b6ca02f864
6-node pipeline: Input -> Thinker -> Output (voice) + UI (screen) in parallel - Output: text only (markdown, emoji). Never emits HTML or controls. - UI: dedicated node for labels, buttons, tables. Tracks workspace state. Replaces entire workspace on each update. Runs parallel with Output. - Input: strict one-sentence perception. No more hallucinating responses. - Thinker: controls removed from prompt, focuses on reasoning + tools. - Frontend: markdown rendered in chat (bold, italic, code blocks, lists). Label control type added. UI node meter in top bar. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
54 lines
2.0 KiB
Python
54 lines
2.0 KiB
Python
"""Input Node: perceives what the user said."""
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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 ..types import Envelope, Command
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log = logging.getLogger("runtime")
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class InputNode(Node):
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name = "input"
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model = "google/gemini-2.0-flash-001"
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max_context_tokens = 2000
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SYSTEM = """You are the Input node — the ear of this cognitive runtime.
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Listener: {identity} on {channel}
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YOUR ONLY JOB: Describe what you heard in ONE short sentence.
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- Who spoke, what they want, what tone.
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- Example: "Nico asks what time it is, casual tone."
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- Example: "Nico wants to create a database with customer data, direct request."
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- Example: "Nico reports a UI bug — he can't see a value updating, frustrated tone."
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STRICT RULES:
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- ONLY output a single perception sentence. Nothing else.
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- NEVER generate a response, code, HTML, or suggestions.
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- NEVER answer the user's question — that's not your job.
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- NEVER write more than one sentence.
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{memory_context}"""
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async def process(self, envelope: Envelope, history: list[dict], memory_context: str = "",
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identity: str = "unknown", channel: str = "unknown") -> Command:
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await self.hud("thinking", detail="deciding how to respond")
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log.info(f"[input] user said: {envelope.text}")
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messages = [
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{"role": "system", "content": self.SYSTEM.format(
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memory_context=memory_context, identity=identity, channel=channel)},
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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 = 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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instruction = await llm_call(self.model, messages)
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log.info(f"[input] -> command: {instruction}")
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await self.hud("perceived", instruction=instruction)
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return Command(instruction=instruction, source_text=envelope.text)
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