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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.
114 lines
3 KiB
YAML
114 lines
3 KiB
YAML
version: 2
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image_name: postgres-demo
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apis:
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- agents
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- inference
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- safety
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- tool_runtime
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- vector_io
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providers:
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inference:
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- provider_id: vllm-inference
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provider_type: remote::vllm
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config:
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url: ${env.VLLM_URL:=http://localhost:8000/v1}
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max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
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api_token: ${env.VLLM_API_TOKEN:=fake}
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tls_verify: ${env.VLLM_TLS_VERIFY:=true}
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- provider_id: sentence-transformers
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provider_type: inline::sentence-transformers
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vector_io:
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- provider_id: ${env.ENABLE_CHROMADB:+chromadb}
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provider_type: remote::chromadb
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config:
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url: ${env.CHROMADB_URL:=}
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persistence:
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namespace: vector_io::chroma_remote
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backend: kv_default
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safety:
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- provider_id: llama-guard
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provider_type: inline::llama-guard
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config:
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excluded_categories: []
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agents:
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- provider_id: meta-reference
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provider_type: inline::meta-reference
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config:
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persistence:
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agent_state:
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namespace: agents
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backend: kv_default
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responses:
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table_name: responses
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backend: sql_default
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max_write_queue_size: 10000
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num_writers: 4
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tool_runtime:
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- provider_id: brave-search
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provider_type: remote::brave-search
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config:
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api_key: ${env.BRAVE_SEARCH_API_KEY:=}
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max_results: 3
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- provider_id: tavily-search
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provider_type: remote::tavily-search
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config:
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api_key: ${env.TAVILY_SEARCH_API_KEY:=}
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max_results: 3
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- provider_id: rag-runtime
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provider_type: inline::rag-runtime
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- provider_id: model-context-protocol
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provider_type: remote::model-context-protocol
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storage:
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backends:
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kv_default:
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type: kv_postgres
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host: ${env.POSTGRES_HOST:=localhost}
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port: ${env.POSTGRES_PORT:=5432}
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db: ${env.POSTGRES_DB:=llamastack}
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user: ${env.POSTGRES_USER:=llamastack}
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password: ${env.POSTGRES_PASSWORD:=llamastack}
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table_name: ${env.POSTGRES_TABLE_NAME:=llamastack_kvstore}
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sql_default:
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type: sql_postgres
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host: ${env.POSTGRES_HOST:=localhost}
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port: ${env.POSTGRES_PORT:=5432}
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db: ${env.POSTGRES_DB:=llamastack}
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user: ${env.POSTGRES_USER:=llamastack}
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password: ${env.POSTGRES_PASSWORD:=llamastack}
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stores:
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metadata:
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namespace: registry
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backend: kv_default
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inference:
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table_name: inference_store
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backend: sql_default
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max_write_queue_size: 10000
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num_writers: 4
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conversations:
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table_name: openai_conversations
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backend: sql_default
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models:
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- metadata: {}
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model_id: ${env.INFERENCE_MODEL}
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provider_id: vllm-inference
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model_type: llm
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- metadata:
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embedding_dimension: 768
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model_id: nomic-embed-text-v1.5
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provider_id: sentence-transformers
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model_type: embedding
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shields:
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- shield_id: meta-llama/Llama-Guard-3-8B
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vector_dbs: []
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datasets: []
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scoring_fns: []
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benchmarks: []
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tool_groups:
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- toolgroup_id: builtin::websearch
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provider_id: tavily-search
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- toolgroup_id: builtin::rag
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provider_id: rag-runtime
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server:
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port: 8321
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telemetry:
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enabled: true
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