61 lines
2.4 KiB
Python
61 lines
2.4 KiB
Python
"""Backend dispatch.
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Reads ``DECNET_REALISM_LLM`` to pick a concrete :class:`LLMBackend`.
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Defaults to ``ollama`` because that's what the prototype proved out and
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what most dev boxes have on hand.
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Supported keys:
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* ``ollama`` — :class:`decnet.realism.llm.impl.ollama.OllamaBackend`
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* ``fake`` — :class:`decnet.realism.llm.impl.fake.FakeBackend`
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(canned output, used by tests so they don't shell out)
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Anthropic / vLLM / llama.cpp slots in here as a third branch when the
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need shows up. Per the provider-subpackages convention, do NOT collapse
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factory dispatch into the impl modules — keeps the ``__init__`` import
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graph cycle-free and the env contract auditable in one place.
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"""
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from __future__ import annotations
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import os
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from typing import Any
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from decnet.realism.llm.base import LLMBackend
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def get_llm(*, model: str | None = None, **kwargs: Any) -> LLMBackend:
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"""Instantiate the LLM backend selected by DB config or environment.
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Resolution order:
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1. Process-level cached backend (populated by the DB config row via
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:func:`decnet.realism.llm.config.apply`). Returned as-is when
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*model* and *kwargs* are both absent — the common case.
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2. Env-var path (``DECNET_REALISM_LLM`` / ``DECNET_REALISM_MODEL`` /
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``DECNET_REALISM_TIMEOUT``) — legacy / default-install fallback.
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*model* (when provided) overrides whatever the backend's own default
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is — e.g. for :class:`OllamaBackend` that's ``llama3.1`` unless
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``DECNET_REALISM_MODEL`` says otherwise. Lets the worker honour
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``decnet orchestrate --model gpt-oss`` without each backend having
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to know about CLI flags.
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"""
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# Fast path: DB-configured cached backend.
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if model is None and not kwargs:
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from decnet.realism.llm.config import get_cached_backend
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cached = get_cached_backend()
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if cached is not None:
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return cached
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backend_key = os.environ.get("DECNET_REALISM_LLM", "ollama").lower()
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if backend_key == "ollama":
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from decnet.realism.llm.impl.ollama import OllamaBackend
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return OllamaBackend(model=model, **kwargs)
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if backend_key == "fake":
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from decnet.realism.llm.impl.fake import FakeBackend
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return FakeBackend(model=model or "fake-model", **kwargs)
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raise ValueError(
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f"Unsupported DECNET_REALISM_LLM={backend_key!r}; "
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"expected one of: ollama, fake"
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)
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