forked from phoenix-oss/llama-stack-mirror
992f865b2e
`EMBEDDING_DEPS` were wrongly associated with `vector_io` providers. They are needed by https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/utils/memory/vector_store.py#L142 and related code and is used by the RAG tool and as such should only be needed by the `inline::rag-runtime` provider.
113 lines
4.6 KiB
Python
113 lines
4.6 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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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=["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.FaissVectorIOConfig",
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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=["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.FaissVectorIOConfig",
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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::sqlite-vec",
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pip_packages=["sqlite-vec"],
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module="llama_stack.providers.inline.vector_io.sqlite_vec",
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config_class="llama_stack.providers.inline.vector_io.sqlite_vec.SQLiteVectorIOConfig",
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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::sqlite_vec",
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pip_packages=["sqlite-vec"],
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module="llama_stack.providers.inline.vector_io.sqlite_vec",
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config_class="llama_stack.providers.inline.vector_io.sqlite_vec.SQLiteVectorIOConfig",
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deprecation_warning="Please use the `inline::sqlite-vec` provider (notice the hyphen instead of underscore) instead.",
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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=["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.ChromaVectorIOConfig",
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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=["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.ChromaVectorIOConfig",
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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=["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.PGVectorVectorIOConfig",
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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=["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.WeaviateVectorIOConfig",
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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.SampleVectorIOConfig",
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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=["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.QdrantVectorIOConfig",
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),
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api_dependencies=[Api.inference],
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),
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]
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