anti
0b9873982d
Lift the format-agnostic pieces from decnet/orchestrator/emailgen/
into the new decnet/realism/ library so file-class content generation
(stage 3 of the realism migration) can reuse them. Email-specific
delivery (RFC 2822 EML, IMAP/POP3 spool, thread chains) stays in
orchestrator/.
Renames (history-preserving git mv):
emailgen/personas.py -> realism/personas.py
emailgen/prompt.py -> realism/prompts/email.py
emailgen/global_pool.py -> realism/personas_pool.py
emailgen/llm/ -> realism/llm/
Env-var clean break (pre-v1, no aliases):
DECNET_EMAILGEN_LLM -> DECNET_REALISM_LLM
DECNET_EMAILGEN_MODEL -> DECNET_REALISM_MODEL
DECNET_EMAILGEN_TIMEOUT -> DECNET_REALISM_TIMEOUT
DECNET_EMAILGEN_PERSONAS -> DECNET_REALISM_PERSONAS
DECNET_EMAILGEN_FAKE_OUTPUT -> DECNET_REALISM_FAKE_OUTPUT
Importers rewritten in: orchestrator/emailgen/scheduler.py,
orchestrator/drivers/email.py, web/router/{emailgen,topology}/
api_personas.py, cli/emailgen.py. Tests for moved modules relocated
to tests/realism/; tests for stay-put modules updated in place.
API URL `/api/v1/emailgen/personas` and CLI `decnet emailgen
import-personas` keep their public names until the service-collapse
commit (stage 5).
48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
"""Backend protocol shared by every LLM transport.
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Deliberately narrow: realism consumers need one async ``generate``
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call that takes a prompt string and returns the model's output text
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plus enough metadata to populate per-event payloads (model name,
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latency, success bit). Streaming, embeddings, multi-turn chat — all
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out of scope here; realism only ever does one-shot single-prompt
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generations.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any, Protocol
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class LLMTimeout(Exception):
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"""Raised when a generation exceeds the backend's wall-clock cap.
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Backends MUST raise this rather than returning silently empty
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output; the driver discriminates timeout from "model produced
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nothing useful" so payloads carry the right ``stage`` value.
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"""
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@dataclass
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class LLMResult:
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"""Outcome of one ``generate`` call.
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``success`` is ``False`` when the backend ran cleanly but produced
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no usable output (e.g. an empty stdout). Hard failures (subprocess
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crash, network error) raise; soft failures land here so the driver
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can persist + log them as one event.
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"""
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success: bool
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text: str
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model: str
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latency_ms: int
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extra: dict[str, Any] = field(default_factory=dict)
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class LLMBackend(Protocol):
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"""Minimal contract for a realism LLM provider."""
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model: str
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timeout: float
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async def generate(self, prompt: str) -> LLMResult: ...
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