import logging
import threading
from typing import Any, Dict, List, Mapping, Optional
import requests
from langchain_core._api.deprecation import deprecated
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import (
AIMessage,
BaseMessage,
ChatMessage,
HumanMessage,
)
from langchain_core.outputs import ChatGeneration, ChatResult
from langchain_core.pydantic_v1 import root_validator
from langchain_core.utils import get_from_dict_or_env
logger = logging.getLogger(__name__)
def _convert_message_to_dict(message: BaseMessage) -> dict:
if isinstance(message, ChatMessage):
message_dict = {"role": message.role, "content": message.content}
elif isinstance(message, HumanMessage):
message_dict = {"role": "user", "content": message.content}
elif isinstance(message, AIMessage):
message_dict = {"role": "assistant", "content": message.content}
else:
raise ValueError(f"Got unknown type {message}")
return message_dict
[docs]@deprecated(
since="0.0.13",
alternative="langchain_community.chat_models.QianfanChatEndpoint",
)
class ErnieBotChat(BaseChatModel):
"""`ERNIE-Bot` 大型语言模型。
ERNIE-Bot 是百度开发的大型语言模型，涵盖了大量的中文数据。
要使用，您应该设置 `ernie_client_id` 和 `ernie_client_secret`，或者设置环境变量 `ERNIE_CLIENT_ID` 和 `ERNIE_CLIENT_SECRET`。
注意：
访问令牌将根据 client_id 和 client_secret 自动生成，并在过期后重新生成（30天）。
默认模型是 `ERNIE-Bot-turbo`，
目前支持的模型有 `ERNIE-Bot-turbo`、`ERNIE-Bot`、`ERNIE-Bot-8K`、
`ERNIE-Bot-4`、`ERNIE-Bot-turbo-AI`。
示例：
.. code-block:: python
from langchain_community.chat_models import ErnieBotChat
chat = ErnieBotChat(model_name='ERNIE-Bot')
弃用说明：
请使用 `QianfanChatEndpoint` 替代此类。
`QianfanChatEndpoint` 是生产环境中更合适的选择。
在切换到 `QianfanChatEndpoint` 后，请始终测试您的代码。
`QianfanChatEndpoint` 示例：
.. code-block:: python
from langchain_community.chat_models import QianfanChatEndpoint
qianfan_chat = QianfanChatEndpoint(model="ERNIE-Bot",
endpoint="your_endpoint", qianfan_ak="your_ak", qianfan_sk="your_sk")"""
ernie_api_base: Optional[str] = None
"""百度应用自定义端点"""
ernie_client_id: Optional[str] = None
"""百度应用客户端ID"""
ernie_client_secret: Optional[str] = None
"""百度应用客户端密钥"""
access_token: Optional[str] = None
"""访问令牌是由客户端ID和客户端密钥生成的，直接设置此值将导致错误。"""
model_name: str = "ERNIE-Bot-turbo"
"""ERNIE的模型名称，默认为`ERNIE-Bot-turbo`。
目前支持的模型有`ERNIE-Bot-turbo`和`ERNIE-Bot`。"""
system: Optional[str] = None
"""系统主要用于模型角色设计，
例如，您是xxx公司生产的AI助手。
系统长度限制为1024个字符。"""
request_timeout: Optional[int] = 60
"""聊天http请求的请求超时"""
streaming: Optional[bool] = False
"""流式模式。目前还不支持。"""
top_p: Optional[float] = 0.8
temperature: Optional[float] = 0.95
penalty_score: Optional[float] = 1
_lock = threading.Lock()
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
values["ernie_api_base"] = get_from_dict_or_env(
values, "ernie_api_base", "ERNIE_API_BASE", "https://aip.baidubce.com"
)
values["ernie_client_id"] = get_from_dict_or_env(
values,
"ernie_client_id",
"ERNIE_CLIENT_ID",
)
values["ernie_client_secret"] = get_from_dict_or_env(
values,
"ernie_client_secret",
"ERNIE_CLIENT_SECRET",
)
return values
def _chat(self, payload: object) -> dict:
base_url = f"{self.ernie_api_base}/rpc/2.0/ai_custom/v1/wenxinworkshop/chat"
model_paths = {
"ERNIE-Bot-turbo": "eb-instant",
"ERNIE-Bot": "completions",
"ERNIE-Bot-8K": "ernie_bot_8k",
"ERNIE-Bot-4": "completions_pro",
"ERNIE-Bot-turbo-AI": "ai_apaas",
"BLOOMZ-7B": "bloomz_7b1",
"Llama-2-7b-chat": "llama_2_7b",
"Llama-2-13b-chat": "llama_2_13b",
"Llama-2-70b-chat": "llama_2_70b",
}
if self.model_name in model_paths:
url = f"{base_url}/{model_paths[self.model_name]}"
else:
raise ValueError(f"Got unknown model_name {self.model_name}")
resp = requests.post(
url,
timeout=self.request_timeout,
headers={
"Content-Type": "application/json",
},
params={"access_token": self.access_token},
json=payload,
)
return resp.json()
def _refresh_access_token_with_lock(self) -> None:
with self._lock:
logger.debug("Refreshing access token")
base_url: str = f"{self.ernie_api_base}/oauth/2.0/token"
resp = requests.post(
base_url,
timeout=10,
headers={
"Content-Type": "application/json",
"Accept": "application/json",
},
params={
"grant_type": "client_credentials",
"client_id": self.ernie_client_id,
"client_secret": self.ernie_client_secret,
},
)
self.access_token = str(resp.json().get("access_token"))
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
if self.streaming:
raise ValueError("`streaming` option currently unsupported.")
if not self.access_token:
self._refresh_access_token_with_lock()
payload = {
"messages": [_convert_message_to_dict(m) for m in messages],
"top_p": self.top_p,
"temperature": self.temperature,
"penalty_score": self.penalty_score,
"system": self.system,
**kwargs,
}
logger.debug(f"Payload for ernie api is {payload}")
resp = self._chat(payload)
if resp.get("error_code"):
if resp.get("error_code") == 111:
logger.debug("access_token expired, refresh it")
self._refresh_access_token_with_lock()
resp = self._chat(payload)
else:
raise ValueError(f"Error from ErnieChat api response: {resp}")
return self._create_chat_result(resp)
def _create_chat_result(self, response: Mapping[str, Any]) -> ChatResult:
if "function_call" in response:
additional_kwargs = {
"function_call": dict(response.get("function_call", {}))
}
else:
additional_kwargs = {}
generations = [
ChatGeneration(
message=AIMessage(
content=response.get("result", ""),
additional_kwargs={**additional_kwargs},
)
)
]
token_usage = response.get("usage", {})
llm_output = {"token_usage": token_usage, "model_name": self.model_name}
return ChatResult(generations=generations, llm_output=llm_output)
@property
def _llm_type(self) -> str:
return "ernie-bot-chat"