# =========== 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.
# 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 @ CAMEL-AI.org. All Rights Reserved. ===========
import ast
import json
import logging
import os
import uuid
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
if TYPE_CHECKING:
from cohere.types import ChatMessageV2, ChatResponse
from camel.configs import COHERE_API_PARAMS, CohereConfig
from camel.messages import OpenAIMessage
from camel.models import BaseModelBackend
from camel.types import ChatCompletion, ModelType
from camel.utils import (
BaseTokenCounter,
OpenAITokenCounter,
api_keys_required,
)
try:
if os.getenv("AGENTOPS_API_KEY") is not None:
from agentops import LLMEvent, record
else:
raise ImportError
except (ImportError, AttributeError):
LLMEvent = None
[docs]
class CohereModel(BaseModelBackend):
r"""Cohere API in a unified BaseModelBackend interface."""
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,
):
import cohere
if model_config_dict is None:
model_config_dict = CohereConfig().as_dict()
api_key = api_key or os.environ.get("COHERE_API_KEY")
url = url or os.environ.get("COHERE_API_BASE_URL")
super().__init__(
model_type, model_config_dict, api_key, url, token_counter
)
self._client = cohere.ClientV2(api_key=self._api_key)
def _to_openai_response(self, response: 'ChatResponse') -> ChatCompletion:
if response.usage and response.usage.tokens:
input_tokens = response.usage.tokens.input_tokens or 0
output_tokens = response.usage.tokens.output_tokens or 0
usage = {
"prompt_tokens": input_tokens,
"completion_tokens": output_tokens,
"total_tokens": input_tokens + output_tokens,
}
else:
usage = {}
tool_calls = response.message.tool_calls
choices = []
if tool_calls:
for tool_call in tool_calls:
openai_tool_calls = [
dict(
id=tool_call.id,
function={
"name": tool_call.function.name,
"arguments": tool_call.function.arguments,
}
if tool_call.function
else {},
type=tool_call.type,
)
]
choice = dict(
index=None,
message={
"role": "assistant",
"content": response.message.tool_plan,
"tool_calls": openai_tool_calls,
},
finish_reason=response.finish_reason
if response.finish_reason
else None,
)
choices.append(choice)
else:
openai_tool_calls = None
choice = dict(
index=None,
message={
"role": "assistant",
"content": response.message.content[0].text, # type: ignore[union-attr,index]
"tool_calls": openai_tool_calls,
},
finish_reason=response.finish_reason
if response.finish_reason
else None,
)
choices.append(choice)
obj = ChatCompletion.construct(
id=response.id,
choices=choices,
created=None,
model=self.model_type,
object="chat.completion",
usage=usage,
)
return obj
def _to_cohere_chatmessage(
self, messages: List[OpenAIMessage]
) -> List["ChatMessageV2"]:
from cohere.types import ToolCallV2Function
from cohere.types.chat_message_v2 import (
AssistantChatMessageV2,
SystemChatMessageV2,
ToolCallV2,
ToolChatMessageV2,
UserChatMessageV2,
)
tool_call_id = None
new_messages = []
for msg in messages:
role = msg.get("role")
content = msg.get("content")
function_call = msg.get("function_call")
if role == "user":
new_message = UserChatMessageV2(role="user", content=content) # type: ignore[arg-type]
elif role in {"tool", "function"}:
new_message = ToolChatMessageV2(
role="tool",
tool_call_id=tool_call_id, # type: ignore[arg-type]
content=content, # type: ignore[assignment,arg-type]
)
elif role == "assistant":
if not function_call:
new_message = AssistantChatMessageV2( # type: ignore[assignment]
role="assistant",
content=content, # type: ignore[arg-type]
)
else:
arguments = function_call.get("arguments") # type: ignore[attr-defined]
arguments_dict = ast.literal_eval(arguments)
arguments_json = json.dumps(arguments_dict)
assis_tool_call_id = str(uuid.uuid4())
tool_call_id = assis_tool_call_id
new_message = AssistantChatMessageV2( # type: ignore[assignment]
role="assistant",
tool_calls=[
ToolCallV2(
id=assis_tool_call_id,
type="function",
function=ToolCallV2Function(
name=function_call.get("name"), # type: ignore[attr-defined]
arguments=arguments_json, # type: ignore[attr-defined]
),
)
],
content=content, # type: ignore[arg-type]
)
elif role == "system":
new_message = SystemChatMessageV2( # type: ignore[assignment]
role="system",
content=content, # type: ignore[arg-type]
)
else:
raise ValueError(f"Unsupported message role: {role}")
new_messages.append(new_message)
return new_messages # type: ignore[return-value]
@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 = OpenAITokenCounter(
model=ModelType.GPT_4O_MINI
)
return self._token_counter
[docs]
@api_keys_required("COHERE_API_KEY")
def run(self, messages: List[OpenAIMessage]) -> ChatCompletion:
r"""Runs inference of Cohere chat completion.
Args:
messages (List[OpenAIMessage]): Message list with the chat history
in OpenAI API format.
Returns:
ChatCompletion.
"""
from cohere.core.api_error import ApiError
cohere_messages = self._to_cohere_chatmessage(messages)
try:
response = self._client.chat(
messages=cohere_messages,
model=self.model_type,
**self.model_config_dict,
)
except ApiError as e:
logging.error(f"Cohere API Error: {e.status_code}")
logging.error(f"Error body: {e.body}")
raise
except Exception as e:
logging.error(f"Unexpected error when calling Cohere API: {e!s}")
raise
openai_response = self._to_openai_response(response)
# Add AgentOps LLM Event tracking
if LLMEvent:
llm_event = LLMEvent(
thread_id=openai_response.id,
prompt=" ".join(
[message.get("content") for message in messages] # type: ignore[misc]
),
prompt_tokens=openai_response.usage.prompt_tokens, # type: ignore[union-attr]
completion=openai_response.choices[0].message.content,
completion_tokens=openai_response.usage.completion_tokens, # type: ignore[union-attr]
model=self.model_type,
)
record(llm_event)
return openai_response
[docs]
def check_model_config(self):
r"""Check whether the model configuration contains any unexpected
arguments to Cohere API.
Raises:
ValueError: If the model configuration dictionary contains any
unexpected arguments to Cohere API.
"""
for param in self.model_config_dict:
if param not in COHERE_API_PARAMS:
raise ValueError(
f"Unexpected argument `{param}` is "
"input into Cohere model backend."
)
@property
def stream(self) -> bool:
r"""Returns whether the model is in stream mode, which sends partial
results each time. Current it's not supported.
Returns:
bool: Whether the model is in stream mode.
"""
return False
