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https://github.com/meta-llama/llama-stack.git
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8e2675f50c
- Replace error with ibm-granite/granite-embedding-125m-english default - Based on issue #2418 for commercial compatibility and better UX - Update tests to verify default fallback behavior - Update documentation to reflect new precedence rules - Remove unused MissingEmbeddingModelError class - Update tip section to clarify fallback behavior Resolves review comment to use default instead of error.
79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from llama_stack.apis.models import ModelType
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from llama_stack.core.routers.vector_io import VectorIORouter
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class _DummyModel:
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def __init__(self, identifier: str, dim: int):
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self.identifier = identifier
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self.model_type = ModelType.embedding
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self.metadata = {"embedding_dimension": dim}
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class _DummyRoutingTable:
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"""Minimal stub satisfying the methods used by VectorIORouter in tests."""
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def __init__(self):
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self._models: list[_DummyModel] = [
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_DummyModel("first-model", 123),
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_DummyModel("second-model", 512),
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]
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async def get_all_with_type(self, _type: str):
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# Only embedding models requested in our tests
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return self._models
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# The following methods are required by the VectorIORouter signature but
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# are not used in these unit tests; stub them out.
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async def register_vector_db(self, *args, **kwargs):
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raise NotImplementedError
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async def get_provider_impl(self, *args, **kwargs):
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raise NotImplementedError
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async def test_global_default_used(monkeypatch):
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"""Router should pick up global default when no explicit model is supplied."""
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monkeypatch.setenv("LLAMA_STACK_DEFAULT_EMBEDDING_MODEL", "env-default-model")
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monkeypatch.setenv("LLAMA_STACK_DEFAULT_EMBEDDING_DIMENSION", "256")
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router = VectorIORouter(routing_table=_DummyRoutingTable())
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model, dim = await router._resolve_embedding_model(None)
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assert model == "env-default-model"
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assert dim == 256
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# Cleanup env vars
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monkeypatch.delenv("LLAMA_STACK_DEFAULT_EMBEDDING_MODEL", raising=False)
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monkeypatch.delenv("LLAMA_STACK_DEFAULT_EMBEDDING_DIMENSION", raising=False)
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async def test_explicit_override(monkeypatch):
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"""Explicit model parameter should override global default."""
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monkeypatch.setenv("LLAMA_STACK_DEFAULT_EMBEDDING_MODEL", "env-default-model")
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router = VectorIORouter(routing_table=_DummyRoutingTable())
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model, dim = await router._resolve_embedding_model("first-model")
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assert model == "first-model"
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assert dim == 123
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monkeypatch.delenv("LLAMA_STACK_DEFAULT_EMBEDDING_MODEL", raising=False)
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async def test_fallback_to_system_default():
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"""Router should use system default when neither explicit nor global default is available."""
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router = VectorIORouter(routing_table=_DummyRoutingTable())
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model, dimension = await router._resolve_embedding_model(None)
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assert model == "ibm-granite/granite-embedding-125m-english"
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assert dimension == 384
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