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Documentation Index

Fetch the complete documentation index at: https://docs.camel-ai.org/llms.txt

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_StreamLogger

class _StreamLogger:
Base for stream logging wrappers.

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init

def __init__(self, log_path: Optional[str], log_enabled: bool):

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_collect

def _collect(self, chunk: ChatCompletionChunk):

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_log

def _log(self):

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_SyncStreamWrapper

class _SyncStreamWrapper(_StreamLogger):
Sync stream wrapper with logging.

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init

def __init__(
    self,
    stream: Union[Stream[ChatCompletionChunk], Generator[ChatCompletionChunk, None, None]],
    log_path: Optional[str],
    log_enabled: bool
):

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iter

def __iter__(self):

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next

def __next__(self):

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enter

def __enter__(self):

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exit

def __exit__(self, *_):

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del

def __del__(self):

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_AsyncStreamWrapper

class _AsyncStreamWrapper(_StreamLogger):
Async stream wrapper with logging.

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init

def __init__(
    self,
    stream: Union[AsyncStream[ChatCompletionChunk], AsyncGenerator[ChatCompletionChunk, None]],
    log_path: Optional[str],
    log_enabled: bool
):

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aiter

def __aiter__(self):

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del

def __del__(self):

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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] = Constants.TIMEOUT_THRESHOLD,
    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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_prepare_request_config

def _prepare_request_config(self, tools: Optional[List[Dict[str, Any]]] = None):
Prepare the request configuration dictionary. Creates a deep copy of the model config and handles tool-related parameters. If no tools are specified, removes parallel_tool_calls as OpenAI API only allows it when tools are present. Parameters:
  • tools (Optional[List[Dict[str, Any]]]): The tools to include in the request. (default: :obj:None)
Returns: Dict[str, Any]: The prepared request configuration.

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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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_log_and_trace

def _log_and_trace(self):
Update Langfuse trace with session metadata. This method updates the current Langfuse trace with agent session information and model metadata. Called at the start of _run() and _arun() methods before API execution.

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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.