mirror of
https://github.com/hwchase17/langchain
synced 2024-11-11 19:11:02 +00:00
229 lines
7.9 KiB
Python
229 lines
7.9 KiB
Python
import logging
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import threading
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from typing import Any, Dict, List, Mapping, Optional
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import requests
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from langchain_core._api.deprecation import deprecated
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from langchain_core.callbacks import CallbackManagerForLLMRun
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from langchain_core.language_models.chat_models import BaseChatModel
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from langchain_core.messages import (
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AIMessage,
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BaseMessage,
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ChatMessage,
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HumanMessage,
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)
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from langchain_core.outputs import ChatGeneration, ChatResult
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from langchain_core.pydantic_v1 import root_validator
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from langchain_core.utils import get_from_dict_or_env
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logger = logging.getLogger(__name__)
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def _convert_message_to_dict(message: BaseMessage) -> dict:
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if isinstance(message, ChatMessage):
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message_dict = {"role": message.role, "content": message.content}
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elif isinstance(message, HumanMessage):
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message_dict = {"role": "user", "content": message.content}
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elif isinstance(message, AIMessage):
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message_dict = {"role": "assistant", "content": message.content}
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else:
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raise ValueError(f"Got unknown type {message}")
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return message_dict
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@deprecated(
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since="0.0.13",
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alternative="langchain_community.chat_models.QianfanChatEndpoint",
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)
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class ErnieBotChat(BaseChatModel):
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"""`ERNIE-Bot` large language model.
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ERNIE-Bot is a large language model developed by Baidu,
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covering a huge amount of Chinese data.
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To use, you should have the `ernie_client_id` and `ernie_client_secret` set,
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or set the environment variable `ERNIE_CLIENT_ID` and `ERNIE_CLIENT_SECRET`.
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Note:
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access_token will be automatically generated based on client_id and client_secret,
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and will be regenerated after expiration (30 days).
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Default model is `ERNIE-Bot-turbo`,
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currently supported models are `ERNIE-Bot-turbo`, `ERNIE-Bot`, `ERNIE-Bot-8K`,
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`ERNIE-Bot-4`, `ERNIE-Bot-turbo-AI`.
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Example:
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.. code-block:: python
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from langchain_community.chat_models import ErnieBotChat
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chat = ErnieBotChat(model_name='ERNIE-Bot')
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Deprecated Note:
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Please use `QianfanChatEndpoint` instead of this class.
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`QianfanChatEndpoint` is a more suitable choice for production.
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Always test your code after changing to `QianfanChatEndpoint`.
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Example of `QianfanChatEndpoint`:
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.. code-block:: python
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from langchain_community.chat_models import QianfanChatEndpoint
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qianfan_chat = QianfanChatEndpoint(model="ERNIE-Bot",
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endpoint="your_endpoint", qianfan_ak="your_ak", qianfan_sk="your_sk")
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"""
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ernie_api_base: Optional[str] = None
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"""Baidu application custom endpoints"""
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ernie_client_id: Optional[str] = None
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"""Baidu application client id"""
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ernie_client_secret: Optional[str] = None
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"""Baidu application client secret"""
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access_token: Optional[str] = None
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"""access token is generated by client id and client secret,
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setting this value directly will cause an error"""
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model_name: str = "ERNIE-Bot-turbo"
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"""model name of ernie, default is `ERNIE-Bot-turbo`.
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Currently supported `ERNIE-Bot-turbo`, `ERNIE-Bot`"""
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system: Optional[str] = None
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"""system is mainly used for model character design,
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for example, you are an AI assistant produced by xxx company.
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The length of the system is limiting of 1024 characters."""
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request_timeout: Optional[int] = 60
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"""request timeout for chat http requests"""
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streaming: Optional[bool] = False
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"""streaming mode. not supported yet."""
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top_p: Optional[float] = 0.8
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temperature: Optional[float] = 0.95
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penalty_score: Optional[float] = 1
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_lock = threading.Lock()
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@root_validator(pre=True)
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def validate_environment(cls, values: Dict) -> Dict:
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values["ernie_api_base"] = get_from_dict_or_env(
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values, "ernie_api_base", "ERNIE_API_BASE", "https://aip.baidubce.com"
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)
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values["ernie_client_id"] = get_from_dict_or_env(
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values,
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"ernie_client_id",
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"ERNIE_CLIENT_ID",
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)
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values["ernie_client_secret"] = get_from_dict_or_env(
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values,
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"ernie_client_secret",
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"ERNIE_CLIENT_SECRET",
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)
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return values
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def _chat(self, payload: object) -> dict:
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base_url = f"{self.ernie_api_base}/rpc/2.0/ai_custom/v1/wenxinworkshop/chat"
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model_paths = {
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"ERNIE-Bot-turbo": "eb-instant",
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"ERNIE-Bot": "completions",
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"ERNIE-Bot-8K": "ernie_bot_8k",
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"ERNIE-Bot-4": "completions_pro",
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"ERNIE-Bot-turbo-AI": "ai_apaas",
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"BLOOMZ-7B": "bloomz_7b1",
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"Llama-2-7b-chat": "llama_2_7b",
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"Llama-2-13b-chat": "llama_2_13b",
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"Llama-2-70b-chat": "llama_2_70b",
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}
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if self.model_name in model_paths:
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url = f"{base_url}/{model_paths[self.model_name]}"
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else:
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raise ValueError(f"Got unknown model_name {self.model_name}")
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resp = requests.post(
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url,
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timeout=self.request_timeout,
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headers={
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"Content-Type": "application/json",
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},
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params={"access_token": self.access_token},
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json=payload,
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)
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return resp.json()
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def _refresh_access_token_with_lock(self) -> None:
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with self._lock:
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logger.debug("Refreshing access token")
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base_url: str = f"{self.ernie_api_base}/oauth/2.0/token"
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resp = requests.post(
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base_url,
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timeout=10,
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headers={
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"Content-Type": "application/json",
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"Accept": "application/json",
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},
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params={
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"grant_type": "client_credentials",
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"client_id": self.ernie_client_id,
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"client_secret": self.ernie_client_secret,
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},
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)
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self.access_token = str(resp.json().get("access_token"))
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def _generate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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if self.streaming:
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raise ValueError("`streaming` option currently unsupported.")
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if not self.access_token:
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self._refresh_access_token_with_lock()
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payload = {
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"messages": [_convert_message_to_dict(m) for m in messages],
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"top_p": self.top_p,
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"temperature": self.temperature,
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"penalty_score": self.penalty_score,
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"system": self.system,
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**kwargs,
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}
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logger.debug(f"Payload for ernie api is {payload}")
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resp = self._chat(payload)
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if resp.get("error_code"):
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if resp.get("error_code") == 111:
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logger.debug("access_token expired, refresh it")
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self._refresh_access_token_with_lock()
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resp = self._chat(payload)
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else:
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raise ValueError(f"Error from ErnieChat api response: {resp}")
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return self._create_chat_result(resp)
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def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
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if "function_call" in response:
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additional_kwargs = {
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"function_call": dict(response.get("function_call", {}))
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}
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else:
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additional_kwargs = {}
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generations = [
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ChatGeneration(
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message=AIMessage(
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content=response.get("result", ""),
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additional_kwargs={**additional_kwargs},
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)
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)
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]
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token_usage = response.get("usage", {})
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llm_output = {"token_usage": token_usage, "model_name": self.model_name}
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return ChatResult(generations=generations, llm_output=llm_output)
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@property
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def _llm_type(self) -> str:
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return "ernie-bot-chat"
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