Source code for langchain_community.chat_models.volcengine_maas

from __future__ import annotations

from typing import Any, Dict, Iterator, List, Mapping, Optional, cast

from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import (
    AIMessage,
    AIMessageChunk,
    BaseMessage,
    FunctionMessage,
    HumanMessage,
    SystemMessage,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult

from langchain_community.llms.volcengine_maas import VolcEngineMaasBase


def _convert_message_to_dict(message: BaseMessage) -> dict:
    if 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}
    elif isinstance(message, FunctionMessage):
        message_dict = {"role": "function", "content": message.content}
    else:
        raise ValueError(f"Got unknown type {message}")
    return message_dict


[docs]def convert_dict_to_message(_dict: Mapping[str, Any]) -> AIMessage: """Convert a dict to a message.""" content = _dict.get("choice", {}).get("message", {}).get("content", "") return AIMessage(content=content)
[docs]class VolcEngineMaasChat(BaseChatModel, VolcEngineMaasBase): """Volc Engine Maas hosts a plethora of models. You can utilize these models through this class. To use, you should have the ``volcengine`` python package installed. and set access key and secret key by environment variable or direct pass those to this class. access key, secret key are required parameters which you could get help https://www.volcengine.com/docs/6291/65568 In order to use them, it is necessary to install the 'volcengine' Python package. The access key and secret key must be set either via environment variables or passed directly to this class. access key and secret key are mandatory parameters for which assistance can be sought at https://www.volcengine.com/docs/6291/65568. The two methods are as follows: * Environment Variable Set the environment variables 'VOLC_ACCESSKEY' and 'VOLC_SECRETKEY' with your access key and secret key. * Pass Directly to Class Example: .. code-block:: python from langchain_community.llms import VolcEngineMaasLLM model = VolcEngineMaasChat(model="skylark-lite-public", volc_engine_maas_ak="your_ak", volc_engine_maas_sk="your_sk") """ @property def _llm_type(self) -> str: """Return type of chat model.""" return "volc-engine-maas-chat"
[docs] @classmethod def is_lc_serializable(cls) -> bool: """Return whether this model can be serialized by Langchain.""" return False
@property def _identifying_params(self) -> Dict[str, Any]: return { **{"endpoint": self.endpoint, "model": self.model}, **super()._identifying_params, } def _convert_prompt_msg_params( self, messages: List[BaseMessage], **kwargs: Any, ) -> Dict[str, Any]: model_req = { "model": { "name": self.model, } } if self.model_version is not None: model_req["model"]["version"] = self.model_version return { **model_req, "messages": [_convert_message_to_dict(message) for message in messages], "parameters": {**self._default_params, **kwargs}, } def _stream( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[ChatGenerationChunk]: if stop is not None: kwargs["stop"] = stop params = self._convert_prompt_msg_params(messages, **kwargs) for res in self.client.stream_chat(params): if res: msg = convert_dict_to_message(res) chunk = ChatGenerationChunk(message=AIMessageChunk(content=msg.content)) if run_manager: run_manager.on_llm_new_token(cast(str, msg.content), chunk=chunk) yield chunk def _generate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: completion = "" if self.streaming: for chunk in self._stream(messages, stop, run_manager, **kwargs): completion += chunk.text else: if stop is not None: kwargs["stop"] = stop params = self._convert_prompt_msg_params(messages, **kwargs) res = self.client.chat(params) msg = convert_dict_to_message(res) completion = cast(str, msg.content) message = AIMessage(content=completion) return ChatResult(generations=[ChatGeneration(message=message)])