feat(P3): mind/mouth split — separate voice model for the final reply (seam, default off)
The mind (chat backend/model) decides, reasons, and runs tools → a draft; the mouth re-voices that draft in her character. Default: no mouth configured → the mind's draft IS the reply, bit-for-bit the old behavior (and old streaming path untouched). - config: MOUTH_BACKEND / MOUTH_MODEL. The slot for an eventual fine-tuned voice. - chat: _mind_loop (tool/generation loop, non-stream, returns draft + tools_run), _voice_pass / mind.voice_messages (re-voice the draft, keep every fact/number), _mouth_target (active only when configured AND != mind). respond + respond_stream branch: mouth off = stream the mind directly (unchanged); mouth on = mind decides + runs tools, then the mouth streams the re-voiced reply. Falls back to the draft on any mouth failure (chat never breaks). - Key payoff: the mouth needs no tool support (the mind handles tools), so it can be a non-tool character model (Dolphin / Claude / fine-tune). Makes the fine-tune easy: teach a small model to *sound* like Lyra, not to be smart. - tests: mouth target on/off, voice_messages shape, voice_pass revoice+fallback. Suite 96 green, ruff clean. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
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@@ -1,9 +1,12 @@
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"""The chat turn: assemble the prompt (lyra.mind) then speak + persist.
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`mind.assemble()` runs the society of parts (perceive → route → compose →
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deliberate) and hands back a ready message list + the active mode; `chat` runs the
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tool/generation loop (the "speak" part) and persists the exchange. Keeping speak
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here (not in mind) is deliberate — it's tangled with streaming and tool dispatch.
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deliberate) and hands back a ready message list + the active mode. Then:
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- the MIND (the chat backend/model) runs the tool/generation loop — decide,
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reason, run tools — and produces a draft.
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- the MOUTH (a separate character model, if configured) re-voices that draft in
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her own voice. Default: no mouth configured → the mind's draft IS the reply
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(bit-for-bit the old behavior). The mouth slot is where a fine-tuned voice lands.
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"""
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from __future__ import annotations
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@@ -16,6 +19,7 @@ MAX_TOOL_ROUNDS = 5 # cap tool-call iterations per turn
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# tools when launched with --jinja; until it is, keep tools to cloud so MI50 chat
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# doesn't 500 on the tools param. Add "mi50" here once that flag is set.
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TOOL_BACKENDS = {"cloud"}
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_TANGLED = "(I got tangled using my tools there — say that again?)"
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def _resolve_model(backend: Backend, model_override: str | None, cfg) -> str:
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@@ -29,15 +33,59 @@ def _resolve_model(backend: Backend, model_override: str | None, cfg) -> str:
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return model
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def _mouth_target(cfg, mind_backend: Backend, mind_model: str | None):
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"""The mouth (backend, model) if configured AND different from the mind; else None
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(mouth == mind → no separate voice pass)."""
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if not cfg.mouth_backend and not cfg.mouth_model:
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return None
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backend = cfg.mouth_backend or mind_backend
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model = cfg.mouth_model or None
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if backend == mind_backend and model == mind_model:
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return None
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return backend, model
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def _maybe_switch_mode(session_id: str, tool_name: str) -> None:
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"""Keep the chat framing aligned with the live data: opening a poker session
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auto-flips this chat into Poker mode (next turn gets the card + full live tools).
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Manual UI switching still overrides anytime."""
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"""Opening a poker session auto-flips this chat into Poker mode. Manual UI switching
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still overrides anytime."""
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if tool_name == "start_session":
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memory.set_session_mode(session_id, modes.CASH.key)
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logbus.log("info", "mode auto-switch", session=session_id, mode=modes.CASH.key)
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def _mind_loop(messages, backend: Backend, model: str | None, tool_specs,
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ctx: dict, session_id: str) -> tuple[str, list[str]]:
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"""Run the tool/generation loop on the MIND model (non-streaming). Mutates
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`messages` with tool calls/results. Returns (draft_reply, tool_names_run)."""
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tools_run: list[str] = []
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reply = ""
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for _ in range(MAX_TOOL_ROUNDS):
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assistant_msg, tool_calls = llm.chat_call(
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messages, backend=backend, model=model, tools=tool_specs
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)
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if not tool_calls:
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reply = assistant_msg.get("content") or ""
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break
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messages.append(assistant_msg)
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for tc in tool_calls:
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result = toolkit.dispatch(tc["name"], tc["arguments"], ctx)
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logbus.log("info", "tool call", session=session_id, tool=tc["name"], result=result[:80])
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messages.append({"role": "tool", "tool_call_id": tc["id"], "content": result})
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_maybe_switch_mode(session_id, tc["name"])
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tools_run.append(tc["name"])
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return reply, tools_run
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def _voice_pass(messages, draft: str, backend: Backend, model: str | None) -> str:
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"""Mouth: re-render the mind's draft in her voice. Falls back to the draft on failure."""
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try:
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out = llm.complete(mind.voice_messages(messages, draft), backend=backend, model=model)
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return (out or "").strip() or draft
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except Exception as exc:
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logbus.log("error", "voice pass failed", error=str(exc)[:160])
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return draft
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def respond(session_id: str, user_msg: str, backend: Backend = "cloud",
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model_override: str | None = None) -> str:
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"""Produce Lyra's reply to a single user message and persist the exchange."""
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@@ -48,28 +96,16 @@ def respond(session_id: str, user_msg: str, backend: Backend = "cloud",
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turn = mind.assemble(session_id, user_msg, backend, model)
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messages = turn.messages
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# Tool loop (speak): offer her tools (scoped to the mode); run any she calls and
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# feed results back until she returns a text reply.
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tool_specs = toolkit.specs(turn.mode.tools) if backend in TOOL_BACKENDS else None
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ctx = {"session_id": session_id, "backend": backend}
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reply = ""
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for _ in range(MAX_TOOL_ROUNDS):
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assistant_msg, tool_calls = llm.chat_call(
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messages, backend=backend, model=model, tools=tool_specs
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)
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if not tool_calls:
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reply = assistant_msg.get("content") or ""
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break
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messages.append(assistant_msg) # her tool-call request
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for tc in tool_calls:
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result = toolkit.dispatch(tc["name"], tc["arguments"], ctx)
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logbus.log("info", "tool call", session=session_id, tool=tc["name"], result=result[:80])
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messages.append({"role": "tool", "tool_call_id": tc["id"], "content": result})
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_maybe_switch_mode(session_id, tc["name"])
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reply, _ = _mind_loop(messages, backend, model, tool_specs, ctx, session_id)
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mouth = _mouth_target(cfg, backend, model)
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if mouth and reply:
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reply = _voice_pass(messages, reply, *mouth)
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if not reply:
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reply = "(I got tangled using my tools there — say that again?)"
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logbus.log("info", "reply", session=session_id, chars=len(reply))
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reply = _TANGLED
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logbus.log("info", "reply", session=session_id, chars=len(reply), voiced=bool(mouth))
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memory.remember(session_id, "user", user_msg)
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memory.remember(session_id, "assistant", reply)
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@@ -79,11 +115,8 @@ def respond(session_id: str, user_msg: str, backend: Backend = "cloud",
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def respond_stream(session_id: str, user_msg: str, backend: Backend = "cloud",
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model_override: str | None = None):
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"""Streaming generator version of `respond`.
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Yields ("delta", text) as content streams in, ("tool", name) when a tool runs,
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and a final ("done", reply). Persists the exchange — same side effects as `respond`.
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"""
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"""Streaming generator version of `respond`. Yields ("delta", text), ("tool", name),
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and a final ("done", reply). Same side effects as `respond`."""
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cfg = config.load()
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model = _resolve_model(backend, model_override, cfg)
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logbus.log("info", "chat request (stream)", session=session_id, backend=backend,
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@@ -93,36 +126,57 @@ def respond_stream(session_id: str, user_msg: str, backend: Backend = "cloud",
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messages = turn.messages
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tool_specs = toolkit.specs(turn.mode.tools) if backend in TOOL_BACKENDS else None
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ctx = {"session_id": session_id, "backend": backend}
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parts: list[str] = []
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for _ in range(MAX_TOOL_ROUNDS):
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assistant_msg = None
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tool_calls = None
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for ev, payload in llm.chat_call_stream(
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messages, backend=backend, model=model, tools=tool_specs
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):
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if ev == "delta":
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parts.append(payload)
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yield ("delta", payload)
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elif ev == "message":
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assistant_msg = payload
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elif ev == "tool_calls":
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tool_calls = payload
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if not tool_calls:
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break
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messages.append(assistant_msg) # her tool-call request
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for tc in tool_calls:
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result = toolkit.dispatch(tc["name"], tc["arguments"], ctx)
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logbus.log("info", "tool call", session=session_id, tool=tc["name"], result=result[:80])
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messages.append({"role": "tool", "tool_call_id": tc["id"], "content": result})
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_maybe_switch_mode(session_id, tc["name"])
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yield ("tool", tc["name"])
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mouth = _mouth_target(cfg, backend, model)
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reply = "".join(parts)
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if not reply:
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reply = "(I got tangled using my tools there — say that again?)"
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yield ("delta", reply)
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logbus.log("info", "reply", session=session_id, chars=len(reply))
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if mouth is None:
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# No separate voice: stream the mind directly (the original path, unchanged).
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parts: list[str] = []
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for _ in range(MAX_TOOL_ROUNDS):
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assistant_msg = None
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tool_calls = None
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for ev, payload in llm.chat_call_stream(
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messages, backend=backend, model=model, tools=tool_specs
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):
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if ev == "delta":
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parts.append(payload)
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yield ("delta", payload)
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elif ev == "message":
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assistant_msg = payload
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elif ev == "tool_calls":
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tool_calls = payload
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if not tool_calls:
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break
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messages.append(assistant_msg)
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for tc in tool_calls:
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result = toolkit.dispatch(tc["name"], tc["arguments"], ctx)
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logbus.log("info", "tool call", session=session_id, tool=tc["name"], result=result[:80])
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messages.append({"role": "tool", "tool_call_id": tc["id"], "content": result})
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_maybe_switch_mode(session_id, tc["name"])
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yield ("tool", tc["name"])
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reply = "".join(parts)
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if not reply:
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reply = _TANGLED
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yield ("delta", reply)
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else:
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# Mind decides + runs tools (non-streamed); mouth re-voices, streamed.
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draft, tools_run = _mind_loop(messages, backend, model, tool_specs, ctx, session_id)
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for name in tools_run:
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yield ("tool", name)
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parts = []
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try:
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for ev, payload in llm.chat_call_stream(
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mind.voice_messages(messages, draft), backend=mouth[0], model=mouth[1], tools=None
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):
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if ev == "delta":
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parts.append(payload)
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yield ("delta", payload)
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except Exception as exc:
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logbus.log("error", "voice stream failed", error=str(exc)[:160])
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reply = "".join(parts).strip() or draft or _TANGLED
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if not parts:
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yield ("delta", reply)
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logbus.log("info", "reply", session=session_id, chars=len(reply), voiced=bool(mouth))
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memory.remember(session_id, "user", user_msg)
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memory.remember(session_id, "assistant", reply)
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summary.maybe_summarize_async(session_id)
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