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fix: store configs (#2593)

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# What does this PR do?
https://github.com/meta-llama/llama-stack/pull/2490 broke postgres_demo,
as the config expected a str but the value was converted to int.

This PR:
1. Updates the type of port in sqlstore to be int
2. template generation uses `dict` instead of `StackRunConfig` so as to
avoid failing pydantic typechecks.
3. Adds `replace_env_vars` to StackRunConfig instantiation in
`configure.py` (not sure why this wasn't needed before).

## Test Plan
`llama stack build --template postgres_demo --image-type conda --run`
This commit is contained in:
ehhuang 2025-07-03 10:07:23 -07:00 • committed by GitHub
parent aa273944fd
commit 3c43a2f529
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47 changed files with 110 additions and 223 deletions
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66
llama_stack/templates/template.py
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@ -15,6 +15,7 @@ from pydantic import BaseModel, Field
from llama_stack.apis.datasets import DatasetPurpose
from llama_stack.apis.models import ModelType
from llama_stack.distribution.datatypes import (
LLAMA_STACK_RUN_CONFIG_VERSION,
Api,
BenchmarkInput,
BuildConfig,
@ -23,14 +24,15 @@ from llama_stack.distribution.datatypes import (
ModelInput,
Provider,
ShieldInput,
StackRunConfig,
ToolGroupInput,
)
from llama_stack.distribution.distribution import get_provider_registry
from llama_stack.distribution.utils.dynamic import instantiate_class_type
from llama_stack.providers.utils.inference.model_registry import ProviderModelEntry
from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig
from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
from llama_stack.providers.utils.kvstore.config import get_pip_packages as get_kv_pip_packages
from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig
from llama_stack.providers.utils.sqlstore.sqlstore import get_pip_packages as get_sql_pip_packages
def get_model_registry(
@ -87,21 +89,24 @@ class RunConfigSettings(BaseModel):
default_tool_groups: list[ToolGroupInput] | None = None
default_datasets: list[DatasetInput] | None = None
default_benchmarks: list[BenchmarkInput] | None = None
metadata_store: KVStoreConfig | None = None
inference_store: SqlStoreConfig | None = None
metadata_store: dict | None = None
inference_store: dict | None = None
def run_config(
self,
name: str,
providers: dict[str, list[str]],
container_image: str | None = None,
) -> StackRunConfig:
) -> dict:
provider_registry = get_provider_registry()
provider_configs = {}
for api_str, provider_types in providers.items():
if api_providers := self.provider_overrides.get(api_str):
provider_configs[api_str] = api_providers
# Convert Provider objects to dicts for YAML serialization
provider_configs[api_str] = [
p.model_dump(exclude_none=True) if isinstance(p, Provider) else p for p in api_providers
]
continue
provider_configs[api_str] = []
@ -128,33 +133,40 @@ class RunConfigSettings(BaseModel):
provider_id=provider_id,
provider_type=provider_type,
config=config,
)
).model_dump(exclude_none=True)
)
# Get unique set of APIs from providers
apis = sorted(providers.keys())
return StackRunConfig(
image_name=name,
container_image=container_image,
apis=apis,
providers=provider_configs,
metadata_store=self.metadata_store
# Return a dict that matches StackRunConfig structure
return {
"version": LLAMA_STACK_RUN_CONFIG_VERSION,
"image_name": name,
"container_image": container_image,
"apis": apis,
"providers": provider_configs,
"metadata_store": self.metadata_store
or SqliteKVStoreConfig.sample_run_config(
__distro_dir__=f"~/.llama/distributions/{name}",
db_name="registry.db",
),
inference_store=self.inference_store
"inference_store": self.inference_store
or SqliteSqlStoreConfig.sample_run_config(
__distro_dir__=f"~/.llama/distributions/{name}",
db_name="inference_store.db",
),
models=self.default_models or [],
shields=self.default_shields or [],
tool_groups=self.default_tool_groups or [],
datasets=self.default_datasets or [],
benchmarks=self.default_benchmarks or [],
)
"models": [m.model_dump(exclude_none=True) for m in (self.default_models or [])],
"shields": [s.model_dump(exclude_none=True) for s in (self.default_shields or [])],
"vector_dbs": [],
"datasets": [d.model_dump(exclude_none=True) for d in (self.default_datasets or [])],
"scoring_fns": [],
"benchmarks": [b.model_dump(exclude_none=True) for b in (self.default_benchmarks or [])],
"tool_groups": [t.model_dump(exclude_none=True) for t in (self.default_tool_groups or [])],
"server": {
"port": 8321,
},
}
class DistributionTemplate(BaseModel):
@ -190,10 +202,12 @@ class DistributionTemplate(BaseModel):
# TODO: This is a hack to get the dependencies for internal APIs into build
# We should have a better way to do this by formalizing the concept of "internal" APIs
# and providers, with a way to specify dependencies for them.
if run_config_.inference_store:
additional_pip_packages.extend(run_config_.inference_store.pip_packages)
if run_config_.metadata_store:
additional_pip_packages.extend(run_config_.metadata_store.pip_packages)
if run_config_.get("inference_store"):
additional_pip_packages.extend(get_sql_pip_packages(run_config_["inference_store"]))
if run_config_.get("metadata_store"):
additional_pip_packages.extend(get_kv_pip_packages(run_config_["metadata_store"]))
if self.additional_pip_packages:
additional_pip_packages.extend(self.additional_pip_packages)
@ -286,7 +300,7 @@ class DistributionTemplate(BaseModel):
run_config = settings.run_config(self.name, self.providers, self.container_image)
with open(yaml_output_dir / yaml_pth, "w") as f:
yaml.safe_dump(
run_config.model_dump(exclude_none=True),
{k: v for k, v in run_config.items() if v is not None},
f,
sort_keys=False,
)