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4f8b73b9e1
# What does this PR do? Moves all the memory providers to use the inference API and improved the memory tests to setup the inference stack correctly and use the embedding models ## Test Plan torchrun $CONDA_PREFIX/bin/pytest -v -s -k "meta_reference" --inference-model="Llama3.2-3B-Instruct" --embedding-model="sentence-transformers/all-MiniLM-L6-v2" llama_stack/providers/tests/inference/test_embeddings.py --env EMBEDDING_DIMENSION=384 pytest -v -s llama_stack/providers/tests/memory/test_memory.py --providers="inference=together,memory=weaviate" --embedding-model="togethercomputer/m2-bert-80M-2k-retrieval" --env EMBEDDING_DIMENSION=768 --env TOGETHER_API_KEY=<API-KEY> --env WEAVIATE_API_KEY=foo --env WEAVIATE_CLUSTER_URL=bar pytest -v -s llama_stack/providers/tests/memory/test_memory.py --providers="inference=together,memory=chroma" --embedding-model="togethercomputer/m2-bert-80M-2k-retrieval" --env EMBEDDING_DIMENSION=768 --env TOGETHER_API_KEY=<API-KEY>--env CHROMA_HOST=localhost --env CHROMA_PORT=8000 pytest -v -s llama_stack/providers/tests/memory/test_memory.py --providers="inference=together,memory=pgvector" --embedding-model="togethercomputer/m2-bert-80M-2k-retrieval" --env PGVECTOR_DB=postgres --env PGVECTOR_USER=postgres --env PGVECTOR_PASSWORD=mysecretpassword --env PGVECTOR_HOST=0.0.0.0 --env EMBEDDING_DIMENSION=768 --env TOGETHER_API_KEY=<API-KEY> pytest -v -s llama_stack/providers/tests/memory/test_memory.py --providers="inference=together,memory=faiss" --embedding-model="togethercomputer/m2-bert-80M-2k-retrieval" --env EMBEDDING_DIMENSION=768 --env TOGETHER_API_KEY=<API-KEY>
103 lines
3.9 KiB
Python
103 lines
3.9 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 typing import List
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from llama_stack.distribution.datatypes import * # noqa: F403
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EMBEDDING_DEPS = [
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"blobfile",
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"chardet",
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"pypdf",
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"tqdm",
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"numpy",
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"scikit-learn",
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"scipy",
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"nltk",
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"sentencepiece",
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"transformers",
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# this happens to work because special dependencies are always installed last
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# so if there was a regular torch installed first, this would be ignored
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# we need a better way to do this to identify potential conflicts, etc.
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# for now, this lets us significantly reduce the size of the container which
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# does not have any "local" inference code (and hence does not need GPU-enabled torch)
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"torch --index-url https://download.pytorch.org/whl/cpu",
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"sentence-transformers --no-deps",
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]
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def available_providers() -> List[ProviderSpec]:
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return [
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InlineProviderSpec(
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api=Api.memory,
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provider_type="inline::meta-reference",
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pip_packages=EMBEDDING_DEPS + ["faiss-cpu"],
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module="llama_stack.providers.inline.memory.faiss",
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config_class="llama_stack.providers.inline.memory.faiss.FaissImplConfig",
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deprecation_warning="Please use the `inline::faiss` provider instead.",
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api_dependencies=[Api.inference],
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),
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InlineProviderSpec(
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api=Api.memory,
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provider_type="inline::faiss",
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pip_packages=EMBEDDING_DEPS + ["faiss-cpu"],
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module="llama_stack.providers.inline.memory.faiss",
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config_class="llama_stack.providers.inline.memory.faiss.FaissImplConfig",
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api_dependencies=[Api.inference],
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),
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remote_provider_spec(
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Api.memory,
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AdapterSpec(
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adapter_type="chromadb",
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pip_packages=EMBEDDING_DEPS + ["chromadb-client"],
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module="llama_stack.providers.remote.memory.chroma",
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config_class="llama_stack.distribution.datatypes.RemoteProviderConfig",
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),
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api_dependencies=[Api.inference],
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),
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remote_provider_spec(
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Api.memory,
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AdapterSpec(
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adapter_type="pgvector",
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pip_packages=EMBEDDING_DEPS + ["psycopg2-binary"],
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module="llama_stack.providers.remote.memory.pgvector",
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config_class="llama_stack.providers.remote.memory.pgvector.PGVectorConfig",
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),
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api_dependencies=[Api.inference],
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),
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remote_provider_spec(
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Api.memory,
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AdapterSpec(
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adapter_type="weaviate",
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pip_packages=EMBEDDING_DEPS + ["weaviate-client"],
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module="llama_stack.providers.remote.memory.weaviate",
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config_class="llama_stack.providers.remote.memory.weaviate.WeaviateConfig",
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provider_data_validator="llama_stack.providers.remote.memory.weaviate.WeaviateRequestProviderData",
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),
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api_dependencies=[Api.inference],
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),
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remote_provider_spec(
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api=Api.memory,
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adapter=AdapterSpec(
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adapter_type="sample",
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pip_packages=[],
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module="llama_stack.providers.remote.memory.sample",
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config_class="llama_stack.providers.remote.memory.sample.SampleConfig",
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),
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api_dependencies=[],
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),
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remote_provider_spec(
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Api.memory,
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AdapterSpec(
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adapter_type="qdrant",
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pip_packages=EMBEDDING_DEPS + ["qdrant-client"],
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module="llama_stack.providers.remote.memory.qdrant",
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config_class="llama_stack.providers.remote.memory.qdrant.QdrantConfig",
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),
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api_dependencies=[Api.inference],
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),
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]
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