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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 ```
136 lines
4.3 KiB
Python
136 lines
4.3 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 os
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import tempfile
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import pytest
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import pytest_asyncio
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from llama_stack.apis.inference import ModelInput, ModelType
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from llama_stack.distribution.datatypes import Api, Provider
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from llama_stack.providers.inline.memory.chroma import ChromaInlineImplConfig
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from llama_stack.providers.inline.memory.faiss import FaissImplConfig
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from llama_stack.providers.remote.memory.chroma import ChromaRemoteImplConfig
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from llama_stack.providers.remote.memory.pgvector import PGVectorConfig
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from llama_stack.providers.remote.memory.weaviate import WeaviateConfig
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from llama_stack.providers.tests.resolver import construct_stack_for_test
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from llama_stack.providers.utils.kvstore import SqliteKVStoreConfig
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from ..conftest import ProviderFixture, remote_stack_fixture
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from ..env import get_env_or_fail
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@pytest.fixture(scope="session")
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def memory_remote() -> ProviderFixture:
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return remote_stack_fixture()
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@pytest.fixture(scope="session")
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def memory_faiss() -> ProviderFixture:
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temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".db")
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return ProviderFixture(
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providers=[
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Provider(
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provider_id="faiss",
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provider_type="inline::faiss",
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config=FaissImplConfig(
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kvstore=SqliteKVStoreConfig(db_path=temp_file.name).model_dump(),
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).model_dump(),
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)
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],
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)
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@pytest.fixture(scope="session")
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def memory_pgvector() -> ProviderFixture:
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return ProviderFixture(
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providers=[
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Provider(
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provider_id="pgvector",
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provider_type="remote::pgvector",
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config=PGVectorConfig(
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host=os.getenv("PGVECTOR_HOST", "localhost"),
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port=os.getenv("PGVECTOR_PORT", 5432),
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db=get_env_or_fail("PGVECTOR_DB"),
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user=get_env_or_fail("PGVECTOR_USER"),
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password=get_env_or_fail("PGVECTOR_PASSWORD"),
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).model_dump(),
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)
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],
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)
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@pytest.fixture(scope="session")
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def memory_weaviate() -> ProviderFixture:
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return ProviderFixture(
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providers=[
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Provider(
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provider_id="weaviate",
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provider_type="remote::weaviate",
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config=WeaviateConfig().model_dump(),
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)
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],
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provider_data=dict(
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weaviate_api_key=get_env_or_fail("WEAVIATE_API_KEY"),
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weaviate_cluster_url=get_env_or_fail("WEAVIATE_CLUSTER_URL"),
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),
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)
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@pytest.fixture(scope="session")
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def memory_chroma() -> ProviderFixture:
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url = os.getenv("CHROMA_URL")
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if url:
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config = ChromaRemoteImplConfig(url=url)
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provider_type = "remote::chromadb"
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else:
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if not os.getenv("CHROMA_DB_PATH"):
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raise ValueError("CHROMA_DB_PATH or CHROMA_URL must be set")
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config = ChromaInlineImplConfig(db_path=os.getenv("CHROMA_DB_PATH"))
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provider_type = "inline::chromadb"
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return ProviderFixture(
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providers=[
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Provider(
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provider_id="chroma",
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provider_type=provider_type,
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config=config.model_dump(),
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)
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]
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)
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MEMORY_FIXTURES = ["faiss", "pgvector", "weaviate", "remote", "chroma"]
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@pytest_asyncio.fixture(scope="session")
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async def memory_stack(embedding_model, request):
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fixture_dict = request.param
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providers = {}
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provider_data = {}
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for key in ["inference", "memory"]:
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fixture = request.getfixturevalue(f"{key}_{fixture_dict[key]}")
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providers[key] = fixture.providers
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if fixture.provider_data:
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provider_data.update(fixture.provider_data)
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test_stack = await construct_stack_for_test(
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[Api.memory, Api.inference],
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providers,
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provider_data,
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models=[
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ModelInput(
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model_id=embedding_model,
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model_type=ModelType.embedding_model,
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metadata={
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"embedding_dimension": get_env_or_fail("EMBEDDING_DIMENSION"),
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},
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)
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],
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)
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return test_stack.impls[Api.memory], test_stack.impls[Api.memory_banks]
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