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Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com> Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com> Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
97 lines
3.9 KiB
Python
97 lines
3.9 KiB
Python
"""
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Workflow generator service.
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Thin facade over ``core.workflow.generator.WorkflowGenerator`` that owns the
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model-manager / model-instance plumbing. Controllers call this; the pure
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domain class never touches the model registry directly.
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Pattern mirrors ``LLMGenerator.generate_rule_config`` — see
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``core/llm_generator/llm_generator.py`` — but lives in ``services/`` because
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the generator output is consumed at the application layer (sync_draft_workflow,
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createApp) rather than from inside another workflow.
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"""
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import logging
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from typing import Any
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from core.app.app_config.entities import ModelConfig
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from core.model_manager import ModelManager
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from core.workflow.generator import WorkflowGenerator
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from core.workflow.generator.tool_catalogue import build_tool_catalogue, format_tool_catalogue, installed_tool_keys
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from core.workflow.generator.types import WorkflowGenerateResultDict, WorkflowGenerationMode
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from graphon.model_runtime.entities.model_entities import ModelType
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logger = logging.getLogger(__name__)
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class WorkflowGeneratorService:
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"""
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Coordinates model resolution with the workflow generator domain logic.
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Single public method (``generate_workflow_graph``) keeps the surface area
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minimal — the cmd+k `/create` flow is the only caller today.
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"""
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@classmethod
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def generate_workflow_graph(
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cls,
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*,
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tenant_id: str,
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mode: WorkflowGenerationMode,
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instruction: str,
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model_config: ModelConfig,
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ideal_output: str = "",
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current_graph: dict[str, Any] | None = None,
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) -> WorkflowGenerateResultDict:
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"""
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Resolve a model instance for the tenant and run the generator.
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``current_graph`` is the existing draft graph for the cmd+k `/refine`
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flow — when present the generator refines it instead of creating a new
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graph from scratch. ``None`` is the `/create` path.
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Errors from the LLM call (auth, quota, invoke) propagate so the
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controller can map them to existing HTTP error envelopes (same
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envelope as ``/rule-generate``).
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"""
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model_manager = ModelManager.for_tenant(tenant_id=tenant_id)
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model_instance = model_manager.get_model_instance(
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tenant_id=tenant_id,
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model_type=ModelType.LLM,
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provider=model_config.provider,
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model=model_config.name,
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)
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model_parameters: dict[str, Any] = dict(model_config.completion_params or {})
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# Build the installed-tool catalogue for this tenant so the planner/
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# builder can pick concrete tools instead of inventing names, AND so
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# the runner's validator can reject hallucinated tool names BEFORE
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# the user clicks Apply. A failure here (plugin daemon unreachable,
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# etc.) must not block generation — log and fall back to the no-tool
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# path, which also disables tool validation in the runner (None
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# sentinel rather than empty set, so we don't reject every tool
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# node just because we couldn't enumerate the catalogue).
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tool_catalogue_text = ""
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installed_tools: set[tuple[str, str]] | None = None
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try:
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entries = build_tool_catalogue(tenant_id)
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tool_catalogue_text = format_tool_catalogue(entries)
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installed_tools = installed_tool_keys(entries)
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except Exception:
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logger.exception("Workflow generator: failed to build tool catalogue for tenant %s", tenant_id)
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return WorkflowGenerator.generate_workflow_graph(
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model_instance=model_instance,
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model_parameters=model_parameters,
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provider=model_config.provider,
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model_name=model_config.name,
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model_mode=model_config.mode.value,
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mode=mode,
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instruction=instruction,
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ideal_output=ideal_output,
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tool_catalogue_text=tool_catalogue_text,
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installed_tools=installed_tools,
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current_graph=current_graph,
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)
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