# =========== 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. ===========
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
from camel.configs import REKA_API_PARAMS, RekaConfig
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,
dependencies_required,
)
if TYPE_CHECKING:
from reka.types import ChatMessage, ChatResponse
try:
import os
if os.getenv("AGENTOPS_API_KEY") is not None:
from agentops import LLMEvent, record
else:
raise ImportError
except (ImportError, AttributeError):
LLMEvent = None
[docs]
class RekaModel(BaseModelBackend):
r"""Reka API in a unified BaseModelBackend interface.
Args:
model_type (Union[ModelType, str]): Model for which a backend is
created, one of REKA_* series.
model_config_dict (Optional[Dict[str, Any]], optional): A dictionary
that will be fed into:obj:`Reka.chat.create()`. If :obj:`None`,
:obj:`RekaConfig().as_dict()` will be used. (default: :obj:`None`)
api_key (Optional[str], optional): The API key for authenticating with
the Reka service. (default: :obj:`None`)
url (Optional[str], optional): The url to the Reka 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`)
"""
@dependencies_required('reka')
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 reka.client import Reka
if model_config_dict is None:
model_config_dict = RekaConfig().as_dict()
api_key = api_key or os.environ.get("REKA_API_KEY")
url = url or os.environ.get("REKA_API_BASE_URL")
super().__init__(
model_type, model_config_dict, api_key, url, token_counter
)
self._client = Reka(api_key=self._api_key, base_url=self._url)
def _convert_reka_to_openai_response(
self, response: 'ChatResponse'
) -> ChatCompletion:
r"""Converts a Reka `ChatResponse` to an OpenAI-style `ChatCompletion`
response.
Args:
response (ChatResponse): The response object from the Reka API.
Returns:
ChatCompletion: An OpenAI-compatible chat completion response.
"""
openai_response = ChatCompletion.construct(
id=response.id,
choices=[
dict(
message={
"role": response.responses[0].message.role,
"content": response.responses[0].message.content,
},
finish_reason=response.responses[0].finish_reason
if response.responses[0].finish_reason
else None,
)
],
created=None,
model=response.model,
object="chat.completion",
usage=response.usage,
)
return openai_response
def _convert_openai_to_reka_messages(
self,
messages: List[OpenAIMessage],
) -> List["ChatMessage"]:
r"""Converts OpenAI API messages to Reka API messages.
Args:
messages (List[OpenAIMessage]): A list of messages in OpenAI
format.
Returns:
List[ChatMessage]: A list of messages converted to Reka's format.
"""
from reka.types import ChatMessage
reka_messages = []
for msg in messages:
role = msg.get("role")
content = str(msg.get("content"))
if role == "user":
reka_messages.append(ChatMessage(role="user", content=content))
elif role == "assistant":
reka_messages.append(
ChatMessage(role="assistant", content=content)
)
elif role == "system":
reka_messages.append(ChatMessage(role="user", content=content))
# Add one more assistant msg since Reka requires conversation
# history must alternate between 'user' and 'assistant',
# starting and ending with 'user'.
reka_messages.append(
ChatMessage(
role="assistant",
content="",
)
)
else:
raise ValueError(f"Unsupported message role: {role}")
return reka_messages
@property
def token_counter(self) -> BaseTokenCounter:
r"""Initialize the token counter for the model backend.
# NOTE: Temporarily using `OpenAITokenCounter`
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("REKA_API_KEY")
def run(
self,
messages: List[OpenAIMessage],
) -> ChatCompletion:
r"""Runs inference of Mistral chat completion.
Args:
messages (List[OpenAIMessage]): Message list with the chat history
in OpenAI API format.
Returns:
ChatCompletion.
"""
reka_messages = self._convert_openai_to_reka_messages(messages)
response = self._client.chat.create(
messages=reka_messages,
model=self.model_type,
**self.model_config_dict,
)
openai_response = self._convert_reka_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.input_tokens, # type: ignore[union-attr]
completion=openai_response.choices[0].message.content,
completion_tokens=openai_response.usage.output_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 Reka API.
Raises:
ValueError: If the model configuration dictionary contains any
unexpected arguments to Reka API.
"""
for param in self.model_config_dict:
if param not in REKA_API_PARAMS:
raise ValueError(
f"Unexpected argument `{param}` is "
"input into Reka 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)
