mirror of
https://github.com/eigent-ai/eigent.git
synced 2026-05-24 05:26:42 +00:00
341 lines
11 KiB
Python
341 lines
11 KiB
Python
# ========= Copyright 2023-2026 @ 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 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========
|
|
import os
|
|
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Type, Union
|
|
|
|
from pydantic import BaseModel
|
|
|
|
from camel.configs import 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,
|
|
update_current_observation,
|
|
)
|
|
|
|
if os.environ.get("LANGFUSE_ENABLED", "False").lower() == "true":
|
|
try:
|
|
from langfuse.decorators import observe
|
|
except ImportError:
|
|
from camel.utils import observe
|
|
else:
|
|
from camel.utils import observe
|
|
|
|
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
|
|
|
|
|
|
class RekaModel(BaseModelBackend):
|
|
r"""Reka API in a unified OpenAICompatibleModel 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`)
|
|
timeout (Optional[float], optional): The timeout value in seconds for
|
|
API calls. If not provided, will fall back to the MODEL_TIMEOUT
|
|
environment variable or default to 180 seconds.
|
|
(default: :obj:`None`)
|
|
**kwargs (Any): Additional arguments to pass to the client
|
|
initialization.
|
|
"""
|
|
|
|
@api_keys_required(
|
|
[
|
|
("api_key", "REKA_API_KEY"),
|
|
]
|
|
)
|
|
@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,
|
|
timeout: Optional[float] = None,
|
|
**kwargs: Any,
|
|
) -> None:
|
|
from reka.client import AsyncReka, 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")
|
|
timeout = timeout or float(os.environ.get("MODEL_TIMEOUT", 180))
|
|
super().__init__(
|
|
model_type,
|
|
model_config_dict,
|
|
api_key,
|
|
url,
|
|
token_counter,
|
|
timeout,
|
|
**kwargs,
|
|
)
|
|
self._client = Reka(
|
|
api_key=self._api_key,
|
|
base_url=self._url,
|
|
timeout=self._timeout,
|
|
**kwargs,
|
|
)
|
|
self._async_client = AsyncReka(
|
|
api_key=self._api_key,
|
|
base_url=self._url,
|
|
timeout=self._timeout,
|
|
**kwargs,
|
|
)
|
|
|
|
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],
|
|
response_format: Optional[Type[BaseModel]] = None,
|
|
tools: Optional[List[str]] = None,
|
|
) -> 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
|
|
|
|
@observe(as_type="generation")
|
|
async def _arun(
|
|
self,
|
|
messages: List[OpenAIMessage],
|
|
response_format: Optional[Type[BaseModel]] = None,
|
|
tools: Optional[List[Dict[str, Any]]] = None,
|
|
) -> ChatCompletion:
|
|
r"""Runs inference of Mistral chat completion.
|
|
|
|
Args:
|
|
messages (List[OpenAIMessage]): Message list with the chat history
|
|
in OpenAI API format.
|
|
|
|
Returns:
|
|
ChatCompletion.
|
|
"""
|
|
|
|
update_current_observation(
|
|
input={
|
|
"messages": messages,
|
|
"tools": tools,
|
|
},
|
|
model=str(self.model_type),
|
|
model_parameters=self.model_config_dict,
|
|
)
|
|
self._log_and_trace()
|
|
|
|
reka_messages = self._convert_openai_to_reka_messages(messages)
|
|
|
|
response = await self._acall_client(
|
|
self._async_client.chat.create,
|
|
messages=reka_messages,
|
|
model=self.model_type,
|
|
**self.model_config_dict,
|
|
)
|
|
|
|
openai_response = self._convert_reka_to_openai_response(response)
|
|
|
|
update_current_observation(
|
|
usage=openai_response.usage,
|
|
)
|
|
|
|
# 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
|
|
|
|
@observe(as_type="generation")
|
|
def _run(
|
|
self,
|
|
messages: List[OpenAIMessage],
|
|
response_format: Optional[Type[BaseModel]] = None,
|
|
tools: Optional[List[Dict[str, Any]]] = None,
|
|
) -> ChatCompletion:
|
|
r"""Runs inference of Mistral chat completion.
|
|
|
|
Args:
|
|
messages (List[OpenAIMessage]): Message list with the chat history
|
|
in OpenAI API format.
|
|
|
|
Returns:
|
|
ChatCompletion.
|
|
"""
|
|
|
|
update_current_observation(
|
|
input={
|
|
"messages": messages,
|
|
"tools": tools,
|
|
},
|
|
model=str(self.model_type),
|
|
model_parameters=self.model_config_dict,
|
|
)
|
|
|
|
self._log_and_trace()
|
|
|
|
reka_messages = self._convert_openai_to_reka_messages(messages)
|
|
|
|
response = self._call_client(
|
|
self._client.chat.create,
|
|
messages=reka_messages,
|
|
model=self.model_type,
|
|
**self.model_config_dict,
|
|
)
|
|
|
|
openai_response = self._convert_reka_to_openai_response(response)
|
|
|
|
update_current_observation(
|
|
usage=openai_response.usage,
|
|
)
|
|
|
|
# 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
|
|
|
|
@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)
|