# ========= Copyright 2023-2024 @ 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.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
import os
from typing import Any, Dict, List, Optional, Type, Union
from pydantic import BaseModel
from camel.configs import ANTHROPIC_API_PARAMS, AnthropicConfig
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,
dependencies_required,
)
[docs]
class AnthropicModel(BaseModelBackend):
r"""Anthropic API in a unified BaseModelBackend interface.
Args:
model_type (Union[ModelType, str]): Model for which a backend is
created, one of CLAUDE_* series.
model_config_dict (Optional[Dict[str, Any]], optional): A dictionary
that will be fed into Anthropic.messages.create(). If
:obj:`None`, :obj:`AnthropicConfig().as_dict()` will be used.
(default: :obj:`None`)
api_key (Optional[str], optional): The API key for authenticating with
the Anthropic service. (default: :obj:`None`)
url (Optional[str], optional): The url to the Anthropic service.
(default: :obj:`None`)
token_counter (Optional[BaseTokenCounter], optional): Token counter to
use for the model. If not provided, :obj:`AnthropicTokenCounter`
will be used. (default: :obj:`None`)
"""
@api_keys_required(
[
("api_key", "ANTHROPIC_API_KEY"),
]
)
@dependencies_required('anthropic')
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,
) -> None:
from anthropic import Anthropic, AsyncAnthropic
if model_config_dict is None:
model_config_dict = AnthropicConfig().as_dict()
api_key = api_key or os.environ.get("ANTHROPIC_API_KEY")
url = url or os.environ.get("ANTHROPIC_API_BASE_URL")
super().__init__(
model_type, model_config_dict, api_key, url, token_counter
)
self.client = Anthropic(api_key=self._api_key, base_url=self._url)
self.async_client = AsyncAnthropic(
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
def _run(
self,
messages: List[OpenAIMessage],
response_format: Optional[Type[BaseModel]] = None,
tools: Optional[List[Dict[str, Any]]] = None,
):
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.
"""
from anthropic import NOT_GIVEN
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,
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
async def _arun(
self,
messages: List[OpenAIMessage],
response_format: Optional[Type[BaseModel]] = None,
tools: Optional[List[Dict[str, Any]]] = None,
) -> ChatCompletion:
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.
"""
from anthropic import NOT_GIVEN
if messages[0]["role"] == "system":
sys_msg = str(messages.pop(0)["content"])
else:
sys_msg = NOT_GIVEN # type: ignore[assignment]
response = await self.async_client.messages.create(
model=self.model_type,
system=sys_msg,
messages=messages, # type: ignore[arg-type]
**self.model_config_dict,
)
# format response to openai format
return self._convert_response_from_anthropic_to_openai(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)
