mirror of
https://github.com/eigent-ai/eigent.git
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277 lines
9.5 KiB
Python
277 lines
9.5 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 Any, Dict, List, Optional, Type, Union
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from pydantic import BaseModel
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from camel.configs import WatsonXConfig
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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, 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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update_current_observation,
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)
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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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logger = get_logger(__name__)
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class WatsonXModel(BaseModelBackend):
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r"""WatsonX API in a unified BaseModelBackend interface.
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Args:
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model_type (Union[ModelType, str]): Model type for which a backend is
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created, one of WatsonX 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:`ModelInference.chat()`.
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If :obj:`None`, :obj:`WatsonXConfig().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 WatsonX service. (default: :obj:`None`)
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url (Optional[str], optional): The url to the WatsonX service.
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(default: :obj:`None`)
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project_id (Optional[str], optional): The project ID authenticating
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with the WatsonX service. (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(
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ModelType.GPT_4O_MINI)` will be used.
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(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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**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", 'WATSONX_API_KEY'),
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("project_id", 'WATSONX_PROJECT_ID'),
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]
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)
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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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project_id: Optional[str] = None,
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token_counter: Optional[BaseTokenCounter] = None,
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timeout: Optional[float] = None,
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**kwargs: Any,
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):
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from ibm_watsonx_ai import APIClient, Credentials
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from ibm_watsonx_ai.foundation_models import ModelInference
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if model_config_dict is None:
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model_config_dict = WatsonXConfig().as_dict()
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api_key = api_key or os.environ.get("WATSONX_API_KEY")
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url = url or os.environ.get(
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"WATSONX_URL", "https://jp-tok.ml.cloud.ibm.com"
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)
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project_id = project_id or os.environ.get("WATSONX_PROJECT_ID")
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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, model_config_dict, api_key, url, token_counter, timeout
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)
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self._project_id = project_id
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credentials = Credentials(api_key=self._api_key, url=self._url)
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client = APIClient(credentials, project_id=self._project_id, **kwargs)
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self._model = ModelInference(
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model_id=self.model_type,
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api_client=client,
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params=model_config_dict,
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)
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def _to_openai_response(self, response: Dict[str, Any]) -> ChatCompletion:
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r"""Convert WatsonX response to OpenAI format."""
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if not response:
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raise ValueError("Empty response from WatsonX API")
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# Extract usage information
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usage = response.get("usage", {})
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# Create OpenAI-compatible response
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obj = ChatCompletion.construct(
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id=response.get("id", ""),
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choices=response.get("choices", []),
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created=response.get("created"),
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model=self.model_type,
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object="chat.completion",
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usage=usage,
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)
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return obj
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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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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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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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import copy
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request_config = copy.deepcopy(self.model_config_dict)
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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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@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 WatsonX 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]], optional): The
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response format. (default: :obj:`None`)
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tools (Optional[List[Dict[str, Any]]], optional): tools to use.
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(default: :obj:`None`)
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Returns:
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ChatCompletion.
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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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try:
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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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# WatsonX expects messages as a list of dictionaries
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response = self._model.chat(
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messages=messages,
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params=request_config,
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tools=tools,
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)
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openai_response = self._to_openai_response(response)
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update_current_observation(
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usage=openai_response.usage,
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)
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return openai_response
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except Exception as e:
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logger.error(f"Unexpected error when calling WatsonX API: {e!s}")
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raise
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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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) -> ChatCompletion:
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r"""Runs inference of WatsonX chat completion asynchronously.
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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]], optional): The
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response format. (default: :obj:`None`)
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tools (Optional[List[Dict[str, Any]]], optional): tools to use.
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(default: :obj:`None`)
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Returns:
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ChatCompletion.
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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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try:
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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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# WatsonX expects messages as a list of dictionaries
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response = await self._model.achat(
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messages=messages,
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params=request_config,
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tools=tools,
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)
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openai_response = self._to_openai_response(response)
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update_current_observation(
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usage=openai_response.usage,
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)
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return openai_response
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except Exception as e:
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logger.error(f"Unexpected error when calling WatsonX API: {e!s}")
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raise
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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.
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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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