Merge branch 'main' into eval_task_register

llama_stack/providers/adapters/safety/bedrock/config.py

@@ -1,16 +0,0 @@
-# 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 pydantic import BaseModel, Field
-
-
-class BedrockSafetyConfig(BaseModel):
-    """Configuration information for a guardrail that you want to use in the request."""
-
-    aws_profile: str = Field(
-        default="default",
-        description="The profile on the machine having valid aws credentials. This will ensure separation of creation to invocation",
-    )

llama_stack/providers/datatypes.py

@@ -145,11 +145,12 @@ Fully-qualified name of the module to import. The module is expected to have:
 
 class RemoteProviderConfig(BaseModel):
     host: str = "localhost"
-    port: int
+    port: int = 0
+    protocol: str = "http"
 
     @property
     def url(self) -> str:
-        return f"http://{self.host}:{self.port}"
+        return f"{self.protocol}://{self.host}:{self.port}"
 
 
 @json_schema_type

llama_stack/providers/inline/braintrust/scoring/braintrust.py

@@ -16,7 +16,7 @@ from llama_stack.apis.datasets import *  # noqa: F403
 from autoevals.llm import Factuality
 from autoevals.ragas import AnswerCorrectness
 from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
-from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.common import (
+from llama_stack.providers.inline.meta_reference.scoring.scoring_fn.common import (
     aggregate_average,
 )
 

llama_stack/providers/inline/meta_reference/agents/config.py

@@ -4,10 +4,11 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 
 from llama_stack.providers.utils.kvstore import KVStoreConfig
+from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
 
 
 class MetaReferenceAgentsImplConfig(BaseModel):
-    persistence_store: KVStoreConfig
+    persistence_store: KVStoreConfig = Field(default=SqliteKVStoreConfig())

llama_stack/providers/inline/meta_reference/agents/safety.py

@@ -32,18 +32,18 @@ class ShieldRunnerMixin:
         self.output_shields = output_shields
 
     async def run_multiple_shields(
-        self, messages: List[Message], shield_types: List[str]
+        self, messages: List[Message], identifiers: List[str]
     ) -> None:
         responses = await asyncio.gather(
             *[
                 self.safety_api.run_shield(
-                    shield_type=shield_type,
+                    identifier=identifier,
                     messages=messages,
                 )
-                for shield_type in shield_types
+                for identifier in identifiers
             ]
         )
-        for shield_type, response in zip(shield_types, responses):
+        for identifier, response in zip(identifiers, responses):
             if not response.violation:
                 continue
 
@@ -52,6 +52,6 @@ class ShieldRunnerMixin:
                 raise SafetyException(violation)
             elif violation.violation_level == ViolationLevel.WARN:
                 cprint(
-                    f"[Warn]{shield_type} raised a warning",
+                    f"[Warn]{identifier} raised a warning",
                     color="red",
                 )

llama_stack/providers/inline/meta_reference/agents/tools/safety.py

@@ -9,7 +9,7 @@ from typing import List
 from llama_stack.apis.inference import Message
 from llama_stack.apis.safety import *  # noqa: F403
 
-from llama_stack.providers.impls.meta_reference.agents.safety import ShieldRunnerMixin
+from llama_stack.providers.inline.meta_reference.agents.safety import ShieldRunnerMixin
 
 from .builtin import BaseTool
 

llama_stack/providers/inline/meta_reference/inference/inference.py

@@ -14,6 +14,11 @@ from llama_models.llama3.api.datatypes import *  # noqa: F403
 from llama_stack.apis.inference import *  # noqa: F403
 from llama_stack.providers.datatypes import ModelDef, ModelsProtocolPrivate
 
+from llama_stack.providers.utils.inference.prompt_adapter import (
+    convert_image_media_to_url,
+    request_has_media,
+)
+
 from .config import MetaReferenceInferenceConfig
 from .generation import Llama
 from .model_parallel import LlamaModelParallelGenerator
@@ -87,6 +92,7 @@ class MetaReferenceInferenceImpl(Inference, ModelsProtocolPrivate):
             logprobs=logprobs,
         )
         self.check_model(request)
+        request = await request_with_localized_media(request)
 
         if request.stream:
             return self._stream_completion(request)
@@ -211,6 +217,7 @@ class MetaReferenceInferenceImpl(Inference, ModelsProtocolPrivate):
             logprobs=logprobs,
         )
         self.check_model(request)
+        request = await request_with_localized_media(request)
 
         if self.config.create_distributed_process_group:
             if SEMAPHORE.locked():
@@ -388,3 +395,31 @@ class MetaReferenceInferenceImpl(Inference, ModelsProtocolPrivate):
         contents: List[InterleavedTextMedia],
     ) -> EmbeddingsResponse:
         raise NotImplementedError()
+
+
+async def request_with_localized_media(
+    request: Union[ChatCompletionRequest, CompletionRequest],
+) -> Union[ChatCompletionRequest, CompletionRequest]:
+    if not request_has_media(request):
+        return request
+
+    async def _convert_single_content(content):
+        if isinstance(content, ImageMedia):
+            url = await convert_image_media_to_url(content, download=True)
+            return ImageMedia(image=URL(uri=url))
+        else:
+            return content
+
+    async def _convert_content(content):
+        if isinstance(content, list):
+            return [await _convert_single_content(c) for c in content]
+        else:
+            return await _convert_single_content(content)
+
+    if isinstance(request, ChatCompletionRequest):
+        for m in request.messages:
+            m.content = await _convert_content(m.content)
+    else:
+        request.content = await _convert_content(request.content)
+
+    return request

llama_stack/providers/inline/meta_reference/inference/quantization/loader.py

@@ -27,7 +27,7 @@ from torchao.quantization.GPTQ import Int8DynActInt4WeightLinear
 
 from llama_stack.apis.inference import QuantizationType
 
-from llama_stack.providers.impls.meta_reference.inference.config import (
+from llama_stack.providers.inline.meta_reference.inference.config import (
     MetaReferenceQuantizedInferenceConfig,
 )
 

llama_stack/providers/inline/meta_reference/memory/config.py

@@ -0,0 +1,21 @@
+# 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 llama_models.schema_utils import json_schema_type
+from pydantic import BaseModel
+
+from llama_stack.distribution.utils.config_dirs import RUNTIME_BASE_DIR
+from llama_stack.providers.utils.kvstore.config import (
+    KVStoreConfig,
+    SqliteKVStoreConfig,
+)
+
+
+@json_schema_type
+class FaissImplConfig(BaseModel):
+    kvstore: KVStoreConfig = SqliteKVStoreConfig(
+        db_path=(RUNTIME_BASE_DIR / "faiss_store.db").as_posix()
+    )  # Uses SQLite config specific to FAISS storage

llama_stack/providers/inline/meta_reference/memory/faiss.py

@@ -16,6 +16,7 @@ from llama_models.llama3.api.datatypes import *  # noqa: F403
 
 from llama_stack.apis.memory import *  # noqa: F403
 from llama_stack.providers.datatypes import MemoryBanksProtocolPrivate
+from llama_stack.providers.utils.kvstore import kvstore_impl
 
 from llama_stack.providers.utils.memory.vector_store import (
     ALL_MINILM_L6_V2_DIMENSION,
@@ -28,6 +29,8 @@ from .config import FaissImplConfig
 
 logger = logging.getLogger(__name__)
 
+MEMORY_BANKS_PREFIX = "memory_banks:"
+
 
 class FaissIndex(EmbeddingIndex):
     id_by_index: Dict[int, str]
@@ -69,10 +72,25 @@ class FaissMemoryImpl(Memory, MemoryBanksProtocolPrivate):
     def __init__(self, config: FaissImplConfig) -> None:
         self.config = config
         self.cache = {}
+        self.kvstore = None
 
-    async def initialize(self) -> None: ...
+    async def initialize(self) -> None:
+        self.kvstore = await kvstore_impl(self.config.kvstore)
+        # Load existing banks from kvstore
+        start_key = MEMORY_BANKS_PREFIX
+        end_key = f"{MEMORY_BANKS_PREFIX}\xff"
+        stored_banks = await self.kvstore.range(start_key, end_key)
 
-    async def shutdown(self) -> None: ...
+        for bank_data in stored_banks:
+            bank = VectorMemoryBankDef.model_validate_json(bank_data)
+            index = BankWithIndex(
+                bank=bank, index=FaissIndex(ALL_MINILM_L6_V2_DIMENSION)
+            )
+            self.cache[bank.identifier] = index
+
+    async def shutdown(self) -> None:
+        # Cleanup if needed
+        pass
 
     async def register_memory_bank(
         self,
@@ -82,6 +100,14 @@ class FaissMemoryImpl(Memory, MemoryBanksProtocolPrivate):
             memory_bank.type == MemoryBankType.vector.value
         ), f"Only vector banks are supported {memory_bank.type}"
 
+        # Store in kvstore
+        key = f"{MEMORY_BANKS_PREFIX}{memory_bank.identifier}"
+        await self.kvstore.set(
+            key=key,
+            value=memory_bank.json(),
+        )
+
+        # Store in cache
         index = BankWithIndex(
             bank=memory_bank, index=FaissIndex(ALL_MINILM_L6_V2_DIMENSION)
         )

llama_stack/providers/inline/meta_reference/memory/tests/test_faiss.py

@@ -0,0 +1,73 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+import tempfile
+
+import pytest
+from llama_stack.apis.memory import MemoryBankType, VectorMemoryBankDef
+from llama_stack.providers.inline.meta_reference.memory.config import FaissImplConfig
+
+from llama_stack.providers.inline.meta_reference.memory.faiss import FaissMemoryImpl
+from llama_stack.providers.utils.kvstore.config import SqliteKVStoreConfig
+
+
+class TestFaissMemoryImpl:
+    @pytest.fixture
+    def faiss_impl(self):
+        # Create a temporary SQLite database file
+        temp_db = tempfile.NamedTemporaryFile(suffix=".db", delete=False)
+        config = FaissImplConfig(kvstore=SqliteKVStoreConfig(db_path=temp_db.name))
+        return FaissMemoryImpl(config)
+
+    @pytest.mark.asyncio
+    async def test_initialize(self, faiss_impl):
+        # Test empty initialization
+        await faiss_impl.initialize()
+        assert len(faiss_impl.cache) == 0
+
+        # Test initialization with existing banks
+        bank = VectorMemoryBankDef(
+            identifier="test_bank",
+            type=MemoryBankType.vector.value,
+            embedding_model="all-MiniLM-L6-v2",
+            chunk_size_in_tokens=512,
+            overlap_size_in_tokens=64,
+        )
+
+        # Register a bank and reinitialize to test loading
+        await faiss_impl.register_memory_bank(bank)
+
+        # Create new instance to test initialization with existing data
+        new_impl = FaissMemoryImpl(faiss_impl.config)
+        await new_impl.initialize()
+
+        assert len(new_impl.cache) == 1
+        assert "test_bank" in new_impl.cache
+
+    @pytest.mark.asyncio
+    async def test_register_memory_bank(self, faiss_impl):
+        bank = VectorMemoryBankDef(
+            identifier="test_bank",
+            type=MemoryBankType.vector.value,
+            embedding_model="all-MiniLM-L6-v2",
+            chunk_size_in_tokens=512,
+            overlap_size_in_tokens=64,
+        )
+
+        await faiss_impl.initialize()
+        await faiss_impl.register_memory_bank(bank)
+
+        assert "test_bank" in faiss_impl.cache
+        assert faiss_impl.cache["test_bank"].bank == bank
+
+        # Verify persistence
+        new_impl = FaissMemoryImpl(faiss_impl.config)
+        await new_impl.initialize()
+        assert "test_bank" in new_impl.cache
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])

llama_stack/providers/inline/meta_reference/scoring/scoring.py

@@ -13,15 +13,15 @@ from llama_stack.apis.datasetio import *  # noqa: F403
 from llama_stack.apis.datasets import *  # noqa: F403
 from llama_stack.apis.inference.inference import Inference
 from llama_stack.providers.datatypes import ScoringFunctionsProtocolPrivate
-from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.equality_scoring_fn import (
+from llama_stack.providers.inline.meta_reference.scoring.scoring_fn.equality_scoring_fn import (
     EqualityScoringFn,
 )
 
-from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.llm_as_judge_scoring_fn import (
+from llama_stack.providers.inline.meta_reference.scoring.scoring_fn.llm_as_judge_scoring_fn import (
     LlmAsJudgeScoringFn,
 )
 
-from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.subset_of_scoring_fn import (
+from llama_stack.providers.inline.meta_reference.scoring.scoring_fn.subset_of_scoring_fn import (
     SubsetOfScoringFn,
 )
 

llama_stack/providers/inline/meta_reference/scoring/scoring_fn/equality_scoring_fn.py

@@ -4,18 +4,18 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.base_scoring_fn import (
+from llama_stack.providers.inline.meta_reference.scoring.scoring_fn.base_scoring_fn import (
     BaseScoringFn,
 )
 from llama_stack.apis.scoring_functions import *  # noqa: F401, F403
 from llama_stack.apis.scoring import *  # noqa: F401, F403
 from llama_stack.apis.common.type_system import *  # noqa: F403
 
-from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.common import (
+from llama_stack.providers.inline.meta_reference.scoring.scoring_fn.common import (
     aggregate_accuracy,
 )
 
-from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.fn_defs.equality import (
+from llama_stack.providers.inline.meta_reference.scoring.scoring_fn.fn_defs.equality import (
     equality,
 )
 

llama_stack/providers/inline/meta_reference/scoring/scoring_fn/llm_as_judge_scoring_fn.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 from llama_stack.apis.inference.inference import Inference
-from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.base_scoring_fn import (
+from llama_stack.providers.inline.meta_reference.scoring.scoring_fn.base_scoring_fn import (
     BaseScoringFn,
 )
 from llama_stack.apis.scoring_functions import *  # noqa: F401, F403
@@ -12,10 +12,10 @@ from llama_stack.apis.scoring import *  # noqa: F401, F403
 from llama_stack.apis.common.type_system import *  # noqa: F403
 import re
 
-from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.common import (
+from llama_stack.providers.inline.meta_reference.scoring.scoring_fn.common import (
     aggregate_average,
 )
-from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.fn_defs.llm_as_judge_8b_correctness import (
+from llama_stack.providers.inline.meta_reference.scoring.scoring_fn.fn_defs.llm_as_judge_8b_correctness import (
     llm_as_judge_8b_correctness,
 )
 

llama_stack/providers/inline/meta_reference/scoring/scoring_fn/subset_of_scoring_fn.py

@@ -4,17 +4,17 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.base_scoring_fn import (
+from llama_stack.providers.inline.meta_reference.scoring.scoring_fn.base_scoring_fn import (
     BaseScoringFn,
 )
 from llama_stack.apis.scoring_functions import *  # noqa: F401, F403
 from llama_stack.apis.scoring import *  # noqa: F401, F403
 from llama_stack.apis.common.type_system import *  # noqa: F403
-from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.common import (
+from llama_stack.providers.inline.meta_reference.scoring.scoring_fn.common import (
     aggregate_accuracy,
 )
 
-from llama_stack.providers.impls.meta_reference.scoring.scoring_fn.fn_defs.subset_of import (
+from llama_stack.providers.inline.meta_reference.scoring.scoring_fn.fn_defs.subset_of import (
     subset_of,
 )
 

llama_stack/providers/registry/agents.py

@@ -22,8 +22,8 @@ def available_providers() -> List[ProviderSpec]:
                 "scikit-learn",
             ]
             + kvstore_dependencies(),
-            module="llama_stack.providers.impls.meta_reference.agents",
-            config_class="llama_stack.providers.impls.meta_reference.agents.MetaReferenceAgentsImplConfig",
+            module="llama_stack.providers.inline.meta_reference.agents",
+            config_class="llama_stack.providers.inline.meta_reference.agents.MetaReferenceAgentsImplConfig",
             api_dependencies=[
                 Api.inference,
                 Api.safety,
@@ -36,8 +36,8 @@ def available_providers() -> List[ProviderSpec]:
             adapter=AdapterSpec(
                 adapter_type="sample",
                 pip_packages=[],
-                module="llama_stack.providers.adapters.agents.sample",
-                config_class="llama_stack.providers.adapters.agents.sample.SampleConfig",
+                module="llama_stack.providers.remote.agents.sample",
+                config_class="llama_stack.providers.remote.agents.sample.SampleConfig",
             ),
         ),
     ]

llama_stack/providers/registry/datasetio.py

@@ -15,8 +15,8 @@ def available_providers() -> List[ProviderSpec]:
             api=Api.datasetio,
             provider_type="meta-reference",
             pip_packages=["pandas"],
-            module="llama_stack.providers.impls.meta_reference.datasetio",
-            config_class="llama_stack.providers.impls.meta_reference.datasetio.MetaReferenceDatasetIOConfig",
+            module="llama_stack.providers.inline.meta_reference.datasetio",
+            config_class="llama_stack.providers.inline.meta_reference.datasetio.MetaReferenceDatasetIOConfig",
             api_dependencies=[],
         ),
     ]

llama_stack/providers/registry/eval.py

@@ -15,8 +15,8 @@ def available_providers() -> List[ProviderSpec]:
             api=Api.eval,
             provider_type="meta-reference",
             pip_packages=[],
-            module="llama_stack.providers.impls.meta_reference.eval",
-            config_class="llama_stack.providers.impls.meta_reference.eval.MetaReferenceEvalConfig",
+            module="llama_stack.providers.inline.meta_reference.eval",
+            config_class="llama_stack.providers.inline.meta_reference.eval.MetaReferenceEvalConfig",
             api_dependencies=[
                 Api.datasetio,
                 Api.datasets,

llama_stack/providers/registry/inference.py

@@ -27,8 +27,8 @@ def available_providers() -> List[ProviderSpec]:
             api=Api.inference,
             provider_type="meta-reference",
             pip_packages=META_REFERENCE_DEPS,
-            module="llama_stack.providers.impls.meta_reference.inference",
-            config_class="llama_stack.providers.impls.meta_reference.inference.MetaReferenceInferenceConfig",
+            module="llama_stack.providers.inline.meta_reference.inference",
+            config_class="llama_stack.providers.inline.meta_reference.inference.MetaReferenceInferenceConfig",
         ),
         InlineProviderSpec(
             api=Api.inference,
@@ -40,16 +40,16 @@ def available_providers() -> List[ProviderSpec]:
                     "torchao==0.5.0",
                 ]
             ),
-            module="llama_stack.providers.impls.meta_reference.inference",
-            config_class="llama_stack.providers.impls.meta_reference.inference.MetaReferenceQuantizedInferenceConfig",
+            module="llama_stack.providers.inline.meta_reference.inference",
+            config_class="llama_stack.providers.inline.meta_reference.inference.MetaReferenceQuantizedInferenceConfig",
         ),
         remote_provider_spec(
             api=Api.inference,
             adapter=AdapterSpec(
                 adapter_type="sample",
                 pip_packages=[],
-                module="llama_stack.providers.adapters.inference.sample",
-                config_class="llama_stack.providers.adapters.inference.sample.SampleConfig",
+                module="llama_stack.providers.remote.inference.sample",
+                config_class="llama_stack.providers.remote.inference.sample.SampleConfig",
             ),
         ),
         remote_provider_spec(
@@ -57,26 +57,26 @@ def available_providers() -> List[ProviderSpec]:
             adapter=AdapterSpec(
                 adapter_type="ollama",
                 pip_packages=["ollama", "aiohttp"],
-                config_class="llama_stack.providers.adapters.inference.ollama.OllamaImplConfig",
-                module="llama_stack.providers.adapters.inference.ollama",
+                config_class="llama_stack.providers.remote.inference.ollama.OllamaImplConfig",
+                module="llama_stack.providers.remote.inference.ollama",
+            ),
+        ),
+        remote_provider_spec(
+            api=Api.inference,
+            adapter=AdapterSpec(
+                adapter_type="vllm",
+                pip_packages=["openai"],
+                module="llama_stack.providers.remote.inference.vllm",
+                config_class="llama_stack.providers.remote.inference.vllm.VLLMInferenceAdapterConfig",
             ),
         ),
-        # remote_provider_spec(
-        #     api=Api.inference,
-        #     adapter=AdapterSpec(
-        #         adapter_type="vllm",
-        #         pip_packages=["openai"],
-        #         module="llama_stack.providers.adapters.inference.vllm",
-        #         config_class="llama_stack.providers.adapters.inference.vllm.VLLMImplConfig",
-        #     ),
-        # ),
         remote_provider_spec(
             api=Api.inference,
             adapter=AdapterSpec(
                 adapter_type="tgi",
                 pip_packages=["huggingface_hub", "aiohttp"],
-                module="llama_stack.providers.adapters.inference.tgi",
-                config_class="llama_stack.providers.adapters.inference.tgi.TGIImplConfig",
+                module="llama_stack.providers.remote.inference.tgi",
+                config_class="llama_stack.providers.remote.inference.tgi.TGIImplConfig",
             ),
         ),
         remote_provider_spec(
@@ -84,8 +84,8 @@ def available_providers() -> List[ProviderSpec]:
             adapter=AdapterSpec(
                 adapter_type="hf::serverless",
                 pip_packages=["huggingface_hub", "aiohttp"],
-                module="llama_stack.providers.adapters.inference.tgi",
-                config_class="llama_stack.providers.adapters.inference.tgi.InferenceAPIImplConfig",
+                module="llama_stack.providers.remote.inference.tgi",
+                config_class="llama_stack.providers.remote.inference.tgi.InferenceAPIImplConfig",
             ),
         ),
         remote_provider_spec(
@@ -93,8 +93,8 @@ def available_providers() -> List[ProviderSpec]:
             adapter=AdapterSpec(
                 adapter_type="hf::endpoint",
                 pip_packages=["huggingface_hub", "aiohttp"],
-                module="llama_stack.providers.adapters.inference.tgi",
-                config_class="llama_stack.providers.adapters.inference.tgi.InferenceEndpointImplConfig",
+                module="llama_stack.providers.remote.inference.tgi",
+                config_class="llama_stack.providers.remote.inference.tgi.InferenceEndpointImplConfig",
             ),
         ),
         remote_provider_spec(
@@ -104,8 +104,8 @@ def available_providers() -> List[ProviderSpec]:
                 pip_packages=[
                     "fireworks-ai",
                 ],
-                module="llama_stack.providers.adapters.inference.fireworks",
-                config_class="llama_stack.providers.adapters.inference.fireworks.FireworksImplConfig",
+                module="llama_stack.providers.remote.inference.fireworks",
+                config_class="llama_stack.providers.remote.inference.fireworks.FireworksImplConfig",
             ),
         ),
         remote_provider_spec(
@@ -115,9 +115,9 @@ def available_providers() -> List[ProviderSpec]:
                 pip_packages=[
                     "together",
                 ],
-                module="llama_stack.providers.adapters.inference.together",
-                config_class="llama_stack.providers.adapters.inference.together.TogetherImplConfig",
-                provider_data_validator="llama_stack.providers.adapters.safety.together.TogetherProviderDataValidator",
+                module="llama_stack.providers.remote.inference.together",
+                config_class="llama_stack.providers.remote.inference.together.TogetherImplConfig",
+                provider_data_validator="llama_stack.providers.remote.safety.together.TogetherProviderDataValidator",
             ),
         ),
         remote_provider_spec(
@@ -125,8 +125,8 @@ def available_providers() -> List[ProviderSpec]:
             adapter=AdapterSpec(
                 adapter_type="bedrock",
                 pip_packages=["boto3"],
-                module="llama_stack.providers.adapters.inference.bedrock",
-                config_class="llama_stack.providers.adapters.inference.bedrock.BedrockConfig",
+                module="llama_stack.providers.remote.inference.bedrock",
+                config_class="llama_stack.providers.remote.inference.bedrock.BedrockConfig",
             ),
         ),
         remote_provider_spec(
@@ -136,8 +136,8 @@ def available_providers() -> List[ProviderSpec]:
                 pip_packages=[
                     "openai",
                 ],
-                module="llama_stack.providers.adapters.inference.databricks",
-                config_class="llama_stack.providers.adapters.inference.databricks.DatabricksImplConfig",
+                module="llama_stack.providers.remote.inference.databricks",
+                config_class="llama_stack.providers.remote.inference.databricks.DatabricksImplConfig",
             ),
         ),
         InlineProviderSpec(
@@ -146,7 +146,7 @@ def available_providers() -> List[ProviderSpec]:
             pip_packages=[
                 "vllm",
             ],
-            module="llama_stack.providers.impls.vllm",
-            config_class="llama_stack.providers.impls.vllm.VLLMConfig",
+            module="llama_stack.providers.inline.vllm",
+            config_class="llama_stack.providers.inline.vllm.VLLMConfig",
         ),
     ]

llama_stack/providers/registry/memory.py

@@ -36,15 +36,15 @@ def available_providers() -> List[ProviderSpec]:
             api=Api.memory,
             provider_type="meta-reference",
             pip_packages=EMBEDDING_DEPS + ["faiss-cpu"],
-            module="llama_stack.providers.impls.meta_reference.memory",
-            config_class="llama_stack.providers.impls.meta_reference.memory.FaissImplConfig",
+            module="llama_stack.providers.inline.meta_reference.memory",
+            config_class="llama_stack.providers.inline.meta_reference.memory.FaissImplConfig",
         ),
         remote_provider_spec(
             Api.memory,
             AdapterSpec(
                 adapter_type="chromadb",
                 pip_packages=EMBEDDING_DEPS + ["chromadb-client"],
-                module="llama_stack.providers.adapters.memory.chroma",
+                module="llama_stack.providers.remote.memory.chroma",
             ),
         ),
         remote_provider_spec(
@@ -52,8 +52,8 @@ def available_providers() -> List[ProviderSpec]:
             AdapterSpec(
                 adapter_type="pgvector",
                 pip_packages=EMBEDDING_DEPS + ["psycopg2-binary"],
-                module="llama_stack.providers.adapters.memory.pgvector",
-                config_class="llama_stack.providers.adapters.memory.pgvector.PGVectorConfig",
+                module="llama_stack.providers.remote.memory.pgvector",
+                config_class="llama_stack.providers.remote.memory.pgvector.PGVectorConfig",
             ),
         ),
         remote_provider_spec(
@@ -61,9 +61,9 @@ def available_providers() -> List[ProviderSpec]:
             AdapterSpec(
                 adapter_type="weaviate",
                 pip_packages=EMBEDDING_DEPS + ["weaviate-client"],
-                module="llama_stack.providers.adapters.memory.weaviate",
-                config_class="llama_stack.providers.adapters.memory.weaviate.WeaviateConfig",
-                provider_data_validator="llama_stack.providers.adapters.memory.weaviate.WeaviateRequestProviderData",
+                module="llama_stack.providers.remote.memory.weaviate",
+                config_class="llama_stack.providers.remote.memory.weaviate.WeaviateConfig",
+                provider_data_validator="llama_stack.providers.remote.memory.weaviate.WeaviateRequestProviderData",
             ),
         ),
         remote_provider_spec(
@@ -71,8 +71,8 @@ def available_providers() -> List[ProviderSpec]:
             adapter=AdapterSpec(
                 adapter_type="sample",
                 pip_packages=[],
-                module="llama_stack.providers.adapters.memory.sample",
-                config_class="llama_stack.providers.adapters.memory.sample.SampleConfig",
+                module="llama_stack.providers.remote.memory.sample",
+                config_class="llama_stack.providers.remote.memory.sample.SampleConfig",
             ),
         ),
         remote_provider_spec(
@@ -80,8 +80,8 @@ def available_providers() -> List[ProviderSpec]:
             AdapterSpec(
                 adapter_type="qdrant",
                 pip_packages=EMBEDDING_DEPS + ["qdrant-client"],
-                module="llama_stack.providers.adapters.memory.qdrant",
-                config_class="llama_stack.providers.adapters.memory.qdrant.QdrantConfig",
+                module="llama_stack.providers.remote.memory.qdrant",
+                config_class="llama_stack.providers.remote.memory.qdrant.QdrantConfig",
             ),
         ),
     ]

llama_stack/providers/registry/safety.py

@@ -24,8 +24,8 @@ def available_providers() -> List[ProviderSpec]:
                 "transformers",
                 "torch --index-url https://download.pytorch.org/whl/cpu",
             ],
-            module="llama_stack.providers.impls.meta_reference.safety",
-            config_class="llama_stack.providers.impls.meta_reference.safety.SafetyConfig",
+            module="llama_stack.providers.inline.meta_reference.safety",
+            config_class="llama_stack.providers.inline.meta_reference.safety.SafetyConfig",
             api_dependencies=[
                 Api.inference,
             ],
@@ -35,8 +35,8 @@ def available_providers() -> List[ProviderSpec]:
             adapter=AdapterSpec(
                 adapter_type="sample",
                 pip_packages=[],
-                module="llama_stack.providers.adapters.safety.sample",
-                config_class="llama_stack.providers.adapters.safety.sample.SampleConfig",
+                module="llama_stack.providers.remote.safety.sample",
+                config_class="llama_stack.providers.remote.safety.sample.SampleConfig",
             ),
         ),
         remote_provider_spec(
@@ -44,8 +44,8 @@ def available_providers() -> List[ProviderSpec]:
             adapter=AdapterSpec(
                 adapter_type="bedrock",
                 pip_packages=["boto3"],
-                module="llama_stack.providers.adapters.safety.bedrock",
-                config_class="llama_stack.providers.adapters.safety.bedrock.BedrockSafetyConfig",
+                module="llama_stack.providers.remote.safety.bedrock",
+                config_class="llama_stack.providers.remote.safety.bedrock.BedrockSafetyConfig",
             ),
         ),
         remote_provider_spec(
@@ -55,9 +55,9 @@ def available_providers() -> List[ProviderSpec]:
                 pip_packages=[
                     "together",
                 ],
-                module="llama_stack.providers.adapters.safety.together",
-                config_class="llama_stack.providers.adapters.safety.together.TogetherSafetyConfig",
-                provider_data_validator="llama_stack.providers.adapters.safety.together.TogetherProviderDataValidator",
+                module="llama_stack.providers.remote.safety.together",
+                config_class="llama_stack.providers.remote.safety.together.TogetherSafetyConfig",
+                provider_data_validator="llama_stack.providers.remote.safety.together.TogetherProviderDataValidator",
             ),
         ),
         InlineProviderSpec(
@@ -66,8 +66,8 @@ def available_providers() -> List[ProviderSpec]:
             pip_packages=[
                 "codeshield",
             ],
-            module="llama_stack.providers.impls.meta_reference.codeshield",
-            config_class="llama_stack.providers.impls.meta_reference.codeshield.CodeShieldConfig",
+            module="llama_stack.providers.inline.meta_reference.codeshield",
+            config_class="llama_stack.providers.inline.meta_reference.codeshield.CodeShieldConfig",
             api_dependencies=[],
         ),
     ]

llama_stack/providers/registry/scoring.py

@@ -15,8 +15,8 @@ def available_providers() -> List[ProviderSpec]:
             api=Api.scoring,
             provider_type="meta-reference",
             pip_packages=[],
-            module="llama_stack.providers.impls.meta_reference.scoring",
-            config_class="llama_stack.providers.impls.meta_reference.scoring.MetaReferenceScoringConfig",
+            module="llama_stack.providers.inline.meta_reference.scoring",
+            config_class="llama_stack.providers.inline.meta_reference.scoring.MetaReferenceScoringConfig",
             api_dependencies=[
                 Api.datasetio,
                 Api.datasets,
@@ -27,8 +27,8 @@ def available_providers() -> List[ProviderSpec]:
             api=Api.scoring,
             provider_type="braintrust",
             pip_packages=["autoevals", "openai"],
-            module="llama_stack.providers.impls.braintrust.scoring",
-            config_class="llama_stack.providers.impls.braintrust.scoring.BraintrustScoringConfig",
+            module="llama_stack.providers.inline.braintrust.scoring",
+            config_class="llama_stack.providers.inline.braintrust.scoring.BraintrustScoringConfig",
             api_dependencies=[
                 Api.datasetio,
                 Api.datasets,