[memory refactor][1/n] Rename Memory -> VectorIO, MemoryBanks -> VectorDBs (#828)

See https://github.com/meta-llama/llama-stack/issues/827 for the broader
design.

This is the first part:

- delete other kinds of memory banks (keyvalue, keyword, graph) for now;
we will introduce a keyvalue store API as part of this design but not
use it in the RAG tool yet.
- renaming of the APIs

llama_stack/providers/registry/vector_io.py

@@ -0,0 +1,116 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import List
+
+from llama_stack.providers.datatypes import (
+    AdapterSpec,
+    Api,
+    InlineProviderSpec,
+    ProviderSpec,
+    remote_provider_spec,
+)
+
+EMBEDDING_DEPS = [
+    "blobfile",
+    "chardet",
+    "pypdf",
+    "tqdm",
+    "numpy",
+    "scikit-learn",
+    "scipy",
+    "nltk",
+    "sentencepiece",
+    "transformers",
+    # this happens to work because special dependencies are always installed last
+    # so if there was a regular torch installed first, this would be ignored
+    # we need a better way to do this to identify potential conflicts, etc.
+    # for now, this lets us significantly reduce the size of the container which
+    # does not have any "local" inference code (and hence does not need GPU-enabled torch)
+    "torch --index-url https://download.pytorch.org/whl/cpu",
+    "sentence-transformers --no-deps",
+]
+
+
+def available_providers() -> List[ProviderSpec]:
+    return [
+        InlineProviderSpec(
+            api=Api.vector_io,
+            provider_type="inline::meta-reference",
+            pip_packages=EMBEDDING_DEPS + ["faiss-cpu"],
+            module="llama_stack.providers.inline.vector_io.faiss",
+            config_class="llama_stack.providers.inline.vector_io.faiss.FaissImplConfig",
+            deprecation_warning="Please use the `inline::faiss` provider instead.",
+            api_dependencies=[Api.inference],
+        ),
+        InlineProviderSpec(
+            api=Api.vector_io,
+            provider_type="inline::faiss",
+            pip_packages=EMBEDDING_DEPS + ["faiss-cpu"],
+            module="llama_stack.providers.inline.vector_io.faiss",
+            config_class="llama_stack.providers.inline.vector_io.faiss.FaissImplConfig",
+            api_dependencies=[Api.inference],
+        ),
+        remote_provider_spec(
+            Api.vector_io,
+            AdapterSpec(
+                adapter_type="chromadb",
+                pip_packages=EMBEDDING_DEPS + ["chromadb-client"],
+                module="llama_stack.providers.remote.vector_io.chroma",
+                config_class="llama_stack.providers.remote.vector_io.chroma.ChromaRemoteImplConfig",
+            ),
+            api_dependencies=[Api.inference],
+        ),
+        InlineProviderSpec(
+            api=Api.vector_io,
+            provider_type="inline::chromadb",
+            pip_packages=EMBEDDING_DEPS + ["chromadb"],
+            module="llama_stack.providers.inline.vector_io.chroma",
+            config_class="llama_stack.providers.inline.vector_io.chroma.ChromaInlineImplConfig",
+            api_dependencies=[Api.inference],
+        ),
+        remote_provider_spec(
+            Api.vector_io,
+            AdapterSpec(
+                adapter_type="pgvector",
+                pip_packages=EMBEDDING_DEPS + ["psycopg2-binary"],
+                module="llama_stack.providers.remote.vector_io.pgvector",
+                config_class="llama_stack.providers.remote.vector_io.pgvector.PGVectorConfig",
+            ),
+            api_dependencies=[Api.inference],
+        ),
+        remote_provider_spec(
+            Api.vector_io,
+            AdapterSpec(
+                adapter_type="weaviate",
+                pip_packages=EMBEDDING_DEPS + ["weaviate-client"],
+                module="llama_stack.providers.remote.vector_io.weaviate",
+                config_class="llama_stack.providers.remote.vector_io.weaviate.WeaviateConfig",
+                provider_data_validator="llama_stack.providers.remote.vector_io.weaviate.WeaviateRequestProviderData",
+            ),
+            api_dependencies=[Api.inference],
+        ),
+        remote_provider_spec(
+            api=Api.vector_io,
+            adapter=AdapterSpec(
+                adapter_type="sample",
+                pip_packages=[],
+                module="llama_stack.providers.remote.vector_io.sample",
+                config_class="llama_stack.providers.remote.vector_io.sample.SampleConfig",
+            ),
+            api_dependencies=[],
+        ),
+        remote_provider_spec(
+            Api.vector_io,
+            AdapterSpec(
+                adapter_type="qdrant",
+                pip_packages=EMBEDDING_DEPS + ["qdrant-client"],
+                module="llama_stack.providers.remote.vector_io.qdrant",
+                config_class="llama_stack.providers.remote.vector_io.qdrant.QdrantConfig",
+            ),
+            api_dependencies=[Api.inference],
+        ),
+    ]