llama-stack-mirror/llama_stack/distribution/stack.py
Charlie Doern 9e3ea654c1 feat: convert provider config to a file path
currently provider.config is a dictionary. Introduce the ability to specify either a file path or the current in-file dictionary. Allowing users to specify a file path
enables more robust config management allowing stack administrators to swap in different provider configs seamlessly

Signed-off-by: Charlie Doern <cdoern@redhat.com>
2025-06-26 16:17:49 -04:00

221 lines
8.2 KiB
Python

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import importlib.resources
import tempfile
from typing import Any
import yaml
from llama_stack.apis.agents import Agents
from llama_stack.apis.batch_inference import BatchInference
from llama_stack.apis.benchmarks import Benchmarks
from llama_stack.apis.datasetio import DatasetIO
from llama_stack.apis.datasets import Datasets
from llama_stack.apis.eval import Eval
from llama_stack.apis.files import Files
from llama_stack.apis.inference import Inference
from llama_stack.apis.inspect import Inspect
from llama_stack.apis.models import Models
from llama_stack.apis.post_training import PostTraining
from llama_stack.apis.providers import Providers
from llama_stack.apis.safety import Safety
from llama_stack.apis.scoring import Scoring
from llama_stack.apis.scoring_functions import ScoringFunctions
from llama_stack.apis.shields import Shields
from llama_stack.apis.synthetic_data_generation import SyntheticDataGeneration
from llama_stack.apis.telemetry import Telemetry
from llama_stack.apis.tools import RAGToolRuntime, ToolGroups, ToolRuntime
from llama_stack.apis.vector_dbs import VectorDBs
from llama_stack.apis.vector_io import VectorIO
from llama_stack.distribution.datatypes import Provider, StackRunConfig
from llama_stack.distribution.distribution import get_provider_registry
from llama_stack.distribution.inspect import DistributionInspectConfig, DistributionInspectImpl
from llama_stack.distribution.providers import ProviderImpl, ProviderImplConfig
from llama_stack.distribution.resolver import ProviderRegistry, resolve_impls
from llama_stack.distribution.store.registry import create_dist_registry
from llama_stack.distribution.utils.dynamic import instantiate_class_type
from llama_stack.distribution.utils.env import replace_env_vars
from llama_stack.log import get_logger
from llama_stack.providers.datatypes import Api
logger = get_logger(name=__name__, category="core")
class LlamaStack(
Providers,
VectorDBs,
Inference,
BatchInference,
Agents,
Safety,
SyntheticDataGeneration,
Datasets,
Telemetry,
PostTraining,
VectorIO,
Eval,
Benchmarks,
Scoring,
ScoringFunctions,
DatasetIO,
Models,
Shields,
Inspect,
ToolGroups,
ToolRuntime,
RAGToolRuntime,
Files,
):
pass
RESOURCES = [
("models", Api.models, "register_model", "list_models"),
("shields", Api.shields, "register_shield", "list_shields"),
("vector_dbs", Api.vector_dbs, "register_vector_db", "list_vector_dbs"),
("datasets", Api.datasets, "register_dataset", "list_datasets"),
(
"scoring_fns",
Api.scoring_functions,
"register_scoring_function",
"list_scoring_functions",
),
("benchmarks", Api.benchmarks, "register_benchmark", "list_benchmarks"),
("tool_groups", Api.tool_groups, "register_tool_group", "list_tool_groups"),
]
async def register_resources(run_config: StackRunConfig, impls: dict[Api, Any]):
for rsrc, api, register_method, list_method in RESOURCES:
objects = getattr(run_config, rsrc)
if api not in impls:
continue
method = getattr(impls[api], register_method)
for obj in objects:
# In complex templates, like our starter template, we may have dynamic model ids
# given by environment variables. This allows those environment variables to have
# a default value of __disabled__ to skip registration of the model if not set.
if (
hasattr(obj, "provider_model_id")
and obj.provider_model_id is not None
and "__disabled__" in obj.provider_model_id
):
continue
# we want to maintain the type information in arguments to method.
# instead of method(**obj.model_dump()), which may convert a typed attr to a dict,
# we use model_dump() to find all the attrs and then getattr to get the still typed value.
await method(**{k: getattr(obj, k) for k in obj.model_dump().keys()})
method = getattr(impls[api], list_method)
response = await method()
objects_to_process = response.data if hasattr(response, "data") else response
for obj in objects_to_process:
logger.debug(
f"{rsrc.capitalize()}: {obj.identifier} served by {obj.provider_id}",
)
def add_internal_implementations(impls: dict[Api, Any], run_config: StackRunConfig) -> None:
"""Add internal implementations (inspect and providers) to the implementations dictionary.
Args:
impls: Dictionary of API implementations
run_config: Stack run configuration
"""
inspect_impl = DistributionInspectImpl(
DistributionInspectConfig(run_config=run_config),
deps=impls,
)
impls[Api.inspect] = inspect_impl
providers_impl = ProviderImpl(
ProviderImplConfig(run_config=run_config),
deps=impls,
)
impls[Api.providers] = providers_impl
# Produces a stack of providers for the given run config. Not all APIs may be
# asked for in the run config.
async def construct_stack(
run_config: StackRunConfig, provider_registry: ProviderRegistry | None = None
) -> dict[Api, Any]:
dist_registry, _ = await create_dist_registry(run_config.metadata_store, run_config.image_name)
policy = run_config.server.auth.access_policy if run_config.server.auth else []
impls = await resolve_impls(
run_config, provider_registry or get_provider_registry(run_config), dist_registry, policy
)
# Add internal implementations after all other providers are resolved
add_internal_implementations(impls, run_config)
await register_resources(run_config, impls)
return impls
def get_stack_run_config_from_template(template: str) -> StackRunConfig:
template_path = importlib.resources.files("llama_stack") / f"templates/{template}/run.yaml"
with importlib.resources.as_file(template_path) as path:
if not path.exists():
raise ValueError(f"Template '{template}' not found at {template_path}")
run_config = yaml.safe_load(path.open())
return StackRunConfig(**replace_env_vars(run_config))
def run_config_from_adhoc_config_spec(
adhoc_config_spec: str, provider_registry: ProviderRegistry | None = None
) -> StackRunConfig:
"""
Create an adhoc distribution from a list of API providers.
The list should be of the form "api=provider", e.g. "inference=fireworks". If you have
multiple pairs, separate them with commas or semicolons, e.g. "inference=fireworks,safety=llama-guard,agents=meta-reference"
"""
api_providers = adhoc_config_spec.replace(";", ",").split(",")
provider_registry = provider_registry or get_provider_registry()
distro_dir = tempfile.mkdtemp()
provider_configs_by_api = {}
for api_provider in api_providers:
api_str, provider = api_provider.split("=")
api = Api(api_str)
providers_by_type = provider_registry[api]
provider_spec = providers_by_type.get(provider)
if not provider_spec:
provider_spec = providers_by_type.get(f"inline::{provider}")
if not provider_spec:
provider_spec = providers_by_type.get(f"remote::{provider}")
if not provider_spec:
raise ValueError(
f"Provider {provider} (or remote::{provider} or inline::{provider}) not found for API {api}"
)
# call method "sample_run_config" on the provider spec config class
provider_config_type = instantiate_class_type(provider_spec.config_class)
provider_config = replace_env_vars(provider_config_type.sample_run_config(__distro_dir__=distro_dir))
provider_configs_by_api[api_str] = [
Provider(
provider_id=provider,
provider_type=provider_spec.provider_type,
config=provider_config,
)
]
config = StackRunConfig(
image_name="distro-test",
apis=list(provider_configs_by_api.keys()),
providers=provider_configs_by_api,
)
return config