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Add a RoutableProvider protocol, support for multiple routing keys (#163)

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* Update configure.py to use multiple routing keys for safety
* Refactor distribution/datatypes into a providers/datatypes
* Cleanup
This commit is contained in:
Ashwin Bharambe 2024-09-30 17:30:21 -07:00 • committed by GitHub
parent 73decb3781
commit eb2d8a31a5
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24 changed files with 600 additions and 577 deletions
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16
llama_stack/distribution/configure.py
View file

@ -117,18 +117,18 @@ def configure_api_providers(
if api_str == "safety":
# TODO: add support for other safety providers, and simplify safety provider config
if p == "meta-reference":
for shield_type in MetaReferenceShieldType:
routing_entries.append(
RoutableProviderConfig(
routing_key=shield_type.value,
provider_id=p,
config=cfg.dict(),
)
routing_entries.append(
RoutableProviderConfig(
routing_key=[s.value for s in MetaReferenceShieldType],
provider_id=p,
config=cfg.dict(),
)
)
else:
cprint(
f"[WARN] Interactive configuration of safety provider {p} is not supported, please manually configure safety shields types in routing_table of run.yaml",
f"[WARN] Interactive configuration of safety provider {p} is not supported. Please look for `{routing_key}` in run.yaml and replace it appropriately.",
"yellow",
attrs=["bold"],
)
routing_entries.append(
RoutableProviderConfig(

222
llama_stack/distribution/datatypes.py
View file

@ -5,228 +5,16 @@
# the root directory of this source tree.
from datetime import datetime
from enum import Enum
from typing import Any, Dict, List, Optional, Protocol, Union
from llama_models.schema_utils import json_schema_type
from typing import Dict, List, Optional, Union
from pydantic import BaseModel, Field
@json_schema_type
class Api(Enum):
inference = "inference"
safety = "safety"
agents = "agents"
memory = "memory"
telemetry = "telemetry"
models = "models"
shields = "shields"
memory_banks = "memory_banks"
from llama_stack.providers.datatypes import * # noqa: F403
@json_schema_type
class ApiEndpoint(BaseModel):
route: str
method: str
name: str
@json_schema_type
class ProviderSpec(BaseModel):
api: Api
provider_id: str
config_class: str = Field(
...,
description="Fully-qualified classname of the config for this provider",
)
api_dependencies: List[Api] = Field(
default_factory=list,
description="Higher-level API surfaces may depend on other providers to provide their functionality",
)
class RoutingTable(Protocol):
def get_routing_keys(self) -> List[str]: ...
def get_provider_impl(self, routing_key: str) -> Any: ...
class GenericProviderConfig(BaseModel):
provider_id: str
config: Dict[str, Any]
class PlaceholderProviderConfig(BaseModel):
"""Placeholder provider config for API whose provider are defined in routing_table"""
providers: List[str]
class RoutableProviderConfig(GenericProviderConfig):
routing_key: str
# Example: /inference, /safety
@json_schema_type
class AutoRoutedProviderSpec(ProviderSpec):
provider_id: str = "router"
config_class: str = ""
docker_image: Optional[str] = None
routing_table_api: Api
module: str = Field(
...,
description="""
Fully-qualified name of the module to import. The module is expected to have:
- `get_router_impl(config, provider_specs, deps)`: returns the router implementation
""",
)
provider_data_validator: Optional[str] = Field(
default=None,
)
@property
def pip_packages(self) -> List[str]:
raise AssertionError("Should not be called on AutoRoutedProviderSpec")
# Example: /models, /shields
@json_schema_type
class RoutingTableProviderSpec(ProviderSpec):
provider_id: str = "routing_table"
config_class: str = ""
docker_image: Optional[str] = None
inner_specs: List[ProviderSpec]
module: str = Field(
...,
description="""
Fully-qualified name of the module to import. The module is expected to have:
- `get_router_impl(config, provider_specs, deps)`: returns the router implementation
""",
)
pip_packages: List[str] = Field(default_factory=list)
@json_schema_type
class AdapterSpec(BaseModel):
adapter_id: str = Field(
...,
description="Unique identifier for this adapter",
)
module: str = Field(
...,
description="""
Fully-qualified name of the module to import. The module is expected to have:
- `get_adapter_impl(config, deps)`: returns the adapter implementation
""",
)
pip_packages: List[str] = Field(
default_factory=list,
description="The pip dependencies needed for this implementation",
)
config_class: Optional[str] = Field(
default=None,
description="Fully-qualified classname of the config for this provider",
)
provider_data_validator: Optional[str] = Field(
default=None,
)
@json_schema_type
class InlineProviderSpec(ProviderSpec):
pip_packages: List[str] = Field(
default_factory=list,
description="The pip dependencies needed for this implementation",
)
docker_image: Optional[str] = Field(
default=None,
description="""
The docker image to use for this implementation. If one is provided, pip_packages will be ignored.
If a provider depends on other providers, the dependencies MUST NOT specify a docker image.
""",
)
module: str = Field(
...,
description="""
Fully-qualified name of the module to import. The module is expected to have:
- `get_provider_impl(config, deps)`: returns the local implementation
""",
)
provider_data_validator: Optional[str] = Field(
default=None,
)
class RemoteProviderConfig(BaseModel):
host: str = "localhost"
port: int
@property
def url(self) -> str:
return f"http://{self.host}:{self.port}"
def remote_provider_id(adapter_id: str) -> str:
return f"remote::{adapter_id}"
@json_schema_type
class RemoteProviderSpec(ProviderSpec):
adapter: Optional[AdapterSpec] = Field(
default=None,
description="""
If some code is needed to convert the remote responses into Llama Stack compatible
API responses, specify the adapter here. If not specified, it indicates the remote
as being "Llama Stack compatible"
""",
)
@property
def docker_image(self) -> Optional[str]:
return None
@property
def module(self) -> str:
if self.adapter:
return self.adapter.module
return f"llama_stack.apis.{self.api.value}.client"
@property
def pip_packages(self) -> List[str]:
if self.adapter:
return self.adapter.pip_packages
return []
@property
def provider_data_validator(self) -> Optional[str]:
if self.adapter:
return self.adapter.provider_data_validator
return None
# Can avoid this by using Pydantic computed_field
def remote_provider_spec(
api: Api, adapter: Optional[AdapterSpec] = None
) -> RemoteProviderSpec:
config_class = (
adapter.config_class
if adapter and adapter.config_class
else "llama_stack.distribution.datatypes.RemoteProviderConfig"
)
provider_id = remote_provider_id(adapter.adapter_id) if adapter else "remote"
return RemoteProviderSpec(
api=api, provider_id=provider_id, config_class=config_class, adapter=adapter
)
LLAMA_STACK_BUILD_CONFIG_VERSION = "v1"
LLAMA_STACK_RUN_CONFIG_VERSION = "v1"
@json_schema_type
@ -247,6 +35,7 @@ in the runtime configuration to help route to the correct provider.""",
@json_schema_type
class StackRunConfig(BaseModel):
version: str = LLAMA_STACK_RUN_CONFIG_VERSION
built_at: datetime
image_name: str = Field(
@ -295,6 +84,7 @@ Provider configurations for each of the APIs provided by this package.
@json_schema_type
class BuildConfig(BaseModel):
version: str = LLAMA_STACK_BUILD_CONFIG_VERSION
name: str
distribution_spec: DistributionSpec = Field(
description="The distribution spec to build including API providers. "

2
llama_stack/distribution/request_headers.py
View file

@ -23,7 +23,7 @@ class NeedsRequestProviderData:
if not validator_class:
raise ValueError(f"Provider {provider_id} does not have a validator")
val = _THREAD_LOCAL.provider_data_header_value
val = getattr(_THREAD_LOCAL, "provider_data_header_value", None)
if not val:
return None

129
llama_stack/distribution/resolver.py Normal file
View file

@ -0,0 +1,129 @@
# 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.
from typing import Any, Dict, List, Set
from llama_stack.distribution.datatypes import * # noqa: F403
from llama_stack.distribution.distribution import (
api_providers,
builtin_automatically_routed_apis,
)
from llama_stack.distribution.utils.dynamic import instantiate_provider
async def resolve_impls_with_routing(run_config: StackRunConfig) -> Dict[Api, Any]:
"""
Does two things:
- flatmaps, sorts and resolves the providers in dependency order
- for each API, produces either a (local, passthrough or router) implementation
"""
all_providers = api_providers()
specs = {}
configs = {}
for api_str, config in run_config.api_providers.items():
api = Api(api_str)
# TODO: check that these APIs are not in the routing table part of the config
providers = all_providers[api]
# skip checks for API whose provider config is specified in routing_table
if isinstance(config, PlaceholderProviderConfig):
continue
if config.provider_id not in providers:
raise ValueError(
f"Unknown provider `{config.provider_id}` is not available for API `{api}`"
)
specs[api] = providers[config.provider_id]
configs[api] = config
apis_to_serve = run_config.apis_to_serve or set(
list(specs.keys()) + list(run_config.routing_table.keys())
)
for info in builtin_automatically_routed_apis():
source_api = info.routing_table_api
assert (
source_api not in specs
), f"Routing table API {source_api} specified in wrong place?"
assert (
info.router_api not in specs
), f"Auto-routed API {info.router_api} specified in wrong place?"
if info.router_api.value not in apis_to_serve:
continue
print("router_api", info.router_api)
if info.router_api.value not in run_config.routing_table:
raise ValueError(f"Routing table for `{source_api.value}` is not provided?")
routing_table = run_config.routing_table[info.router_api.value]
providers = all_providers[info.router_api]
inner_specs = []
inner_deps = []
for rt_entry in routing_table:
if rt_entry.provider_id not in providers:
raise ValueError(
f"Unknown provider `{rt_entry.provider_id}` is not available for API `{api}`"
)
inner_specs.append(providers[rt_entry.provider_id])
inner_deps.extend(providers[rt_entry.provider_id].api_dependencies)
specs[source_api] = RoutingTableProviderSpec(
api=source_api,
module="llama_stack.distribution.routers",
api_dependencies=inner_deps,
inner_specs=inner_specs,
)
configs[source_api] = routing_table
specs[info.router_api] = AutoRoutedProviderSpec(
api=info.router_api,
module="llama_stack.distribution.routers",
routing_table_api=source_api,
api_dependencies=[source_api],
)
configs[info.router_api] = {}
sorted_specs = topological_sort(specs.values())
print(f"Resolved {len(sorted_specs)} providers in topological order")
for spec in sorted_specs:
print(f" {spec.api}: {spec.provider_id}")
print("")
impls = {}
for spec in sorted_specs:
api = spec.api
deps = {api: impls[api] for api in spec.api_dependencies}
impl = await instantiate_provider(spec, deps, configs[api])
impls[api] = impl
return impls, specs
def topological_sort(providers: List[ProviderSpec]) -> List[ProviderSpec]:
by_id = {x.api: x for x in providers}
def dfs(a: ProviderSpec, visited: Set[Api], stack: List[Api]):
visited.add(a.api)
for api in a.api_dependencies:
if api not in visited:
dfs(by_id[api], visited, stack)
stack.append(a.api)
visited = set()
stack = []
for a in providers:
if a.api not in visited:
dfs(a, visited, stack)
return [by_id[x] for x in stack]

27
llama_stack/distribution/routers/routing_tables.py
View file

@ -19,18 +19,35 @@ from llama_stack.distribution.datatypes import * # noqa: F403
class CommonRoutingTableImpl(RoutingTable):
def __init__(
self,
inner_impls: List[Tuple[str, Any]],
inner_impls: List[Tuple[RoutingKey, Any]],
routing_table_config: Dict[str, List[RoutableProviderConfig]],
) -> None:
self.providers = {k: v for k, v in inner_impls}
self.routing_keys = list(self.providers.keys())
self.unique_providers = []
self.providers = {}
self.routing_keys = []
for key, impl in inner_impls:
keys = key if isinstance(key, list) else [key]
self.unique_providers.append((keys, impl))
for k in keys:
if k in self.providers:
raise ValueError(f"Duplicate routing key {k}")
self.providers[k] = impl
self.routing_keys.append(k)
self.routing_table_config = routing_table_config
async def initialize(self) -> None:
pass
for keys, p in self.unique_providers:
spec = p.__provider_spec__
if isinstance(spec, RemoteProviderSpec) and spec.adapter is None:
continue
await p.validate_routing_keys(keys)
async def shutdown(self) -> None:
for p in self.providers.values():
for _, p in self.unique_providers:
await p.shutdown()
def get_provider_impl(self, routing_key: str) -> Any:

138
llama_stack/distribution/server/server.py
View file

@ -17,16 +17,7 @@ from collections.abc import (
from contextlib import asynccontextmanager
from http import HTTPStatus
from ssl import SSLError
from typing import (
Any,
AsyncGenerator,
AsyncIterator,
Dict,
get_type_hints,
List,
Optional,
Set,
)
from typing import Any, AsyncGenerator, AsyncIterator, Dict, get_type_hints, Optional
import fire
import httpx
@ -48,13 +39,9 @@ from llama_stack.providers.utils.telemetry.tracing import (
)
from llama_stack.distribution.datatypes import * # noqa: F403
from llama_stack.distribution.distribution import (
api_endpoints,
api_providers,
builtin_automatically_routed_apis,
)
from llama_stack.distribution.distribution import api_endpoints
from llama_stack.distribution.request_headers import set_request_provider_data
from llama_stack.distribution.utils.dynamic import instantiate_provider
from llama_stack.distribution.resolver import resolve_impls_with_routing
def is_async_iterator_type(typ):
@ -289,125 +276,6 @@ def create_dynamic_typed_route(func: Any, method: str):
return endpoint
def topological_sort(providers: List[ProviderSpec]) -> List[ProviderSpec]:
by_id = {x.api: x for x in providers}
def dfs(a: ProviderSpec, visited: Set[Api], stack: List[Api]):
visited.add(a.api)
for api in a.api_dependencies:
if api not in visited:
dfs(by_id[api], visited, stack)
stack.append(a.api)
visited = set()
stack = []
for a in providers:
if a.api not in visited:
dfs(a, visited, stack)
return [by_id[x] for x in stack]
def snake_to_camel(snake_str):
return "".join(word.capitalize() for word in snake_str.split("_"))
async def resolve_impls_with_routing(run_config: StackRunConfig) -> Dict[Api, Any]:
"""
Does two things:
- flatmaps, sorts and resolves the providers in dependency order
- for each API, produces either a (local, passthrough or router) implementation
"""
all_providers = api_providers()
specs = {}
configs = {}
for api_str, config in run_config.api_providers.items():
api = Api(api_str)
# TODO: check that these APIs are not in the routing table part of the config
providers = all_providers[api]
# skip checks for API whose provider config is specified in routing_table
if isinstance(config, PlaceholderProviderConfig):
continue
if config.provider_id not in providers:
raise ValueError(
f"Unknown provider `{config.provider_id}` is not available for API `{api}`"
)
specs[api] = providers[config.provider_id]
configs[api] = config
apis_to_serve = run_config.apis_to_serve or set(
list(specs.keys()) + list(run_config.routing_table.keys())
)
for info in builtin_automatically_routed_apis():
source_api = info.routing_table_api
assert (
source_api not in specs
), f"Routing table API {source_api} specified in wrong place?"
assert (
info.router_api not in specs
), f"Auto-routed API {info.router_api} specified in wrong place?"
if info.router_api.value not in apis_to_serve:
continue
print("router_api", info.router_api)
if info.router_api.value not in run_config.routing_table:
raise ValueError(f"Routing table for `{source_api.value}` is not provided?")
routing_table = run_config.routing_table[info.router_api.value]
providers = all_providers[info.router_api]
inner_specs = []
inner_deps = []
for rt_entry in routing_table:
if rt_entry.provider_id not in providers:
raise ValueError(
f"Unknown provider `{rt_entry.provider_id}` is not available for API `{api}`"
)
inner_specs.append(providers[rt_entry.provider_id])
inner_deps.extend(providers[rt_entry.provider_id].api_dependencies)
specs[source_api] = RoutingTableProviderSpec(
api=source_api,
module="llama_stack.distribution.routers",
api_dependencies=inner_deps,
inner_specs=inner_specs,
)
configs[source_api] = routing_table
specs[info.router_api] = AutoRoutedProviderSpec(
api=info.router_api,
module="llama_stack.distribution.routers",
routing_table_api=source_api,
api_dependencies=[source_api],
)
configs[info.router_api] = {}
sorted_specs = topological_sort(specs.values())
print(f"Resolved {len(sorted_specs)} providers in topological order")
for spec in sorted_specs:
print(f" {spec.api}: {spec.provider_id}")
print("")
impls = {}
for spec in sorted_specs:
api = spec.api
deps = {api: impls[api] for api in spec.api_dependencies}
impl = await instantiate_provider(spec, deps, configs[api])
impls[api] = impl
return impls, specs
def main(
yaml_config: str = "llamastack-run.yaml",
port: int = 5000,

17
llama_stack/distribution/templates/docker/llamastack-local-cpu/run.yaml
View file

@ -42,22 +42,7 @@ routing_table:
config:
llama_guard_shield: null
prompt_guard_shield: null
routing_key: llama_guard
- provider_id: meta-reference
config:
llama_guard_shield: null
prompt_guard_shield: null
routing_key: code_scanner_guard
- provider_id: meta-reference
config:
llama_guard_shield: null
prompt_guard_shield: null
routing_key: injection_shield
- provider_id: meta-reference
config:
llama_guard_shield: null
prompt_guard_shield: null
routing_key: jailbreak_shield
routing_key: ["llama_guard", "code_scanner_guard", "injection_shield", "jailbreak_shield"]
memory:
- provider_id: meta-reference
config: {}

17
llama_stack/distribution/templates/docker/llamastack-local-gpu/run.yaml
View file

@ -45,22 +45,7 @@ routing_table:
config:
llama_guard_shield: null
prompt_guard_shield: null
routing_key: llama_guard
- provider_id: meta-reference
config:
llama_guard_shield: null
prompt_guard_shield: null
routing_key: code_scanner_guard
- provider_id: meta-reference
config:
llama_guard_shield: null
prompt_guard_shield: null
routing_key: injection_shield
- provider_id: meta-reference
config:
llama_guard_shield: null
prompt_guard_shield: null
routing_key: jailbreak_shield
routing_key: ["llama_guard", "code_scanner_guard", "injection_shield", "jailbreak_shield"]
memory:
- provider_id: meta-reference
config: {}

34
llama_stack/providers/adapters/inference/bedrock/bedrock.py
View file

@ -12,20 +12,21 @@ from botocore.config import Config
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.tokenizer import Tokenizer
from llama_models.sku_list import resolve_model
from llama_stack.providers.utils.inference.routable import RoutableProviderForModels
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.providers.adapters.inference.bedrock.config import BedrockConfig
# mapping of Model SKUs to ollama models
BEDROCK_SUPPORTED_MODELS = {
"Meta-Llama3.1-8B-Instruct": "meta.llama3-1-8b-instruct-v1:0",
"Meta-Llama3.1-70B-Instruct": "meta.llama3-1-70b-instruct-v1:0",
"Meta-Llama3.1-405B-Instruct": "meta.llama3-1-405b-instruct-v1:0",
"Llama3.1-8B-Instruct": "meta.llama3-1-8b-instruct-v1:0",
"Llama3.1-70B-Instruct": "meta.llama3-1-70b-instruct-v1:0",
"Llama3.1-405B-Instruct": "meta.llama3-1-405b-instruct-v1:0",
}
class BedrockInferenceAdapter(Inference):
class BedrockInferenceAdapter(Inference, RoutableProviderForModels):
@staticmethod
def _create_bedrock_client(config: BedrockConfig) -> BaseClient:
@ -68,6 +69,9 @@ class BedrockInferenceAdapter(Inference):
return boto3_session.client("bedrock-runtime", config=boto3_config)
def __init__(self, config: BedrockConfig) -> None:
RoutableProviderForModels.__init__(
self, stack_to_provider_models_map=BEDROCK_SUPPORTED_MODELS
)
self._config = config
self._client = BedrockInferenceAdapter._create_bedrock_client(config)
@ -94,22 +98,6 @@ class BedrockInferenceAdapter(Inference):
) -> Union[CompletionResponse, CompletionResponseStreamChunk]:
raise NotImplementedError()
@staticmethod
def resolve_bedrock_model(model_name: str) -> str:
model = resolve_model(model_name)
assert (
model is not None
and model.descriptor(shorten_default_variant=True)
in BEDROCK_SUPPORTED_MODELS
), (
f"Unsupported model: {model_name}, use one of the supported models: "
f"{','.join(BEDROCK_SUPPORTED_MODELS.keys())}"
)
return BEDROCK_SUPPORTED_MODELS.get(
model.descriptor(shorten_default_variant=True)
)
@staticmethod
def _bedrock_stop_reason_to_stop_reason(bedrock_stop_reason: str) -> StopReason:
if bedrock_stop_reason == "max_tokens":
@ -350,7 +338,7 @@ class BedrockInferenceAdapter(Inference):
) -> (
AsyncGenerator
): # Union[ChatCompletionResponse, ChatCompletionResponseStreamChunk]:
bedrock_model = BedrockInferenceAdapter.resolve_bedrock_model(model)
bedrock_model = self.map_to_provider_model(model)
inference_config = BedrockInferenceAdapter.get_bedrock_inference_config(
sampling_params
)

23
llama_stack/providers/adapters/inference/fireworks/fireworks.py
View file

@ -12,7 +12,8 @@ from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.datatypes import Message, StopReason
from llama_models.llama3.api.tokenizer import Tokenizer
from llama_models.sku_list import resolve_model
from llama_stack.providers.utils.inference.routable import RoutableProviderForModels
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.providers.utils.inference.augment_messages import (
@ -21,6 +22,7 @@ from llama_stack.providers.utils.inference.augment_messages import (
from .config import FireworksImplConfig
FIREWORKS_SUPPORTED_MODELS = {
"Llama3.1-8B-Instruct": "fireworks/llama-v3p1-8b-instruct",
"Llama3.1-70B-Instruct": "fireworks/llama-v3p1-70b-instruct",
@ -28,8 +30,11 @@ FIREWORKS_SUPPORTED_MODELS = {
}
class FireworksInferenceAdapter(Inference):
class FireworksInferenceAdapter(Inference, RoutableProviderForModels):
def __init__(self, config: FireworksImplConfig) -> None:
RoutableProviderForModels.__init__(
self, stack_to_provider_models_map=FIREWORKS_SUPPORTED_MODELS
)
self.config = config
tokenizer = Tokenizer.get_instance()
self.formatter = ChatFormat(tokenizer)
@ -65,18 +70,6 @@ class FireworksInferenceAdapter(Inference):
return fireworks_messages
def resolve_fireworks_model(self, model_name: str) -> str:
model = resolve_model(model_name)
assert (
model is not None
and model.descriptor(shorten_default_variant=True)
in FIREWORKS_SUPPORTED_MODELS
), f"Unsupported model: {model_name}, use one of the supported models: {','.join(FIREWORKS_SUPPORTED_MODELS.keys())}"
return FIREWORKS_SUPPORTED_MODELS.get(
model.descriptor(shorten_default_variant=True)
)
def get_fireworks_chat_options(self, request: ChatCompletionRequest) -> dict:
options = {}
if request.sampling_params is not None:
@ -112,7 +105,7 @@ class FireworksInferenceAdapter(Inference):
# accumulate sampling params and other options to pass to fireworks
options = self.get_fireworks_chat_options(request)
fireworks_model = self.resolve_fireworks_model(request.model)
fireworks_model = self.map_to_provider_model(request.model)
if not request.stream:
r = await self.client.chat.completions.acreate(

18
llama_stack/providers/adapters/inference/ollama/ollama.py
View file

@ -11,7 +11,6 @@ import httpx
from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.datatypes import Message, StopReason
from llama_models.llama3.api.tokenizer import Tokenizer
from llama_models.sku_list import resolve_model
from ollama import AsyncClient
@ -19,6 +18,7 @@ from llama_stack.apis.inference import * # noqa: F403
from llama_stack.providers.utils.inference.augment_messages import (
augment_messages_for_tools,
)
from llama_stack.providers.utils.inference.routable import RoutableProviderForModels
# TODO: Eventually this will move to the llama cli model list command
# mapping of Model SKUs to ollama models
@ -29,8 +29,11 @@ OLLAMA_SUPPORTED_SKUS = {
}
class OllamaInferenceAdapter(Inference):
class OllamaInferenceAdapter(Inference, RoutableProviderForModels):
def __init__(self, url: str) -> None:
RoutableProviderForModels.__init__(
self, stack_to_provider_models_map=OLLAMA_SUPPORTED_SKUS
)
self.url = url
tokenizer = Tokenizer.get_instance()
self.formatter = ChatFormat(tokenizer)
@ -72,15 +75,6 @@ class OllamaInferenceAdapter(Inference):
return ollama_messages
def resolve_ollama_model(self, model_name: str) -> str:
model = resolve_model(model_name)
assert (
model is not None
and model.descriptor(shorten_default_variant=True) in OLLAMA_SUPPORTED_SKUS
), f"Unsupported model: {model_name}, use one of the supported models: {','.join(OLLAMA_SUPPORTED_SKUS.keys())}"
return OLLAMA_SUPPORTED_SKUS.get(model.descriptor(shorten_default_variant=True))
def get_ollama_chat_options(self, request: ChatCompletionRequest) -> dict:
options = {}
if request.sampling_params is not None:
@ -120,7 +114,7 @@ class OllamaInferenceAdapter(Inference):
messages = augment_messages_for_tools(request)
# accumulate sampling params and other options to pass to ollama
options = self.get_ollama_chat_options(request)
ollama_model = self.resolve_ollama_model(request.model)
ollama_model = self.map_to_provider_model(request.model)
res = await self.client.ps()
need_model_pull = True

9
llama_stack/providers/adapters/inference/tgi/tgi.py
View file

@ -13,6 +13,8 @@ from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.datatypes import StopReason
from llama_models.llama3.api.tokenizer import Tokenizer
from llama_stack.distribution.datatypes import RoutableProvider
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.providers.utils.inference.augment_messages import (
augment_messages_for_tools,
@ -23,7 +25,7 @@ from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImpl
logger = logging.getLogger(__name__)
class _HfAdapter(Inference):
class _HfAdapter(Inference, RoutableProvider):
client: AsyncInferenceClient
max_tokens: int
model_id: str
@ -32,6 +34,11 @@ class _HfAdapter(Inference):
self.tokenizer = Tokenizer.get_instance()
self.formatter = ChatFormat(self.tokenizer)
async def validate_routing_keys(self, routing_keys: list[str]) -> None:
# these are the model names the Llama Stack will use to route requests to this provider
# perform validation here if necessary
pass
async def shutdown(self) -> None:
pass

6
llama_stack/providers/adapters/inference/together/config.py
View file

@ -4,6 +4,8 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Optional
from llama_models.schema_utils import json_schema_type
from pydantic import BaseModel, Field
@ -14,7 +16,7 @@ class TogetherImplConfig(BaseModel):
default="https://api.together.xyz/v1",
description="The URL for the Together AI server",
)
api_key: str = Field(
default="",
api_key: Optional[str] = Field(
default=None,
description="The Together AI API Key",
)

52
llama_stack/providers/adapters/inference/together/together.py
View file

@ -10,7 +10,6 @@ from llama_models.llama3.api.chat_format import ChatFormat
from llama_models.llama3.api.datatypes import Message, StopReason
from llama_models.llama3.api.tokenizer import Tokenizer
from llama_models.sku_list import resolve_model
from together import Together
@ -19,9 +18,11 @@ from llama_stack.distribution.request_headers import NeedsRequestProviderData
from llama_stack.providers.utils.inference.augment_messages import (
augment_messages_for_tools,
)
from llama_stack.providers.utils.inference.routable import RoutableProviderForModels
from .config import TogetherImplConfig
TOGETHER_SUPPORTED_MODELS = {
"Llama3.1-8B-Instruct": "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
"Llama3.1-70B-Instruct": "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo",
@ -32,8 +33,13 @@ TOGETHER_SUPPORTED_MODELS = {
}
class TogetherInferenceAdapter(Inference, NeedsRequestProviderData):
class TogetherInferenceAdapter(
Inference, NeedsRequestProviderData, RoutableProviderForModels
):
def __init__(self, config: TogetherImplConfig) -> None:
RoutableProviderForModels.__init__(
self, stack_to_provider_models_map=TOGETHER_SUPPORTED_MODELS
)
self.config = config
tokenizer = Tokenizer.get_instance()
self.formatter = ChatFormat(tokenizer)
@ -69,18 +75,6 @@ class TogetherInferenceAdapter(Inference, NeedsRequestProviderData):
return together_messages
def resolve_together_model(self, model_name: str) -> str:
model = resolve_model(model_name)
assert (
model is not None
and model.descriptor(shorten_default_variant=True)
in TOGETHER_SUPPORTED_MODELS
), f"Unsupported model: {model_name}, use one of the supported models: {','.join(TOGETHER_SUPPORTED_MODELS.keys())}"
return TOGETHER_SUPPORTED_MODELS.get(
model.descriptor(shorten_default_variant=True)
)
def get_together_chat_options(self, request: ChatCompletionRequest) -> dict:
options = {}
if request.sampling_params is not None:
@ -103,12 +97,15 @@ class TogetherInferenceAdapter(Inference, NeedsRequestProviderData):
) -> AsyncGenerator:
together_api_key = None
provider_data = self.get_request_provider_data()
if provider_data is None or not provider_data.together_api_key:
raise ValueError(
'Pass Together API Key in the header X-LlamaStack-ProviderData as { "together_api_key": <your api key>}'
)
together_api_key = provider_data.together_api_key
if self.config.api_key is not None:
together_api_key = self.config.api_key
else:
provider_data = self.get_request_provider_data()
if provider_data is None or not provider_data.together_api_key:
raise ValueError(
'Pass Together API Key in the header X-LlamaStack-ProviderData as { "together_api_key": <your api key>}'
)
together_api_key = provider_data.together_api_key
client = Together(api_key=together_api_key)
# wrapper request to make it easier to pass around (internal only, not exposed to API)
@ -125,7 +122,7 @@ class TogetherInferenceAdapter(Inference, NeedsRequestProviderData):
# accumulate sampling params and other options to pass to together
options = self.get_together_chat_options(request)
together_model = self.resolve_together_model(request.model)
together_model = self.map_to_provider_model(request.model)
messages = augment_messages_for_tools(request)
if not request.stream:
@ -171,17 +168,10 @@ class TogetherInferenceAdapter(Inference, NeedsRequestProviderData):
stream=True,
**options,
):
if chunk.choices[0].finish_reason:
if (
stop_reason is None and chunk.choices[0].finish_reason == "stop"
) or (
stop_reason is None and chunk.choices[0].finish_reason == "eos"
):
if finish_reason := chunk.choices[0].finish_reason:
if stop_reason is None and finish_reason in ["stop", "eos"]:
stop_reason = StopReason.end_of_turn
elif (
stop_reason is None
and chunk.choices[0].finish_reason == "length"
):
elif stop_reason is None and finish_reason == "length":
stop_reason = StopReason.out_of_tokens
break

8
llama_stack/providers/adapters/memory/chroma/chroma.py
View file

@ -13,7 +13,7 @@ import chromadb
from numpy.typing import NDArray
from llama_stack.apis.memory import * # noqa: F403
from llama_stack.distribution.datatypes import RoutableProvider
from llama_stack.providers.utils.memory.vector_store import (
BankWithIndex,
@ -65,7 +65,7 @@ class ChromaIndex(EmbeddingIndex):
return QueryDocumentsResponse(chunks=chunks, scores=scores)
class ChromaMemoryAdapter(Memory):
class ChromaMemoryAdapter(Memory, RoutableProvider):
def __init__(self, url: str) -> None:
print(f"Initializing ChromaMemoryAdapter with url: {url}")
url = url.rstrip("/")
@ -93,6 +93,10 @@ class ChromaMemoryAdapter(Memory):
async def shutdown(self) -> None:
pass
async def validate_routing_keys(self, routing_keys: List[str]) -> None:
print(f"[chroma] Registering memory bank routing keys: {routing_keys}")
pass
async def create_memory_bank(
self,
name: str,

13
llama_stack/providers/adapters/memory/pgvector/pgvector.py
View file

@ -5,16 +5,17 @@
# the root directory of this source tree.
import uuid
from typing import List, Tuple
import psycopg2
from numpy.typing import NDArray
from psycopg2 import sql
from psycopg2.extras import execute_values, Json
from pydantic import BaseModel
from llama_stack.apis.memory import * # noqa: F403
from pydantic import BaseModel
from llama_stack.apis.memory import * # noqa: F403
from llama_stack.distribution.datatypes import RoutableProvider
from llama_stack.providers.utils.memory.vector_store import (
ALL_MINILM_L6_V2_DIMENSION,
@ -118,7 +119,7 @@ class PGVectorIndex(EmbeddingIndex):
return QueryDocumentsResponse(chunks=chunks, scores=scores)
class PGVectorMemoryAdapter(Memory):
class PGVectorMemoryAdapter(Memory, RoutableProvider):
def __init__(self, config: PGVectorConfig) -> None:
print(f"Initializing PGVectorMemoryAdapter -> {config.host}:{config.port}")
self.config = config
@ -160,6 +161,10 @@ class PGVectorMemoryAdapter(Memory):
async def shutdown(self) -> None:
pass
async def validate_routing_keys(self, routing_keys: List[str]) -> None:
print(f"[pgvector] Registering memory bank routing keys: {routing_keys}")
pass
async def create_memory_bank(
self,
name: str,

39
llama_stack/providers/adapters/safety/bedrock/bedrock.py
View file

@ -4,47 +4,58 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import json
import logging
import traceback
from typing import Any, Dict, List
from .config import BedrockSafetyConfig
import boto3
from llama_stack.apis.safety import * # noqa
from llama_models.llama3.api.datatypes import * # noqa: F403
import json
import logging
from llama_stack.distribution.datatypes import RoutableProvider
import boto3
from .config import BedrockSafetyConfig
logger = logging.getLogger(__name__)
class BedrockSafetyAdapter(Safety):
SUPPORTED_SHIELD_TYPES = [
"bedrock_guardrail",
]
class BedrockSafetyAdapter(Safety, RoutableProvider):
def __init__(self, config: BedrockSafetyConfig) -> None:
if not config.aws_profile:
raise ValueError(f"Missing boto_client aws_profile in model info::{config}")
self.config = config
async def initialize(self) -> None:
if not self.config.aws_profile:
raise RuntimeError(
f"Missing boto_client aws_profile in model info::{self.config}"
)
try:
print(f"initializing with profile --- > {self.config}::")
self.boto_client_profile = self.config.aws_profile
print(f"initializing with profile --- > {self.config}")
self.boto_client = boto3.Session(
profile_name=self.boto_client_profile
profile_name=self.config.aws_profile
).client("bedrock-runtime")
except Exception as e:
raise RuntimeError(f"Error initializing BedrockSafetyAdapter: {e}") from e
raise RuntimeError("Error initializing BedrockSafetyAdapter") from e
async def shutdown(self) -> None:
pass
async def validate_routing_keys(self, routing_keys: List[str]) -> None:
for key in routing_keys:
if key not in SUPPORTED_SHIELD_TYPES:
raise ValueError(f"Unknown safety shield type: {key}")
async def run_shield(
self, shield_type: str, messages: List[Message], params: Dict[str, Any] = None
) -> RunShieldResponse:
if shield_type not in SUPPORTED_SHIELD_TYPES:
raise ValueError(f"Unknown safety shield type: {shield_type}")
"""This is the implementation for the bedrock guardrails. The input to the guardrails is to be of this format
```content = [
{

50
llama_stack/providers/adapters/safety/together/together.py
View file

@ -3,7 +3,6 @@
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from llama_models.sku_list import resolve_model
from together import Together
from llama_models.llama3.api.datatypes import * # noqa: F403
@ -13,53 +12,52 @@ from llama_stack.apis.safety import (
SafetyViolation,
ViolationLevel,
)
from llama_stack.distribution.datatypes import RoutableProvider
from llama_stack.distribution.request_headers import NeedsRequestProviderData
from .config import TogetherSafetyConfig
SAFETY_SHIELD_TYPES = {
"llama_guard": "meta-llama/Meta-Llama-Guard-3-8B",
"Llama-Guard-3-8B": "meta-llama/Meta-Llama-Guard-3-8B",
"Llama-Guard-3-11B-Vision": "meta-llama/Llama-Guard-3-11B-Vision-Turbo",
}
def shield_type_to_model_name(shield_type: str) -> str:
if shield_type == "llama_guard":
shield_type = "Llama-Guard-3-8B"
model = resolve_model(shield_type)
if (
model is None
or not model.descriptor(shorten_default_variant=True) in SAFETY_SHIELD_TYPES
or model.model_family is not ModelFamily.safety
):
raise ValueError(
f"{shield_type} is not supported, please use of {','.join(SAFETY_SHIELD_TYPES.keys())}"
)
return SAFETY_SHIELD_TYPES.get(model.descriptor(shorten_default_variant=True))
class TogetherSafetyImpl(Safety, NeedsRequestProviderData):
class TogetherSafetyImpl(Safety, NeedsRequestProviderData, RoutableProvider):
def __init__(self, config: TogetherSafetyConfig) -> None:
self.config = config
async def initialize(self) -> None:
pass
async def shutdown(self) -> None:
pass
async def validate_routing_keys(self, routing_keys: List[str]) -> None:
for key in routing_keys:
if key not in SAFETY_SHIELD_TYPES:
raise ValueError(f"Unknown safety shield type: {key}")
async def run_shield(
self, shield_type: str, messages: List[Message], params: Dict[str, Any] = None
) -> RunShieldResponse:
if shield_type not in SAFETY_SHIELD_TYPES:
raise ValueError(f"Unknown safety shield type: {shield_type}")
together_api_key = None
provider_data = self.get_request_provider_data()
if provider_data is None or not provider_data.together_api_key:
raise ValueError(
'Pass Together API Key in the header X-LlamaStack-ProviderData as { "together_api_key": <your api key>}'
)
together_api_key = provider_data.together_api_key
if self.config.api_key is not None:
together_api_key = self.config.api_key
else:
provider_data = self.get_request_provider_data()
if provider_data is None or not provider_data.together_api_key:
raise ValueError(
'Pass Together API Key in the header X-LlamaStack-ProviderData as { "together_api_key": <your api key>}'
)
together_api_key = provider_data.together_api_key
model_name = shield_type_to_model_name(shield_type)
model_name = SAFETY_SHIELD_TYPES[shield_type]
# messages can have role assistant or user
api_messages = []

240
llama_stack/providers/datatypes.py Normal file
View file

@ -0,0 +1,240 @@
# 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.
from enum import Enum
from typing import Any, Dict, List, Optional, Protocol, Union
from llama_models.schema_utils import json_schema_type
from pydantic import BaseModel, Field
@json_schema_type
class Api(Enum):
inference = "inference"
safety = "safety"
agents = "agents"
memory = "memory"
telemetry = "telemetry"
models = "models"
shields = "shields"
memory_banks = "memory_banks"
@json_schema_type
class ApiEndpoint(BaseModel):
route: str
method: str
name: str
@json_schema_type
class ProviderSpec(BaseModel):
api: Api
provider_id: str
config_class: str = Field(
...,
description="Fully-qualified classname of the config for this provider",
)
api_dependencies: List[Api] = Field(
default_factory=list,
description="Higher-level API surfaces may depend on other providers to provide their functionality",
)
class RoutingTable(Protocol):
def get_routing_keys(self) -> List[str]: ...
def get_provider_impl(self, routing_key: str) -> Any: ...
class RoutableProvider(Protocol):
"""
A provider which sits behind the RoutingTable and can get routed to.
All Inference / Safety / Memory providers fall into this bucket.
"""
async def validate_routing_keys(self, keys: List[str]) -> None: ...
class GenericProviderConfig(BaseModel):
provider_id: str
config: Dict[str, Any]
class PlaceholderProviderConfig(BaseModel):
"""Placeholder provider config for API whose provider are defined in routing_table"""
providers: List[str]
RoutingKey = Union[str, List[str]]
class RoutableProviderConfig(GenericProviderConfig):
routing_key: RoutingKey
# Example: /inference, /safety
@json_schema_type
class AutoRoutedProviderSpec(ProviderSpec):
provider_id: str = "router"
config_class: str = ""
docker_image: Optional[str] = None
routing_table_api: Api
module: str = Field(
...,
description="""
Fully-qualified name of the module to import. The module is expected to have:
- `get_router_impl(config, provider_specs, deps)`: returns the router implementation
""",
)
provider_data_validator: Optional[str] = Field(
default=None,
)
@property
def pip_packages(self) -> List[str]:
raise AssertionError("Should not be called on AutoRoutedProviderSpec")
# Example: /models, /shields
@json_schema_type
class RoutingTableProviderSpec(ProviderSpec):
provider_id: str = "routing_table"
config_class: str = ""
docker_image: Optional[str] = None
inner_specs: List[ProviderSpec]
module: str = Field(
...,
description="""
Fully-qualified name of the module to import. The module is expected to have:
- `get_router_impl(config, provider_specs, deps)`: returns the router implementation
""",
)
pip_packages: List[str] = Field(default_factory=list)
@json_schema_type
class AdapterSpec(BaseModel):
adapter_id: str = Field(
...,
description="Unique identifier for this adapter",
)
module: str = Field(
...,
description="""
Fully-qualified name of the module to import. The module is expected to have:
- `get_adapter_impl(config, deps)`: returns the adapter implementation
""",
)
pip_packages: List[str] = Field(
default_factory=list,
description="The pip dependencies needed for this implementation",
)
config_class: Optional[str] = Field(
default=None,
description="Fully-qualified classname of the config for this provider",
)
provider_data_validator: Optional[str] = Field(
default=None,
)
@json_schema_type
class InlineProviderSpec(ProviderSpec):
pip_packages: List[str] = Field(
default_factory=list,
description="The pip dependencies needed for this implementation",
)
docker_image: Optional[str] = Field(
default=None,
description="""
The docker image to use for this implementation. If one is provided, pip_packages will be ignored.
If a provider depends on other providers, the dependencies MUST NOT specify a docker image.
""",
)
module: str = Field(
...,
description="""
Fully-qualified name of the module to import. The module is expected to have:
- `get_provider_impl(config, deps)`: returns the local implementation
""",
)
provider_data_validator: Optional[str] = Field(
default=None,
)
class RemoteProviderConfig(BaseModel):
host: str = "localhost"
port: int
@property
def url(self) -> str:
return f"http://{self.host}:{self.port}"
def remote_provider_id(adapter_id: str) -> str:
return f"remote::{adapter_id}"
@json_schema_type
class RemoteProviderSpec(ProviderSpec):
adapter: Optional[AdapterSpec] = Field(
default=None,
description="""
If some code is needed to convert the remote responses into Llama Stack compatible
API responses, specify the adapter here. If not specified, it indicates the remote
as being "Llama Stack compatible"
""",
)
@property
def docker_image(self) -> Optional[str]:
return None
@property
def module(self) -> str:
if self.adapter:
return self.adapter.module
return f"llama_stack.apis.{self.api.value}.client"
@property
def pip_packages(self) -> List[str]:
if self.adapter:
return self.adapter.pip_packages
return []
@property
def provider_data_validator(self) -> Optional[str]:
if self.adapter:
return self.adapter.provider_data_validator
return None
# Can avoid this by using Pydantic computed_field
def remote_provider_spec(
api: Api, adapter: Optional[AdapterSpec] = None
) -> RemoteProviderSpec:
config_class = (
adapter.config_class
if adapter and adapter.config_class
else "llama_stack.distribution.datatypes.RemoteProviderConfig"
)
provider_id = remote_provider_id(adapter.adapter_id) if adapter else "remote"
return RemoteProviderSpec(
api=api, provider_id=provider_id, config_class=config_class, adapter=adapter
)

27
llama_stack/providers/impls/meta_reference/inference/inference.py
View file

@ -6,21 +6,13 @@
import asyncio
from typing import AsyncIterator, Union
from typing import AsyncIterator, List, Union
from llama_models.llama3.api.datatypes import StopReason
from llama_models.sku_list import resolve_model
from llama_stack.apis.inference import (
ChatCompletionRequest,
ChatCompletionResponse,
ChatCompletionResponseEvent,
ChatCompletionResponseEventType,
ChatCompletionResponseStreamChunk,
Inference,
ToolCallDelta,
ToolCallParseStatus,
)
from llama_models.llama3.api.datatypes import * # noqa: F403
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.distribution.datatypes import RoutableProvider
from llama_stack.providers.utils.inference.augment_messages import (
augment_messages_for_tools,
)
@ -28,15 +20,12 @@ from llama_stack.providers.utils.inference.augment_messages import (
from .config import MetaReferenceImplConfig
from .model_parallel import LlamaModelParallelGenerator
from llama_models.llama3.api.datatypes import * # noqa: F403
from llama_stack.apis.inference import * # noqa: F403
# there's a single model parallel process running serving the model. for now,
# we don't support multiple concurrent requests to this process.
SEMAPHORE = asyncio.Semaphore(1)
class MetaReferenceInferenceImpl(Inference):
class MetaReferenceInferenceImpl(Inference, RoutableProvider):
def __init__(self, config: MetaReferenceImplConfig) -> None:
self.config = config
model = resolve_model(config.model)
@ -49,6 +38,12 @@ class MetaReferenceInferenceImpl(Inference):
self.generator = LlamaModelParallelGenerator(self.config)
self.generator.start()
async def validate_routing_keys(self, routing_keys: List[str]) -> None:
assert (
len(routing_keys) == 1
), f"Only one routing key is supported {routing_keys}"
assert routing_keys[0] == self.config.model
async def shutdown(self) -> None:
self.generator.stop()

7
llama_stack/providers/impls/meta_reference/memory/faiss.py
View file

@ -14,6 +14,7 @@ import numpy as np
from numpy.typing import NDArray
from llama_models.llama3.api.datatypes import * # noqa: F403
from llama_stack.distribution.datatypes import RoutableProvider
from llama_stack.apis.memory import * # noqa: F403
from llama_stack.providers.utils.memory.vector_store import (
@ -62,7 +63,7 @@ class FaissIndex(EmbeddingIndex):
return QueryDocumentsResponse(chunks=chunks, scores=scores)
class FaissMemoryImpl(Memory):
class FaissMemoryImpl(Memory, RoutableProvider):
def __init__(self, config: FaissImplConfig) -> None:
self.config = config
self.cache = {}
@ -71,6 +72,10 @@ class FaissMemoryImpl(Memory):
async def shutdown(self) -> None: ...
async def validate_routing_keys(self, routing_keys: List[str]) -> None:
print(f"[faiss] Registering memory bank routing keys: {routing_keys}")
pass
async def create_memory_bank(
self,
name: str,

15
llama_stack/providers/impls/meta_reference/safety/safety.py
View file

@ -4,13 +4,15 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from typing import Any, Dict, List
from llama_models.sku_list import resolve_model
from llama_stack.distribution.utils.model_utils import model_local_dir
from llama_stack.apis.inference import * # noqa: F403
from llama_stack.apis.safety import * # noqa: F403
from llama_models.llama3.api.datatypes import * # noqa: F403
from llama_stack.distribution.datatypes import Api
from llama_stack.distribution.datatypes import Api, RoutableProvider
from llama_stack.providers.impls.meta_reference.safety.shields.base import (
OnViolationAction,
@ -35,7 +37,7 @@ def resolve_and_get_path(model_name: str) -> str:
return model_dir
class MetaReferenceSafetyImpl(Safety):
class MetaReferenceSafetyImpl(Safety, RoutableProvider):
def __init__(self, config: SafetyConfig, deps) -> None:
self.config = config
self.inference_api = deps[Api.inference]
@ -46,6 +48,15 @@ class MetaReferenceSafetyImpl(Safety):
model_dir = resolve_and_get_path(shield_cfg.model)
_ = PromptGuardShield.instance(model_dir)
async def shutdown(self) -> None:
pass
async def validate_routing_keys(self, routing_keys: List[str]) -> None:
available_shields = [v.value for v in MetaReferenceShieldType]
for key in routing_keys:
if key not in available_shields:
raise ValueError(f"Unknown safety shield type: {key}")
async def run_shield(
self,
shield_type: str,

36
llama_stack/providers/utils/inference/routable.py Normal file
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@ -0,0 +1,36 @@
# 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.
from typing import Dict, List
from llama_models.sku_list import resolve_model
from llama_stack.distribution.datatypes import RoutableProvider
class RoutableProviderForModels(RoutableProvider):
def __init__(self, stack_to_provider_models_map: Dict[str, str]):
self.stack_to_provider_models_map = stack_to_provider_models_map
async def validate_routing_keys(self, routing_keys: List[str]):
for routing_key in routing_keys:
if routing_key not in self.stack_to_provider_models_map:
raise ValueError(
f"Routing key {routing_key} not found in map {self.stack_to_provider_models_map}"
)
def map_to_provider_model(self, routing_key: str) -> str:
model = resolve_model(routing_key)
if not model:
raise ValueError(f"Unknown model: `{routing_key}`")
if routing_key not in self.stack_to_provider_models_map:
raise ValueError(
f"Model {routing_key} not found in map {self.stack_to_provider_models_map}"
)
return self.stack_to_provider_models_map[routing_key]