response to log.

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_run

def _run(
    self,
    messages: List[OpenAIMessage],
    response_format: Optional[Type[BaseModel]] = None,
    tools: Optional[List[Dict[str, Any]]] = None
):
Runs the query to the backend model in a non-stream mode. Parameters:
  • messages (List[OpenAIMessage]): Message list with the chat history in OpenAI API format.
  • response_format (Optional[Type[BaseModel]]): The format of the response.
  • tools (Optional[List[Dict[str, Any]]]): The schema of the tools to use for the request.
Returns: Union[ChatCompletion, Stream[ChatCompletionChunk], Any]: ChatCompletion in the non-stream mode, or Stream[ChatCompletionChunk] in the stream mode, or ChatCompletionStreamManager[BaseModel] in the structured stream mode.

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run

def run(
    self,
    messages: List[OpenAIMessage],
    response_format: Optional[Type[BaseModel]] = None,
    tools: Optional[List[Dict[str, Any]]] = None
):
Runs the query to the backend model. Parameters:
  • messages (List[OpenAIMessage]): Message list with the chat history in OpenAI API format.
  • response_format (Optional[Type[BaseModel]]): The response format to use for the model. (default: :obj:None)
  • tools (Optional[List[Tool]]): The schema of tools to use for the model for this request. Will override the tools specified in the model configuration (but not change the configuration). (default: :obj:None)
Returns: Union[ChatCompletion, Stream[ChatCompletionChunk], Any]: ChatCompletion in the non-stream mode, Stream[ChatCompletionChunk] in the stream mode, or ChatCompletionStreamManager[BaseModel] in the structured stream mode.

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count_tokens_from_messages

def count_tokens_from_messages(self, messages: List[OpenAIMessage]):
Count the number of tokens in the messages using the specific tokenizer. Parameters:
  • messages (List[Dict]): message list with the chat history in OpenAI API format.
Returns: int: Number of tokens in the messages.

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_to_chat_completion

def _to_chat_completion(self, response: ParsedChatCompletion):

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token_limit

def token_limit(self):
Returns: int: The maximum token limit for the given model.

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stream

def stream(self):
Returns: bool: Whether the model is in stream mode.