74d1a284eb* Studio: hide infra models from the hub cached inventory The hub inventory scans behind /api/hub/cached-gguf and /api/hub/cached-models returned the llama.cpp install validation probe (ggml-org/models) and the RAG embedder (unsloth/bge-small-en-v1.5[-GGUF]) as on-device models. Share the hidden-model check from routes/models.py via utils/models/hidden_models.py and apply it in both scans. A GGUF infra repo stays visible when the user explicitly downloaded a variant through the Hub, since variant manifests only exist for user-initiated downloads. * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Studio: make On Device trust the hub inventory, match repo ids exactly, lighten the hidden-model import Follow-up on the hub cached-inventory hidden-model change, addressing the review. On Device now trusts the Hub inventory API for cached rows. The backend already hides the RAG embedder and the llama.cpp probe and re-includes a GGUF infra repo once the user downloads a variant through the Hub, but the frontend was re-hiding it by repo id, so the user-downloaded variant never appeared in the On Device list or the count. isVisibleInventoryRow now short-circuits cached rows (kind === "cache") to visible and keeps client-side needle hiding only for local filesystem rows and Discover. is_hidden_model matches Hub repo ids exactly (case-insensitive) against the probe plus the effective embedder and its GGUF companion, instead of substring matching the configured-embedder basename. A custom embedder with a generic basename like org/model no longer hides unrelated cached repos such as user/model-chat or org/model-instruct. The probe filename and local-path embedders keep exact matching. The helper moves to utils/hidden_models.py and is imported at module scope in the hub cache scanner, so it no longer pulls in utils/models/__init__ (the eager model-config/checkpoint stack) and a broken import fails at startup instead of being swallowed per-repo and silently emptying the inventory. routes.models keeps the _is_hidden_model and _safe_resolve aliases and drops the unused _HF_REPO_ID_RE re-export that was failing source lint. Tests: exact repo-id matching with a custom embedder, the cached-models scan keeping an unrelated repo, and a clean-interpreter check that the helper imports without the model-config stack. * Studio: match the llama.cpp probe filename on both path separators The hidden-model check compared the probe's on-disk filename with Path(value).name, which on a POSIX interpreter does not split a Windows-style path ("...\stories260K.gguf") and would let the probe through. Split on both separators so the probe is matched regardless of which OS produced the path, matching the tolerance of the previous substring check. Adds a Windows-path assertion to the probe test. * Studio: harden hidden infra model handling * Fix hidden cache row confirmation * Fix hidden local rows and confirmed hint merges * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Handle snapshot-configured hidden models * Hide basename-only default embedders * Fix dynamic embedder inventory filtering * Studio: hide the configured RAG embedder from Discover and feed rows --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: danielhanchen <unslothshared@gmail.com> Co-authored-by: Daniel Han <23090290+danielhanchen@users.noreply.github.com>
62 lines
2.4 KiB
Python
62 lines
2.4 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""Test for the customizable RAG embedding model: a saved override becomes the
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effective model and derives its GGUF companion for the llama-server backend."""
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from pathlib import Path
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import sys
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import types as _types
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_BACKEND_DIR = str(Path(__file__).resolve().parent.parent)
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if _BACKEND_DIR not in sys.path:
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sys.path.insert(0, _BACKEND_DIR)
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_loggers_stub = _types.ModuleType("loggers")
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_loggers_stub.get_logger = lambda name: __import__("logging").getLogger(name)
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sys.modules.setdefault("loggers", _loggers_stub)
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import pytest
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import utils.embedding_model_settings as ems
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from core.rag import config as rag_config
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@pytest.fixture
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def settings_store(monkeypatch):
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"""In-memory app_settings store patched under the module's lazy imports."""
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import storage.studio_db as studio_db
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store: dict = {}
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monkeypatch.setattr(
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studio_db, "get_app_setting", lambda key, fallback = None: store.get(key, fallback)
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)
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monkeypatch.setattr(
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studio_db, "upsert_app_settings", lambda settings: store.update(settings) or store
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)
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ems._invalidate_cache()
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yield store
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ems._invalidate_cache()
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def test_custom_model_overrides_default_and_derives_gguf(settings_store, monkeypatch):
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"""The core contract: with nothing stored the default is in effect; a saved
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custom model becomes the effective embedding model and derives its -GGUF
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companion (what the llama-server backend loads); reset clears the override."""
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monkeypatch.delenv("RAG_EMBED_GGUF_REPO", raising = False)
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assert ems.get_rag_embedding_model() == rag_config.EMBEDDING_MODEL
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assert rag_config.effective_gguf_repo() == rag_config.EMBED_GGUF_REPO
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assert ems.set_rag_embedding_model(" org/my-embedder ") == "org/my-embedder"
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assert rag_config.effective_embedding_model() == "org/my-embedder"
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assert rag_config.effective_gguf_repo() == "org/my-embedder-GGUF"
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assert ems.reset_rag_embedding_model() == rag_config.EMBEDDING_MODEL
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assert ems.get_stored_embedding_model() is None
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def test_env_default_derives_its_gguf_companion(monkeypatch):
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monkeypatch.delenv("RAG_EMBED_GGUF_REPO", raising = False)
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monkeypatch.setattr(rag_config, "EMBEDDING_MODEL", "org/env-default-embedder")
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assert rag_config.default_gguf_repo() == "org/env-default-embedder-GGUF"
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