import json
import logging
from typing import Any, Dict, Iterator, List, Mapping, Optional, Type
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,
SystemMessage,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.utils import (
convert_to_secret_str,
get_from_dict_or_env,
get_pydantic_field_names,
pre_init,
)
from pydantic import ConfigDict, Field, SecretStr, model_validator
logger = logging.getLogger(__name__)
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, SystemMessage):
message_dict = {"Role": "system", "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 == "system":
return SystemMessage(content=_dict.get("Content", "") or "")
elif 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]
def _create_chat_result(response: Mapping[str, Any]) -> ChatResult:
generations = []
for choice in response["Choices"]:
message = _convert_dict_to_message(choice["Message"])
message.id = response.get("Id", "")
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):
"""Tencent Hunyuan chat models API by Tencent.
For more information, see 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_app_id: Optional[int] = None
"""Hunyuan App ID"""
hunyuan_secret_id: Optional[str] = None
"""Hunyuan Secret ID"""
hunyuan_secret_key: Optional[SecretStr] = None
"""Hunyuan Secret Key"""
streaming: bool = False
"""Whether to stream the results or not."""
request_timeout: int = 60
"""Timeout for requests to Hunyuan API. Default is 60 seconds."""
temperature: float = 1.0
"""What sampling temperature to use."""
top_p: float = 1.0
"""What probability mass to use."""
model: str = "hunyuan-lite"
"""What Model to use.
Optional model:
- hunyuan-lite
- hunyuan-standard
- hunyuan-standard-256K
- hunyuan-pro
- hunyuan-code
- hunyuan-role
- hunyuan-functioncall
- hunyuan-vision
"""
stream_moderation: bool = False
"""Whether to review the results or not when streaming is true."""
enable_enhancement: bool = True
"""Whether to enhancement the results or not."""
model_kwargs: Dict[str, Any] = Field(default_factory=dict)
"""Holds any model parameters valid for API call not explicitly specified."""
model_config = ConfigDict(
populate_by_name=True,
)
@model_validator(mode="before")
@classmethod
def build_extra(cls, values: Dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in."""
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
[docs]
@pre_init
def validate_environment(cls, values: Dict) -> Dict:
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]:
"""Get the default parameters for calling Hunyuan API."""
normal_params = {
"Temperature": self.temperature,
"TopP": self.top_p,
"Model": self.model,
"Stream": self.streaming,
"StreamModeration": self.stream_moderation,
"EnableEnhancement": self.enable_enhancement,
}
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)
return _create_chat_result(json.loads(res.to_json_string()))
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:
chunk = chunk.get("data", "")
if len(chunk) == 0:
continue
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
)
chunk.id = response.get("Id", "")
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) -> Any:
if self.hunyuan_secret_key is None:
raise ValueError("Hunyuan secret key is not set.")
try:
from tencentcloud.common import credential
from tencentcloud.hunyuan.v20230901 import hunyuan_client, models
except ImportError:
raise ImportError(
"Could not import tencentcloud python package. "
"Please install it with `pip install tencentcloud-sdk-python`."
)
parameters = {**self._default_params, **kwargs}
cred = credential.Credential(
self.hunyuan_secret_id, str(self.hunyuan_secret_key.get_secret_value())
)
client = hunyuan_client.HunyuanClient(cred, "")
req = models.ChatCompletionsRequest()
params = {
"Messages": [_convert_message_to_dict(m) for m in messages],
**parameters,
}
req.from_json_string(json.dumps(params))
resp = client.ChatCompletions(req)
return resp
@property
def _llm_type(self) -> str:
return "hunyuan-chat"
