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https://github.com/meta-llama/llama-stack.git
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d362d2d740
This PR adds the ability to generate embeddings in all supported inference providers. ``` pytest -v -s llama_stack/providers/tests/inference/test_embeddings.py -k "bedrock" --inference-model="amazon.titan-embed-text-v2:0" --env EMBEDDING_DIMENSION=1024 pytest -v -s -k "vllm" --inferrence-model="intfloat/e5-mistral-7b-instruct" llama_stack/providers/tests/inference/test_embeddings.py --env EMBEDDING_DIMENSION=4096 --env VLLM_URL="http://localhost:9798/v1" pytest -v -s --inference-model="nomic-ai/nomic-embed-text-v1.5" llama_stack/providers/tests/inference/test_embeddings.py -k "fireworks" --env FIREWORKS_API_KEY=<API_KEY>--env EMBEDDING_DIMENSION=128 pytest -v -s --inference-model="togethercomputer/m2-bert-80M-2k-retrieval" llama_stack/providers/tests/inference/test_embeddings.py -k "together" --env TOGETHER_API_KEY=<API_KEY>--env EMBEDDING_DIMENSION=768 pytest -v -s -k "ollama" --inference-model="all-minilm:v8" llama_stack/providers/tests/inference/test_embeddings.py --env EMBEDDING_DIMENSION=384 torchrun $CONDA_PREFIX/bin/pytest -v -s -k "meta_reference" --inference-model="sentence-transformers/all-MiniLM-L6-v2" llama_stack/providers/tests/inference/test_embeddings.py --env EMBEDDING_DIMENSION=384 ```
95 lines
2.5 KiB
Python
95 lines
2.5 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 get_provider_fixture_overrides
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from ..inference.fixtures import INFERENCE_FIXTURES
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from .fixtures import MEMORY_FIXTURES
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DEFAULT_PROVIDER_COMBINATIONS = [
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pytest.param(
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{
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"inference": "meta_reference",
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"memory": "faiss",
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},
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id="meta_reference",
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marks=pytest.mark.meta_reference,
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),
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pytest.param(
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{
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"inference": "ollama",
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"memory": "pgvector",
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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": "together",
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"memory": "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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"memory": "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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"memory": "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_addoption(parser):
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parser.addoption(
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"--embedding-model",
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action="store",
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default=None,
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help="Specify the embedding model to use for testing",
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)
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def pytest_configure(config):
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for fixture_name in MEMORY_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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if "embedding_model" in metafunc.fixturenames:
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model = metafunc.config.getoption("--embedding-model")
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if not model:
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raise ValueError(
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"No embedding model specified. Please provide a valid embedding model."
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)
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params = [pytest.param(model, id="")]
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metafunc.parametrize("embedding_model", params, indirect=True)
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if "memory_stack" in metafunc.fixturenames:
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available_fixtures = {
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"inference": INFERENCE_FIXTURES,
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"memory": MEMORY_FIXTURES,
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}
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combinations = (
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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("memory_stack", combinations, indirect=True)
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