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Charles YaoandClaude Opus 4.8
27be3de30a
analyze_impact/propose_edit_plan/build_edit_intents now call services.dify_builder.agent.edit.* via _model_or_none() instead of delegating to the canned PlaceholderAgent. 9 of the Protocol's 14 Build/Edit/Fix methods now use real LLM cognition; only generate_mock_inputs and propose_build_repair (returns []) remain deferred pending the live-test step. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
90 lines
4.1 KiB
Python
90 lines
4.1 KiB
Python
"""The real Dify Builder agent shell.
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Owns the resolved model. Of the Protocol's 14 methods (Fix 4 + Build 7 + Edit 3),
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9 now use real LLM cognition: the 3 Fix methods that reason about a failed run --
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``diagnose``, ``diagnose_checklist``, ``propose_repair`` -- via
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``services.dify_builder.agent.fix``; 6 of the 7 Build methods -- ``analyze_goal``,
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``propose_plan_v1``, ``discover_resources``, ``bind_resources``, ``build_nodes``,
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``learn_from_build`` -- via ``services.dify_builder.agent.build``; and all 3 Edit
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methods -- ``analyze_impact``, ``propose_edit_plan``, ``build_edit_intents`` -- via
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``services.dify_builder.agent.edit``. Each resolves the model through
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``_model_or_none`` (which degrades to ``None`` on resolution failure rather than
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crashing the advance; ``fix.*``/``build.*``/``edit.*`` handle that).
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``generate_mock_inputs`` and ``propose_build_repair`` remain deferred;
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``propose_build_repair`` returns ``[]`` pending the live-test step.
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"""
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from typing import Any
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from core.dify_builder.placeholder_agent import PlaceholderAgent
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from core.model_manager import ModelInstance
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from services.dify_builder.agent import build, edit, fix
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from services.dify_builder.agent.model_resolver import resolve_model_instance
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class LlmBuilderAgent:
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def __init__(self, tenant_id: str, model_config: dict[str, Any] | None = None) -> None:
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self._tenant_id = tenant_id
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self._model_config = model_config or {}
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self._canned = PlaceholderAgent()
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self._model_instance: ModelInstance | None = None
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def _model(self) -> ModelInstance:
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"""Lazily resolve + memoize the chosen model. Used by cognition (next slice)."""
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if self._model_instance is None:
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self._model_instance = resolve_model_instance(self._tenant_id, self._model_config)
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return self._model_instance
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def _model_or_none(self) -> ModelInstance | None:
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"""The resolved model, or None if it can't resolve — fix.* degrades on None
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rather than crashing the advance."""
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try:
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return self._model()
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except Exception: # any resolution failure -> degrade path (fix.* handles None)
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return None
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# -- Fix cognition (delegated) --
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def diagnose(self, failed_run, graph, node_outputs):
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return fix.diagnose(self._model_or_none(), failed_run, graph, node_outputs)
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def diagnose_checklist(self, errors, graph):
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return fix.diagnose_checklist(self._model_or_none(), errors, graph)
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def propose_repair(self, diagnosis, graph):
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return fix.propose_repair(self._model_or_none(), diagnosis, graph)
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def generate_mock_inputs(self, schema, prior_failed):
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return self._canned.generate_mock_inputs(schema, prior_failed)
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# -- Build cognition (real) --
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def analyze_goal(self, goal_text):
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return build.analyze_goal(self._model_or_none(), goal_text)
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def propose_plan_v1(self, requirements):
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return build.propose_plan_v1(self._model_or_none(), requirements)
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def discover_resources(self, plan_items):
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return build.discover_resources(self._model_or_none(), self._tenant_id, plan_items)
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def bind_resources(self, plan_items, resource_ids, conflict_policy):
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return build.bind_resources(self._model_or_none(), self._tenant_id, plan_items, resource_ids, conflict_policy)
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def build_nodes(self, plan_items):
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return build.build_nodes(self._tenant_id, self._model_config, plan_items)
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def propose_build_repair(self, built_node_ids):
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return [] # no real failure signal until the live test step is de-canned (deferred)
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def learn_from_build(self, goal_text, requirements, plan_items, built_node_ids):
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return build.learn_from_build(self._model_or_none(), goal_text, requirements, plan_items, built_node_ids)
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# -- Edit cognition (real) --
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def analyze_impact(self, goal_text, graph):
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return edit.analyze_impact(self._model_or_none(), goal_text, graph)
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def propose_edit_plan(self, edit_rules, graph):
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return edit.propose_edit_plan(self._model_or_none(), edit_rules, graph)
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def build_edit_intents(self, edit_rules, graph):
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return edit.build_edit_intents(self._model_or_none(), edit_rules, graph)
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