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92 lines
3.1 KiB
Python
92 lines
3.1 KiB
Python
from collections.abc import Sequence
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from typing import cast
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from core.model_manager import ModelInstance
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from dify_graph.file.models import File
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from dify_graph.model_runtime.entities import PromptMessageRole
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from dify_graph.model_runtime.entities.message_entities import (
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ImagePromptMessageContent,
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PromptMessage,
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TextPromptMessageContent,
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)
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from dify_graph.model_runtime.entities.model_entities import AIModelEntity
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from dify_graph.model_runtime.memory import PromptMessageMemory
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from dify_graph.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
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from dify_graph.runtime import VariablePool
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from dify_graph.variables.segments import ArrayAnySegment, ArrayFileSegment, FileSegment, NoneSegment
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from .exc import InvalidVariableTypeError
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def fetch_model_schema(*, model_instance: ModelInstance) -> AIModelEntity:
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model_schema = cast(LargeLanguageModel, model_instance.model_type_instance).get_model_schema(
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model_instance.model_name,
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model_instance.credentials,
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)
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if not model_schema:
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raise ValueError(f"Model schema not found for {model_instance.model_name}")
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return model_schema
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def fetch_files(variable_pool: VariablePool, selector: Sequence[str]) -> Sequence["File"]:
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variable = variable_pool.get(selector)
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if variable is None:
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return []
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elif isinstance(variable, FileSegment):
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return [variable.value]
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elif isinstance(variable, ArrayFileSegment):
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return variable.value
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elif isinstance(variable, NoneSegment | ArrayAnySegment):
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return []
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raise InvalidVariableTypeError(f"Invalid variable type: {type(variable)}")
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def convert_history_messages_to_text(
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*,
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history_messages: Sequence[PromptMessage],
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human_prefix: str,
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ai_prefix: str,
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) -> str:
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string_messages: list[str] = []
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for message in history_messages:
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if message.role == PromptMessageRole.USER:
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role = human_prefix
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elif message.role == PromptMessageRole.ASSISTANT:
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role = ai_prefix
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else:
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continue
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if isinstance(message.content, list):
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content_parts = []
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for content in message.content:
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if isinstance(content, TextPromptMessageContent):
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content_parts.append(content.data)
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elif isinstance(content, ImagePromptMessageContent):
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content_parts.append("[image]")
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inner_msg = "\n".join(content_parts)
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string_messages.append(f"{role}: {inner_msg}")
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else:
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string_messages.append(f"{role}: {message.content}")
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return "\n".join(string_messages)
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def fetch_memory_text(
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*,
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memory: PromptMessageMemory,
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max_token_limit: int,
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message_limit: int | None = None,
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human_prefix: str = "Human",
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ai_prefix: str = "Assistant",
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) -> str:
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history_messages = memory.get_history_prompt_messages(
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max_token_limit=max_token_limit,
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message_limit=message_limit,
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)
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return convert_history_messages_to_text(
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history_messages=history_messages,
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human_prefix=human_prefix,
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ai_prefix=ai_prefix,
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)
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