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Merge branch 'main' into opengauss-add

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llama_stack/providers/registry/vector_io.py
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@ -395,7 +395,7 @@ That means you'll get fast and efficient vector retrieval.
To use PGVector in your Llama Stack project, follow these steps:
1. Install the necessary dependencies.
2. Configure your Llama Stack project to use Faiss.
2. Configure your Llama Stack project to use pgvector. (e.g. remote::pgvector).
3. Start storing and querying vectors.
## Installation
@ -410,6 +410,7 @@ See [PGVector's documentation](https://github.com/pgvector/pgvector) for more de
""",
),
api_dependencies=[Api.inference],
optional_api_dependencies=[Api.files],
),
remote_provider_spec(
Api.vector_io,
@ -497,6 +498,7 @@ See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more
module="llama_stack.providers.inline.vector_io.qdrant",
config_class="llama_stack.providers.inline.vector_io.qdrant.QdrantVectorIOConfig",
api_dependencies=[Api.inference],
optional_api_dependencies=[Api.files],
description=r"""
[Qdrant](https://qdrant.tech/documentation/) is an inline and remote vector database provider for Llama Stack. It
allows you to store and query vectors directly in memory.
@ -553,6 +555,7 @@ Please refer to the inline provider documentation.
""",
),
api_dependencies=[Api.inference],
optional_api_dependencies=[Api.files],
),
remote_provider_spec(
Api.vector_io,