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https://github.com/langgenius/dify.git
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44491e427c
Resolve the full dependency chain to enable all previously disabled controllers: Enabled routes: - sandbox_files: sandbox file browser API - sandbox_providers: sandbox provider management API - app_asset: app asset management API - skills: skill extraction API - CLI API blueprint: DifyCli callback endpoints (/cli/api/*) Dependencies extracted (64 files, ~8000 lines): - models/sandbox.py, models/app_asset.py: DB models - core/zip_sandbox/: zip-based sandbox execution - core/session/: CLI API session management - core/memory/: base memory + node token buffer - core/helper/creators.py: helper utilities - core/llm_generator/: context models, output models, utils - core/workflow/nodes/command/: command node type - core/workflow/nodes/file_upload/: file upload node type - core/app/entities/: app_asset_entities, app_bundle_entities, llm_generation_entities - services/: asset_content, skill, workflow_collaboration, workflow_comment - controllers/console/app/error.py: AppAsset error classes - core/tools/utils/system_encryption.py Import fixes: - dify_graph.enums -> graphon.enums in skill_service.py - get_signed_file_url_for_plugin -> get_signed_file_url in cli_api.py All 5 controllers verified: import OK, Flask starts successfully. 46 existing tests still pass. Made-with: Cursor
83 lines
2.5 KiB
Python
83 lines
2.5 KiB
Python
"""
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Base memory interfaces and types.
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This module defines the common protocol for memory implementations.
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"""
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from abc import ABC, abstractmethod
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from collections.abc import Sequence
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from graphon.model_runtime.entities import ImagePromptMessageContent, PromptMessage
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class BaseMemory(ABC):
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"""
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Abstract base class for memory implementations.
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Provides a common interface for both conversation-level and node-level memory.
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"""
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@abstractmethod
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def get_history_prompt_messages(
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self,
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max_token_limit: int = 2000,
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message_limit: int | None = None,
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) -> Sequence[PromptMessage]:
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"""
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Get history prompt messages.
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:param max_token_limit: Maximum tokens for history
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:param message_limit: Maximum number of messages
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:return: Sequence of PromptMessage for LLM context
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"""
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pass
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def get_history_prompt_text(
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self,
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human_prefix: str = "Human",
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ai_prefix: str = "Assistant",
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max_token_limit: int = 2000,
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message_limit: int | None = None,
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) -> str:
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"""
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Get history prompt as formatted text.
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:param human_prefix: Prefix for human messages
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:param ai_prefix: Prefix for assistant messages
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:param max_token_limit: Maximum tokens for history
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:param message_limit: Maximum number of messages
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:return: Formatted history text
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"""
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from graphon.model_runtime.entities import (
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PromptMessageRole,
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TextPromptMessageContent,
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)
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prompt_messages = self.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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string_messages = []
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for m in prompt_messages:
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if m.role == PromptMessageRole.USER:
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role = human_prefix
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elif m.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(m.content, list):
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inner_msg = ""
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for content in m.content:
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if isinstance(content, TextPromptMessageContent):
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inner_msg += f"{content.data}\n"
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elif isinstance(content, ImagePromptMessageContent):
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inner_msg += "[image]\n"
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string_messages.append(f"{role}: {inner_msg.strip()}")
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else:
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message = f"{role}: {m.content}"
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string_messages.append(message)
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return "\n".join(string_messages)
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