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pewdiepie-archdaemon
76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
"""Shared resolver for background-task AI endpoints."""
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from src.endpoint_resolver import (
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resolve_chat_fallback_candidates,
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resolve_endpoint,
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resolve_utility_fallback_candidates,
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)
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from src.llm_core import llm_call_async_with_fallback
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def resolve_task_endpoint(fallback_url=None, fallback_model=None, fallback_headers=None, owner=None):
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"""Return (endpoint_url, model, headers) for background tasks.
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Reads task_endpoint_id / task_model from admin settings.
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Falls back to the provided values when the setting is empty or the
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endpoint cannot be resolved.
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"""
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return resolve_endpoint("task", fallback_url, fallback_model, fallback_headers, owner=owner)
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def resolve_task_candidates(
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fallback_url=None,
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fallback_model=None,
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fallback_headers=None,
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owner=None,
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):
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"""Return ordered background-task LLM candidates.
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Order:
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1. configured Background Tasks endpoint/model, or caller fallback
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2. Utility endpoint/model
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3. Default endpoint/model
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4. Utility fallback chain
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5. Default fallback chain
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"""
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candidates = []
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def _append(url, model, headers):
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if not url or not model:
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return
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key = (url, model)
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if any((u, m) == key for u, m, _ in candidates):
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return
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candidates.append((url, model, headers or {}))
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_append(*resolve_task_endpoint(fallback_url, fallback_model, fallback_headers, owner=owner))
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_append(*resolve_endpoint("utility", owner=owner))
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_append(*resolve_endpoint("default", owner=owner))
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for url, model, headers in resolve_utility_fallback_candidates(owner=owner):
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_append(url, model, headers)
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for url, model, headers in resolve_chat_fallback_candidates(owner=owner):
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_append(url, model, headers)
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return candidates
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async def task_llm_call_async(
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messages,
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*,
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fallback_url=None,
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fallback_model=None,
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fallback_headers=None,
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owner=None,
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**kwargs,
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):
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"""Call the shared background-task LLM candidate chain."""
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candidates = resolve_task_candidates(
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fallback_url=fallback_url,
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fallback_model=fallback_model,
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fallback_headers=fallback_headers,
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owner=owner,
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)
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if not candidates:
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raise RuntimeError("No LLM endpoint available for background task")
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return await llm_call_async_with_fallback(candidates, messages=messages, **kwargs)
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