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https://github.com/meta-llama/llama-stack.git
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2c43285e22
**This PR changes configurations in a backward incompatible way.**
Run configs today repeat full SQLite/Postgres snippets everywhere a
store is needed, which means duplicated credentials, extra connection
pools, and lots of drift between files. This PR introduces named storage
backends so the stack and providers can share a single catalog and
reference those backends by name.
## Key Changes
- Add `storage.backends` to `StackRunConfig`, register each KV/SQL
backend once at startup, and validate that references point to the right
family.
- Move server stores under `storage.stores` with lightweight references
(backend + namespace/table) instead of full configs.
- Update every provider/config/doc to use the new reference style;
docs/codegen now surface the simplified YAML.
## Migration
Before:
```yaml
metadata_store:
type: sqlite
db_path: ~/.llama/distributions/foo/registry.db
inference_store:
type: postgres
host: ${env.POSTGRES_HOST}
port: ${env.POSTGRES_PORT}
db: ${env.POSTGRES_DB}
user: ${env.POSTGRES_USER}
password: ${env.POSTGRES_PASSWORD}
conversations_store:
type: postgres
host: ${env.POSTGRES_HOST}
port: ${env.POSTGRES_PORT}
db: ${env.POSTGRES_DB}
user: ${env.POSTGRES_USER}
password: ${env.POSTGRES_PASSWORD}
```
After:
```yaml
storage:
backends:
kv_default:
type: kv_sqlite
db_path: ~/.llama/distributions/foo/kvstore.db
sql_default:
type: sql_postgres
host: ${env.POSTGRES_HOST}
port: ${env.POSTGRES_PORT}
db: ${env.POSTGRES_DB}
user: ${env.POSTGRES_USER}
password: ${env.POSTGRES_PASSWORD}
stores:
metadata:
backend: kv_default
namespace: registry
inference:
backend: sql_default
table_name: inference_store
max_write_queue_size: 10000
num_writers: 4
conversations:
backend: sql_default
table_name: openai_conversations
```
Provider configs follow the same pattern—for example, a Chroma vector
adapter switches from:
```yaml
providers:
vector_io:
- provider_id: chromadb
provider_type: remote::chromadb
config:
url: ${env.CHROMADB_URL}
kvstore:
type: sqlite
db_path: ~/.llama/distributions/foo/chroma.db
```
to:
```yaml
providers:
vector_io:
- provider_id: chromadb
provider_type: remote::chromadb
config:
url: ${env.CHROMADB_URL}
persistence:
backend: kv_default
namespace: vector_io::chroma_remote
```
Once the backends are declared, everything else just points at them, so
rotating credentials or swapping to Postgres happens in one place and
the stack reuses a single connection pool.
42 lines
1.9 KiB
Python
42 lines
1.9 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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from typing import Any
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from pydantic import BaseModel, Field
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from llama_stack.core.storage.datatypes import SqlStoreReference
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class S3FilesImplConfig(BaseModel):
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"""Configuration for S3-based files provider."""
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bucket_name: str = Field(description="S3 bucket name to store files")
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region: str = Field(default="us-east-1", description="AWS region where the bucket is located")
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aws_access_key_id: str | None = Field(default=None, description="AWS access key ID (optional if using IAM roles)")
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aws_secret_access_key: str | None = Field(
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default=None, description="AWS secret access key (optional if using IAM roles)"
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)
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endpoint_url: str | None = Field(default=None, description="Custom S3 endpoint URL (for MinIO, LocalStack, etc.)")
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auto_create_bucket: bool = Field(
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default=False, description="Automatically create the S3 bucket if it doesn't exist"
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)
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metadata_store: SqlStoreReference = Field(description="SQL store configuration for file metadata")
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@classmethod
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def sample_run_config(cls, __distro_dir__: str) -> dict[str, Any]:
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return {
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"bucket_name": "${env.S3_BUCKET_NAME}", # no default, buckets must be globally unique
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"region": "${env.AWS_REGION:=us-east-1}",
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"aws_access_key_id": "${env.AWS_ACCESS_KEY_ID:=}",
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"aws_secret_access_key": "${env.AWS_SECRET_ACCESS_KEY:=}",
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"endpoint_url": "${env.S3_ENDPOINT_URL:=}",
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"auto_create_bucket": "${env.S3_AUTO_CREATE_BUCKET:=false}",
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"metadata_store": SqlStoreReference(
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backend="sql_default",
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table_name="s3_files_metadata",
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).model_dump(exclude_none=True),
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}
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