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https://github.com/langgenius/dify.git
synced 2026-05-03 17:08:03 +08:00
refactor(skill): transition from artifact set to bundle structure
- Replaced SkillArtifactSet with SkillBundle across various components, enhancing the organization of skill dependencies and references. - Updated SkillManager methods to load and save bundles instead of artifacts, improving clarity in asset management. - Refactored SkillCompiler to compile skills into bundles, streamlining the dependency resolution process. - Adjusted DifyCli and SandboxBashSession to utilize ToolDependencies, ensuring consistent handling of tool references. - Introduced AssetReferences for better management of file dependencies within skill bundles.
This commit is contained in:
Harry
@ -57,9 +57,9 @@ from core.sandbox import Sandbox
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from core.sandbox.bash.session import SandboxBashSession
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from core.sandbox.entities.config import AppAssets
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from core.skill.constants import SkillAttrs
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from core.skill.entities.skill_artifact_set import SkillArtifactSet
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from core.skill.entities.skill_bundle import SkillBundle
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from core.skill.entities.skill_document import SkillDocument
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from core.skill.entities.tool_artifact import ToolArtifact
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from core.skill.entities.tool_dependencies import ToolDependencies
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from core.skill.skill_compiler import SkillCompiler
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from core.tools.__base.tool import Tool
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from core.tools.signature import sign_upload_file
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@ -299,14 +299,14 @@ class LLMNode(Node[LLMNodeData]):
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sandbox = self.graph_runtime_state.sandbox
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if sandbox:
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tool_artifact = self._extract_tool_artifact()
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tool_dependencies = self._extract_tool_dependencies()
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generator = self._invoke_llm_with_sandbox(
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sandbox=sandbox,
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model_instance=model_instance,
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prompt_messages=prompt_messages,
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stop=stop,
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variable_pool=variable_pool,
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tool_artifact=tool_artifact,
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tool_dependencies=tool_dependencies,
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)
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elif self.tool_call_enabled:
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generator = self._invoke_llm_with_tools(
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@ -1492,11 +1492,10 @@ class LLMNode(Node[LLMNodeData]):
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) -> Sequence[PromptMessage]:
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prompt_messages: list[PromptMessage] = []
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# Extract skill compilation context from sandbox if available
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artifact_set: SkillArtifactSet | None = None
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bundle: SkillBundle | None = None
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file_tree: AppAssetFileTree | None = None
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if sandbox:
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artifact_set = sandbox.attrs.get(SkillAttrs.ARTIFACT_SET)
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bundle = sandbox.attrs.get(SkillAttrs.BUNDLE)
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file_tree = sandbox.attrs.get(AppAssetsAttrs.FILE_TREE)
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for message in messages:
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@ -1507,29 +1506,26 @@ class LLMNode(Node[LLMNodeData]):
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variable_pool=variable_pool,
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)
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# Compile skill references after jinja2 rendering
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if artifact_set is not None and file_tree is not None:
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skill_artifact = SkillCompiler().compile_one(
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artifact_set=artifact_set,
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if bundle is not None and file_tree is not None:
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skill_entry = SkillCompiler().compile_one(
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bundle=bundle,
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document=SkillDocument(skill_id="anonymous", content=result_text, metadata={}),
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file_tree=file_tree,
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base_path=AppAssets.PATH,
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)
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result_text = skill_artifact.content
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result_text = skill_entry.content
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prompt_message = _combine_message_content_with_role(
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contents=[TextPromptMessageContent(data=result_text)], role=message.role
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)
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prompt_messages.append(prompt_message)
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else:
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# Get segment group from basic message
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if context:
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template = message.text.replace("{#context#}", context)
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else:
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template = message.text
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segment_group = variable_pool.convert_template(template)
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# Process segments for images
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file_contents = []
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for segment in segment_group.value:
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if isinstance(segment, ArrayFileSegment):
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@ -1547,18 +1543,16 @@ class LLMNode(Node[LLMNodeData]):
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)
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file_contents.append(file_content)
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# Create message with text from all segments
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plain_text = segment_group.text
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# Compile skill references after context and variable substitution
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if plain_text and artifact_set is not None and file_tree is not None:
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skill_artifact = SkillCompiler().compile_one(
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artifact_set=artifact_set,
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if plain_text and bundle is not None and file_tree is not None:
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skill_entry = SkillCompiler().compile_one(
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bundle=bundle,
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document=SkillDocument(skill_id="anonymous", content=plain_text, metadata={}),
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file_tree=file_tree,
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base_path=AppAssets.PATH,
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)
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plain_text = skill_artifact.content
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plain_text = skill_entry.content
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if plain_text:
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prompt_message = _combine_message_content_with_role(
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@ -1813,30 +1807,30 @@ class LLMNode(Node[LLMNodeData]):
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generation_data,
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)
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def _extract_tool_artifact(self) -> ToolArtifact | None:
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def _extract_tool_dependencies(self) -> ToolDependencies | None:
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"""Extract tool artifact from prompt template."""
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sandbox = self.graph_runtime_state.sandbox
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if not sandbox:
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raise LLMNodeError("Sandbox not found")
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artifact_set = sandbox.attrs.get(SkillAttrs.ARTIFACT_SET)
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bundle = sandbox.attrs.get(SkillAttrs.BUNDLE)
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file_tree = sandbox.attrs.get(AppAssetsAttrs.FILE_TREE)
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tool_artifacts: list[ToolArtifact] = []
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tool_deps_list: list[ToolDependencies] = []
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for prompt in self.node_data.prompt_template:
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if isinstance(prompt, LLMNodeChatModelMessage):
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skill_artifact = SkillCompiler().compile_one(
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artifact_set=artifact_set,
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skill_entry = SkillCompiler().compile_one(
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bundle=bundle,
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document=SkillDocument(skill_id="anonymous", content=prompt.text, metadata={}),
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file_tree=file_tree,
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base_path=AppAssets.PATH,
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)
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tool_artifacts.append(skill_artifact.tools)
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tool_deps_list.append(skill_entry.tools)
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if len(tool_artifacts) == 0:
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if len(tool_deps_list) == 0:
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return None
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return reduce(lambda x, y: x.merge(y), tool_artifacts)
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return reduce(lambda x, y: x.merge(y), tool_deps_list)
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def _invoke_llm_with_tools(
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self,
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@ -1889,11 +1883,11 @@ class LLMNode(Node[LLMNodeData]):
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prompt_messages: Sequence[PromptMessage],
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stop: Sequence[str] | None,
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variable_pool: VariablePool,
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tool_artifact: ToolArtifact | None,
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tool_dependencies: ToolDependencies | None,
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) -> Generator[NodeEventBase, None, LLMGenerationData]:
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result: LLMGenerationData | None = None
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with SandboxBashSession(sandbox=sandbox, node_id=self.id, tools=tool_artifact) as session:
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with SandboxBashSession(sandbox=sandbox, node_id=self.id, tools=tool_dependencies) as session:
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prompt_files = self._extract_prompt_files(variable_pool)
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model_features = self._get_model_features(model_instance)
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