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) 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 @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"