Source code for camel.models.anthropic_model

# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
# Licensed under the Apache License, Version 2.0 (the “License”);
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# =========== Copyright 2023 @ CAMEL-AI.org. All Rights Reserved. ===========
import os
from typing import Any, Dict, List, Optional

from anthropic import NOT_GIVEN, Anthropic

from camel.configs import ANTHROPIC_API_PARAMS
from camel.messages import OpenAIMessage
from camel.models.base_model import BaseModelBackend
from camel.types import ChatCompletion, ModelType
from camel.utils import (
    AnthropicTokenCounter,
    BaseTokenCounter,
    api_keys_required,
)


[docs] class AnthropicModel(BaseModelBackend): r"""Anthropic API in a unified BaseModelBackend interface.""" def __init__( self, model_type: ModelType, model_config_dict: Dict[str, Any], api_key: Optional[str] = None, url: Optional[str] = None, token_counter: Optional[BaseTokenCounter] = None, ) -> None: r"""Constructor for Anthropic backend. Args: model_type (ModelType): Model for which a backend is created, one of CLAUDE_* series. model_config_dict (Dict[str, Any]): A dictionary that will be fed into Anthropic.messages.create(). api_key (Optional[str]): The API key for authenticating with the Anthropic service. (default: :obj:`None`) url (Optional[str]): The url to the Anthropic service. (default: :obj:`None`) token_counter (Optional[BaseTokenCounter]): Token counter to use for the model. If not provided, `AnthropicTokenCounter` will be used. """ super().__init__( model_type, model_config_dict, api_key, url, token_counter ) self._api_key = api_key or os.environ.get("ANTHROPIC_API_KEY") self._url = url or os.environ.get("ANTHROPIC_API_BASE_URL") self.client = Anthropic(api_key=self._api_key, base_url=self._url) def _convert_response_from_anthropic_to_openai(self, response): # openai ^1.0.0 format, reference openai/types/chat/chat_completion.py obj = ChatCompletion.construct( id=None, choices=[ dict( index=0, message={ "role": "assistant", "content": response.content[0].text, }, finish_reason=response.stop_reason, ) ], created=None, model=response.model, object="chat.completion", ) return obj @property def token_counter(self) -> BaseTokenCounter: r"""Initialize the token counter for the model backend. Returns: BaseTokenCounter: The token counter following the model's tokenization style. """ if not self._token_counter: self._token_counter = AnthropicTokenCounter(self.model_type) return self._token_counter
[docs] def count_tokens_from_prompt(self, prompt: str) -> int: r"""Count the number of tokens from a prompt. Args: prompt (str): The prompt string. Returns: int: The number of tokens in the prompt. """ return self.client.count_tokens(prompt)
[docs] @api_keys_required("ANTHROPIC_API_KEY") def run( self, messages: List[OpenAIMessage], ): r"""Run inference of Anthropic chat completion. Args: messages (List[OpenAIMessage]): Message list with the chat history in OpenAI API format. Returns: ChatCompletion: Response in the OpenAI API format. """ if messages[0]["role"] == "system": sys_msg = str(messages.pop(0)["content"]) else: sys_msg = NOT_GIVEN # type: ignore[assignment] response = self.client.messages.create( model=self.model_type.value, system=sys_msg, messages=messages, # type: ignore[arg-type] **self.model_config_dict, ) # format response to openai format response = self._convert_response_from_anthropic_to_openai(response) return response
[docs] def check_model_config(self): r"""Check whether the model configuration is valid for anthropic model backends. Raises: ValueError: If the model configuration dictionary contains any unexpected arguments to OpenAI API, or it does not contain :obj:`model_path` or :obj:`server_url`. """ for param in self.model_config_dict: if param not in ANTHROPIC_API_PARAMS: raise ValueError( f"Unexpected argument `{param}` is " "input into Anthropic model backend." )
@property def stream(self) -> bool: r"""Returns whether the model is in stream mode, which sends partial results each time. Returns: bool: Whether the model is in stream mode. """ return self.model_config_dict.get("stream", False)