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refactor: convert providers to be installed via package

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currently providers have a `pip_package` list. Rather than make our own form of python dependency management, we should use `pyproject.toml` files in each provider declaring the dependencies in a more trackable manner.
Each provider can then be installed using the already in place `module` field in the ProviderSpec, pointing to the directory the provider lives in
we can then simply `uv pip install` this directory as opposed to installing the dependencies one by one

Signed-off-by: Charlie Doern <cdoern@redhat.com>
This commit is contained in:
Charlie Doern 2025-07-29 15:18:54 -04:00
parent a1301911e4
commit 41431d8bdd
76 changed files with 1294 additions and 134 deletions
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12
llama_stack/providers/registry/vector_io.py
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@ -18,7 +18,6 @@ def available_providers() -> list[ProviderSpec]:
InlineProviderSpec(
api=Api.vector_io,
provider_type="inline::meta-reference",
pip_packages=["faiss-cpu"],
module="llama_stack.providers.inline.vector_io.faiss",
config_class="llama_stack.providers.inline.vector_io.faiss.FaissVectorIOConfig",
deprecation_warning="Please use the `inline::faiss` provider instead.",
@ -29,7 +28,6 @@ def available_providers() -> list[ProviderSpec]:
InlineProviderSpec(
api=Api.vector_io,
provider_type="inline::faiss",
pip_packages=["faiss-cpu"],
module="llama_stack.providers.inline.vector_io.faiss",
config_class="llama_stack.providers.inline.vector_io.faiss.FaissVectorIOConfig",
api_dependencies=[Api.inference],
@ -82,7 +80,6 @@ more details about Faiss in general.
InlineProviderSpec(
api=Api.vector_io,
provider_type="inline::sqlite-vec",
pip_packages=["sqlite-vec"],
module="llama_stack.providers.inline.vector_io.sqlite_vec",
config_class="llama_stack.providers.inline.vector_io.sqlite_vec.SQLiteVectorIOConfig",
api_dependencies=[Api.inference],
@ -289,7 +286,6 @@ See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) f
InlineProviderSpec(
api=Api.vector_io,
provider_type="inline::sqlite_vec",
pip_packages=["sqlite-vec"],
module="llama_stack.providers.inline.vector_io.sqlite_vec",
config_class="llama_stack.providers.inline.vector_io.sqlite_vec.SQLiteVectorIOConfig",
deprecation_warning="Please use the `inline::sqlite-vec` provider (notice the hyphen instead of underscore) instead.",
@ -303,7 +299,6 @@ Please refer to the sqlite-vec provider documentation.
api=Api.vector_io,
adapter_type="chromadb",
provider_type="remote::chromadb",
pip_packages=["chromadb-client"],
module="llama_stack.providers.remote.vector_io.chroma",
config_class="llama_stack.providers.remote.vector_io.chroma.ChromaVectorIOConfig",
api_dependencies=[Api.inference],
@ -345,7 +340,6 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
InlineProviderSpec(
api=Api.vector_io,
provider_type="inline::chromadb",
pip_packages=["chromadb"],
module="llama_stack.providers.inline.vector_io.chroma",
config_class="llama_stack.providers.inline.vector_io.chroma.ChromaVectorIOConfig",
api_dependencies=[Api.inference],
@ -389,7 +383,6 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
api=Api.vector_io,
adapter_type="pgvector",
provider_type="remote::pgvector",
pip_packages=["psycopg2-binary"],
module="llama_stack.providers.remote.vector_io.pgvector",
config_class="llama_stack.providers.remote.vector_io.pgvector.PGVectorVectorIOConfig",
api_dependencies=[Api.inference],
@ -500,7 +493,6 @@ See [PGVector's documentation](https://github.com/pgvector/pgvector) for more de
api=Api.vector_io,
adapter_type="weaviate",
provider_type="remote::weaviate",
pip_packages=["weaviate-client"],
module="llama_stack.providers.remote.vector_io.weaviate",
config_class="llama_stack.providers.remote.vector_io.weaviate.WeaviateVectorIOConfig",
provider_data_validator="llama_stack.providers.remote.vector_io.weaviate.WeaviateRequestProviderData",
@ -541,7 +533,6 @@ See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more
InlineProviderSpec(
api=Api.vector_io,
provider_type="inline::qdrant",
pip_packages=["qdrant-client"],
module="llama_stack.providers.inline.vector_io.qdrant",
config_class="llama_stack.providers.inline.vector_io.qdrant.QdrantVectorIOConfig",
api_dependencies=[Api.inference],
@ -594,7 +585,6 @@ See the [Qdrant documentation](https://qdrant.tech/documentation/) for more deta
api=Api.vector_io,
adapter_type="qdrant",
provider_type="remote::qdrant",
pip_packages=["qdrant-client"],
module="llama_stack.providers.remote.vector_io.qdrant",
config_class="llama_stack.providers.remote.vector_io.qdrant.QdrantVectorIOConfig",
api_dependencies=[Api.inference],
@ -607,7 +597,6 @@ Please refer to the inline provider documentation.
api=Api.vector_io,
adapter_type="milvus",
provider_type="remote::milvus",
pip_packages=["pymilvus>=2.4.10"],
module="llama_stack.providers.remote.vector_io.milvus",
config_class="llama_stack.providers.remote.vector_io.milvus.MilvusVectorIOConfig",
api_dependencies=[Api.inference],
@ -813,7 +802,6 @@ For more details on TLS configuration, refer to the [TLS setup guide](https://mi
InlineProviderSpec(
api=Api.vector_io,
provider_type="inline::milvus",
pip_packages=["pymilvus[milvus-lite]>=2.4.10"],
module="llama_stack.providers.inline.vector_io.milvus",
config_class="llama_stack.providers.inline.vector_io.milvus.MilvusVectorIOConfig",
api_dependencies=[Api.inference],