# =========== 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 os
from typing import TYPE_CHECKING, Any, Dict, List, Optional
if TYPE_CHECKING:
from mistralai.models import (
ChatCompletionResponse,
Messages,
)
from camel.configs import MISTRAL_API_PARAMS
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:
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 MistralModel(BaseModelBackend):
r"""Mistral API in a unified BaseModelBackend interface."""
def __init__(
self,
model_type: ModelType,
model_config_dict: Dict[str, Any],
api_key: Optional[str] = None,
url: Optional[str] = None,
token_counter: Optional[BaseTokenCounter] = None,
) -> None:
r"""Constructor for Mistral backend.
Args:
model_type (ModelType): Model for which a backend is created,
one of MISTRAL_* series.
model_config_dict (Dict[str, Any]): A dictionary that will
be fed into `MistralClient.chat`.
api_key (Optional[str]): The API key for authenticating with the
mistral service. (default: :obj:`None`)
url (Optional[str]): The url to the mistral service.
token_counter (Optional[BaseTokenCounter]): Token counter to use
for the model. If not provided, `OpenAITokenCounter` will be
used.
"""
super().__init__(
model_type, model_config_dict, api_key, url, token_counter
)
self._api_key = api_key or os.environ.get("MISTRAL_API_KEY")
self._url = url or os.environ.get("MISTRAL_SERVER_URL")
from mistralai import Mistral
self._client = Mistral(api_key=self._api_key, server_url=self._url)
self._token_counter: Optional[BaseTokenCounter] = None
def _to_openai_response(
self, response: 'ChatCompletionResponse'
) -> ChatCompletion:
tool_calls = None
if (
response.choices
and response.choices[0].message
and response.choices[0].message.tool_calls is not None
):
tool_calls = [
dict(
id=tool_call.id, # type: ignore[union-attr]
function={
"name": tool_call.function.name, # type: ignore[union-attr]
"arguments": tool_call.function.arguments, # type: ignore[union-attr]
},
type=tool_call.type, # type: ignore[union-attr]
)
for tool_call in response.choices[0].message.tool_calls
]
obj = ChatCompletion.construct(
id=response.id,
choices=[
dict(
index=response.choices[0].index, # type: ignore[index]
message={
"role": response.choices[0].message.role, # type: ignore[index,union-attr]
"content": response.choices[0].message.content, # type: ignore[index,union-attr]
"tool_calls": tool_calls,
},
finish_reason=response.choices[0].finish_reason # type: ignore[index]
if response.choices[0].finish_reason # type: ignore[index]
else None,
)
],
created=response.created,
model=response.model,
object="chat.completion",
usage=response.usage,
)
return obj
def _to_mistral_chatmessage(
self,
messages: List[OpenAIMessage],
) -> List["Messages"]:
import uuid
from mistralai.models import (
AssistantMessage,
FunctionCall,
SystemMessage,
ToolCall,
ToolMessage,
UserMessage,
)
new_messages = []
for msg in messages:
tool_id = uuid.uuid4().hex[:9]
tool_call_id = uuid.uuid4().hex[:9]
role = msg.get("role")
function_call = msg.get("function_call")
content = msg.get("content")
mistral_function_call = None
if function_call:
mistral_function_call = FunctionCall(
name=function_call.get("name"), # type: ignore[attr-defined]
arguments=function_call.get("arguments"), # type: ignore[attr-defined]
)
tool_calls = None
if mistral_function_call:
tool_calls = [
ToolCall(function=mistral_function_call, id=tool_id)
]
if role == "user":
new_messages.append(UserMessage(content=content)) # type: ignore[arg-type]
elif role == "assistant":
new_messages.append(
AssistantMessage(content=content, tool_calls=tool_calls) # type: ignore[arg-type]
)
elif role == "system":
new_messages.append(SystemMessage(content=content)) # type: ignore[arg-type]
elif role in {"tool", "function"}:
new_messages.append(
ToolMessage(
content=content, # type: ignore[arg-type]
tool_call_id=tool_call_id,
name=msg.get("name"), # type: ignore[arg-type]
)
)
else:
raise ValueError(f"Unsupported message role: {role}")
return new_messages # type: ignore[return-value]
@property
def token_counter(self) -> BaseTokenCounter:
r"""Initialize the token counter for the model backend.
# NOTE: Temporarily using `OpenAITokenCounter` due to a current issue
# with installing `mistral-common` alongside `mistralai`.
# Refer to: https://github.com/mistralai/mistral-common/issues/37
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("MISTRAL_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.
"""
mistral_messages = self._to_mistral_chatmessage(messages)
response = self._client.chat.complete(
messages=mistral_messages,
model=self.model_type.value,
**self.model_config_dict,
)
openai_response = self._to_openai_response(response) # type: ignore[arg-type]
# 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.value,
)
record(llm_event)
return openai_response
[docs]
def check_model_config(self):
r"""Check whether the model configuration contains any
unexpected arguments to Mistral API.
Raises:
ValueError: If the model configuration dictionary contains any
unexpected arguments to Mistral API.
"""
for param in self.model_config_dict:
if param not in MISTRAL_API_PARAMS:
raise ValueError(
f"Unexpected argument `{param}` is "
"input into Mistral 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
