# ========= 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 openai import AsyncOpenAI, AsyncStream, OpenAI, Stream
from pydantic import BaseModel
from camel.configs import Gemini_API_PARAMS, GeminiConfig
from camel.messages import OpenAIMessage
from camel.models import BaseModelBackend
from camel.types import (
ChatCompletion,
ChatCompletionChunk,
ModelType,
)
from camel.utils import (
BaseTokenCounter,
OpenAITokenCounter,
api_keys_required,
)
[docs]
class GeminiModel(BaseModelBackend):
r"""Gemini API in a unified BaseModelBackend interface.
Args:
model_type (Union[ModelType, str]): Model for which a backend is
created, one of Gemini series.
model_config_dict (Optional[Dict[str, Any]], optional): A dictionary
that will be fed into:obj:`openai.ChatCompletion.create()`. If
:obj:`None`, :obj:`GeminiConfig().as_dict()` will be used.
(default: :obj:`None`)
api_key (Optional[str], optional): The API key for authenticating with
the Gemini service. (default: :obj:`None`)
url (Optional[str], optional): The url to the Gemini service.
(default: :obj:`https://generativelanguage.googleapis.com/v1beta/
openai/`)
token_counter (Optional[BaseTokenCounter], optional): Token counter to
use for the model. If not provided, :obj:`OpenAITokenCounter(
ModelType.GPT_4O_MINI)` will be used.
(default: :obj:`None`)
"""
@api_keys_required(
[
("api_key", 'GEMINI_API_KEY'),
]
)
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:
if model_config_dict is None:
model_config_dict = GeminiConfig().as_dict()
api_key = api_key or os.environ.get("GEMINI_API_KEY")
url = url or os.environ.get(
"GEMINI_API_BASE_URL",
"https://generativelanguage.googleapis.com/v1beta/openai/",
)
super().__init__(
model_type, model_config_dict, api_key, url, token_counter
)
self._client = OpenAI(
timeout=180,
max_retries=3,
api_key=self._api_key,
base_url=self._url,
)
self._async_client = AsyncOpenAI(
timeout=180,
max_retries=3,
api_key=self._api_key,
base_url=self._url,
)
def _process_messages(self, messages) -> List[OpenAIMessage]:
r"""Process the messages for Gemini API to ensure no empty content,
which is not accepted by Gemini.
"""
processed_messages = []
for msg in messages:
msg_copy = msg.copy()
if 'content' in msg_copy and msg_copy['content'] == '':
msg_copy['content'] = 'null'
processed_messages.append(msg_copy)
return processed_messages
def _run(
self,
messages: List[OpenAIMessage],
response_format: Optional[Type[BaseModel]] = None,
tools: Optional[List[Dict[str, Any]]] = None,
) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]:
r"""Runs inference of Gemini chat completion.
Args:
messages (List[OpenAIMessage]): Message list with the chat history
in OpenAI API format.
response_format (Optional[Type[BaseModel]]): The format of the
response.
tools (Optional[List[Dict[str, Any]]]): The schema of the tools to
use for the request.
Returns:
Union[ChatCompletion, Stream[ChatCompletionChunk]]:
`ChatCompletion` in the non-stream mode, or
`Stream[ChatCompletionChunk]` in the stream mode.
"""
response_format = response_format or self.model_config_dict.get(
"response_format", None
)
messages = self._process_messages(messages)
if response_format:
return self._request_parse(messages, response_format)
else:
return self._request_chat_completion(messages, tools)
async def _arun(
self,
messages: List[OpenAIMessage],
response_format: Optional[Type[BaseModel]] = None,
tools: Optional[List[Dict[str, Any]]] = None,
) -> Union[ChatCompletion, AsyncStream[ChatCompletionChunk]]:
r"""Runs inference of OpenAI chat completion in async mode.
Args:
messages (List[OpenAIMessage]): Message list with the chat history
in OpenAI API format.
response_format (Optional[Type[BaseModel]]): The format of the
response.
tools (Optional[List[Dict[str, Any]]]): The schema of the tools to
use for the request.
Returns:
Union[ChatCompletion, AsyncStream[ChatCompletionChunk]]:
`ChatCompletion` in the non-stream mode, or
`AsyncStream[ChatCompletionChunk]` in the stream mode.
"""
response_format = response_format or self.model_config_dict.get(
"response_format", None
)
messages = self._process_messages(messages)
if response_format:
return await self._arequest_parse(messages, response_format)
else:
return await self._arequest_chat_completion(messages, tools)
def _request_chat_completion(
self,
messages: List[OpenAIMessage],
tools: Optional[List[Dict[str, Any]]] = None,
) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]:
request_config = self.model_config_dict.copy()
if tools:
for tool in tools:
function_dict = tool.get('function', {})
function_dict.pop("strict", None)
request_config["tools"] = tools
return self._client.chat.completions.create(
messages=messages,
model=self.model_type,
**request_config,
)
async def _arequest_chat_completion(
self,
messages: List[OpenAIMessage],
tools: Optional[List[Dict[str, Any]]] = None,
) -> Union[ChatCompletion, AsyncStream[ChatCompletionChunk]]:
request_config = self.model_config_dict.copy()
if tools:
for tool in tools:
function_dict = tool.get('function', {})
function_dict.pop("strict", None)
request_config["tools"] = tools
return await self._async_client.chat.completions.create(
messages=messages,
model=self.model_type,
**request_config,
)
def _request_parse(
self,
messages: List[OpenAIMessage],
response_format: Type[BaseModel],
) -> ChatCompletion:
request_config = self.model_config_dict.copy()
request_config["response_format"] = response_format
request_config.pop("stream", None)
return self._client.beta.chat.completions.parse(
messages=messages,
model=self.model_type,
**request_config,
)
async def _arequest_parse(
self,
messages: List[OpenAIMessage],
response_format: Type[BaseModel],
) -> ChatCompletion:
request_config = self.model_config_dict.copy()
request_config["response_format"] = response_format
request_config.pop("stream", None)
return await self._async_client.beta.chat.completions.parse(
messages=messages,
model=self.model_type,
**request_config,
)
@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(ModelType.GPT_4O_MINI)
return self._token_counter
[docs]
def check_model_config(self):
r"""Check whether the model configuration contains any
unexpected arguments to Gemini API.
Raises:
ValueError: If the model configuration dictionary contains any
unexpected arguments to Gemini API.
"""
for param in self.model_config_dict:
if param not in Gemini_API_PARAMS:
raise ValueError(
f"Unexpected argument `{param}` is "
"input into Gemini 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)
