q792602257
a5aeb8889c
Tests / Test failed: 2, passed: 849
- ToolCallDecision captures thinking blocks and pre-tool text output
- AnthropicToolCallingClient supports optional extended thinking (budget_tokens + beta header)
- PlannedStep carries rationale and thinking from each LLM decision
- WorldEvent replaces scene_summary with rationale/thinking/page fields (backward-compatible)
- AI planner system prompt instructs reflection before each tool call
- _history_summary() emits compact {page, rationale, action, success} dicts
- Cloud DB migration 0011 adds nullable rationale/thinking columns to planner_decision_log
- OpenAI client extracts reasoning_content into thinking field
86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
from __future__ import annotations
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from typing import TYPE_CHECKING, Any
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from core.errors import TaskFailedError
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from core.models import Scene
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from runtime.context import TaskContext
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from runtime.planner import PlannedStep, Planner
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from runtime.planner_config import PlannerConfig, load_config
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from runtime.planner_prompts import PLANNER_SYSTEM_PROMPT, planner_user_prompt
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from runtime.tool_calling_client import ToolCallingClient, build_client
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from runtime.tool_specs import ALL_TOOL_SPECS
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if TYPE_CHECKING:
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from world.models import WorldState
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FINISH_TASK_TOOL = "finish_task"
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class AIPlanner(Planner):
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def __init__(
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self,
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*,
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client: ToolCallingClient | None = None,
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config: PlannerConfig | None = None,
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) -> None:
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self.config = config or load_config()
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self.client = client or build_client(self.config)
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def plan(
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self,
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*,
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goal: str,
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scene: Scene,
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context: TaskContext,
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world: "WorldState | None" = None,
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screenshot: bytes | None = None,
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) -> list[PlannedStep]:
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user_prompt = planner_user_prompt(
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goal=goal,
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scene_json=scene.to_dict(),
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history_summary=_history_summary(world),
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)
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decision = self.client.decide(
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system_prompt=PLANNER_SYSTEM_PROMPT,
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user_prompt=user_prompt,
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screenshot=screenshot,
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tools=ALL_TOOL_SPECS,
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timeout=self.config.timeout,
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)
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if decision.tool_name == FINISH_TASK_TOOL:
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if decision.arguments.get("success"):
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return []
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raise TaskFailedError(decision.arguments.get("reason") or "task failed")
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return [
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PlannedStep(
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action=decision.tool_name,
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description=f"AI planner: {decision.tool_name}({decision.arguments})",
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args=dict(decision.arguments),
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prompt=decision.user_prompt or user_prompt,
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rationale=decision.text_output,
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thinking=decision.thinking,
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)
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]
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def goal_reached(self, *, goal: str, scene: Scene, context: TaskContext) -> bool:
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# Completion is signaled exclusively via the finish_task tool call
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# (mapped to an empty plan above), never via this hook.
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return False
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def _history_summary(world: "WorldState | None") -> list[dict[str, Any]]:
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if world is None:
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return []
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return [
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{
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"page": event.page,
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"action": event.action,
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"rationale": event.rationale,
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"success": event.success,
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}
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for event in world.history
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]
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