wehub-resource-sync
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107 lines
3.9 KiB
Python
107 lines
3.9 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Shared constants for skillspector (env-driven where applicable)."""
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import logging
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import os
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from skillspector.providers import get_metadata_provider
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logger = logging.getLogger(__name__)
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# % of model's max tokens used for input. 1-MAX_INPUT_TOKENS_PCT is used for output.
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MAX_INPUT_TOKENS_PCT = 0.75
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# Fallback context length when no metadata API or registry entry is available.
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DEFAULT_CONTEXT_LENGTH = 128_000
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# Risk score threshold above which a scan is treated as unsafe.
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RISK_THRESHOLD = 50
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# Default-model selection lives on each provider (see providers/<name>/provider.py
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# for ``DEFAULT_MODEL`` and ``SLOT_DEFAULTS``). The active provider's
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# ``resolve_model`` runs the waterfall: ``SKILLSPECTOR_MODEL`` env > slot
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# default > general default. OSS users pointing at build.nvidia.com or
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# stock OpenAI inherit ``NvBuildProvider``'s default model automatically.
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_provider = get_metadata_provider()
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# Exposed for analyzers that need a final fallback symbol (e.g.,
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# ``model = state.model or MODEL_CONFIG[ANALYZER_ID] or _SKILLSPECTOR_DEFAULT_MODEL``).
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_SKILLSPECTOR_DEFAULT_MODEL = _provider.DEFAULT_MODEL
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_MODEL_SLOTS: tuple[str, ...] = (
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"default",
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"mcp_least_privilege",
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"mcp_rug_pull",
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"mcp_tool_poisoning",
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"semantic_developer_intent",
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"semantic_quality_policy",
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"semantic_security_discovery",
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"meta_analyzer",
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)
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def _resolve_slot_model(slot: str) -> str:
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"""Resolve the model for *slot* with per-slot env var override support.
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Precedence: ``SKILLSPECTOR_MODEL_{SLOT}`` env var > provider
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``resolve_model(slot)`` (which itself runs ``SKILLSPECTOR_MODEL`` env >
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provider slot default > provider ``DEFAULT_MODEL``).
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"""
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env_key = f"SKILLSPECTOR_MODEL_{slot.upper()}"
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env_val = os.environ.get(env_key, "").strip()
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if env_val:
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return env_val
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return _provider.resolve_model(slot)
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MODEL_CONFIG: dict[str, str] = {slot: _resolve_slot_model(slot) for slot in _MODEL_SLOTS}
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def _validate_model_config() -> None:
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"""Warn about models not found in the provider's model registry.
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When ``SKILLSPECTOR_STRICT_MODEL_VALIDATION=true``, raises
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``ValueError`` instead of logging warnings.
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"""
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unknown: list[str] = []
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for slot, model in MODEL_CONFIG.items():
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ctx = _provider.get_context_length(model) # type: ignore[attr-defined]
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if ctx is None:
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unknown.append(f" {slot}: {model}")
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logger.warning(
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"Model '%s' (slot: %s) not found in model_registry.yaml. "
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"Using fallback context length (%d). Token budgeting may be "
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"inaccurate — add the model to the registry or verify the "
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"model ID.",
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model,
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slot,
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DEFAULT_CONTEXT_LENGTH,
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)
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strict = os.environ.get("SKILLSPECTOR_STRICT_MODEL_VALIDATION", "").lower() == "true"
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if strict and unknown:
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raise ValueError(
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"Strict model validation enabled. Unknown models:\n"
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+ "\n".join(unknown)
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+ "\nAdd them to model_registry.yaml or disable "
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"SKILLSPECTOR_STRICT_MODEL_VALIDATION."
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)
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_validate_model_config()
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# Log level: from env or fallback (DEBUG, INFO, WARNING, ERROR).
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SKILLSPECTOR_LOG_LEVEL = os.environ.get("SKILLSPECTOR_LOG_LEVEL", "WARNING")
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