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402 lines
14 KiB
Python
402 lines
14 KiB
Python
# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========
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import os
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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Type, Union
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from pydantic import BaseModel
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if TYPE_CHECKING:
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from mistralai.models import (
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ChatCompletionResponse,
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Messages,
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)
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from openai import AsyncStream
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from camel.configs import MistralConfig
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from camel.logger import get_logger
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from camel.messages import OpenAIMessage
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from camel.models import BaseModelBackend
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from camel.models._utils import try_modify_message_with_format
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from camel.types import ChatCompletion, ChatCompletionChunk, ModelType
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from camel.utils import (
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BaseTokenCounter,
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OpenAITokenCounter,
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api_keys_required,
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dependencies_required,
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update_current_observation,
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)
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logger = get_logger(__name__)
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try:
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if os.getenv("AGENTOPS_API_KEY") is not None:
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from agentops import LLMEvent, record
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else:
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raise ImportError
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except (ImportError, AttributeError):
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LLMEvent = None
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if os.environ.get("LANGFUSE_ENABLED", "False").lower() == "true":
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try:
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from langfuse.decorators import observe
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except ImportError:
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from camel.utils import observe
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else:
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from camel.utils import observe
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class MistralModel(BaseModelBackend):
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r"""Mistral API in a unified BaseModelBackend interface.
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Args:
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model_type (Union[ModelType, str]): Model for which a backend is
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created, one of MISTRAL_* series.
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model_config_dict (Optional[Dict[str, Any]], optional): A dictionary
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that will be fed into:obj:`Mistral.chat.complete()`.
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If:obj:`None`, :obj:`MistralConfig().as_dict()` will be used.
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(default: :obj:`None`)
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api_key (Optional[str], optional): The API key for authenticating with
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the mistral service. (default: :obj:`None`)
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url (Optional[str], optional): The url to the mistral service.
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(default: :obj:`None`)
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token_counter (Optional[BaseTokenCounter], optional): Token counter to
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use for the model. If not provided, :obj:`OpenAITokenCounter` will
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be used. (default: :obj:`None`)
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timeout (Optional[float], optional): The timeout value in seconds for
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API calls. If not provided, will fall back to the MODEL_TIMEOUT
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environment variable or default to 180 seconds.
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(default: :obj:`None`)
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max_retries (int, optional): Maximum number of retries
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for API calls. (default: :obj:`3`)
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**kwargs (Any): Additional arguments to pass to the client
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initialization.
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"""
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@api_keys_required(
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[
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("api_key", "MISTRAL_API_KEY"),
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]
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)
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@dependencies_required('mistralai')
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def __init__(
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self,
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model_type: Union[ModelType, str],
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model_config_dict: Optional[Dict[str, Any]] = None,
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api_key: Optional[str] = None,
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url: Optional[str] = None,
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token_counter: Optional[BaseTokenCounter] = None,
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timeout: Optional[float] = None,
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max_retries: int = 3,
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**kwargs: Any,
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) -> None:
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from mistralai import Mistral
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if model_config_dict is None:
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model_config_dict = MistralConfig().as_dict()
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api_key = api_key or os.environ.get("MISTRAL_API_KEY")
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url = url or os.environ.get("MISTRAL_API_BASE_URL")
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timeout = timeout or float(os.environ.get("MODEL_TIMEOUT", 180))
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super().__init__(
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model_type,
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model_config_dict,
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api_key,
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url,
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token_counter,
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timeout,
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max_retries,
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**kwargs,
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)
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self._client = Mistral(
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timeout_ms=int(self._timeout * 1000)
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if self._timeout is not None
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else None,
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api_key=self._api_key,
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server_url=self._url,
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**kwargs,
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)
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def _to_openai_response(
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self, response: 'ChatCompletionResponse'
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) -> ChatCompletion:
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tool_calls = None
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if (
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response.choices
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and response.choices[0].message
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and response.choices[0].message.tool_calls is not None
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):
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tool_calls = [
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dict(
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id=tool_call.id, # type: ignore[union-attr]
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function={
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"name": tool_call.function.name, # type: ignore[union-attr]
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"arguments": tool_call.function.arguments, # type: ignore[union-attr]
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},
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type=tool_call.type, # type: ignore[union-attr]
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)
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for tool_call in response.choices[0].message.tool_calls
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]
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obj = ChatCompletion.construct(
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id=response.id,
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choices=[
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dict(
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index=response.choices[0].index, # type: ignore[index]
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message={
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"role": response.choices[0].message.role, # type: ignore[index,union-attr]
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"content": response.choices[0].message.content, # type: ignore[index,union-attr]
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"tool_calls": tool_calls,
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},
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finish_reason=response.choices[0].finish_reason # type: ignore[index]
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if response.choices[0].finish_reason # type: ignore[index]
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else None,
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)
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],
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created=response.created,
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model=response.model,
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object="chat.completion",
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usage=response.usage,
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)
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return obj
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def _to_mistral_chatmessage(
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self,
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messages: List[OpenAIMessage],
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) -> List["Messages"]:
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from mistralai.models import (
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AssistantMessage,
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FunctionCall,
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SystemMessage,
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ToolCall,
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ToolMessage,
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UserMessage,
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)
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new_messages: List["Messages"] = []
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for msg in messages:
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role = msg.get("role")
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tool_calls = msg.get("tool_calls")
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content = msg.get("content")
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mistral_tool_calls = None
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if tool_calls:
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# Ensure tool_calls is treated as a list
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tool_calls_list = (
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tool_calls
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if isinstance(tool_calls, list)
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else [tool_calls]
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)
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mistral_tool_calls = []
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for tool_call in tool_calls_list:
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function_call = FunctionCall(
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name=tool_call["function"].get("name"), # type: ignore[attr-defined]
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arguments=tool_call["function"].get("arguments"), # type: ignore[attr-defined]
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)
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# Preserve the original tool call id to keep tool result
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# ordering valid across turns.
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mistral_tool_calls.append(
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ToolCall(
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function=function_call,
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id=tool_call.get("id"), # type: ignore[attr-defined]
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)
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)
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if role == "user":
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new_messages.append(UserMessage(content=content)) # type: ignore[arg-type]
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elif role == "assistant":
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new_messages.append(
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AssistantMessage(
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content=content, # type: ignore[arg-type]
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tool_calls=mistral_tool_calls,
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)
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)
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elif role == "system":
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new_messages.append(SystemMessage(content=content)) # type: ignore[arg-type]
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elif role in {"tool", "function"}:
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new_messages.append(
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ToolMessage(
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content=content, # type: ignore[arg-type]
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tool_call_id=msg.get("tool_call_id"), # type: ignore[arg-type]
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name=msg.get("name"), # type: ignore[arg-type]
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)
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)
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else:
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raise ValueError(f"Unsupported message role: {role}")
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return new_messages # type: ignore[return-value]
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@property
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def token_counter(self) -> BaseTokenCounter:
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r"""Initialize the token counter for the model backend.
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# NOTE: Temporarily using `OpenAITokenCounter` due to a current issue
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# with installing `mistral-common` alongside `mistralai`.
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# Refer to: https://github.com/mistralai/mistral-common/issues/37
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Returns:
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BaseTokenCounter: The token counter following the model's
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tokenization style.
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"""
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if not self._token_counter:
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self._token_counter = OpenAITokenCounter(
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model=ModelType.GPT_4O_MINI
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)
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return self._token_counter
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@observe(as_type="generation")
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async def _arun(
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self,
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messages: List[OpenAIMessage],
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response_format: Optional[Type[BaseModel]] = None,
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tools: Optional[List[Dict[str, Any]]] = None,
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) -> Union[ChatCompletion, AsyncStream[ChatCompletionChunk]]:
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logger.warning(
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"Mistral does not support async inference, using sync "
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"inference instead."
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)
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update_current_observation(
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input={
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"messages": messages,
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"response_format": response_format,
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"tools": tools,
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},
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model=str(self.model_type),
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model_parameters=self.model_config_dict,
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)
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self._log_and_trace()
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request_config = self._prepare_request(
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messages, response_format, tools
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)
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mistral_messages = self._to_mistral_chatmessage(messages)
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response = self._call_client(
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self._client.chat.complete,
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messages=mistral_messages,
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model=self.model_type,
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**request_config,
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)
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openai_response = self._to_openai_response(response) # type: ignore[arg-type]
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update_current_observation(
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usage=openai_response.usage,
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)
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# Add AgentOps LLM Event tracking
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if LLMEvent:
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llm_event = LLMEvent(
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thread_id=openai_response.id,
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prompt=" ".join(
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[message.get("content") for message in messages] # type: ignore[misc]
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),
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prompt_tokens=openai_response.usage.prompt_tokens, # type: ignore[union-attr]
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completion=openai_response.choices[0].message.content,
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completion_tokens=openai_response.usage.completion_tokens, # type: ignore[union-attr]
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model=self.model_type,
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)
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record(llm_event)
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return openai_response
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@observe(as_type="generation")
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def _run(
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self,
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messages: List[OpenAIMessage],
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response_format: Optional[Type[BaseModel]] = None,
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tools: Optional[List[Dict[str, Any]]] = None,
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) -> ChatCompletion:
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r"""Runs inference of Mistral chat completion.
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Args:
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messages (List[OpenAIMessage]): Message list with the chat history
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in OpenAI API format.
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response_format (Optional[Type[BaseModel]]): The format of the
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response for this query.
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tools (Optional[List[Dict[str, Any]]]): The tools to use for this
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query.
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Returns:
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ChatCompletion: The response from the model.
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"""
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update_current_observation(
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input={
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"messages": messages,
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"tools": tools,
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},
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model=str(self.model_type),
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model_parameters=self.model_config_dict,
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)
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self._log_and_trace()
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request_config = self._prepare_request(
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messages, response_format, tools
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)
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mistral_messages = self._to_mistral_chatmessage(messages)
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response = self._call_client(
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self._client.chat.complete,
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messages=mistral_messages,
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model=self.model_type,
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**request_config,
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)
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openai_response = self._to_openai_response(response) # type: ignore[arg-type]
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update_current_observation(
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usage=openai_response.usage,
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)
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# Add AgentOps LLM Event tracking
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if LLMEvent:
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llm_event = LLMEvent(
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thread_id=openai_response.id,
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prompt=" ".join(
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[message.get("content") for message in messages] # type: ignore[misc]
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),
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prompt_tokens=openai_response.usage.prompt_tokens, # type: ignore[union-attr]
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completion=openai_response.choices[0].message.content,
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completion_tokens=openai_response.usage.completion_tokens, # type: ignore[union-attr]
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model=self.model_type,
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)
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record(llm_event)
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return openai_response
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def _prepare_request(
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self,
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messages: List[OpenAIMessage],
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response_format: Optional[Type[BaseModel]] = None,
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tools: Optional[List[Dict[str, Any]]] = None,
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) -> Dict[str, Any]:
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request_config = self.model_config_dict.copy()
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if tools:
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request_config["tools"] = tools
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elif response_format:
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try_modify_message_with_format(messages[-1], response_format)
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request_config["response_format"] = {"type": "json_object"}
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return request_config
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@property
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def stream(self) -> bool:
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r"""Returns whether the model is in stream mode, which sends partial
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results each time. Current it's not supported.
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Returns:
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bool: Whether the model is in stream mode.
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"""
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return False
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