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63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
"""Normalized embedding configuration resolved from the model catalog."""
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from __future__ import annotations
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from dataclasses import dataclass
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from deeptutor.services.config import resolve_embedding_runtime_config
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@dataclass
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class EmbeddingConfig:
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"""Embedding runtime configuration."""
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model: str
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api_key: str
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base_url: str | None = None
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effective_url: str | None = None
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binding: str = "openai"
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provider_name: str = "openai"
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provider_mode: str = "standard"
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api_version: str | None = None
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extra_headers: dict[str, str] | None = None
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dim: int = 0
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send_dimensions: bool | None = None
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request_timeout: int = 60
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batch_size: int = 10
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batch_delay: float = 0.0
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def get_embedding_config() -> EmbeddingConfig:
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"""Load embedding config from provider runtime resolver."""
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resolved = resolve_embedding_runtime_config()
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if not resolved.model:
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raise ValueError("Embedding model not set. Please configure it in Settings > Catalog.")
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if not resolved.effective_url:
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raise ValueError(
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"No effective embedding endpoint resolved. Please configure base_url/host for the active profile."
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)
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if resolved.provider_mode != "local" and not resolved.api_key:
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raise ValueError(
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"Embedding API key not set. Please configure the active profile in Settings > Catalog."
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)
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return EmbeddingConfig(
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model=resolved.model,
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api_key=resolved.api_key,
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base_url=resolved.base_url,
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effective_url=resolved.effective_url,
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binding=resolved.binding,
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provider_name=resolved.provider_name,
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provider_mode=resolved.provider_mode,
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api_version=resolved.api_version,
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extra_headers=resolved.extra_headers,
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dim=resolved.dimension,
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send_dimensions=resolved.send_dimensions,
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request_timeout=max(1, resolved.request_timeout),
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batch_size=max(1, resolved.batch_size),
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batch_delay=max(0.0, resolved.batch_delay),
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)
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