feat: separate CHAT_MODEL (gpt-4o) for persona fidelity
Mid-size models (gpt-4o-mini, qwen2.5-14b) resist persona instructions — help-desk closers and feelings-disclaimers leak through regardless. Route live chat to a stronger model while keeping bulk consolidation cheap: - config: CHAT_MODEL (default gpt-4o), distinct from CLOUD_MODEL (gpt-4o-mini) - llm.complete gains a `model` override; chat.respond uses chat_model on cloud, consolidation paths keep cloud_model - persona: reword the "no sign-off" rule so genuine questions are welcome and only reflexive customer-service closers are discouraged Verified: on gpt-4o she owns her mood without disclaimers and drops most help-desk tails — clearly more in-character than mini/qwen. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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@@ -17,24 +17,26 @@ class Message(TypedDict):
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Backend = Literal["local", "cloud", "mi50"]
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def complete(messages: list[Message], backend: Backend = "local") -> str:
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def complete(messages: list[Message], backend: Backend = "local", model: str | None = None) -> str:
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"""Generate a completion. `model` overrides the backend's default model
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(used so live chat can run a stronger cloud model than bulk consolidation)."""
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cfg = load()
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if backend == "cloud":
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if not cfg.openai_api_key:
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raise RuntimeError("OPENAI_API_KEY is not set")
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client = OpenAI(api_key=cfg.openai_api_key)
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resp = client.chat.completions.create(model=cfg.cloud_model, messages=messages)
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resp = client.chat.completions.create(model=model or cfg.cloud_model, messages=messages)
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return resp.choices[0].message.content or ""
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if backend == "mi50":
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# MI50 box runs an OpenAI-compatible llama.cpp server; key is unused.
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client = OpenAI(api_key="not-needed", base_url=cfg.mi50_base_url)
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resp = client.chat.completions.create(model=cfg.mi50_model, messages=messages)
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resp = client.chat.completions.create(model=model or cfg.mi50_model, messages=messages)
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return resp.choices[0].message.content or ""
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resp = httpx.post(
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f"{cfg.local_base_url}/api/chat",
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json={"model": cfg.local_model, "messages": messages, "stream": False},
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json={"model": model or cfg.local_model, "messages": messages, "stream": False},
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timeout=120,
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)
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resp.raise_for_status()
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