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https://github.com/langgenius/dify-plugin-daemon.git
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eux
111 lines
3.0 KiB
Python
111 lines
3.0 KiB
Python
import logging
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from collections.abc import Generator
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from typing import Optional, Union
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from dify_plugin import LargeLanguageModel
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from dify_plugin.entities import I18nObject
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from dify_plugin.errors.model import (
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CredentialsValidateFailedError,
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)
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from dify_plugin.entities.model import (
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AIModelEntity,
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FetchFrom,
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ModelType,
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)
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from dify_plugin.entities.model.llm import (
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LLMResult,
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)
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from dify_plugin.entities.model.message import (
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PromptMessage,
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PromptMessageTool,
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)
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logger = logging.getLogger(__name__)
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class {{ .PluginName | SnakeToCamel }}LargeLanguageModel(LargeLanguageModel):
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"""
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Model class for {{ .PluginName }} large language model.
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"""
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def _invoke(
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self,
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model: str,
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credentials: dict,
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prompt_messages: list[PromptMessage],
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model_parameters: dict,
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tools: Optional[list[PromptMessageTool]] = None,
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stop: Optional[list[str]] = None,
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stream: bool = True,
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user: Optional[str] = None,
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) -> Union[LLMResult, Generator]:
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"""
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Invoke large language model
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:param model: model name
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:param credentials: model credentials
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:param prompt_messages: prompt messages
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:param model_parameters: model parameters
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:param tools: tools for tool calling
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:param stop: stop words
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:param stream: is stream response
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:param user: unique user id
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:return: full response or stream response chunk generator result
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"""
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pass
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def get_num_tokens(
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self,
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model: str,
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credentials: dict,
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prompt_messages: list[PromptMessage],
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tools: Optional[list[PromptMessageTool]] = None,
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) -> int:
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"""
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Get number of tokens for given prompt messages
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:param model: model name
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:param credentials: model credentials
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:param prompt_messages: prompt messages
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:param tools: tools for tool calling
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:return:
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"""
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return 0
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def validate_credentials(self, model: str, credentials: dict) -> None:
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"""
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Validate model credentials
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:param model: model name
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:param credentials: model credentials
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:return:
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"""
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try:
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pass
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except Exception as ex:
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raise CredentialsValidateFailedError(str(ex))
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def get_customizable_model_schema(
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self, model: str, credentials: dict
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) -> AIModelEntity:
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"""
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If your model supports fine-tuning, this method returns the schema of the base model
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but renamed to the fine-tuned model name.
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:param model: model name
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:param credentials: credentials
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:return: model schema
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"""
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entity = AIModelEntity(
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model=model,
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label=I18nObject(zh_Hans=model, en_US=model),
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model_type=ModelType.LLM,
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features=[],
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fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
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model_properties={},
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parameter_rules=[],
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)
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return entity
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