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feat: Enable setting a default embedding model in the stack
Signed-off-by: Francisco Javier Arceo <farceo@redhat.com>
This commit is contained in:
Francisco Javier Arceo
parent
007efa6eb5
commit
86c1e3b217
27 changed files with 435 additions and 403 deletions
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@ -26,7 +26,7 @@ def available_providers() -> list[ProviderSpec]:
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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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optional_api_dependencies=[Api.files],
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optional_api_dependencies=[Api.files, Api.models],
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description="Meta's reference implementation of a vector database.",
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),
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InlineProviderSpec(
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@ -36,7 +36,7 @@ def available_providers() -> list[ProviderSpec]:
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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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optional_api_dependencies=[Api.files],
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optional_api_dependencies=[Api.files, Api.models],
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description="""
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[Faiss](https://github.com/facebookresearch/faiss) is an inline vector database provider for Llama Stack. It
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allows you to store and query vectors directly in memory.
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@ -89,7 +89,7 @@ more details about Faiss in general.
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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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optional_api_dependencies=[Api.files],
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optional_api_dependencies=[Api.files, Api.models],
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description="""
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[SQLite-Vec](https://github.com/asg017/sqlite-vec) is an inline vector database provider for Llama Stack. It
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allows you to store and query vectors directly within an SQLite database.
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@ -297,7 +297,7 @@ See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) f
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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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optional_api_dependencies=[Api.files],
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optional_api_dependencies=[Api.files, Api.models],
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description="""
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Please refer to the sqlite-vec provider documentation.
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""",
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@ -310,7 +310,7 @@ Please refer to the sqlite-vec provider documentation.
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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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api_dependencies=[Api.inference],
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optional_api_dependencies=[Api.files],
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optional_api_dependencies=[Api.files, Api.models],
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description="""
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[Chroma](https://www.trychroma.com/) is an inline and remote vector
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database provider for Llama Stack. It allows you to store and query vectors directly within a Chroma database.
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@ -352,7 +352,7 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
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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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optional_api_dependencies=[Api.files],
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optional_api_dependencies=[Api.files, Api.models],
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description="""
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[Chroma](https://www.trychroma.com/) is an inline and remote vector
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database provider for Llama Stack. It allows you to store and query vectors directly within a Chroma database.
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@ -396,7 +396,7 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
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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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api_dependencies=[Api.inference],
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optional_api_dependencies=[Api.files],
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optional_api_dependencies=[Api.files, Api.models],
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description="""
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[PGVector](https://github.com/pgvector/pgvector) is a remote vector database provider for Llama Stack. It
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allows you to store and query vectors directly in memory.
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@ -508,7 +508,7 @@ See [PGVector's documentation](https://github.com/pgvector/pgvector) for more de
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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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api_dependencies=[Api.inference],
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optional_api_dependencies=[Api.files],
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optional_api_dependencies=[Api.files, Api.models],
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description="""
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[Weaviate](https://weaviate.io/) is a vector database provider for Llama Stack.
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It allows you to store and query vectors directly within a Weaviate database.
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@ -548,7 +548,7 @@ See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more
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module="llama_stack.providers.inline.vector_io.qdrant",
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config_class="llama_stack.providers.inline.vector_io.qdrant.QdrantVectorIOConfig",
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api_dependencies=[Api.inference],
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optional_api_dependencies=[Api.files],
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optional_api_dependencies=[Api.files, Api.models],
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description=r"""
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[Qdrant](https://qdrant.tech/documentation/) is an inline and remote vector database provider for Llama Stack. It
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allows you to store and query vectors directly in memory.
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@ -601,7 +601,7 @@ See the [Qdrant documentation](https://qdrant.tech/documentation/) for more deta
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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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api_dependencies=[Api.inference],
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optional_api_dependencies=[Api.files],
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optional_api_dependencies=[Api.files, Api.models],
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description="""
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Please refer to the inline provider documentation.
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""",
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@ -614,7 +614,7 @@ Please refer to the inline provider documentation.
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module="llama_stack.providers.remote.vector_io.milvus",
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config_class="llama_stack.providers.remote.vector_io.milvus.MilvusVectorIOConfig",
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api_dependencies=[Api.inference],
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optional_api_dependencies=[Api.files],
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optional_api_dependencies=[Api.files, Api.models],
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description="""
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[Milvus](https://milvus.io/) is an inline and remote vector database provider for Llama Stack. It
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allows you to store and query vectors directly within a Milvus database.
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@ -820,7 +820,7 @@ For more details on TLS configuration, refer to the [TLS setup guide](https://mi
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module="llama_stack.providers.inline.vector_io.milvus",
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config_class="llama_stack.providers.inline.vector_io.milvus.MilvusVectorIOConfig",
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api_dependencies=[Api.inference],
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optional_api_dependencies=[Api.files],
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optional_api_dependencies=[Api.files, Api.models],
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description="""
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Please refer to the remote provider documentation.
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""",
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