mirror of
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.
125 lines
4.4 KiB
Python
125 lines
4.4 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 llama_stack.apis.models import ModelType
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from llama_stack.core.datatypes import (
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BuildProvider,
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ModelInput,
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Provider,
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ShieldInput,
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ToolGroupInput,
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)
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from llama_stack.distributions.template import (
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DistributionTemplate,
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RunConfigSettings,
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)
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from llama_stack.providers.inline.inference.sentence_transformers import SentenceTransformersInferenceConfig
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from llama_stack.providers.remote.inference.vllm import VLLMInferenceAdapterConfig
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from llama_stack.providers.remote.vector_io.chroma.config import ChromaVectorIOConfig
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from llama_stack.providers.utils.kvstore.config import PostgresKVStoreConfig
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from llama_stack.providers.utils.sqlstore.sqlstore import PostgresSqlStoreConfig
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def get_distribution_template() -> DistributionTemplate:
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inference_providers = [
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Provider(
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provider_id="vllm-inference",
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provider_type="remote::vllm",
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config=VLLMInferenceAdapterConfig.sample_run_config(
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url="${env.VLLM_URL:=http://localhost:8000/v1}",
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),
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),
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]
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providers = {
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"inference": [
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BuildProvider(provider_type="remote::vllm"),
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BuildProvider(provider_type="inline::sentence-transformers"),
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],
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"vector_io": [BuildProvider(provider_type="remote::chromadb")],
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"safety": [BuildProvider(provider_type="inline::llama-guard")],
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"agents": [BuildProvider(provider_type="inline::meta-reference")],
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"tool_runtime": [
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BuildProvider(provider_type="remote::brave-search"),
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BuildProvider(provider_type="remote::tavily-search"),
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BuildProvider(provider_type="inline::rag-runtime"),
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BuildProvider(provider_type="remote::model-context-protocol"),
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],
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}
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name = "postgres-demo"
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vector_io_providers = [
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Provider(
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provider_id="${env.ENABLE_CHROMADB:+chromadb}",
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provider_type="remote::chromadb",
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config=ChromaVectorIOConfig.sample_run_config(
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f"~/.llama/distributions/{name}",
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url="${env.CHROMADB_URL:=}",
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),
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),
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]
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default_tool_groups = [
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ToolGroupInput(
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toolgroup_id="builtin::websearch",
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provider_id="tavily-search",
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),
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ToolGroupInput(
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toolgroup_id="builtin::rag",
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provider_id="rag-runtime",
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),
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]
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default_models = [
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ModelInput(
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model_id="${env.INFERENCE_MODEL}",
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provider_id="vllm-inference",
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)
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]
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embedding_provider = Provider(
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provider_id="sentence-transformers",
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provider_type="inline::sentence-transformers",
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config=SentenceTransformersInferenceConfig.sample_run_config(),
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)
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embedding_model = ModelInput(
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model_id="nomic-embed-text-v1.5",
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provider_id=embedding_provider.provider_id,
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model_type=ModelType.embedding,
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metadata={
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"embedding_dimension": 768,
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},
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)
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return DistributionTemplate(
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name=name,
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distro_type="self_hosted",
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description="Quick start template for running Llama Stack with several popular providers",
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container_image=None,
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template_path=None,
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providers=providers,
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available_models_by_provider={},
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run_configs={
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"run.yaml": RunConfigSettings(
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provider_overrides={
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"inference": inference_providers + [embedding_provider],
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"vector_io": vector_io_providers,
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},
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default_models=default_models + [embedding_model],
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default_tool_groups=default_tool_groups,
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default_shields=[ShieldInput(shield_id="meta-llama/Llama-Guard-3-8B")],
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storage_backends={
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"kv_default": PostgresKVStoreConfig.sample_run_config(
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table_name="llamastack_kvstore",
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),
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"sql_default": PostgresSqlStoreConfig.sample_run_config(),
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},
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),
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},
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run_config_env_vars={
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"LLAMA_STACK_PORT": (
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"8321",
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"Port for the Llama Stack distribution server",
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),
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},
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)
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