Split 1161-line monolith into agent/ package: auth, llm, types, process, runtime, api, and nodes/ (base, sensor, input, output, thinker, memorizer). No logic changes — pure structural split. uvicorn agent:app entrypoint unchanged. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
167 lines
6.5 KiB
Python
167 lines
6.5 KiB
Python
"""Thinker Node: S3 — control, reasoning, tool use."""
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import json
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import logging
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import re
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from .base import Node
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from ..llm import llm_call
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from ..process import ProcessManager
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from ..types import Command, ThoughtResult
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log = logging.getLogger("runtime")
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class ThinkerNode(Node):
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name = "thinker"
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model = "google/gemini-2.5-flash-preview"
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max_context_tokens = 4000
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SYSTEM = """You are the Thinker node — the brain of this cognitive runtime.
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You receive a perception of what the user said. Decide: answer directly, use a tool, or show UI controls.
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TOOLS — write a ```python code block and it WILL be executed. Use print() for output.
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- For math, databases, file ops, any computation: write python. NEVER describe code — write it.
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- For simple conversation: respond directly as text.
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UI CONTROLS — to show interactive elements, include a JSON block:
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```controls
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[
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{{"type": "table", "data": [...], "columns": ["id", "name", "email"]}},
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{{"type": "button", "label": "Add Customer", "action": "add_customer"}},
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{{"type": "button", "label": "Refresh", "action": "refresh_customers"}}
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]
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```
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Controls render in the chat. User clicks flow back as actions you can handle.
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You can combine text + code + controls in one response.
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{memory_context}"""
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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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def _parse_tool_call(self, response: str) -> tuple[str, str] | None:
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"""Parse tool calls. Supports TOOL: format and auto-detects python code blocks."""
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text = response.strip()
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if text.startswith("TOOL:"):
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lines = text.split("\n")
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tool_name = lines[0].replace("TOOL:", "").strip()
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code_lines = []
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in_code = False
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for line in lines[1:]:
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if line.strip().startswith("```") and not in_code:
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in_code = True
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continue
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elif line.strip().startswith("```") and in_code:
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break
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elif in_code:
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code_lines.append(line)
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elif line.strip().startswith("CODE:"):
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continue
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return (tool_name, "\n".join(code_lines)) if code_lines else None
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block_match = re.search(r'```(?:python|py|sql|sqlite|sh|bash|tool_code)?\s*\n(.*?)```', text, re.DOTALL)
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if block_match:
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code = block_match.group(1).strip()
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if code and len(code.split("\n")) > 0:
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if "```sql" in text or "```sqlite" in text or ("SELECT" in code.upper() and "CREATE" in code.upper()):
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wrapped = f'''import sqlite3
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conn = sqlite3.connect("/tmp/cog_db.sqlite")
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cursor = conn.cursor()
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for stmt in """{code}""".split(";"):
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stmt = stmt.strip()
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if stmt:
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cursor.execute(stmt)
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conn.commit()
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cursor.execute("SELECT name FROM sqlite_master WHERE type='table'")
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tables = cursor.fetchall()
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for t in tables:
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cursor.execute(f"SELECT * FROM {{t[0]}}")
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rows = cursor.fetchall()
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cols = [d[0] for d in cursor.description]
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print(f"Table: {{t[0]}}")
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print(" | ".join(cols))
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for row in rows:
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print(" | ".join(str(c) for c in row))
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conn.close()'''
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return ("python", wrapped)
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return ("python", code)
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return None
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def _parse_controls(self, response: str) -> list[dict]:
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"""Extract ```controls JSON blocks from response."""
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controls = []
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if "```controls" not in response:
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return controls
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parts = response.split("```controls")
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for part in parts[1:]:
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end = part.find("```")
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if end != -1:
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try:
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controls.extend(json.loads(part[:end].strip()))
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except json.JSONDecodeError:
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pass
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return controls
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def _strip_blocks(self, response: str) -> str:
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"""Remove code and control blocks, return plain text."""
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text = re.sub(r'```(?:python|py|controls).*?```', '', response, flags=re.DOTALL)
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return text.strip()
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async def process(self, command: Command, history: list[dict], memory_context: str = "") -> ThoughtResult:
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await self.hud("thinking", detail="reasoning about response")
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messages = [
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{"role": "system", "content": self.SYSTEM.format(memory_context=memory_context)},
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]
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for msg in history[-12:]:
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messages.append(msg)
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messages.append({"role": "system", "content": f"Input perception: {command.instruction}"})
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messages = self.trim_context(messages)
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await self.hud("context", messages=messages, tokens=self.last_context_tokens,
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max_tokens=self.max_context_tokens, fill_pct=self.context_fill_pct)
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response = await llm_call(self.model, messages)
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log.info(f"[thinker] response: {response[:200]}")
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controls = self._parse_controls(response)
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if controls:
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await self.hud("controls", controls=controls)
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tool_call = self._parse_tool_call(response)
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if tool_call:
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tool_name, code = tool_call
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if self.pm and tool_name == "python":
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proc = await self.pm.execute(tool_name, code)
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tool_output = "\n".join(proc.output_lines)
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else:
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tool_output = f"[unknown tool: {tool_name}]"
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log.info(f"[thinker] tool output: {tool_output[:200]}")
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messages.append({"role": "assistant", "content": response})
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messages.append({"role": "system", "content": f"Tool output:\n{tool_output}"})
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messages.append({"role": "user", "content": "Respond to the user based on the tool output. If showing data, include a ```controls block with a table. Be natural and concise."})
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messages = self.trim_context(messages)
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final = await llm_call(self.model, messages)
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more_controls = self._parse_controls(final)
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if more_controls:
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controls.extend(more_controls)
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await self.hud("controls", controls=more_controls)
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clean_text = self._strip_blocks(final)
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await self.hud("decided", instruction=clean_text[:200])
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return ThoughtResult(response=clean_text, tool_used=tool_name,
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tool_output=tool_output, controls=controls)
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clean_text = self._strip_blocks(response) or response
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await self.hud("decided", instruction="direct response (no tools)")
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return ThoughtResult(response=clean_text, controls=controls)
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