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https://github.com/meta-llama/llama-stack.git
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f98efe68c9
- Make sure torch + torchvision go together as deps, otherwise bad stuff happens - Add a pre-commit for requirements.txt
116 lines
4.5 KiB
Python
116 lines
4.5 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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from typing import List
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from llama_stack.providers.datatypes import (
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AdapterSpec,
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Api,
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InlineProviderSpec,
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ProviderSpec,
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remote_provider_spec,
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)
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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 torchvision --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.vector_io,
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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.vector_io.faiss",
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config_class="llama_stack.providers.inline.vector_io.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.vector_io,
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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.vector_io.faiss",
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config_class="llama_stack.providers.inline.vector_io.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.vector_io,
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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.vector_io.chroma",
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config_class="llama_stack.providers.remote.vector_io.chroma.ChromaRemoteImplConfig",
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),
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api_dependencies=[Api.inference],
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),
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InlineProviderSpec(
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api=Api.vector_io,
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provider_type="inline::chromadb",
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pip_packages=EMBEDDING_DEPS + ["chromadb"],
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module="llama_stack.providers.inline.vector_io.chroma",
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config_class="llama_stack.providers.inline.vector_io.chroma.ChromaInlineImplConfig",
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api_dependencies=[Api.inference],
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),
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remote_provider_spec(
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Api.vector_io,
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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.vector_io.pgvector",
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config_class="llama_stack.providers.remote.vector_io.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.vector_io,
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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.vector_io.weaviate",
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config_class="llama_stack.providers.remote.vector_io.weaviate.WeaviateConfig",
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provider_data_validator="llama_stack.providers.remote.vector_io.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.vector_io,
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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.vector_io.sample",
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config_class="llama_stack.providers.remote.vector_io.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.vector_io,
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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.vector_io.qdrant",
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config_class="llama_stack.providers.remote.vector_io.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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