forked from phoenix-oss/llama-stack-mirror
78a481bb22
See https://github.com/meta-llama/llama-stack/issues/827 for the broader design. Second part: - updates routing table / router code - updates the faiss implementation ## Test Plan ``` pytest -s -v -k sentence test_vector_io.py --env EMBEDDING_DIMENSION=384 ```
96 lines
2.7 KiB
Python
96 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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import pytest
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from ..conftest import (
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get_provider_fixture_overrides,
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get_provider_fixture_overrides_from_test_config,
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get_test_config_for_api,
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)
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from ..inference.fixtures import INFERENCE_FIXTURES
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from .fixtures import VECTOR_IO_FIXTURES
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DEFAULT_PROVIDER_COMBINATIONS = [
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pytest.param(
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{
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"inference": "sentence_transformers",
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"vector_io": "faiss",
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},
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id="sentence_transformers",
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marks=pytest.mark.sentence_transformers,
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),
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pytest.param(
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{
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"inference": "ollama",
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"vector_io": "faiss",
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},
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id="ollama",
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marks=pytest.mark.ollama,
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),
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pytest.param(
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{
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"inference": "sentence_transformers",
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"vector_io": "chroma",
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},
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id="chroma",
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marks=pytest.mark.chroma,
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),
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pytest.param(
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{
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"inference": "bedrock",
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"vector_io": "qdrant",
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},
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id="qdrant",
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marks=pytest.mark.qdrant,
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),
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pytest.param(
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{
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"inference": "fireworks",
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"vector_io": "weaviate",
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},
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id="weaviate",
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marks=pytest.mark.weaviate,
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),
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]
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def pytest_configure(config):
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for fixture_name in VECTOR_IO_FIXTURES:
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config.addinivalue_line(
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"markers",
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f"{fixture_name}: marks tests as {fixture_name} specific",
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)
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def pytest_generate_tests(metafunc):
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test_config = get_test_config_for_api(metafunc.config, "vector_io")
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if "embedding_model" in metafunc.fixturenames:
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model = getattr(test_config, "embedding_model", None)
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# Fall back to the default if not specified by the config file
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model = model or metafunc.config.getoption("--embedding-model")
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if model:
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params = [pytest.param(model, id="")]
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else:
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params = [pytest.param("all-MiniLM-L6-v2", id="")]
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metafunc.parametrize("embedding_model", params, indirect=True)
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if "vector_io_stack" in metafunc.fixturenames:
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available_fixtures = {
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"inference": INFERENCE_FIXTURES,
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"vector_io": VECTOR_IO_FIXTURES,
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}
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combinations = (
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get_provider_fixture_overrides_from_test_config(
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metafunc.config, "vector_io", DEFAULT_PROVIDER_COMBINATIONS
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)
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or get_provider_fixture_overrides(metafunc.config, available_fixtures)
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or DEFAULT_PROVIDER_COMBINATIONS
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)
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metafunc.parametrize("vector_io_stack", combinations, indirect=True)
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