f6939d47f5
Output node upgraded from dumb echo to device-aware renderer: - Knows it's rendering to HTML/browser, uses markdown formatting - Receives full ThoughtResult (response + tool output + controls) - Always in pipeline: Input perceives, Thinker reasons, Output renders - Keeps user's language, weaves tool results into natural responses Awareness panel (3-column layout): - State: mood, topic, language, facts from Memorizer - Sensors: clock, idle, memo deltas from Sensor ticks - Processes: live cards with cancel during tool execution - Workspace: docked controls (tables/buttons) persist across messages Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
83 lines
3.3 KiB
Python
83 lines
3.3 KiB
Python
"""Output Node: renders Thinker's reasoning into device-appropriate responses."""
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import json
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import logging
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from fastapi import WebSocket
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from .base import Node
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from ..llm import llm_call
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from ..types import Command, ThoughtResult
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log = logging.getLogger("runtime")
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class OutputNode(Node):
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name = "output"
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model = "google/gemini-2.0-flash-001"
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max_context_tokens = 4000
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SYSTEM = """You are the Output node — the renderer of this cognitive runtime.
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DEVICE: The user is on a web browser (Chrome, desktop). Your output renders in an HTML chat panel.
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You can use markdown: **bold**, *italic*, `code`, ```code blocks```, lists, headers.
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The chat panel renders markdown to HTML — use it for structure when helpful.
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YOUR JOB: Transform the Thinker's reasoning into a polished, user-facing response.
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- The Thinker reasons and may use tools. You receive its output and render it for the human.
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- NEVER echo internal node names, perceptions, or system details.
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- NEVER say "the Thinker decided..." or "I'll process..." — just deliver the answer.
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- If the Thinker ran a tool and got output, weave the results into a natural response.
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- If the Thinker gave a direct answer, refine and format it — don't just repeat it.
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- Keep the user's language — if they wrote German, respond in German.
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- Be concise but complete. Use formatting to make data scannable.
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{memory_context}"""
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async def process(self, thought: ThoughtResult, history: list[dict],
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ws: WebSocket, memory_context: str = "") -> str:
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await self.hud("streaming")
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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[-20:]:
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messages.append(msg)
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# Give Output the full Thinker result to render
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thinker_ctx = f"Thinker response: {thought.response}"
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if thought.tool_used:
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thinker_ctx += f"\n\nTool used: {thought.tool_used}\nTool output:\n{thought.tool_output}"
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if thought.controls:
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thinker_ctx += f"\n\n(UI controls were also sent to the user: {len(thought.controls)} elements)"
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messages.append({"role": "system", "content": thinker_ctx})
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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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client, resp = await llm_call(self.model, messages, stream=True)
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full_response = ""
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try:
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async for line in resp.aiter_lines():
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if not line.startswith("data: "):
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continue
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payload = line[6:]
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if payload == "[DONE]":
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break
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chunk = json.loads(payload)
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delta = chunk["choices"][0].get("delta", {})
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token = delta.get("content", "")
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if token:
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full_response += token
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await ws.send_text(json.dumps({"type": "delta", "content": token}))
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finally:
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await resp.aclose()
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await client.aclose()
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log.info(f"[output] response: {full_response[:100]}...")
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await ws.send_text(json.dumps({"type": "done"}))
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await self.hud("done")
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return full_response
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