phoenix-oss/llama-stack-mirror
0
0
Fork 1
mirror of https://github.com/meta-llama/llama-stack.git synced 2025-10-04 04:04:14 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

feat: combine ProviderSpec datatypes (#3378)
Some checks failed
Unit Tests / unit-tests (3.13) (push) Failing after 3s
Details
UI Tests / ui-tests (22) (push) Successful in 36s
Details
Update ReadTheDocs / update-readthedocs (push) Failing after 3s
Details
Test Llama Stack Build / build (push) Failing after 4s
Details
Pre-commit / pre-commit (push) Successful in 1m12s
Details
SqlStore Integration Tests / test-postgres (3.13) (push) Failing after 1s
Details
Integration Auth Tests / test-matrix (oauth2_token) (push) Failing after 2s
Details
Integration Tests (Replay) / Integration Tests (, , , client=, ) (push) Failing after 3s
Details
Test External Providers Installed via Module / test-external-providers-from-module (venv) (push) Has been skipped
Details
Python Package Build Test / build (3.13) (push) Failing after 1s
Details
Test Llama Stack Build / build-single-provider (push) Failing after 3s
Details
Test Llama Stack Build / build-custom-container-distribution (push) Failing after 3s
Details
Unit Tests / unit-tests (3.12) (push) Failing after 3s
Details
Python Package Build Test / build (3.12) (push) Failing after 2s
Details
SqlStore Integration Tests / test-postgres (3.12) (push) Failing after 7s
Details
Vector IO Integration Tests / test-matrix (push) Failing after 5s
Details
API Conformance Tests / check-schema-compatibility (push) Successful in 7s
Details
Test Llama Stack Build / generate-matrix (push) Successful in 5s
Details
Test External API and Providers / test-external (venv) (push) Failing after 4s
Details
Test Llama Stack Build / build-ubi9-container-distribution (push) Failing after 3s
Details

Browse source 
# What does this PR do?

currently `RemoteProviderSpec` has an `AdapterSpec` embedded in it.
Remove `AdapterSpec`, and put its leftover fields into
`RemoteProviderSpec`.

Additionally, many of the fields were duplicated between
`InlineProviderSpec` and `RemoteProviderSpec`. Move these to
`ProviderSpec` so they are shared.

Fixup the distro codegen to use `RemoteProviderSpec` directly rather
than `remote_provider_spec` which took an AdapterSpec and returned a
full provider spec

## Test Plan

existing distro tests should pass.

Signed-off-by: Charlie Doern <cdoern@redhat.com>
This commit is contained in:
Charlie Doern 2025-09-18 10:10:00 -04:00 • committed by GitHub
parent e66103c09d
commit 8422bd102a
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
15 changed files with 381 additions and 503 deletions
Download patch file Download diff file Expand all files Collapse all files

4
llama_stack/core/datatypes.py
View file

@ -121,10 +121,6 @@ class AutoRoutedProviderSpec(ProviderSpec):
default=None, default=None,
) )
@property
def pip_packages(self) -> list[str]:
raise AssertionError("Should not be called on AutoRoutedProviderSpec")
# Example: /models, /shields # Example: /models, /shields
class RoutingTableProviderSpec(ProviderSpec): class RoutingTableProviderSpec(ProviderSpec):

22
llama_stack/core/distribution.py
View file

@ -16,11 +16,10 @@ from llama_stack.core.datatypes import BuildConfig, DistributionSpec
from llama_stack.core.external import load_external_apis from llama_stack.core.external import load_external_apis
from llama_stack.log import get_logger from llama_stack.log import get_logger
from llama_stack.providers.datatypes import ( from llama_stack.providers.datatypes import (
AdapterSpec,
Api, Api,
InlineProviderSpec, InlineProviderSpec,
ProviderSpec, ProviderSpec,
remote_provider_spec, RemoteProviderSpec,
) )
logger = get_logger(name=__name__, category="core") logger = get_logger(name=__name__, category="core")
@ -77,27 +76,12 @@ def providable_apis() -> list[Api]:
def _load_remote_provider_spec(spec_data: dict[str, Any], api: Api) -> ProviderSpec: def _load_remote_provider_spec(spec_data: dict[str, Any], api: Api) -> ProviderSpec:
adapter = AdapterSpec(**spec_data["adapter"]) spec = RemoteProviderSpec(api=api, provider_type=f"remote::{spec_data['adapter_type']}", **spec_data)
spec = remote_provider_spec(
api=api,
adapter=adapter,
api_dependencies=[Api(dep) for dep in spec_data.get("api_dependencies", [])],
)
return spec return spec
def _load_inline_provider_spec(spec_data: dict[str, Any], api: Api, provider_name: str) -> ProviderSpec: def _load_inline_provider_spec(spec_data: dict[str, Any], api: Api, provider_name: str) -> ProviderSpec:
spec = InlineProviderSpec( spec = InlineProviderSpec(api=api, provider_type=f"inline::{provider_name}", **spec_data)
api=api,
provider_type=f"inline::{provider_name}",
pip_packages=spec_data.get("pip_packages", []),
module=spec_data["module"],
config_class=spec_data["config_class"],
api_dependencies=[Api(dep) for dep in spec_data.get("api_dependencies", [])],
optional_api_dependencies=[Api(dep) for dep in spec_data.get("optional_api_dependencies", [])],
provider_data_validator=spec_data.get("provider_data_validator"),
container_image=spec_data.get("container_image"),
)
return spec return spec

4
llama_stack/distributions/starter/starter.py
View file

@ -78,12 +78,12 @@ def get_remote_inference_providers() -> list[Provider]:
remote_providers = [ remote_providers = [
provider provider
for provider in available_providers() for provider in available_providers()
if isinstance(provider, RemoteProviderSpec) and provider.adapter.adapter_type in ENABLED_INFERENCE_PROVIDERS if isinstance(provider, RemoteProviderSpec) and provider.adapter_type in ENABLED_INFERENCE_PROVIDERS
] ]
inference_providers = [] inference_providers = []
for provider_spec in remote_providers: for provider_spec in remote_providers:
provider_type = provider_spec.adapter.adapter_type provider_type = provider_spec.adapter_type
if provider_type in INFERENCE_PROVIDER_IDS: if provider_type in INFERENCE_PROVIDER_IDS:
provider_id = INFERENCE_PROVIDER_IDS[provider_type] provider_id = INFERENCE_PROVIDER_IDS[provider_type]

88
llama_stack/providers/datatypes.py
View file

@ -131,6 +131,15 @@ class ProviderSpec(BaseModel):
""", """,
) )
pip_packages: list[str] = Field(
default_factory=list,
description="The pip dependencies needed for this implementation",
)
provider_data_validator: str | None = Field(
default=None,
)
is_external: bool = Field(default=False, description="Notes whether this provider is an external provider.") is_external: bool = Field(default=False, description="Notes whether this provider is an external provider.")
# used internally by the resolver; this is a hack for now # used internally by the resolver; this is a hack for now
@ -145,45 +154,8 @@ class RoutingTable(Protocol):
async def get_provider_impl(self, routing_key: str) -> Any: ... async def get_provider_impl(self, routing_key: str) -> Any: ...
# TODO: this can now be inlined into RemoteProviderSpec
@json_schema_type
class AdapterSpec(BaseModel):
adapter_type: str = Field(
...,
description="Unique identifier for this adapter",
)
module: str = Field(
default_factory=str,
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: str = Field(
description="Fully-qualified classname of the config for this provider",
)
provider_data_validator: str | None = Field(
default=None,
)
description: str | None = Field(
default=None,
description="""
A description of the provider. This is used to display in the documentation.
""",
)
@json_schema_type @json_schema_type
class InlineProviderSpec(ProviderSpec): class InlineProviderSpec(ProviderSpec):
pip_packages: list[str] = Field(
default_factory=list,
description="The pip dependencies needed for this implementation",
)
container_image: str | None = Field( container_image: str | None = Field(
default=None, default=None,
description=""" description="""
@ -191,10 +163,6 @@ The container image to use for this implementation. If one is provided, pip_pack
If a provider depends on other providers, the dependencies MUST NOT specify a container image. If a provider depends on other providers, the dependencies MUST NOT specify a container image.
""", """,
) )
# module field is inherited from ProviderSpec
provider_data_validator: str | None = Field(
default=None,
)
description: str | None = Field( description: str | None = Field(
default=None, default=None,
description=""" description="""
@ -223,10 +191,15 @@ class RemoteProviderConfig(BaseModel):
@json_schema_type @json_schema_type
class RemoteProviderSpec(ProviderSpec): class RemoteProviderSpec(ProviderSpec):
adapter: AdapterSpec = Field( adapter_type: str = Field(
...,
description="Unique identifier for this adapter",
)
description: str | None = Field(
default=None,
description=""" description="""
If some code is needed to convert the remote responses into Llama Stack compatible A description of the provider. This is used to display in the documentation.
API responses, specify the adapter here.
""", """,
) )
@ -234,33 +207,6 @@ API responses, specify the adapter here.
def container_image(self) -> str | None: def container_image(self) -> str | None:
return None return None
# module field is inherited from ProviderSpec
@property
def pip_packages(self) -> list[str]:
return self.adapter.pip_packages
@property
def provider_data_validator(self) -> str | None:
return self.adapter.provider_data_validator
def remote_provider_spec(
api: Api,
adapter: AdapterSpec,
api_dependencies: list[Api] | None = None,
optional_api_dependencies: list[Api] | None = None,
) -> RemoteProviderSpec:
return RemoteProviderSpec(
api=api,
provider_type=f"remote::{adapter.adapter_type}",
config_class=adapter.config_class,
module=adapter.module,
adapter=adapter,
api_dependencies=api_dependencies or [],
optional_api_dependencies=optional_api_dependencies or [],
)
class HealthStatus(StrEnum): class HealthStatus(StrEnum):
OK = "OK" OK = "OK"

41
llama_stack/providers/registry/datasetio.py
View file

@ -6,11 +6,10 @@
from llama_stack.providers.datatypes import ( from llama_stack.providers.datatypes import (
AdapterSpec,
Api, Api,
InlineProviderSpec, InlineProviderSpec,
ProviderSpec, ProviderSpec,
remote_provider_spec, RemoteProviderSpec,
) )
@ -25,28 +24,26 @@ def available_providers() -> list[ProviderSpec]:
api_dependencies=[], api_dependencies=[],
description="Local filesystem-based dataset I/O provider for reading and writing datasets to local storage.", description="Local filesystem-based dataset I/O provider for reading and writing datasets to local storage.",
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.datasetio, api=Api.datasetio,
adapter=AdapterSpec( adapter_type="huggingface",
adapter_type="huggingface", provider_type="remote::huggingface",
pip_packages=[ pip_packages=[
"datasets>=4.0.0", "datasets>=4.0.0",
], ],
module="llama_stack.providers.remote.datasetio.huggingface", module="llama_stack.providers.remote.datasetio.huggingface",
config_class="llama_stack.providers.remote.datasetio.huggingface.HuggingfaceDatasetIOConfig", config_class="llama_stack.providers.remote.datasetio.huggingface.HuggingfaceDatasetIOConfig",
description="HuggingFace datasets provider for accessing and managing datasets from the HuggingFace Hub.", description="HuggingFace datasets provider for accessing and managing datasets from the HuggingFace Hub.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.datasetio, api=Api.datasetio,
adapter=AdapterSpec( adapter_type="nvidia",
adapter_type="nvidia", provider_type="remote::nvidia",
pip_packages=[ module="llama_stack.providers.remote.datasetio.nvidia",
"datasets>=4.0.0", config_class="llama_stack.providers.remote.datasetio.nvidia.NvidiaDatasetIOConfig",
], pip_packages=[
module="llama_stack.providers.remote.datasetio.nvidia", "datasets>=4.0.0",
config_class="llama_stack.providers.remote.datasetio.nvidia.NvidiaDatasetIOConfig", ],
description="NVIDIA's dataset I/O provider for accessing datasets from NVIDIA's data platform.", description="NVIDIA's dataset I/O provider for accessing datasets from NVIDIA's data platform.",
),
), ),
] ]

21
llama_stack/providers/registry/eval.py
View file

@ -5,7 +5,7 @@
# the root directory of this source tree. # the root directory of this source tree.
from llama_stack.providers.datatypes import AdapterSpec, Api, InlineProviderSpec, ProviderSpec, remote_provider_spec from llama_stack.providers.datatypes import Api, InlineProviderSpec, ProviderSpec, RemoteProviderSpec
def available_providers() -> list[ProviderSpec]: def available_providers() -> list[ProviderSpec]:
@ -25,17 +25,16 @@ def available_providers() -> list[ProviderSpec]:
], ],
description="Meta's reference implementation of evaluation tasks with support for multiple languages and evaluation metrics.", description="Meta's reference implementation of evaluation tasks with support for multiple languages and evaluation metrics.",
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.eval, api=Api.eval,
adapter=AdapterSpec( adapter_type="nvidia",
adapter_type="nvidia", pip_packages=[
pip_packages=[ "requests",
"requests", ],
], provider_type="remote::nvidia",
module="llama_stack.providers.remote.eval.nvidia", module="llama_stack.providers.remote.eval.nvidia",
config_class="llama_stack.providers.remote.eval.nvidia.NVIDIAEvalConfig", config_class="llama_stack.providers.remote.eval.nvidia.NVIDIAEvalConfig",
description="NVIDIA's evaluation provider for running evaluation tasks on NVIDIA's platform.", description="NVIDIA's evaluation provider for running evaluation tasks on NVIDIA's platform.",
),
api_dependencies=[ api_dependencies=[
Api.datasetio, Api.datasetio,
Api.datasets, Api.datasets,

23
llama_stack/providers/registry/files.py
View file

@ -4,13 +4,7 @@
# This source code is licensed under the terms described in the LICENSE file in # This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree. # the root directory of this source tree.
from llama_stack.providers.datatypes import ( from llama_stack.providers.datatypes import Api, InlineProviderSpec, ProviderSpec, RemoteProviderSpec
AdapterSpec,
Api,
InlineProviderSpec,
ProviderSpec,
remote_provider_spec,
)
from llama_stack.providers.utils.sqlstore.sqlstore import sql_store_pip_packages from llama_stack.providers.utils.sqlstore.sqlstore import sql_store_pip_packages
@ -25,14 +19,13 @@ def available_providers() -> list[ProviderSpec]:
config_class="llama_stack.providers.inline.files.localfs.config.LocalfsFilesImplConfig", config_class="llama_stack.providers.inline.files.localfs.config.LocalfsFilesImplConfig",
description="Local filesystem-based file storage provider for managing files and documents locally.", description="Local filesystem-based file storage provider for managing files and documents locally.",
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.files, api=Api.files,
adapter=AdapterSpec( provider_type="remote::s3",
adapter_type="s3", adapter_type="s3",
pip_packages=["boto3"] + sql_store_pip_packages, pip_packages=["boto3"] + sql_store_pip_packages,
module="llama_stack.providers.remote.files.s3", module="llama_stack.providers.remote.files.s3",
config_class="llama_stack.providers.remote.files.s3.config.S3FilesImplConfig", config_class="llama_stack.providers.remote.files.s3.config.S3FilesImplConfig",
description="AWS S3-based file storage provider for scalable cloud file management with metadata persistence.", description="AWS S3-based file storage provider for scalable cloud file management with metadata persistence.",
),
), ),
] ]

380
llama_stack/providers/registry/inference.py
View file

@ -6,11 +6,10 @@
from llama_stack.providers.datatypes import ( from llama_stack.providers.datatypes import (
AdapterSpec,
Api, Api,
InlineProviderSpec, InlineProviderSpec,
ProviderSpec, ProviderSpec,
remote_provider_spec, RemoteProviderSpec,
) )
META_REFERENCE_DEPS = [ META_REFERENCE_DEPS = [
@ -49,177 +48,167 @@ def available_providers() -> list[ProviderSpec]:
config_class="llama_stack.providers.inline.inference.sentence_transformers.config.SentenceTransformersInferenceConfig", config_class="llama_stack.providers.inline.inference.sentence_transformers.config.SentenceTransformersInferenceConfig",
description="Sentence Transformers inference provider for text embeddings and similarity search.", description="Sentence Transformers inference provider for text embeddings and similarity search.",
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="cerebras",
adapter_type="cerebras", provider_type="remote::cerebras",
pip_packages=[ pip_packages=[
"cerebras_cloud_sdk", "cerebras_cloud_sdk",
], ],
module="llama_stack.providers.remote.inference.cerebras", module="llama_stack.providers.remote.inference.cerebras",
config_class="llama_stack.providers.remote.inference.cerebras.CerebrasImplConfig", config_class="llama_stack.providers.remote.inference.cerebras.CerebrasImplConfig",
description="Cerebras inference provider for running models on Cerebras Cloud platform.", description="Cerebras inference provider for running models on Cerebras Cloud platform.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="ollama",
adapter_type="ollama", provider_type="remote::ollama",
pip_packages=["ollama", "aiohttp", "h11>=0.16.0"], pip_packages=["ollama", "aiohttp", "h11>=0.16.0"],
config_class="llama_stack.providers.remote.inference.ollama.OllamaImplConfig", config_class="llama_stack.providers.remote.inference.ollama.OllamaImplConfig",
module="llama_stack.providers.remote.inference.ollama", module="llama_stack.providers.remote.inference.ollama",
description="Ollama inference provider for running local models through the Ollama runtime.", description="Ollama inference provider for running local models through the Ollama runtime.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="vllm",
adapter_type="vllm", provider_type="remote::vllm",
pip_packages=[], pip_packages=[],
module="llama_stack.providers.remote.inference.vllm", module="llama_stack.providers.remote.inference.vllm",
config_class="llama_stack.providers.remote.inference.vllm.VLLMInferenceAdapterConfig", config_class="llama_stack.providers.remote.inference.vllm.VLLMInferenceAdapterConfig",
provider_data_validator="llama_stack.providers.remote.inference.vllm.VLLMProviderDataValidator", provider_data_validator="llama_stack.providers.remote.inference.vllm.VLLMProviderDataValidator",
description="Remote vLLM inference provider for connecting to vLLM servers.", description="Remote vLLM inference provider for connecting to vLLM servers.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="tgi",
adapter_type="tgi", provider_type="remote::tgi",
pip_packages=["huggingface_hub", "aiohttp"], pip_packages=["huggingface_hub", "aiohttp"],
module="llama_stack.providers.remote.inference.tgi", module="llama_stack.providers.remote.inference.tgi",
config_class="llama_stack.providers.remote.inference.tgi.TGIImplConfig", config_class="llama_stack.providers.remote.inference.tgi.TGIImplConfig",
description="Text Generation Inference (TGI) provider for HuggingFace model serving.", description="Text Generation Inference (TGI) provider for HuggingFace model serving.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="hf::serverless",
adapter_type="hf::serverless", provider_type="remote::hf::serverless",
pip_packages=["huggingface_hub", "aiohttp"], pip_packages=["huggingface_hub", "aiohttp"],
module="llama_stack.providers.remote.inference.tgi", module="llama_stack.providers.remote.inference.tgi",
config_class="llama_stack.providers.remote.inference.tgi.InferenceAPIImplConfig", config_class="llama_stack.providers.remote.inference.tgi.InferenceAPIImplConfig",
description="HuggingFace Inference API serverless provider for on-demand model inference.", description="HuggingFace Inference API serverless provider for on-demand model inference.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( provider_type="remote::hf::endpoint",
adapter_type="hf::endpoint", adapter_type="hf::endpoint",
pip_packages=["huggingface_hub", "aiohttp"], pip_packages=["huggingface_hub", "aiohttp"],
module="llama_stack.providers.remote.inference.tgi", module="llama_stack.providers.remote.inference.tgi",
config_class="llama_stack.providers.remote.inference.tgi.InferenceEndpointImplConfig", config_class="llama_stack.providers.remote.inference.tgi.InferenceEndpointImplConfig",
description="HuggingFace Inference Endpoints provider for dedicated model serving.", description="HuggingFace Inference Endpoints provider for dedicated model serving.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="fireworks",
adapter_type="fireworks", provider_type="remote::fireworks",
pip_packages=[ pip_packages=[
"fireworks-ai<=0.17.16", "fireworks-ai<=0.17.16",
], ],
module="llama_stack.providers.remote.inference.fireworks", module="llama_stack.providers.remote.inference.fireworks",
config_class="llama_stack.providers.remote.inference.fireworks.FireworksImplConfig", config_class="llama_stack.providers.remote.inference.fireworks.FireworksImplConfig",
provider_data_validator="llama_stack.providers.remote.inference.fireworks.FireworksProviderDataValidator", provider_data_validator="llama_stack.providers.remote.inference.fireworks.FireworksProviderDataValidator",
description="Fireworks AI inference provider for Llama models and other AI models on the Fireworks platform.", description="Fireworks AI inference provider for Llama models and other AI models on the Fireworks platform.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="together",
adapter_type="together", provider_type="remote::together",
pip_packages=[ pip_packages=[
"together", "together",
], ],
module="llama_stack.providers.remote.inference.together", module="llama_stack.providers.remote.inference.together",
config_class="llama_stack.providers.remote.inference.together.TogetherImplConfig", config_class="llama_stack.providers.remote.inference.together.TogetherImplConfig",
provider_data_validator="llama_stack.providers.remote.inference.together.TogetherProviderDataValidator", provider_data_validator="llama_stack.providers.remote.inference.together.TogetherProviderDataValidator",
description="Together AI inference provider for open-source models and collaborative AI development.", description="Together AI inference provider for open-source models and collaborative AI development.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="bedrock",
adapter_type="bedrock", provider_type="remote::bedrock",
pip_packages=["boto3"], pip_packages=["boto3"],
module="llama_stack.providers.remote.inference.bedrock", module="llama_stack.providers.remote.inference.bedrock",
config_class="llama_stack.providers.remote.inference.bedrock.BedrockConfig", config_class="llama_stack.providers.remote.inference.bedrock.BedrockConfig",
description="AWS Bedrock inference provider for accessing various AI models through AWS's managed service.", description="AWS Bedrock inference provider for accessing various AI models through AWS's managed service.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="databricks",
adapter_type="databricks", provider_type="remote::databricks",
pip_packages=[], pip_packages=[],
module="llama_stack.providers.remote.inference.databricks", module="llama_stack.providers.remote.inference.databricks",
config_class="llama_stack.providers.remote.inference.databricks.DatabricksImplConfig", config_class="llama_stack.providers.remote.inference.databricks.DatabricksImplConfig",
description="Databricks inference provider for running models on Databricks' unified analytics platform.", description="Databricks inference provider for running models on Databricks' unified analytics platform.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="nvidia",
adapter_type="nvidia", provider_type="remote::nvidia",
pip_packages=[], pip_packages=[],
module="llama_stack.providers.remote.inference.nvidia", module="llama_stack.providers.remote.inference.nvidia",
config_class="llama_stack.providers.remote.inference.nvidia.NVIDIAConfig", config_class="llama_stack.providers.remote.inference.nvidia.NVIDIAConfig",
description="NVIDIA inference provider for accessing NVIDIA NIM models and AI services.", description="NVIDIA inference provider for accessing NVIDIA NIM models and AI services.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="runpod",
adapter_type="runpod", provider_type="remote::runpod",
pip_packages=[], pip_packages=[],
module="llama_stack.providers.remote.inference.runpod", module="llama_stack.providers.remote.inference.runpod",
config_class="llama_stack.providers.remote.inference.runpod.RunpodImplConfig", config_class="llama_stack.providers.remote.inference.runpod.RunpodImplConfig",
description="RunPod inference provider for running models on RunPod's cloud GPU platform.", description="RunPod inference provider for running models on RunPod's cloud GPU platform.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="openai",
adapter_type="openai", provider_type="remote::openai",
pip_packages=["litellm"], pip_packages=["litellm"],
module="llama_stack.providers.remote.inference.openai", module="llama_stack.providers.remote.inference.openai",
config_class="llama_stack.providers.remote.inference.openai.OpenAIConfig", config_class="llama_stack.providers.remote.inference.openai.OpenAIConfig",
provider_data_validator="llama_stack.providers.remote.inference.openai.config.OpenAIProviderDataValidator", provider_data_validator="llama_stack.providers.remote.inference.openai.config.OpenAIProviderDataValidator",
description="OpenAI inference provider for accessing GPT models and other OpenAI services.", description="OpenAI inference provider for accessing GPT models and other OpenAI services.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="anthropic",
adapter_type="anthropic", provider_type="remote::anthropic",
pip_packages=["litellm"], pip_packages=["litellm"],
module="llama_stack.providers.remote.inference.anthropic", module="llama_stack.providers.remote.inference.anthropic",
config_class="llama_stack.providers.remote.inference.anthropic.AnthropicConfig", config_class="llama_stack.providers.remote.inference.anthropic.AnthropicConfig",
provider_data_validator="llama_stack.providers.remote.inference.anthropic.config.AnthropicProviderDataValidator", provider_data_validator="llama_stack.providers.remote.inference.anthropic.config.AnthropicProviderDataValidator",
description="Anthropic inference provider for accessing Claude models and Anthropic's AI services.", description="Anthropic inference provider for accessing Claude models and Anthropic's AI services.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="gemini",
adapter_type="gemini", provider_type="remote::gemini",
pip_packages=["litellm"], pip_packages=[
module="llama_stack.providers.remote.inference.gemini", "litellm",
config_class="llama_stack.providers.remote.inference.gemini.GeminiConfig", ],
provider_data_validator="llama_stack.providers.remote.inference.gemini.config.GeminiProviderDataValidator", module="llama_stack.providers.remote.inference.gemini",
description="Google Gemini inference provider for accessing Gemini models and Google's AI services.", config_class="llama_stack.providers.remote.inference.gemini.GeminiConfig",
), provider_data_validator="llama_stack.providers.remote.inference.gemini.config.GeminiProviderDataValidator",
description="Google Gemini inference provider for accessing Gemini models and Google's AI services.",
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="vertexai",
adapter_type="vertexai", provider_type="remote::vertexai",
pip_packages=["litellm", "google-cloud-aiplatform"], pip_packages=[
module="llama_stack.providers.remote.inference.vertexai", "litellm",
config_class="llama_stack.providers.remote.inference.vertexai.VertexAIConfig", "google-cloud-aiplatform",
provider_data_validator="llama_stack.providers.remote.inference.vertexai.config.VertexAIProviderDataValidator", ],
description="""Google Vertex AI inference provider enables you to use Google's Gemini models through Google Cloud's Vertex AI platform, providing several advantages: module="llama_stack.providers.remote.inference.vertexai",
config_class="llama_stack.providers.remote.inference.vertexai.VertexAIConfig",
provider_data_validator="llama_stack.providers.remote.inference.vertexai.config.VertexAIProviderDataValidator",
description="""Google Vertex AI inference provider enables you to use Google's Gemini models through Google Cloud's Vertex AI platform, providing several advantages:
• Enterprise-grade security: Uses Google Cloud's security controls and IAM • Enterprise-grade security: Uses Google Cloud's security controls and IAM
• Better integration: Seamless integration with other Google Cloud services • Better integration: Seamless integration with other Google Cloud services
@ -239,76 +228,73 @@ Available Models:
- vertex_ai/gemini-2.0-flash - vertex_ai/gemini-2.0-flash
- vertex_ai/gemini-2.5-flash - vertex_ai/gemini-2.5-flash
- vertex_ai/gemini-2.5-pro""", - vertex_ai/gemini-2.5-pro""",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="groq",
adapter_type="groq", provider_type="remote::groq",
pip_packages=["litellm"], pip_packages=[
module="llama_stack.providers.remote.inference.groq", "litellm",
config_class="llama_stack.providers.remote.inference.groq.GroqConfig", ],
provider_data_validator="llama_stack.providers.remote.inference.groq.config.GroqProviderDataValidator", module="llama_stack.providers.remote.inference.groq",
description="Groq inference provider for ultra-fast inference using Groq's LPU technology.", config_class="llama_stack.providers.remote.inference.groq.GroqConfig",
), provider_data_validator="llama_stack.providers.remote.inference.groq.config.GroqProviderDataValidator",
description="Groq inference provider for ultra-fast inference using Groq's LPU technology.",
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="llama-openai-compat",
adapter_type="llama-openai-compat", provider_type="remote::llama-openai-compat",
pip_packages=["litellm"], pip_packages=["litellm"],
module="llama_stack.providers.remote.inference.llama_openai_compat", module="llama_stack.providers.remote.inference.llama_openai_compat",
config_class="llama_stack.providers.remote.inference.llama_openai_compat.config.LlamaCompatConfig", config_class="llama_stack.providers.remote.inference.llama_openai_compat.config.LlamaCompatConfig",
provider_data_validator="llama_stack.providers.remote.inference.llama_openai_compat.config.LlamaProviderDataValidator", provider_data_validator="llama_stack.providers.remote.inference.llama_openai_compat.config.LlamaProviderDataValidator",
description="Llama OpenAI-compatible provider for using Llama models with OpenAI API format.", description="Llama OpenAI-compatible provider for using Llama models with OpenAI API format.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="sambanova",
adapter_type="sambanova", provider_type="remote::sambanova",
pip_packages=["litellm"], pip_packages=[
module="llama_stack.providers.remote.inference.sambanova", "litellm",
config_class="llama_stack.providers.remote.inference.sambanova.SambaNovaImplConfig", ],
provider_data_validator="llama_stack.providers.remote.inference.sambanova.config.SambaNovaProviderDataValidator", module="llama_stack.providers.remote.inference.sambanova",
description="SambaNova inference provider for running models on SambaNova's dataflow architecture.", config_class="llama_stack.providers.remote.inference.sambanova.SambaNovaImplConfig",
), provider_data_validator="llama_stack.providers.remote.inference.sambanova.config.SambaNovaProviderDataValidator",
description="SambaNova inference provider for running models on SambaNova's dataflow architecture.",
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="passthrough",
adapter_type="passthrough", provider_type="remote::passthrough",
pip_packages=[], pip_packages=[],
module="llama_stack.providers.remote.inference.passthrough", module="llama_stack.providers.remote.inference.passthrough",
config_class="llama_stack.providers.remote.inference.passthrough.PassthroughImplConfig", config_class="llama_stack.providers.remote.inference.passthrough.PassthroughImplConfig",
provider_data_validator="llama_stack.providers.remote.inference.passthrough.PassthroughProviderDataValidator", provider_data_validator="llama_stack.providers.remote.inference.passthrough.PassthroughProviderDataValidator",
description="Passthrough inference provider for connecting to any external inference service not directly supported.", description="Passthrough inference provider for connecting to any external inference service not directly supported.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( adapter_type="watsonx",
adapter_type="watsonx", provider_type="remote::watsonx",
pip_packages=["ibm_watsonx_ai"], pip_packages=["ibm_watsonx_ai"],
module="llama_stack.providers.remote.inference.watsonx", module="llama_stack.providers.remote.inference.watsonx",
config_class="llama_stack.providers.remote.inference.watsonx.WatsonXConfig", config_class="llama_stack.providers.remote.inference.watsonx.WatsonXConfig",
provider_data_validator="llama_stack.providers.remote.inference.watsonx.WatsonXProviderDataValidator", provider_data_validator="llama_stack.providers.remote.inference.watsonx.WatsonXProviderDataValidator",
description="IBM WatsonX inference provider for accessing AI models on IBM's WatsonX platform.", description="IBM WatsonX inference provider for accessing AI models on IBM's WatsonX platform.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.inference, api=Api.inference,
adapter=AdapterSpec( provider_type="remote::azure",
adapter_type="azure", adapter_type="azure",
pip_packages=["litellm"], pip_packages=["litellm"],
module="llama_stack.providers.remote.inference.azure", module="llama_stack.providers.remote.inference.azure",
config_class="llama_stack.providers.remote.inference.azure.AzureConfig", config_class="llama_stack.providers.remote.inference.azure.AzureConfig",
provider_data_validator="llama_stack.providers.remote.inference.azure.config.AzureProviderDataValidator", provider_data_validator="llama_stack.providers.remote.inference.azure.config.AzureProviderDataValidator",
description=""" description="""
Azure OpenAI inference provider for accessing GPT models and other Azure services. Azure OpenAI inference provider for accessing GPT models and other Azure services.
Provider documentation Provider documentation
https://learn.microsoft.com/en-us/azure/ai-foundry/openai/overview https://learn.microsoft.com/en-us/azure/ai-foundry/openai/overview
""", """,
),
), ),
] ]

17
llama_stack/providers/registry/post_training.py
View file

@ -7,7 +7,7 @@
from typing import cast from typing import cast
from llama_stack.providers.datatypes import AdapterSpec, Api, InlineProviderSpec, ProviderSpec, remote_provider_spec from llama_stack.providers.datatypes import Api, InlineProviderSpec, ProviderSpec, RemoteProviderSpec
# We provide two versions of these providers so that distributions can package the appropriate version of torch. # We provide two versions of these providers so that distributions can package the appropriate version of torch.
# The CPU version is used for distributions that don't have GPU support -- they result in smaller container images. # The CPU version is used for distributions that don't have GPU support -- they result in smaller container images.
@ -57,14 +57,13 @@ def available_providers() -> list[ProviderSpec]:
], ],
description="HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem.", description="HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem.",
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.post_training, api=Api.post_training,
adapter=AdapterSpec( adapter_type="nvidia",
adapter_type="nvidia", provider_type="remote::nvidia",
pip_packages=["requests", "aiohttp"], pip_packages=["requests", "aiohttp"],
module="llama_stack.providers.remote.post_training.nvidia", module="llama_stack.providers.remote.post_training.nvidia",
config_class="llama_stack.providers.remote.post_training.nvidia.NvidiaPostTrainingConfig", config_class="llama_stack.providers.remote.post_training.nvidia.NvidiaPostTrainingConfig",
description="NVIDIA's post-training provider for fine-tuning models on NVIDIA's platform.", description="NVIDIA's post-training provider for fine-tuning models on NVIDIA's platform.",
),
), ),
] ]

50
llama_stack/providers/registry/safety.py
View file

@ -6,11 +6,10 @@
from llama_stack.providers.datatypes import ( from llama_stack.providers.datatypes import (
AdapterSpec,
Api, Api,
InlineProviderSpec, InlineProviderSpec,
ProviderSpec, ProviderSpec,
remote_provider_spec, RemoteProviderSpec,
) )
@ -48,35 +47,32 @@ def available_providers() -> list[ProviderSpec]:
config_class="llama_stack.providers.inline.safety.code_scanner.CodeScannerConfig", config_class="llama_stack.providers.inline.safety.code_scanner.CodeScannerConfig",
description="Code Scanner safety provider for detecting security vulnerabilities and unsafe code patterns.", description="Code Scanner safety provider for detecting security vulnerabilities and unsafe code patterns.",
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.safety, api=Api.safety,
adapter=AdapterSpec( adapter_type="bedrock",
adapter_type="bedrock", provider_type="remote::bedrock",
pip_packages=["boto3"], pip_packages=["boto3"],
module="llama_stack.providers.remote.safety.bedrock", module="llama_stack.providers.remote.safety.bedrock",
config_class="llama_stack.providers.remote.safety.bedrock.BedrockSafetyConfig", config_class="llama_stack.providers.remote.safety.bedrock.BedrockSafetyConfig",
description="AWS Bedrock safety provider for content moderation using AWS's safety services.", description="AWS Bedrock safety provider for content moderation using AWS's safety services.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.safety, api=Api.safety,
adapter=AdapterSpec( adapter_type="nvidia",
adapter_type="nvidia", provider_type="remote::nvidia",
pip_packages=["requests"], pip_packages=["requests"],
module="llama_stack.providers.remote.safety.nvidia", module="llama_stack.providers.remote.safety.nvidia",
config_class="llama_stack.providers.remote.safety.nvidia.NVIDIASafetyConfig", config_class="llama_stack.providers.remote.safety.nvidia.NVIDIASafetyConfig",
description="NVIDIA's safety provider for content moderation and safety filtering.", description="NVIDIA's safety provider for content moderation and safety filtering.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.safety, api=Api.safety,
adapter=AdapterSpec( adapter_type="sambanova",
adapter_type="sambanova", provider_type="remote::sambanova",
pip_packages=["litellm", "requests"], pip_packages=["litellm", "requests"],
module="llama_stack.providers.remote.safety.sambanova", module="llama_stack.providers.remote.safety.sambanova",
config_class="llama_stack.providers.remote.safety.sambanova.SambaNovaSafetyConfig", config_class="llama_stack.providers.remote.safety.sambanova.SambaNovaSafetyConfig",
provider_data_validator="llama_stack.providers.remote.safety.sambanova.config.SambaNovaProviderDataValidator", provider_data_validator="llama_stack.providers.remote.safety.sambanova.config.SambaNovaProviderDataValidator",
description="SambaNova's safety provider for content moderation and safety filtering.", description="SambaNova's safety provider for content moderation and safety filtering.",
),
), ),
] ]

88
llama_stack/providers/registry/tool_runtime.py
View file

@ -6,11 +6,10 @@
from llama_stack.providers.datatypes import ( from llama_stack.providers.datatypes import (
AdapterSpec,
Api, Api,
InlineProviderSpec, InlineProviderSpec,
ProviderSpec, ProviderSpec,
remote_provider_spec, RemoteProviderSpec,
) )
@ -35,59 +34,54 @@ def available_providers() -> list[ProviderSpec]:
api_dependencies=[Api.vector_io, Api.inference, Api.files], api_dependencies=[Api.vector_io, Api.inference, Api.files],
description="RAG (Retrieval-Augmented Generation) tool runtime for document ingestion, chunking, and semantic search.", description="RAG (Retrieval-Augmented Generation) tool runtime for document ingestion, chunking, and semantic search.",
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.tool_runtime, api=Api.tool_runtime,
adapter=AdapterSpec( adapter_type="brave-search",
adapter_type="brave-search", provider_type="remote::brave-search",
module="llama_stack.providers.remote.tool_runtime.brave_search", module="llama_stack.providers.remote.tool_runtime.brave_search",
config_class="llama_stack.providers.remote.tool_runtime.brave_search.config.BraveSearchToolConfig", config_class="llama_stack.providers.remote.tool_runtime.brave_search.config.BraveSearchToolConfig",
pip_packages=["requests"], pip_packages=["requests"],
provider_data_validator="llama_stack.providers.remote.tool_runtime.brave_search.BraveSearchToolProviderDataValidator", provider_data_validator="llama_stack.providers.remote.tool_runtime.brave_search.BraveSearchToolProviderDataValidator",
description="Brave Search tool for web search capabilities with privacy-focused results.", description="Brave Search tool for web search capabilities with privacy-focused results.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.tool_runtime, api=Api.tool_runtime,
adapter=AdapterSpec( adapter_type="bing-search",
adapter_type="bing-search", provider_type="remote::bing-search",
module="llama_stack.providers.remote.tool_runtime.bing_search", module="llama_stack.providers.remote.tool_runtime.bing_search",
config_class="llama_stack.providers.remote.tool_runtime.bing_search.config.BingSearchToolConfig", config_class="llama_stack.providers.remote.tool_runtime.bing_search.config.BingSearchToolConfig",
pip_packages=["requests"], pip_packages=["requests"],
provider_data_validator="llama_stack.providers.remote.tool_runtime.bing_search.BingSearchToolProviderDataValidator", provider_data_validator="llama_stack.providers.remote.tool_runtime.bing_search.BingSearchToolProviderDataValidator",
description="Bing Search tool for web search capabilities using Microsoft's search engine.", description="Bing Search tool for web search capabilities using Microsoft's search engine.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.tool_runtime, api=Api.tool_runtime,
adapter=AdapterSpec( adapter_type="tavily-search",
adapter_type="tavily-search", provider_type="remote::tavily-search",
module="llama_stack.providers.remote.tool_runtime.tavily_search", module="llama_stack.providers.remote.tool_runtime.tavily_search",
config_class="llama_stack.providers.remote.tool_runtime.tavily_search.config.TavilySearchToolConfig", config_class="llama_stack.providers.remote.tool_runtime.tavily_search.config.TavilySearchToolConfig",
pip_packages=["requests"], pip_packages=["requests"],
provider_data_validator="llama_stack.providers.remote.tool_runtime.tavily_search.TavilySearchToolProviderDataValidator", provider_data_validator="llama_stack.providers.remote.tool_runtime.tavily_search.TavilySearchToolProviderDataValidator",
description="Tavily Search tool for AI-optimized web search with structured results.", description="Tavily Search tool for AI-optimized web search with structured results.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.tool_runtime, api=Api.tool_runtime,
adapter=AdapterSpec( adapter_type="wolfram-alpha",
adapter_type="wolfram-alpha", provider_type="remote::wolfram-alpha",
module="llama_stack.providers.remote.tool_runtime.wolfram_alpha", module="llama_stack.providers.remote.tool_runtime.wolfram_alpha",
config_class="llama_stack.providers.remote.tool_runtime.wolfram_alpha.config.WolframAlphaToolConfig", config_class="llama_stack.providers.remote.tool_runtime.wolfram_alpha.config.WolframAlphaToolConfig",
pip_packages=["requests"], pip_packages=["requests"],
provider_data_validator="llama_stack.providers.remote.tool_runtime.wolfram_alpha.WolframAlphaToolProviderDataValidator", provider_data_validator="llama_stack.providers.remote.tool_runtime.wolfram_alpha.WolframAlphaToolProviderDataValidator",
description="Wolfram Alpha tool for computational knowledge and mathematical calculations.", description="Wolfram Alpha tool for computational knowledge and mathematical calculations.",
),
), ),
remote_provider_spec( RemoteProviderSpec(
api=Api.tool_runtime, api=Api.tool_runtime,
adapter=AdapterSpec( adapter_type="model-context-protocol",
adapter_type="model-context-protocol", provider_type="remote::model-context-protocol",
module="llama_stack.providers.remote.tool_runtime.model_context_protocol", module="llama_stack.providers.remote.tool_runtime.model_context_protocol",
config_class="llama_stack.providers.remote.tool_runtime.model_context_protocol.config.MCPProviderConfig", config_class="llama_stack.providers.remote.tool_runtime.model_context_protocol.config.MCPProviderConfig",
pip_packages=["mcp>=1.8.1"], pip_packages=["mcp>=1.8.1"],
provider_data_validator="llama_stack.providers.remote.tool_runtime.model_context_protocol.config.MCPProviderDataValidator", provider_data_validator="llama_stack.providers.remote.tool_runtime.model_context_protocol.config.MCPProviderDataValidator",
description="Model Context Protocol (MCP) tool for standardized tool calling and context management.", description="Model Context Protocol (MCP) tool for standardized tool calling and context management.",
),
), ),
] ]

108
llama_stack/providers/registry/vector_io.py
View file

@ -6,11 +6,10 @@
from llama_stack.providers.datatypes import ( from llama_stack.providers.datatypes import (
AdapterSpec,
Api, Api,
InlineProviderSpec, InlineProviderSpec,
ProviderSpec, ProviderSpec,
remote_provider_spec, RemoteProviderSpec,
) )
@ -300,14 +299,16 @@ See [sqlite-vec's GitHub repo](https://github.com/asg017/sqlite-vec/tree/main) f
Please refer to the sqlite-vec provider documentation. Please refer to the sqlite-vec provider documentation.
""", """,
), ),
remote_provider_spec( RemoteProviderSpec(
Api.vector_io, api=Api.vector_io,
AdapterSpec( adapter_type="chromadb",
adapter_type="chromadb", provider_type="remote::chromadb",
pip_packages=["chromadb-client"], pip_packages=["chromadb-client"],
module="llama_stack.providers.remote.vector_io.chroma", module="llama_stack.providers.remote.vector_io.chroma",
config_class="llama_stack.providers.remote.vector_io.chroma.ChromaVectorIOConfig", config_class="llama_stack.providers.remote.vector_io.chroma.ChromaVectorIOConfig",
description=""" api_dependencies=[Api.inference],
optional_api_dependencies=[Api.files],
description="""
[Chroma](https://www.trychroma.com/) is an inline and remote vector [Chroma](https://www.trychroma.com/) is an inline and remote vector
database provider for Llama Stack. It allows you to store and query vectors directly within a Chroma database. database provider for Llama Stack. It allows you to store and query vectors directly within a Chroma database.
That means you're not limited to storing vectors in memory or in a separate service. That means you're not limited to storing vectors in memory or in a separate service.
@ -340,9 +341,6 @@ pip install chromadb
## Documentation ## Documentation
See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introduction) for more details about Chroma in general. See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introduction) for more details about Chroma in general.
""", """,
),
api_dependencies=[Api.inference],
optional_api_dependencies=[Api.files],
), ),
InlineProviderSpec( InlineProviderSpec(
api=Api.vector_io, api=Api.vector_io,
@ -387,14 +385,16 @@ See [Chroma's documentation](https://docs.trychroma.com/docs/overview/introducti
""", """,
), ),
remote_provider_spec( RemoteProviderSpec(
Api.vector_io, api=Api.vector_io,
AdapterSpec( adapter_type="pgvector",
adapter_type="pgvector", provider_type="remote::pgvector",
pip_packages=["psycopg2-binary"], pip_packages=["psycopg2-binary"],
module="llama_stack.providers.remote.vector_io.pgvector", module="llama_stack.providers.remote.vector_io.pgvector",
config_class="llama_stack.providers.remote.vector_io.pgvector.PGVectorVectorIOConfig", config_class="llama_stack.providers.remote.vector_io.pgvector.PGVectorVectorIOConfig",
description=""" api_dependencies=[Api.inference],
optional_api_dependencies=[Api.files],
description="""
[PGVector](https://github.com/pgvector/pgvector) is a remote vector database provider for Llama Stack. It [PGVector](https://github.com/pgvector/pgvector) is a remote vector database provider for Llama Stack. It
allows you to store and query vectors directly in memory. allows you to store and query vectors directly in memory.
That means you'll get fast and efficient vector retrieval. That means you'll get fast and efficient vector retrieval.
@ -495,19 +495,18 @@ docker pull pgvector/pgvector:pg17
## Documentation ## Documentation
See [PGVector's documentation](https://github.com/pgvector/pgvector) for more details about PGVector in general. See [PGVector's documentation](https://github.com/pgvector/pgvector) for more details about PGVector in general.
""", """,
), ),
RemoteProviderSpec(
api=Api.vector_io,
adapter_type="weaviate",
provider_type="remote::weaviate",
pip_packages=["weaviate-client"],
module="llama_stack.providers.remote.vector_io.weaviate",
config_class="llama_stack.providers.remote.vector_io.weaviate.WeaviateVectorIOConfig",
provider_data_validator="llama_stack.providers.remote.vector_io.weaviate.WeaviateRequestProviderData",
api_dependencies=[Api.inference], api_dependencies=[Api.inference],
optional_api_dependencies=[Api.files], optional_api_dependencies=[Api.files],
), description="""
remote_provider_spec(
Api.vector_io,
AdapterSpec(
adapter_type="weaviate",
pip_packages=["weaviate-client"],
module="llama_stack.providers.remote.vector_io.weaviate",
config_class="llama_stack.providers.remote.vector_io.weaviate.WeaviateVectorIOConfig",
provider_data_validator="llama_stack.providers.remote.vector_io.weaviate.WeaviateRequestProviderData",
description="""
[Weaviate](https://weaviate.io/) is a vector database provider for Llama Stack. [Weaviate](https://weaviate.io/) is a vector database provider for Llama Stack.
It allows you to store and query vectors directly within a Weaviate database. It allows you to store and query vectors directly within a Weaviate database.
That means you're not limited to storing vectors in memory or in a separate service. That means you're not limited to storing vectors in memory or in a separate service.
@ -538,9 +537,6 @@ To install Weaviate see the [Weaviate quickstart documentation](https://weaviate
## Documentation ## Documentation
See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more details about Weaviate in general. See [Weaviate's documentation](https://weaviate.io/developers/weaviate) for more details about Weaviate in general.
""", """,
),
api_dependencies=[Api.inference],
optional_api_dependencies=[Api.files],
), ),
InlineProviderSpec( InlineProviderSpec(
api=Api.vector_io, api=Api.vector_io,
@ -594,28 +590,29 @@ docker pull qdrant/qdrant
See the [Qdrant documentation](https://qdrant.tech/documentation/) for more details about Qdrant in general. See the [Qdrant documentation](https://qdrant.tech/documentation/) for more details about Qdrant in general.
""", """,
), ),
remote_provider_spec( RemoteProviderSpec(
Api.vector_io, api=Api.vector_io,
AdapterSpec( adapter_type="qdrant",
adapter_type="qdrant", provider_type="remote::qdrant",
pip_packages=["qdrant-client"], pip_packages=["qdrant-client"],
module="llama_stack.providers.remote.vector_io.qdrant", module="llama_stack.providers.remote.vector_io.qdrant",
config_class="llama_stack.providers.remote.vector_io.qdrant.QdrantVectorIOConfig", config_class="llama_stack.providers.remote.vector_io.qdrant.QdrantVectorIOConfig",
description="""
Please refer to the inline provider documentation.
""",
),
api_dependencies=[Api.inference], api_dependencies=[Api.inference],
optional_api_dependencies=[Api.files], optional_api_dependencies=[Api.files],
description="""
Please refer to the inline provider documentation.
""",
), ),
remote_provider_spec( RemoteProviderSpec(
Api.vector_io, api=Api.vector_io,
AdapterSpec( adapter_type="milvus",
adapter_type="milvus", provider_type="remote::milvus",
pip_packages=["pymilvus>=2.4.10"], pip_packages=["pymilvus>=2.4.10"],
module="llama_stack.providers.remote.vector_io.milvus", module="llama_stack.providers.remote.vector_io.milvus",
config_class="llama_stack.providers.remote.vector_io.milvus.MilvusVectorIOConfig", config_class="llama_stack.providers.remote.vector_io.milvus.MilvusVectorIOConfig",
description=""" api_dependencies=[Api.inference],
optional_api_dependencies=[Api.files],
description="""
[Milvus](https://milvus.io/) is an inline and remote vector database provider for Llama Stack. It [Milvus](https://milvus.io/) is an inline and remote vector database provider for Llama Stack. It
allows you to store and query vectors directly within a Milvus database. allows you to store and query vectors directly within a Milvus database.
That means you're not limited to storing vectors in memory or in a separate service. That means you're not limited to storing vectors in memory or in a separate service.
@ -806,9 +803,6 @@ See the [Milvus documentation](https://milvus.io/docs/install-overview.md) for m
For more details on TLS configuration, refer to the [TLS setup guide](https://milvus.io/docs/tls.md). For more details on TLS configuration, refer to the [TLS setup guide](https://milvus.io/docs/tls.md).
""", """,
),
api_dependencies=[Api.inference],
optional_api_dependencies=[Api.files],
), ),
InlineProviderSpec( InlineProviderSpec(
api=Api.vector_io, api=Api.vector_io,

9
tests/external/kaze.yaml vendored
View file

@ -1,6 +1,5 @@
adapter: adapter_type: kaze
adapter_type: kaze pip_packages: ["tests/external/llama-stack-provider-kaze"]
pip_packages: ["tests/external/llama-stack-provider-kaze"] config_class: llama_stack_provider_kaze.config.KazeProviderConfig
config_class: llama_stack_provider_kaze.config.KazeProviderConfig module: llama_stack_provider_kaze
module: llama_stack_provider_kaze
optional_api_dependencies: [] optional_api_dependencies: []

11
tests/external/llama-stack-api-weather/src/llama_stack_api_weather/weather.py vendored
View file

@ -6,7 +6,7 @@
from typing import Protocol from typing import Protocol
from llama_stack.providers.datatypes import AdapterSpec, Api, ProviderSpec, RemoteProviderSpec from llama_stack.providers.datatypes import Api, ProviderSpec, RemoteProviderSpec
from llama_stack.schema_utils import webmethod from llama_stack.schema_utils import webmethod
@ -16,12 +16,9 @@ def available_providers() -> list[ProviderSpec]:
api=Api.weather, api=Api.weather,
provider_type="remote::kaze", provider_type="remote::kaze",
config_class="llama_stack_provider_kaze.KazeProviderConfig", config_class="llama_stack_provider_kaze.KazeProviderConfig",
adapter=AdapterSpec( adapter_type="kaze",
adapter_type="kaze", module="llama_stack_provider_kaze",
module="llama_stack_provider_kaze", pip_packages=["llama_stack_provider_kaze"],
pip_packages=["llama_stack_provider_kaze"],
config_class="llama_stack_provider_kaze.KazeProviderConfig",
),
), ),
] ]

18
tests/unit/distribution/test_distribution.py
View file

@ -66,10 +66,9 @@ def base_config(tmp_path):
def provider_spec_yaml(): def provider_spec_yaml():
"""Common provider spec YAML for testing.""" """Common provider spec YAML for testing."""
return """ return """
adapter: adapter_type: test_provider
adapter_type: test_provider config_class: test_provider.config.TestProviderConfig
config_class: test_provider.config.TestProviderConfig module: test_provider
module: test_provider
api_dependencies: api_dependencies:
- safety - safety
""" """
@ -182,9 +181,9 @@ class TestProviderRegistry:
assert Api.inference in registry assert Api.inference in registry
assert "remote::test_provider" in registry[Api.inference] assert "remote::test_provider" in registry[Api.inference]
provider = registry[Api.inference]["remote::test_provider"] provider = registry[Api.inference]["remote::test_provider"]
assert provider.adapter.adapter_type == "test_provider" assert provider.adapter_type == "test_provider"
assert provider.adapter.module == "test_provider" assert provider.module == "test_provider"
assert provider.adapter.config_class == "test_provider.config.TestProviderConfig" assert provider.config_class == "test_provider.config.TestProviderConfig"
assert Api.safety in provider.api_dependencies assert Api.safety in provider.api_dependencies
def test_external_inline_providers(self, api_directories, mock_providers, base_config, inline_provider_spec_yaml): def test_external_inline_providers(self, api_directories, mock_providers, base_config, inline_provider_spec_yaml):
@ -246,8 +245,7 @@ class TestProviderRegistry:
"""Test handling of malformed remote provider spec (missing required fields).""" """Test handling of malformed remote provider spec (missing required fields)."""
remote_dir, _ = api_directories remote_dir, _ = api_directories
malformed_spec = """ malformed_spec = """
adapter: adapter_type: test_provider
adapter_type: test_provider
# Missing required fields # Missing required fields
api_dependencies: api_dependencies:
- safety - safety
@ -270,7 +268,7 @@ pip_packages:
with open(inline_dir / "malformed.yaml", "w") as f: with open(inline_dir / "malformed.yaml", "w") as f:
f.write(malformed_spec) f.write(malformed_spec)
with pytest.raises(KeyError) as exc_info: with pytest.raises(ValidationError) as exc_info:
get_provider_registry(base_config) get_provider_registry(base_config)
assert "config_class" in str(exc_info.value) assert "config_class" in str(exc_info.value)