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109 lines
4.7 KiB
Python
109 lines
4.7 KiB
Python
"""The real Dify Builder agent shell.
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Owns the resolved model. All of the Protocol's methods use real cognition: the
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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``; the 6 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`` (``bind_resources``
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is deterministic, not LLM-driven); and all 3 Edit methods -- ``analyze_impact``,
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``propose_edit_plan``, ``build_edit_intents`` -- via ``services.dify_builder.agent.edit``;
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``generate_mock_inputs`` via ``services.dify_builder.agent.mock_inputs``, and
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multi-turn chat via ``services.dify_builder.agent.chat``. Chat uses native model
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streaming; the other cognition methods resolve the model through
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``_model_or_none`` and degrade when no model is available.
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"""
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import logging
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from typing import Any
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from core.model_manager import ModelInstance
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from services.dify_builder.agent import build, chat, edit, fix, mock_inputs
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from services.dify_builder.agent.model_resolver import resolve_model_instance
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logger = logging.getLogger(__name__)
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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._model_instance: ModelInstance | None = None
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self._resolution_failed = False
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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 — cognition degrades on
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None rather than crashing the advance. Logs the resolution failure once per
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agent instance (per advance) so a misconfigured llm-mode tenant is visible."""
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try:
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return self._model()
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except Exception:
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if not self._resolution_failed:
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self._resolution_failed = True
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logger.warning(
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"Dify Builder: model resolution failed for tenant %s in llm mode; degrading to heuristic cognition",
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self._tenant_id,
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exc_info=True,
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)
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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 mock_inputs.generate(self._model_or_none(), 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 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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def respond_to_message(self, state, context, history, graph, text, on_delta=None):
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return chat.respond(
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self._model_or_none(),
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self._model_config,
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state,
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context,
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history,
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graph,
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text,
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on_delta,
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)
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