Netherwarlord
7a81468925
- Updated package.json to include Vitest and testing dependencies. - Created test cases for SuitePicker component to validate selection logic. - Added tests for ReportsPage to ensure correct report grouping and ordering. - Implemented read-only enforcement tests for SuitePage to prevent actions on built-in suites. - Introduced a setup file for Vitest to include jest-dom matchers and cleanup after tests. - Modified ReportsPage to group reports by model and display them accordingly. - Enhanced API types to include suite_scope and question_count for better report handling.
100 lines
3.8 KiB
Python
100 lines
3.8 KiB
Python
from __future__ import annotations
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import hashlib
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import os
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from pathlib import Path
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from typing import Dict, List, Optional
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from config import MODELS_DIR
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from engine_loader import EngineLoadError, load_engine_class
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from gguf import EMBEDDING, GENERATIVE, PROJECTOR, classify
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from .base import ModelInfo, Provider, ProviderError
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class LocalEngineProvider(Provider):
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name = "Local Engine"
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def __init__(self, search_paths: Optional[List[Path]] = None) -> None:
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try:
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runtime_cls = load_engine_class()
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self.runtime = runtime_cls()
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self.runtime.setup()
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except EngineLoadError as exc:
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raise ProviderError(str(exc)) from exc
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except RuntimeError as exc:
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raise ProviderError(str(exc)) from exc
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paths: List[Path] = [MODELS_DIR]
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env_override = os.getenv("LOCAL_LLM_PATHS")
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if env_override:
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for entry in env_override.split(os.pathsep):
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entry_clean = entry.strip()
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if not entry_clean:
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continue
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entry_path = Path(entry_clean).expanduser()
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if entry_path not in paths:
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paths.append(entry_path)
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if search_paths:
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for candidate in search_paths:
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if candidate not in paths:
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paths.append(candidate)
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self.search_paths = paths
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self._model_index: Dict[str, Path] = {}
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def list_models(self) -> List[ModelInfo]:
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discovered = self.runtime.discover_gguf_models(self.search_paths)
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self._model_index.clear()
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models: List[ModelInfo] = []
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skipped: Dict[str, int] = {}
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for path in discovered:
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# Vision projectors and embedding models are valid GGUF files that
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# cannot answer a prompt. Offering them produced an empty report
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# with no error, which read as the suite failing rather than the
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# model being the wrong kind. UNKNOWN stays listed: only positive
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# evidence should hide something.
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kind = classify(path) if path.suffix == ".gguf" else GENERATIVE
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if kind in (PROJECTOR, EMBEDDING):
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skipped[kind] = skipped.get(kind, 0) + 1
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continue
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model_id = self._register_model(path)
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models.append(ModelInfo(id=model_id, display_name=path.stem))
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if not models:
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if skipped:
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detail = ", ".join(f"{count} {kind}" for kind, count in sorted(skipped.items()))
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raise ProviderError(
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f"No runnable models were found — the only GGUF files present are "
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f"{detail}, which cannot generate text. Add a chat or instruct model."
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)
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raise ProviderError(
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"No GGUF models were found. Place models inside the 'models' directory or set LOCAL_LLM_PATHS."
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)
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return models
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def run_prompt(self, model_id: str, prompt: str, *, temperature: float) -> Dict[str, object]:
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try:
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model_path = self._model_index[model_id]
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except KeyError as exc:
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raise ProviderError(f"Model '{model_id}' is not registered. Refresh the model list and try again.") from exc
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self.runtime.load_model(model_path)
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return self.runtime.generate(prompt, temperature=temperature)
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def _register_model(self, path: Path) -> str:
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for existing_id, existing_path in self._model_index.items():
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if path == existing_path:
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return existing_id
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base_id = path.stem
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candidate = base_id
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counter = 1
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while candidate in self._model_index:
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digest = hashlib.sha1(str(path).encode("utf-8")).hexdigest()[:6]
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candidate = f"{base_id}-{digest}-{counter}"
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counter += 1
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self._model_index[candidate] = path
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return candidate
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