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chore: add storage sane defaults (#4182)
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# What does this PR do? since `StackRunConfig` requires certain parts of `StorageConfig`, it'd probably make sense to template in some defaults that will "just work" for most usecases specifically introduce`ServerStoresConfig` defaults for inference, metadata, conversations and prompts. We already actually funnel in defaults for these sections ad-hoc throughout the codebase additionally set some `backends` defaults for the `StorageConfig`. This will alleviate some weirdness for `--providers` for run/list-deps and also some work I have to better align our list-deps/run datatypes --------- Signed-off-by: Charlie Doern <cdoern@redhat.com>
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3 changed files with 49 additions and 7 deletions
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@ -12,6 +12,8 @@ from typing import Annotated, Literal
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from pydantic import BaseModel, Field, field_validator
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from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR
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class StorageBackendType(StrEnum):
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KV_REDIS = "kv_redis"
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@ -256,15 +258,24 @@ class ResponsesStoreReference(InferenceStoreReference):
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class ServerStoresConfig(BaseModel):
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metadata: KVStoreReference | None = Field(
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default=None,
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default=KVStoreReference(
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backend="kv_default",
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namespace="registry",
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),
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description="Metadata store configuration (uses KV backend)",
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)
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inference: InferenceStoreReference | None = Field(
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default=None,
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default=InferenceStoreReference(
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backend="sql_default",
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table_name="inference_store",
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),
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description="Inference store configuration (uses SQL backend)",
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)
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conversations: SqlStoreReference | None = Field(
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default=None,
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default=SqlStoreReference(
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backend="sql_default",
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table_name="openai_conversations",
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),
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description="Conversations store configuration (uses SQL backend)",
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)
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responses: ResponsesStoreReference | None = Field(
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@ -272,13 +283,21 @@ class ServerStoresConfig(BaseModel):
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description="Responses store configuration (uses SQL backend)",
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)
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prompts: KVStoreReference | None = Field(
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default=None,
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default=KVStoreReference(backend="kv_default", namespace="prompts"),
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description="Prompts store configuration (uses KV backend)",
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)
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class StorageConfig(BaseModel):
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backends: dict[str, StorageBackendConfig] = Field(
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default={
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"kv_default": SqliteKVStoreConfig(
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db_path=f"${{env.SQLITE_STORE_DIR:={DISTRIBS_BASE_DIR}}}/kvstore.db",
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),
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"sql_default": SqliteSqlStoreConfig(
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db_path=f"${{env.SQLITE_STORE_DIR:={DISTRIBS_BASE_DIR}}}/sql_store.db",
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),
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},
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description="Named backend configurations (e.g., 'default', 'cache')",
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)
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stores: ServerStoresConfig = Field(
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@ -38,6 +38,9 @@ async def service():
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},
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stores=ServerStoresConfig(
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conversations=SqlStoreReference(backend="sql_test", table_name="openai_conversations"),
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metadata=None,
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inference=None,
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prompts=None,
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),
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)
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register_sqlstore_backends({"sql_test": storage.backends["sql_test"]})
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@ -142,6 +145,9 @@ async def test_policy_configuration():
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},
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stores=ServerStoresConfig(
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conversations=SqlStoreReference(backend="sql_test", table_name="openai_conversations"),
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metadata=None,
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inference=None,
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prompts=None,
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),
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)
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register_sqlstore_backends({"sql_test": storage.backends["sql_test"]})
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@ -10,8 +10,9 @@ from unittest.mock import AsyncMock
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import pytest
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from llama_stack.core.datatypes import QualifiedModel, SafetyConfig, StackRunConfig, StorageConfig, VectorStoresConfig
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from llama_stack.core.datatypes import QualifiedModel, SafetyConfig, StackRunConfig, VectorStoresConfig
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from llama_stack.core.stack import validate_safety_config, validate_vector_stores_config
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from llama_stack.core.storage.datatypes import ServerStoresConfig, StorageConfig
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from llama_stack_api import Api, ListModelsResponse, ListShieldsResponse, Model, ModelType, Shield
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@ -21,7 +22,15 @@ class TestVectorStoresValidation:
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run_config = StackRunConfig(
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image_name="test",
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providers={},
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storage=StorageConfig(backends={}, stores={}),
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storage=StorageConfig(
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backends={},
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stores=ServerStoresConfig(
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metadata=None,
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inference=None,
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conversations=None,
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prompts=None,
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),
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),
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vector_stores=VectorStoresConfig(
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default_provider_id="faiss",
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default_embedding_model=QualifiedModel(
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@ -41,7 +50,15 @@ class TestVectorStoresValidation:
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run_config = StackRunConfig(
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image_name="test",
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providers={},
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storage=StorageConfig(backends={}, stores={}),
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storage=StorageConfig(
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backends={},
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stores=ServerStoresConfig(
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metadata=None,
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inference=None,
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conversations=None,
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prompts=None,
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),
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),
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vector_stores=VectorStoresConfig(
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default_provider_id="faiss",
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default_embedding_model=QualifiedModel(
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