925fff731f
- Memorizer tracks user_expectation (conversational/delegated/waiting_input/observing) - Output node adjusts phrasing per expectation - PA retry loop: reformulates job on expert failure (all retries exhausted or tool skip) - Machine state in PA context: get_machine_summary includes current state, buttons, stored data - Expert writes to machine state via update_machine + transition_machine - Expanded baked schema coverage - Awareness panel shows color-coded expectation state - Dashboard and workspace component updates Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
113 lines
4.1 KiB
Python
113 lines
4.1 KiB
Python
"""Thinker Node v2: pure executor — runs tools as directed by Director."""
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import asyncio
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import json
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import logging
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from .base import Node
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from ..llm import llm_call
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from ..db import run_db_query
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from ..process import ProcessManager
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from ..types import Command, DirectorPlan, ThoughtResult
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log = logging.getLogger("runtime")
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class ThinkerV2Node(Node):
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name = "thinker_v2"
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model = "google/gemini-2.0-flash-001" # Fast model — just executes
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max_context_tokens = 4000
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RESPONSE_SYSTEM = """You are the Thinker — a fast executor in a cognitive runtime.
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The Director (a smart model) already decided what to do. You just executed the tools.
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Now write a natural response to the user based on the results.
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{hint}
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Rules:
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- Be concise and natural.
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- If tool results contain data, summarize it clearly.
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- NEVER apologize. NEVER say "I" — you are part of a team.
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- Keep it short: 1-3 sentences for simple responses.
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- For data: reference the numbers, don't repeat raw output."""
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def __init__(self, send_hud, process_manager: ProcessManager = None):
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super().__init__(send_hud)
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self.pm = process_manager
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async def process(self, command: Command, plan: DirectorPlan,
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history: list[dict], memory_context: str = "") -> ThoughtResult:
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"""Execute Director's plan and produce ThoughtResult."""
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await self.hud("thinking", detail=f"executing plan: {plan.goal}")
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actions = []
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state_updates = {}
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display_items = []
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machine_ops = []
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tool_used = ""
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tool_output = ""
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# Execute tool_sequence in order
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for step in plan.tool_sequence:
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tool = step.get("tool", "")
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args = step.get("args", {})
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await self.hud("tool_call", tool=tool, args=args)
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if tool == "emit_actions":
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actions.extend(args.get("actions", []))
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elif tool == "set_state":
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key = args.get("key", "")
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if key:
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state_updates[key] = args.get("value")
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elif tool == "emit_display":
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display_items.extend(args.get("items", []))
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elif tool == "create_machine":
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machine_ops.append({"op": "create", **args})
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elif tool == "add_state":
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machine_ops.append({"op": "add_state", **args})
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elif tool == "reset_machine":
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machine_ops.append({"op": "reset", **args})
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elif tool == "destroy_machine":
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machine_ops.append({"op": "destroy", **args})
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elif tool == "query_db":
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query = args.get("query", "")
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database = args.get("database", "eras2_production")
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try:
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result = await asyncio.to_thread(run_db_query, query, database)
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tool_used = "query_db"
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tool_output = result
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await self.hud("tool_result", tool="query_db", output=result[:200])
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except Exception as e:
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tool_used = "query_db"
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tool_output = f"Error: {e}"
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await self.hud("tool_result", tool="query_db", output=str(e)[:200])
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# Generate text response
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hint = plan.response_hint or f"Goal: {plan.goal}"
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if tool_output:
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hint += f"\nTool result:\n{tool_output[:500]}"
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messages = [
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{"role": "system", "content": self.RESPONSE_SYSTEM.replace("{hint}", hint)},
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]
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for msg in history[-8:]:
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messages.append(msg)
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messages.append({"role": "user", "content": command.source_text})
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messages = self.trim_context(messages)
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response = await llm_call(self.model, messages)
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if not response:
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response = "[no response]"
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await self.hud("decided", instruction=response[:200])
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return ThoughtResult(
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response=response,
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tool_used=tool_used,
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tool_output=tool_output,
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actions=actions,
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state_updates=state_updates,
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display_items=display_items,
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machine_ops=machine_ops,
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)
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