import base64
import hashlib
import hmac
import json
import logging
import time
from typing import Any, Dict, Iterator, List, Mapping, Optional, Type
from urllib.parse import urlparse
import requests
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.chat_models import (
BaseChatModel,
generate_from_stream,
)
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
BaseMessageChunk,
ChatMessage,
ChatMessageChunk,
HumanMessage,
HumanMessageChunk,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
from langchain_core.utils import (
convert_to_secret_str,
get_from_dict_or_env,
get_pydantic_field_names,
)
logger = logging.getLogger(__name__)
DEFAULT_API_BASE = "https://hunyuan.cloud.tencent.com"
DEFAULT_PATH = "/hyllm/v1/chat/completions"
def _convert_message_to_dict(message: BaseMessage) -> dict:
message_dict: Dict[str, Any]
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 TypeError(f"Got unknown type {message}")
return message_dict
def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage:
role = _dict["role"]
if role == "user":
return HumanMessage(content=_dict["content"])
elif role == "assistant":
return AIMessage(content=_dict.get("content", "") or "")
else:
return ChatMessage(content=_dict["content"], role=role)
def _convert_delta_to_message_chunk(
_dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
) -> BaseMessageChunk:
role = _dict.get("role")
content = _dict.get("content") or ""
if role == "user" or default_class == HumanMessageChunk:
return HumanMessageChunk(content=content)
elif role == "assistant" or default_class == AIMessageChunk:
return AIMessageChunk(content=content)
elif role or default_class == ChatMessageChunk:
return ChatMessageChunk(content=content, role=role) # type: ignore[arg-type]
else:
return default_class(content=content) # type: ignore[call-arg]
# signature generation
# https://cloud.tencent.com/document/product/1729/97732#532252ce-e960-48a7-8821-940a9ce2ccf3
def _signature(secret_key: SecretStr, url: str, payload: Dict[str, Any]) -> str:
sorted_keys = sorted(payload.keys())
url_info = urlparse(url)
sign_str = url_info.netloc + url_info.path + "?"
for key in sorted_keys:
value = payload[key]
if isinstance(value, list) or isinstance(value, dict):
value = json.dumps(value, separators=(",", ":"))
elif isinstance(value, float):
value = "%g" % value
sign_str = sign_str + key + "=" + str(value) + "&"
sign_str = sign_str[:-1]
hmacstr = hmac.new(
key=secret_key.get_secret_value().encode("utf-8"),
msg=sign_str.encode("utf-8"),
digestmod=hashlib.sha1,
).digest()
return base64.b64encode(hmacstr).decode("utf-8")
def _create_chat_result(response: Mapping[str, Any]) -> ChatResult:
generations = []
for choice in response["choices"]:
message = _convert_dict_to_message(choice["messages"])
generations.append(ChatGeneration(message=message))
token_usage = response["usage"]
llm_output = {"token_usage": token_usage}
return ChatResult(generations=generations, llm_output=llm_output)
[docs]class ChatHunyuan(BaseChatModel):
"""腾讯混元聊天模型API由腾讯提供。
更多信息，请参见https://cloud.tencent.com/document/product/1729"""
@property
def lc_secrets(self) -> Dict[str, str]:
return {
"hunyuan_app_id": "HUNYUAN_APP_ID",
"hunyuan_secret_id": "HUNYUAN_SECRET_ID",
"hunyuan_secret_key": "HUNYUAN_SECRET_KEY",
}
@property
def lc_serializable(self) -> bool:
return True
hunyuan_api_base: str = Field(default=DEFAULT_API_BASE)
"""混元自定义端点"""
hunyuan_app_id: Optional[int] = None
"""混元应用程序ID"""
hunyuan_secret_id: Optional[str] = None
"""混元秘籍ID"""
hunyuan_secret_key: Optional[SecretStr] = None
"""混元秘钥"""
streaming: bool = False
"""是否要流式传输结果。"""
request_timeout: int = 60
"""请求Hunyuan API 的超时时间。默认为60秒。"""
query_id: Optional[str] = None
"""用于故障排除的查询ID"""
temperature: float = 1.0
"""使用哪种采样温度。"""
top_p: float = 1.0
"""选择要使用的概率质量。"""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""保存API调用中未明确指定的任何模型参数。"""
class Config:
"""此pydantic对象的配置。"""
allow_population_by_field_name = True
@root_validator(pre=True)
def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
"""从传入的额外参数构建额外的kwargs。"""
all_required_field_names = get_pydantic_field_names(cls)
extra = values.get("model_kwargs", {})
for field_name in list(values):
if field_name in extra:
raise ValueError(f"Found {field_name} supplied twice.")
if field_name not in all_required_field_names:
logger.warning(
f"""WARNING! {field_name} is not default parameter.
{field_name} was transferred to model_kwargs.
Please confirm that {field_name} is what you intended."""
)
extra[field_name] = values.pop(field_name)
invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
if invalid_model_kwargs:
raise ValueError(
f"Parameters {invalid_model_kwargs} should be specified explicitly. "
f"Instead they were passed in as part of `model_kwargs` parameter."
)
values["model_kwargs"] = extra
return values
@root_validator()
def validate_environment(cls, values: Dict) -> Dict:
values["hunyuan_api_base"] = get_from_dict_or_env(
values,
"hunyuan_api_base",
"HUNYUAN_API_BASE",
DEFAULT_API_BASE,
)
values["hunyuan_app_id"] = get_from_dict_or_env(
values,
"hunyuan_app_id",
"HUNYUAN_APP_ID",
)
values["hunyuan_secret_id"] = get_from_dict_or_env(
values,
"hunyuan_secret_id",
"HUNYUAN_SECRET_ID",
)
values["hunyuan_secret_key"] = convert_to_secret_str(
get_from_dict_or_env(
values,
"hunyuan_secret_key",
"HUNYUAN_SECRET_KEY",
)
)
return values
@property
def _default_params(self) -> Dict[str, Any]:
"""获取调用Hunyuan API 的默认参数。"""
normal_params = {
"app_id": self.hunyuan_app_id,
"secret_id": self.hunyuan_secret_id,
"temperature": self.temperature,
"top_p": self.top_p,
}
if self.query_id is not None:
normal_params["query_id"] = self.query_id
return {**normal_params, **self.model_kwargs}
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
if self.streaming:
stream_iter = self._stream(
messages=messages, stop=stop, run_manager=run_manager, **kwargs
)
return generate_from_stream(stream_iter)
res = self._chat(messages, **kwargs)
response = res.json()
if "error" in response:
raise ValueError(f"Error from Hunyuan api response: {response}")
return _create_chat_result(response)
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
res = self._chat(messages, **kwargs)
default_chunk_class = AIMessageChunk
for chunk in res.iter_lines():
response = json.loads(chunk)
if "error" in response:
raise ValueError(f"Error from Hunyuan api response: {response}")
for choice in response["choices"]:
chunk = _convert_delta_to_message_chunk(
choice["delta"], default_chunk_class
)
default_chunk_class = chunk.__class__
cg_chunk = ChatGenerationChunk(message=chunk)
if run_manager:
run_manager.on_llm_new_token(chunk.content, chunk=cg_chunk)
yield cg_chunk
def _chat(self, messages: List[BaseMessage], **kwargs: Any) -> requests.Response:
if self.hunyuan_secret_key is None:
raise ValueError("Hunyuan secret key is not set.")
parameters = {**self._default_params, **kwargs}
headers = parameters.pop("headers", {})
timestamp = parameters.pop("timestamp", int(time.time()))
expired = parameters.pop("expired", timestamp + 24 * 60 * 60)
payload = {
"timestamp": timestamp,
"expired": expired,
"messages": [_convert_message_to_dict(m) for m in messages],
**parameters,
}
if self.streaming:
payload["stream"] = 1
url = self.hunyuan_api_base + DEFAULT_PATH
res = requests.post(
url=url,
timeout=self.request_timeout,
headers={
"Content-Type": "application/json",
"Authorization": _signature(
secret_key=self.hunyuan_secret_key, url=url, payload=payload
),
**headers,
},
json=payload,
stream=self.streaming,
)
return res
@property
def _llm_type(self) -> str:
return "hunyuan-chat"