Merge branch 'main' into HuggingfacePostTrainingConfig-branch

llama_stack/apis/batches/batches.py

@@ -29,12 +29,16 @@ class ListBatchesResponse(BaseModel):
 
 @runtime_checkable
 class Batches(Protocol):
-    """Protocol for batch processing API operations.
-
+    """
     The Batches API enables efficient processing of multiple requests in a single operation,
     particularly useful for processing large datasets, batch evaluation workflows, and
     cost-effective inference at scale.
 
+    The API is designed to allow use of openai client libraries for seamless integration.
+
+    This API provides the following extensions:
+     - idempotent batch creation
+
     Note: This API is currently under active development and may undergo changes.
     """
 
@@ -45,6 +49,7 @@ class Batches(Protocol):
         endpoint: str,
         completion_window: Literal["24h"],
         metadata: dict[str, str] | None = None,
+        idempotency_key: str | None = None,
     ) -> BatchObject:
         """Create a new batch for processing multiple API requests.
 
@@ -52,6 +57,7 @@ class Batches(Protocol):
         :param endpoint: The endpoint to be used for all requests in the batch.
         :param completion_window: The time window within which the batch should be processed.
         :param metadata: Optional metadata for the batch.
+        :param idempotency_key: Optional idempotency key. When provided, enables idempotent behavior.
         :returns: The created batch object.
         """
         ...

llama_stack/apis/inference/inference.py

@@ -473,6 +473,28 @@ class EmbeddingsResponse(BaseModel):
     embeddings: list[list[float]]
 
 
+@json_schema_type
+class RerankData(BaseModel):
+    """A single rerank result from a reranking response.
+
+    :param index: The original index of the document in the input list
+    :param relevance_score: The relevance score from the model output. Values are inverted when applicable so that higher scores indicate greater relevance.
+    """
+
+    index: int
+    relevance_score: float
+
+
+@json_schema_type
+class RerankResponse(BaseModel):
+    """Response from a reranking request.
+
+    :param data: List of rerank result objects, sorted by relevance score (descending)
+    """
+
+    data: list[RerankData]
+
+
 @json_schema_type
 class OpenAIChatCompletionContentPartTextParam(BaseModel):
     """Text content part for OpenAI-compatible chat completion messages.
@@ -1046,6 +1068,7 @@ class InferenceProvider(Protocol):
         :returns: A BatchCompletionResponse with the full completions.
         """
         raise NotImplementedError("Batch completion is not implemented")
+        return  # this is so mypy's safe-super rule will consider the method concrete
 
     @webmethod(route="/inference/chat-completion", method="POST")
     async def chat_completion(
@@ -1110,6 +1133,7 @@ class InferenceProvider(Protocol):
         :returns: A BatchChatCompletionResponse with the full completions.
         """
         raise NotImplementedError("Batch chat completion is not implemented")
+        return  # this is so mypy's safe-super rule will consider the method concrete
 
     @webmethod(route="/inference/embeddings", method="POST")
     async def embeddings(
@@ -1131,6 +1155,25 @@ class InferenceProvider(Protocol):
         """
         ...
 
+    @webmethod(route="/inference/rerank", method="POST", experimental=True)
+    async def rerank(
+        self,
+        model: str,
+        query: str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam,
+        items: list[str | OpenAIChatCompletionContentPartTextParam | OpenAIChatCompletionContentPartImageParam],
+        max_num_results: int | None = None,
+    ) -> RerankResponse:
+        """Rerank a list of documents based on their relevance to a query.
+
+        :param model: The identifier of the reranking model to use.
+        :param query: The search query to rank items against. Can be a string, text content part, or image content part. The input must not exceed the model's max input token length.
+        :param items: List of items to rerank. Each item can be a string, text content part, or image content part. Each input must not exceed the model's max input token length.
+        :param max_num_results: (Optional) Maximum number of results to return. Default: returns all.
+        :returns: RerankResponse with indices sorted by relevance score (descending).
+        """
+        raise NotImplementedError("Reranking is not implemented")
+        return  # this is so mypy's safe-super rule will consider the method concrete
+
     @webmethod(route="/openai/v1/completions", method="POST")
     async def openai_completion(
         self,

llama_stack/apis/telemetry/telemetry.py

@@ -386,6 +386,7 @@ class MetricDataPoint(BaseModel):
 
     timestamp: int
     value: float
+    unit: str
 
 
 @json_schema_type
@@ -518,7 +519,7 @@ class Telemetry(Protocol):
         metric_name: str,
         start_time: int,
         end_time: int | None = None,
-        granularity: str | None = "1d",
+        granularity: str | None = None,
         query_type: MetricQueryType = MetricQueryType.RANGE,
         label_matchers: list[MetricLabelMatcher] | None = None,
     ) -> QueryMetricsResponse:

llama_stack/cli/stack/run.py

@@ -15,7 +15,7 @@ from llama_stack.log import get_logger
 
 REPO_ROOT = Path(__file__).parent.parent.parent.parent
 
-logger = get_logger(name=__name__, category="server")
+logger = get_logger(name=__name__, category="cli")
 
 
 class StackRun(Subcommand):

llama_stack/core/build.py

@@ -5,7 +5,6 @@
 # the root directory of this source tree.
 
 import importlib.resources
-import logging
 import sys
 
 from pydantic import BaseModel
@@ -17,9 +16,10 @@ from llama_stack.core.external import load_external_apis
 from llama_stack.core.utils.exec import run_command
 from llama_stack.core.utils.image_types import LlamaStackImageType
 from llama_stack.distributions.template import DistributionTemplate
+from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import Api
 
-log = logging.getLogger(__name__)
+log = get_logger(name=__name__, category="core")
 
 # These are the dependencies needed by the distribution server.
 # `llama-stack` is automatically installed by the installation script.

llama_stack/core/configure.py

@@ -3,7 +3,6 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
-import logging
 import textwrap
 from typing import Any
 
@@ -21,9 +20,10 @@ from llama_stack.core.stack import cast_image_name_to_string, replace_env_vars
 from llama_stack.core.utils.config_dirs import EXTERNAL_PROVIDERS_DIR
 from llama_stack.core.utils.dynamic import instantiate_class_type
 from llama_stack.core.utils.prompt_for_config import prompt_for_config
+from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import Api, ProviderSpec
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="core")
 
 
 def configure_single_provider(registry: dict[str, ProviderSpec], provider: Provider) -> Provider:

llama_stack/core/datatypes.py

@@ -318,6 +318,41 @@ class QuotaConfig(BaseModel):
     period: QuotaPeriod = Field(default=QuotaPeriod.DAY, description="Quota period to set")
 
 
+class CORSConfig(BaseModel):
+    allow_origins: list[str] = Field(default_factory=list)
+    allow_origin_regex: str | None = Field(default=None)
+    allow_methods: list[str] = Field(default=["OPTIONS"])
+    allow_headers: list[str] = Field(default_factory=list)
+    allow_credentials: bool = Field(default=False)
+    expose_headers: list[str] = Field(default_factory=list)
+    max_age: int = Field(default=600, ge=0)
+
+    @model_validator(mode="after")
+    def validate_credentials_config(self) -> Self:
+        if self.allow_credentials and (self.allow_origins == ["*"] or "*" in self.allow_origins):
+            raise ValueError("Cannot use wildcard origins with credentials enabled")
+        return self
+
+
+def process_cors_config(cors_config: bool | CORSConfig | None) -> CORSConfig | None:
+    if cors_config is False or cors_config is None:
+        return None
+
+    if cors_config is True:
+        # dev mode: allow localhost on any port
+        return CORSConfig(
+            allow_origins=[],
+            allow_origin_regex=r"https?://localhost:\d+",
+            allow_methods=["GET", "POST", "PUT", "DELETE", "OPTIONS"],
+            allow_headers=["Content-Type", "Authorization", "X-Requested-With"],
+        )
+
+    if isinstance(cors_config, CORSConfig):
+        return cors_config
+
+    raise ValueError(f"Expected bool or CORSConfig, got {type(cors_config).__name__}")
+
+
 class ServerConfig(BaseModel):
     port: int = Field(
         default=8321,
@@ -349,6 +384,12 @@ class ServerConfig(BaseModel):
         default=None,
         description="Per client quota request configuration",
     )
+    cors: bool | CORSConfig | None = Field(
+        default=None,
+        description="CORS configuration for cross-origin requests. Can be:\n"
+        "- true: Enable localhost CORS for development\n"
+        "- {allow_origins: [...], allow_methods: [...], ...}: Full configuration",
+    )
 
 
 class StackRunConfig(BaseModel):

llama_stack/core/library_client.py

@@ -7,7 +7,7 @@
 import asyncio
 import inspect
 import json
-import logging
+import logging  # allow-direct-logging
 import os
 import sys
 from concurrent.futures import ThreadPoolExecutor
@@ -48,6 +48,7 @@ from llama_stack.core.stack import (
 from llama_stack.core.utils.config import redact_sensitive_fields
 from llama_stack.core.utils.context import preserve_contexts_async_generator
 from llama_stack.core.utils.exec import in_notebook
+from llama_stack.log import get_logger
 from llama_stack.providers.utils.telemetry.tracing import (
     CURRENT_TRACE_CONTEXT,
     end_trace,
@@ -55,7 +56,7 @@ from llama_stack.providers.utils.telemetry.tracing import (
     start_trace,
 )
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="core")
 
 T = TypeVar("T")
 
@@ -145,39 +146,26 @@ class LlamaStackAsLibraryClient(LlamaStackClient):
     ):
         super().__init__()
         self.async_client = AsyncLlamaStackAsLibraryClient(
-            config_path_or_distro_name, custom_provider_registry, provider_data
+            config_path_or_distro_name, custom_provider_registry, provider_data, skip_logger_removal
         )
         self.pool_executor = ThreadPoolExecutor(max_workers=4)
-        self.skip_logger_removal = skip_logger_removal
         self.provider_data = provider_data
 
         self.loop = asyncio.new_event_loop()
 
-    def initialize(self):
-        if in_notebook():
-            import nest_asyncio
-
-            nest_asyncio.apply()
-            if not self.skip_logger_removal:
-                self._remove_root_logger_handlers()
-
         # use a new event loop to avoid interfering with the main event loop
         loop = asyncio.new_event_loop()
         asyncio.set_event_loop(loop)
         try:
-            return loop.run_until_complete(self.async_client.initialize())
+            loop.run_until_complete(self.async_client.initialize())
         finally:
             asyncio.set_event_loop(None)
 
-    def _remove_root_logger_handlers(self):
+    def initialize(self):
         """
-        Remove all handlers from the root logger. Needed to avoid polluting the console with logs.
+        Deprecated method for backward compatibility.
         """
-        root_logger = logging.getLogger()
-
-        for handler in root_logger.handlers[:]:
-            root_logger.removeHandler(handler)
-            logger.info(f"Removed handler {handler.__class__.__name__} from root logger")
+        pass
 
     def request(self, *args, **kwargs):
         loop = self.loop
@@ -215,6 +203,7 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         config_path_or_distro_name: str,
         custom_provider_registry: ProviderRegistry | None = None,
         provider_data: dict[str, Any] | None = None,
+        skip_logger_removal: bool = False,
     ):
         super().__init__()
         # when using the library client, we should not log to console since many
@@ -222,6 +211,13 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         current_sinks = os.environ.get("TELEMETRY_SINKS", "sqlite").split(",")
         os.environ["TELEMETRY_SINKS"] = ",".join(sink for sink in current_sinks if sink != "console")
 
+        if in_notebook():
+            import nest_asyncio
+
+            nest_asyncio.apply()
+            if not skip_logger_removal:
+                self._remove_root_logger_handlers()
+
         if config_path_or_distro_name.endswith(".yaml"):
             config_path = Path(config_path_or_distro_name)
             if not config_path.exists():
@@ -238,7 +234,24 @@ class AsyncLlamaStackAsLibraryClient(AsyncLlamaStackClient):
         self.provider_data = provider_data
         self.route_impls: RouteImpls | None = None  # Initialize to None to prevent AttributeError
 
+    def _remove_root_logger_handlers(self):
+        """
+        Remove all handlers from the root logger. Needed to avoid polluting the console with logs.
+        """
+        root_logger = logging.getLogger()
+
+        for handler in root_logger.handlers[:]:
+            root_logger.removeHandler(handler)
+            logger.info(f"Removed handler {handler.__class__.__name__} from root logger")
+
     async def initialize(self) -> bool:
+        """
+        Initialize the async client.
+
+        Returns:
+            bool: True if initialization was successful
+        """
+
         try:
             self.route_impls = None
             self.impls = await construct_stack(self.config, self.custom_provider_registry)

llama_stack/core/request_headers.py

@@ -6,15 +6,15 @@
 
 import contextvars
 import json
-import logging
 from contextlib import AbstractContextManager
 from typing import Any
 
 from llama_stack.core.datatypes import User
+from llama_stack.log import get_logger
 
 from .utils.dynamic import instantiate_class_type
 
-log = logging.getLogger(__name__)
+log = get_logger(name=__name__, category="core")
 
 # Context variable for request provider data and auth attributes
 PROVIDER_DATA_VAR = contextvars.ContextVar("provider_data", default=None)

llama_stack/core/routers/datasets.py

@@ -12,7 +12,7 @@ from llama_stack.apis.datasets import DatasetPurpose, DataSource
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import RoutingTable
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routers")
 
 
 class DatasetIORouter(DatasetIO):

llama_stack/core/routers/eval_scoring.py

@@ -16,7 +16,7 @@ from llama_stack.apis.scoring import (
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import RoutingTable
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routers")
 
 
 class ScoringRouter(Scoring):

llama_stack/core/routers/inference.py

@@ -65,7 +65,7 @@ from llama_stack.providers.datatypes import HealthResponse, HealthStatus, Routin
 from llama_stack.providers.utils.inference.inference_store import InferenceStore
 from llama_stack.providers.utils.telemetry.tracing import get_current_span
 
-logger = get_logger(name=__name__, category="inference")
+logger = get_logger(name=__name__, category="core::routers")
 
 
 class InferenceRouter(Inference):

llama_stack/core/routers/safety.py

@@ -13,7 +13,7 @@ from llama_stack.apis.shields import Shield
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import RoutingTable
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routers")
 
 
 class SafetyRouter(Safety):

llama_stack/core/routers/tool_runtime.py

@@ -22,7 +22,7 @@ from llama_stack.log import get_logger
 
 from ..routing_tables.toolgroups import ToolGroupsRoutingTable
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routers")
 
 
 class ToolRuntimeRouter(ToolRuntime):

llama_stack/core/routers/vector_io.py

@@ -30,7 +30,7 @@ from llama_stack.apis.vector_io import (
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import HealthResponse, HealthStatus, RoutingTable
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routers")
 
 
 class VectorIORouter(VectorIO):

llama_stack/core/routing_tables/benchmarks.py

@@ -14,7 +14,7 @@ from llama_stack.log import get_logger
 
 from .common import CommonRoutingTableImpl
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routing_tables")
 
 
 class BenchmarksRoutingTable(CommonRoutingTableImpl, Benchmarks):

llama_stack/core/routing_tables/common.py

@@ -23,7 +23,7 @@ from llama_stack.core.store import DistributionRegistry
 from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import Api, RoutingTable
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routing_tables")
 
 
 def get_impl_api(p: Any) -> Api:

llama_stack/core/routing_tables/datasets.py

@@ -26,7 +26,7 @@ from llama_stack.log import get_logger
 
 from .common import CommonRoutingTableImpl
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routing_tables")
 
 
 class DatasetsRoutingTable(CommonRoutingTableImpl, Datasets):

llama_stack/core/routing_tables/models.py

@@ -17,7 +17,7 @@ from llama_stack.log import get_logger
 
 from .common import CommonRoutingTableImpl, lookup_model
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routing_tables")
 
 
 class ModelsRoutingTable(CommonRoutingTableImpl, Models):

llama_stack/core/routing_tables/scoring_functions.py

@@ -19,7 +19,7 @@ from llama_stack.log import get_logger
 
 from .common import CommonRoutingTableImpl
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routing_tables")
 
 
 class ScoringFunctionsRoutingTable(CommonRoutingTableImpl, ScoringFunctions):

llama_stack/core/routing_tables/shields.py

@@ -15,7 +15,7 @@ from llama_stack.log import get_logger
 
 from .common import CommonRoutingTableImpl
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routing_tables")
 
 
 class ShieldsRoutingTable(CommonRoutingTableImpl, Shields):

llama_stack/core/routing_tables/toolgroups.py

@@ -14,7 +14,7 @@ from llama_stack.log import get_logger
 
 from .common import CommonRoutingTableImpl
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routing_tables")
 
 
 def parse_toolgroup_from_toolgroup_name_pair(toolgroup_name_with_maybe_tool_name: str) -> str | None:

llama_stack/core/routing_tables/vector_dbs.py

@@ -30,7 +30,7 @@ from llama_stack.log import get_logger
 
 from .common import CommonRoutingTableImpl, lookup_model
 
-logger = get_logger(name=__name__, category="core")
+logger = get_logger(name=__name__, category="core::routing_tables")
 
 
 class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):

llama_stack/core/server/auth.py

@@ -15,7 +15,7 @@ from llama_stack.core.server.auth_providers import create_auth_provider
 from llama_stack.core.server.routes import find_matching_route, initialize_route_impls
 from llama_stack.log import get_logger
 
-logger = get_logger(name=__name__, category="auth")
+logger = get_logger(name=__name__, category="core::auth")
 
 
 class AuthenticationMiddleware:

llama_stack/core/server/auth_providers.py

@@ -23,7 +23,7 @@ from llama_stack.core.datatypes import (
 )
 from llama_stack.log import get_logger
 
-logger = get_logger(name=__name__, category="auth")
+logger = get_logger(name=__name__, category="core::auth")
 
 
 class AuthResponse(BaseModel):

llama_stack/core/server/quota.py

@@ -15,7 +15,7 @@ from llama_stack.providers.utils.kvstore.api import KVStore
 from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
 from llama_stack.providers.utils.kvstore.kvstore import kvstore_impl
 
-logger = get_logger(name=__name__, category="quota")
+logger = get_logger(name=__name__, category="core::server")
 
 
 class QuotaMiddleware:

llama_stack/core/server/server.py

@@ -9,7 +9,7 @@ import asyncio
 import functools
 import inspect
 import json
-import logging
+import logging  # allow-direct-logging
 import os
 import ssl
 import sys
@@ -28,6 +28,7 @@ from aiohttp import hdrs
 from fastapi import Body, FastAPI, HTTPException, Request, Response
 from fastapi import Path as FastapiPath
 from fastapi.exceptions import RequestValidationError
+from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse, StreamingResponse
 from openai import BadRequestError
 from pydantic import BaseModel, ValidationError
@@ -40,6 +41,7 @@ from llama_stack.core.datatypes import (
     AuthenticationRequiredError,
     LoggingConfig,
     StackRunConfig,
+    process_cors_config,
 )
 from llama_stack.core.distribution import builtin_automatically_routed_apis
 from llama_stack.core.external import ExternalApiSpec, load_external_apis
@@ -82,7 +84,7 @@ from .quota import QuotaMiddleware
 
 REPO_ROOT = Path(__file__).parent.parent.parent.parent
 
-logger = get_logger(name=__name__, category="server")
+logger = get_logger(name=__name__, category="core::server")
 
 
 def warn_with_traceback(message, category, filename, lineno, file=None, line=None):
@@ -413,7 +415,7 @@ def main(args: argparse.Namespace | None = None):
         config_contents = yaml.safe_load(fp)
         if isinstance(config_contents, dict) and (cfg := config_contents.get("logging_config")):
             logger_config = LoggingConfig(**cfg)
-        logger = get_logger(name=__name__, category="server", config=logger_config)
+        logger = get_logger(name=__name__, category="core::server", config=logger_config)
         if args.env:
             for env_pair in args.env:
                 try:
@@ -483,6 +485,12 @@ def main(args: argparse.Namespace | None = None):
             window_seconds=window_seconds,
         )
 
+    if config.server.cors:
+        logger.info("Enabling CORS")
+        cors_config = process_cors_config(config.server.cors)
+        if cors_config:
+            app.add_middleware(CORSMiddleware, **cors_config.model_dump())
+
     if Api.telemetry in impls:
         setup_logger(impls[Api.telemetry])
     else:

llama_stack/core/store/registry.py

@@ -16,7 +16,7 @@ from llama_stack.log import get_logger
 from llama_stack.providers.utils.kvstore import KVStore, kvstore_impl
 from llama_stack.providers.utils.kvstore.config import KVStoreConfig, SqliteKVStoreConfig
 
-logger = get_logger(__name__, category="core")
+logger = get_logger(__name__, category="core::registry")
 
 
 class DistributionRegistry(Protocol):

llama_stack/core/utils/config_resolution.py

@@ -10,7 +10,7 @@ from pathlib import Path
 from llama_stack.core.utils.config_dirs import DISTRIBS_BASE_DIR
 from llama_stack.log import get_logger
 
-logger = get_logger(name=__name__, category="config_resolution")
+logger = get_logger(name=__name__, category="core")
 
 
 DISTRO_DIR = Path(__file__).parent.parent.parent.parent / "llama_stack" / "distributions"

llama_stack/core/utils/exec.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.
 
-import logging
+import importlib
 import os
 import signal
 import subprocess
@@ -12,9 +12,9 @@ import sys
 
 from termcolor import cprint
 
-log = logging.getLogger(__name__)
+from llama_stack.log import get_logger
 
-import importlib
+log = get_logger(name=__name__, category="core")
 
 
 def formulate_run_args(image_type: str, image_name: str) -> list:

llama_stack/core/utils/prompt_for_config.py

@@ -6,7 +6,6 @@
 
 import inspect
 import json
-import logging
 from enum import Enum
 from typing import Annotated, Any, Literal, Union, get_args, get_origin
 
@@ -14,7 +13,9 @@ from pydantic import BaseModel
 from pydantic.fields import FieldInfo
 from pydantic_core import PydanticUndefinedType
 
-log = logging.getLogger(__name__)
+from llama_stack.log import get_logger
+
+log = get_logger(name=__name__, category="core")
 
 
 def is_list_of_primitives(field_type):

llama_stack/distributions/ci-tests/build.yaml

@@ -34,7 +34,7 @@ distribution_spec:
     telemetry:
     - provider_type: inline::meta-reference
     post_training:
-    - provider_type: inline::huggingface
+    - provider_type: inline::huggingface-cpu
     eval:
     - provider_type: inline::meta-reference
     datasetio:

llama_stack/distributions/ci-tests/run.yaml

@@ -156,8 +156,8 @@ providers:
       sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/ci-tests}/trace_store.db
       otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
   post_training:
-  - provider_id: huggingface
-    provider_type: inline::huggingface
+  - provider_id: huggingface-cpu
+    provider_type: inline::huggingface-cpu
     config:
       checkpoint_format: huggingface
       distributed_backend: null

llama_stack/distributions/starter-gpu/__init__.py

@@ -0,0 +1,7 @@
+# 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 .starter_gpu import get_distribution_template  # noqa: F401

llama_stack/distributions/starter-gpu/build.yaml

@@ -0,0 +1,59 @@
+version: 2
+distribution_spec:
+  description: Quick start template for running Llama Stack with several popular providers.
+    This distribution is intended for GPU-enabled environments.
+  providers:
+    inference:
+    - provider_type: remote::cerebras
+    - provider_type: remote::ollama
+    - provider_type: remote::vllm
+    - provider_type: remote::tgi
+    - provider_type: remote::fireworks
+    - provider_type: remote::together
+    - provider_type: remote::bedrock
+    - provider_type: remote::nvidia
+    - provider_type: remote::openai
+    - provider_type: remote::anthropic
+    - provider_type: remote::gemini
+    - provider_type: remote::vertexai
+    - provider_type: remote::groq
+    - provider_type: remote::sambanova
+    - provider_type: inline::sentence-transformers
+    vector_io:
+    - provider_type: inline::faiss
+    - provider_type: inline::sqlite-vec
+    - provider_type: inline::milvus
+    - provider_type: remote::chromadb
+    - provider_type: remote::pgvector
+    files:
+    - provider_type: inline::localfs
+    safety:
+    - provider_type: inline::llama-guard
+    - provider_type: inline::code-scanner
+    agents:
+    - provider_type: inline::meta-reference
+    telemetry:
+    - provider_type: inline::meta-reference
+    post_training:
+    - provider_type: inline::torchtune-gpu
+    eval:
+    - provider_type: inline::meta-reference
+    datasetio:
+    - provider_type: remote::huggingface
+    - provider_type: inline::localfs
+    scoring:
+    - provider_type: inline::basic
+    - provider_type: inline::llm-as-judge
+    - provider_type: inline::braintrust
+    tool_runtime:
+    - provider_type: remote::brave-search
+    - provider_type: remote::tavily-search
+    - provider_type: inline::rag-runtime
+    - provider_type: remote::model-context-protocol
+    batches:
+    - provider_type: inline::reference
+image_type: venv
+additional_pip_packages:
+- aiosqlite
+- asyncpg
+- sqlalchemy[asyncio]

llama_stack/distributions/starter-gpu/run.yaml

@@ -0,0 +1,238 @@
+version: 2
+image_name: starter-gpu
+apis:
+- agents
+- batches
+- datasetio
+- eval
+- files
+- inference
+- post_training
+- safety
+- scoring
+- telemetry
+- tool_runtime
+- vector_io
+providers:
+  inference:
+  - provider_id: ${env.CEREBRAS_API_KEY:+cerebras}
+    provider_type: remote::cerebras
+    config:
+      base_url: https://api.cerebras.ai
+      api_key: ${env.CEREBRAS_API_KEY:=}
+  - provider_id: ${env.OLLAMA_URL:+ollama}
+    provider_type: remote::ollama
+    config:
+      url: ${env.OLLAMA_URL:=http://localhost:11434}
+  - provider_id: ${env.VLLM_URL:+vllm}
+    provider_type: remote::vllm
+    config:
+      url: ${env.VLLM_URL:=}
+      max_tokens: ${env.VLLM_MAX_TOKENS:=4096}
+      api_token: ${env.VLLM_API_TOKEN:=fake}
+      tls_verify: ${env.VLLM_TLS_VERIFY:=true}
+  - provider_id: ${env.TGI_URL:+tgi}
+    provider_type: remote::tgi
+    config:
+      url: ${env.TGI_URL:=}
+  - provider_id: fireworks
+    provider_type: remote::fireworks
+    config:
+      url: https://api.fireworks.ai/inference/v1
+      api_key: ${env.FIREWORKS_API_KEY:=}
+  - provider_id: together
+    provider_type: remote::together
+    config:
+      url: https://api.together.xyz/v1
+      api_key: ${env.TOGETHER_API_KEY:=}
+  - provider_id: bedrock
+    provider_type: remote::bedrock
+  - provider_id: ${env.NVIDIA_API_KEY:+nvidia}
+    provider_type: remote::nvidia
+    config:
+      url: ${env.NVIDIA_BASE_URL:=https://integrate.api.nvidia.com}
+      api_key: ${env.NVIDIA_API_KEY:=}
+      append_api_version: ${env.NVIDIA_APPEND_API_VERSION:=True}
+  - provider_id: openai
+    provider_type: remote::openai
+    config:
+      api_key: ${env.OPENAI_API_KEY:=}
+      base_url: ${env.OPENAI_BASE_URL:=https://api.openai.com/v1}
+  - provider_id: anthropic
+    provider_type: remote::anthropic
+    config:
+      api_key: ${env.ANTHROPIC_API_KEY:=}
+  - provider_id: gemini
+    provider_type: remote::gemini
+    config:
+      api_key: ${env.GEMINI_API_KEY:=}
+  - provider_id: ${env.VERTEX_AI_PROJECT:+vertexai}
+    provider_type: remote::vertexai
+    config:
+      project: ${env.VERTEX_AI_PROJECT:=}
+      location: ${env.VERTEX_AI_LOCATION:=us-central1}
+  - provider_id: groq
+    provider_type: remote::groq
+    config:
+      url: https://api.groq.com
+      api_key: ${env.GROQ_API_KEY:=}
+  - provider_id: sambanova
+    provider_type: remote::sambanova
+    config:
+      url: https://api.sambanova.ai/v1
+      api_key: ${env.SAMBANOVA_API_KEY:=}
+  - provider_id: sentence-transformers
+    provider_type: inline::sentence-transformers
+  vector_io:
+  - provider_id: faiss
+    provider_type: inline::faiss
+    config:
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/faiss_store.db
+  - provider_id: sqlite-vec
+    provider_type: inline::sqlite-vec
+    config:
+      db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec.db
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/sqlite_vec_registry.db
+  - provider_id: ${env.MILVUS_URL:+milvus}
+    provider_type: inline::milvus
+    config:
+      db_path: ${env.MILVUS_DB_PATH:=~/.llama/distributions/starter}/milvus.db
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/milvus_registry.db
+  - provider_id: ${env.CHROMADB_URL:+chromadb}
+    provider_type: remote::chromadb
+    config:
+      url: ${env.CHROMADB_URL:=}
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter/}/chroma_remote_registry.db
+  - provider_id: ${env.PGVECTOR_DB:+pgvector}
+    provider_type: remote::pgvector
+    config:
+      host: ${env.PGVECTOR_HOST:=localhost}
+      port: ${env.PGVECTOR_PORT:=5432}
+      db: ${env.PGVECTOR_DB:=}
+      user: ${env.PGVECTOR_USER:=}
+      password: ${env.PGVECTOR_PASSWORD:=}
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/pgvector_registry.db
+  files:
+  - provider_id: meta-reference-files
+    provider_type: inline::localfs
+    config:
+      storage_dir: ${env.FILES_STORAGE_DIR:=~/.llama/distributions/starter/files}
+      metadata_store:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/files_metadata.db
+  safety:
+  - provider_id: llama-guard
+    provider_type: inline::llama-guard
+    config:
+      excluded_categories: []
+  - provider_id: code-scanner
+    provider_type: inline::code-scanner
+  agents:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      persistence_store:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/agents_store.db
+      responses_store:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/responses_store.db
+  telemetry:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      service_name: "${env.OTEL_SERVICE_NAME:=\u200B}"
+      sinks: ${env.TELEMETRY_SINKS:=console,sqlite}
+      sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/trace_store.db
+      otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
+  post_training:
+  - provider_id: torchtune-gpu
+    provider_type: inline::torchtune-gpu
+    config:
+      checkpoint_format: meta
+  eval:
+  - provider_id: meta-reference
+    provider_type: inline::meta-reference
+    config:
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/meta_reference_eval.db
+  datasetio:
+  - provider_id: huggingface
+    provider_type: remote::huggingface
+    config:
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/huggingface_datasetio.db
+  - provider_id: localfs
+    provider_type: inline::localfs
+    config:
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/localfs_datasetio.db
+  scoring:
+  - provider_id: basic
+    provider_type: inline::basic
+  - provider_id: llm-as-judge
+    provider_type: inline::llm-as-judge
+  - provider_id: braintrust
+    provider_type: inline::braintrust
+    config:
+      openai_api_key: ${env.OPENAI_API_KEY:=}
+  tool_runtime:
+  - provider_id: brave-search
+    provider_type: remote::brave-search
+    config:
+      api_key: ${env.BRAVE_SEARCH_API_KEY:=}
+      max_results: 3
+  - provider_id: tavily-search
+    provider_type: remote::tavily-search
+    config:
+      api_key: ${env.TAVILY_SEARCH_API_KEY:=}
+      max_results: 3
+  - provider_id: rag-runtime
+    provider_type: inline::rag-runtime
+  - provider_id: model-context-protocol
+    provider_type: remote::model-context-protocol
+  batches:
+  - provider_id: reference
+    provider_type: inline::reference
+    config:
+      kvstore:
+        type: sqlite
+        db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/batches.db
+metadata_store:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/registry.db
+inference_store:
+  type: sqlite
+  db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter-gpu}/inference_store.db
+models: []
+shields:
+- shield_id: llama-guard
+  provider_id: ${env.SAFETY_MODEL:+llama-guard}
+  provider_shield_id: ${env.SAFETY_MODEL:=}
+- shield_id: code-scanner
+  provider_id: ${env.CODE_SCANNER_MODEL:+code-scanner}
+  provider_shield_id: ${env.CODE_SCANNER_MODEL:=}
+vector_dbs: []
+datasets: []
+scoring_fns: []
+benchmarks: []
+tool_groups:
+- toolgroup_id: builtin::websearch
+  provider_id: tavily-search
+- toolgroup_id: builtin::rag
+  provider_id: rag-runtime
+server:
+  port: 8321

llama_stack/distributions/starter-gpu/starter_gpu.py

@@ -0,0 +1,22 @@
+# 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_stack.distributions.template import BuildProvider, DistributionTemplate
+
+from ..starter.starter import get_distribution_template as get_starter_distribution_template
+
+
+def get_distribution_template() -> DistributionTemplate:
+    template = get_starter_distribution_template()
+    name = "starter-gpu"
+    template.name = name
+    template.description = "Quick start template for running Llama Stack with several popular providers. This distribution is intended for GPU-enabled environments."
+
+    template.providers["post_training"] = [
+        BuildProvider(provider_type="inline::torchtune-gpu"),
+    ]
+    return template

llama_stack/distributions/starter/build.yaml

@@ -1,6 +1,7 @@
 version: 2
 distribution_spec:
-  description: Quick start template for running Llama Stack with several popular providers
+  description: Quick start template for running Llama Stack with several popular providers.
+    This distribution is intended for CPU-only environments.
   providers:
     inference:
     - provider_type: remote::cerebras
@@ -34,7 +35,7 @@ distribution_spec:
     telemetry:
     - provider_type: inline::meta-reference
     post_training:
-    - provider_type: inline::huggingface
+    - provider_type: inline::huggingface-cpu
     eval:
     - provider_type: inline::meta-reference
     datasetio:

llama_stack/distributions/starter/run.yaml

@@ -156,8 +156,8 @@ providers:
       sqlite_db_path: ${env.SQLITE_STORE_DIR:=~/.llama/distributions/starter}/trace_store.db
       otel_exporter_otlp_endpoint: ${env.OTEL_EXPORTER_OTLP_ENDPOINT:=}
   post_training:
-  - provider_id: huggingface
-    provider_type: inline::huggingface
+  - provider_id: huggingface-cpu
+    provider_type: inline::huggingface-cpu
     config:
       checkpoint_format: huggingface
       distributed_backend: null

llama_stack/distributions/starter/starter.py

@@ -120,7 +120,7 @@ def get_distribution_template() -> DistributionTemplate:
         ],
         "agents": [BuildProvider(provider_type="inline::meta-reference")],
         "telemetry": [BuildProvider(provider_type="inline::meta-reference")],
-        "post_training": [BuildProvider(provider_type="inline::huggingface")],
+        "post_training": [BuildProvider(provider_type="inline::huggingface-cpu")],
         "eval": [BuildProvider(provider_type="inline::meta-reference")],
         "datasetio": [
             BuildProvider(provider_type="remote::huggingface"),
@@ -178,7 +178,7 @@ def get_distribution_template() -> DistributionTemplate:
     return DistributionTemplate(
         name=name,
         distro_type="self_hosted",
-        description="Quick start template for running Llama Stack with several popular providers",
+        description="Quick start template for running Llama Stack with several popular providers. This distribution is intended for CPU-only environments.",
         container_image=None,
         template_path=None,
         providers=providers,

llama_stack/log.py

@@ -4,10 +4,10 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import logging
+import logging  # allow-direct-logging
 import os
 import re
-from logging.config import dictConfig
+from logging.config import dictConfig  # allow-direct-logging
 
 from rich.console import Console
 from rich.errors import MarkupError

llama_stack/models/llama/llama3/multimodal/encoder_utils.py

@@ -13,14 +13,15 @@
 
 # Copyright (c) Meta Platforms, Inc. and its affiliates.
 import math
-from logging import getLogger
 
 import torch
 import torch.nn.functional as F
 
+from llama_stack.log import get_logger
+
 from .utils import get_negative_inf_value, to_2tuple
 
-logger = getLogger()
+logger = get_logger(name=__name__, category="models::llama")
 
 
 def resize_local_position_embedding(orig_pos_embed, grid_size):

llama_stack/models/llama/llama3/multimodal/image_transform.py

@@ -13,7 +13,6 @@
 
 import math
 from collections import defaultdict
-from logging import getLogger
 from typing import Any
 
 import torch
@@ -21,9 +20,11 @@ import torchvision.transforms as tv
 from PIL import Image
 from torchvision.transforms import functional as F
 
+from llama_stack.log import get_logger
+
 IMAGE_RES = 224
 
-logger = getLogger()
+logger = get_logger(name=__name__, category="models::llama")
 
 
 class VariableSizeImageTransform:

llama_stack/models/llama/llama3/multimodal/model.py

@@ -3,8 +3,6 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
-
-import logging
 import math
 from collections.abc import Callable
 from functools import partial
@@ -22,6 +20,8 @@ from PIL import Image as PIL_Image
 from torch import Tensor, nn
 from torch.distributed import _functional_collectives as funcol
 
+from llama_stack.log import get_logger
+
 from ..model import ModelArgs, RMSNorm, apply_rotary_emb, precompute_freqs_cis
 from .encoder_utils import (
     build_encoder_attention_mask,
@@ -34,9 +34,10 @@ from .encoder_utils import (
 from .image_transform import VariableSizeImageTransform
 from .utils import get_negative_inf_value, to_2tuple
 
-logger = logging.getLogger(__name__)
 MP_SCALE = 8
 
+logger = get_logger(name=__name__, category="models::llama")
+
 
 def reduce_from_tensor_model_parallel_region(input_):
     """All-reduce the input tensor across model parallel group."""
@@ -771,7 +772,7 @@ class TilePositionEmbedding(nn.Module):
         if embed is not None:
             # reshape the weights to the correct shape
             nt_old, nt_old, _, w = embed.shape
-            logging.info(f"Resizing tile embedding from {nt_old}x{nt_old} to {self.num_tiles}x{self.num_tiles}")
+            logger.info(f"Resizing tile embedding from {nt_old}x{nt_old} to {self.num_tiles}x{self.num_tiles}")
             embed_new = TilePositionEmbedding._dynamic_resize(embed, self.num_tiles)
             # assign the weights to the module
             state_dict[prefix + "embedding"] = embed_new

llama_stack/models/llama/llama3/tokenizer.py

@@ -4,8 +4,8 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
+
 from collections.abc import Collection, Iterator, Sequence, Set
-from logging import getLogger
 from pathlib import Path
 from typing import (
     Literal,
@@ -14,11 +14,9 @@ from typing import (
 
 import tiktoken
 
+from llama_stack.log import get_logger
 from llama_stack.models.llama.tokenizer_utils import load_bpe_file
 
-logger = getLogger(__name__)
-
-
 # The tiktoken tokenizer can handle <=400k chars without
 # pyo3_runtime.PanicException.
 TIKTOKEN_MAX_ENCODE_CHARS = 400_000
@@ -31,6 +29,8 @@ MAX_NO_WHITESPACES_CHARS = 25_000
 
 _INSTANCE = None
 
+logger = get_logger(name=__name__, category="models::llama")
+
 
 class Tokenizer:
     """

llama_stack/models/llama/llama3/tool_utils.py

@@ -11,7 +11,7 @@ from llama_stack.log import get_logger
 
 from ..datatypes import BuiltinTool, RecursiveType, ToolCall, ToolPromptFormat
 
-logger = get_logger(name=__name__, category="inference")
+logger = get_logger(name=__name__, category="models::llama")
 
 BUILTIN_TOOL_PATTERN = r'\b(?P<tool_name>\w+)\.call\(query="(?P<query>[^"]*)"\)'
 CUSTOM_TOOL_CALL_PATTERN = re.compile(r"<function=(?P<function_name>[^}]+)>(?P<args>{.*?})")

llama_stack/models/llama/llama4/quantization/loader.py

@@ -4,7 +4,6 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import logging
 import os
 from collections.abc import Callable
 
@@ -13,11 +12,13 @@ from fairscale.nn.model_parallel.initialize import get_model_parallel_rank
 from torch import Tensor, nn
 from torch.nn import functional as F
 
+from llama_stack.log import get_logger
+
 from ...datatypes import QuantizationMode
 from ..model import Transformer, TransformerBlock
 from ..moe import MoE
 
-log = logging.getLogger(__name__)
+log = get_logger(name=__name__, category="models::llama")
 
 
 def swiglu_wrapper_no_reduce(

llama_stack/models/llama/llama4/tokenizer.py

@@ -5,7 +5,6 @@
 # the root directory of this source tree.
 
 from collections.abc import Collection, Iterator, Sequence, Set
-from logging import getLogger
 from pathlib import Path
 from typing import (
     Literal,
@@ -14,11 +13,9 @@ from typing import (
 
 import tiktoken
 
+from llama_stack.log import get_logger
 from llama_stack.models.llama.tokenizer_utils import load_bpe_file
 
-logger = getLogger(__name__)
-
-
 # The tiktoken tokenizer can handle <=400k chars without
 # pyo3_runtime.PanicException.
 TIKTOKEN_MAX_ENCODE_CHARS = 400_000
@@ -101,6 +98,8 @@ BASIC_SPECIAL_TOKENS = [
     "<|fim_suffix|>",
 ]
 
+logger = get_logger(name=__name__, category="models::llama")
+
 
 class Tokenizer:
     """

llama_stack/models/llama/quantize_impls.py

@@ -6,9 +6,10 @@
 
 # type: ignore
 import collections
-import logging
 
-log = logging.getLogger(__name__)
+from llama_stack.log import get_logger
+
+log = get_logger(name=__name__, category="models::llama")
 
 try:
     import fbgemm_gpu.experimental.gen_ai  # noqa: F401

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

@@ -84,7 +84,7 @@ MEMORY_QUERY_TOOL = "knowledge_search"
 WEB_SEARCH_TOOL = "web_search"
 RAG_TOOL_GROUP = "builtin::rag"
 
-logger = get_logger(name=__name__, category="agents")
+logger = get_logger(name=__name__, category="agents::meta_reference")
 
 
 class ChatAgent(ShieldRunnerMixin):

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

@@ -4,7 +4,6 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import logging
 import uuid
 from collections.abc import AsyncGenerator
 from datetime import UTC, datetime
@@ -42,6 +41,7 @@ from llama_stack.apis.safety import Safety
 from llama_stack.apis.tools import ToolGroups, ToolRuntime
 from llama_stack.apis.vector_io import VectorIO
 from llama_stack.core.datatypes import AccessRule
+from llama_stack.log import get_logger
 from llama_stack.providers.utils.kvstore import InmemoryKVStoreImpl, kvstore_impl
 from llama_stack.providers.utils.pagination import paginate_records
 from llama_stack.providers.utils.responses.responses_store import ResponsesStore
@@ -51,7 +51,7 @@ from .config import MetaReferenceAgentsImplConfig
 from .persistence import AgentInfo
 from .responses.openai_responses import OpenAIResponsesImpl
 
-logger = logging.getLogger()
+logger = get_logger(name=__name__, category="agents::meta_reference")
 
 
 class MetaReferenceAgentsImpl(Agents):

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

@@ -5,7 +5,6 @@
 # the root directory of this source tree.
 
 import json
-import logging
 import uuid
 from datetime import UTC, datetime
 
@@ -15,9 +14,10 @@ from llama_stack.core.access_control.access_control import AccessDeniedError, is
 from llama_stack.core.access_control.datatypes import AccessRule
 from llama_stack.core.datatypes import User
 from llama_stack.core.request_headers import get_authenticated_user
+from llama_stack.log import get_logger
 from llama_stack.providers.utils.kvstore import KVStore
 
-log = logging.getLogger(__name__)
+log = get_logger(name=__name__, category="agents::meta_reference")
 
 
 class AgentSessionInfo(Session):

llama_stack/providers/inline/agents/meta_reference/responses/openai_responses.py

@@ -41,7 +41,7 @@ from .utils import (
     convert_response_text_to_chat_response_format,
 )
 
-logger = get_logger(name=__name__, category="responses")
+logger = get_logger(name=__name__, category="openai::responses")
 
 
 class OpenAIResponsePreviousResponseWithInputItems(BaseModel):

llama_stack/providers/inline/agents/meta_reference/responses/streaming.py

@@ -47,7 +47,7 @@ from llama_stack.log import get_logger
 from .types import ChatCompletionContext, ChatCompletionResult
 from .utils import convert_chat_choice_to_response_message, is_function_tool_call
 
-logger = get_logger(name=__name__, category="responses")
+logger = get_logger(name=__name__, category="agents::meta_reference")
 
 
 class StreamingResponseOrchestrator:

llama_stack/providers/inline/agents/meta_reference/responses/tool_executor.py

@@ -38,7 +38,7 @@ from llama_stack.log import get_logger
 
 from .types import ChatCompletionContext, ToolExecutionResult
 
-logger = get_logger(name=__name__, category="responses")
+logger = get_logger(name=__name__, category="agents::meta_reference")
 
 
 class ToolExecutor:

llama_stack/providers/inline/agents/meta_reference/responses/utils.py

@@ -17,6 +17,8 @@ from llama_stack.apis.agents.openai_responses import (
     OpenAIResponseOutputMessageContent,
     OpenAIResponseOutputMessageContentOutputText,
     OpenAIResponseOutputMessageFunctionToolCall,
+    OpenAIResponseOutputMessageMCPCall,
+    OpenAIResponseOutputMessageMCPListTools,
     OpenAIResponseText,
 )
 from llama_stack.apis.inference import (
@@ -99,14 +101,22 @@ async def convert_response_input_to_chat_messages(
     """
     messages: list[OpenAIMessageParam] = []
     if isinstance(input, list):
+        # extract all OpenAIResponseInputFunctionToolCallOutput items
+        # so their corresponding OpenAIToolMessageParam instances can
+        # be added immediately following the corresponding
+        # OpenAIAssistantMessageParam
+        tool_call_results = {}
         for input_item in input:
             if isinstance(input_item, OpenAIResponseInputFunctionToolCallOutput):
-                messages.append(
-                    OpenAIToolMessageParam(
-                        content=input_item.output,
-                        tool_call_id=input_item.call_id,
-                    )
+                tool_call_results[input_item.call_id] = OpenAIToolMessageParam(
+                    content=input_item.output,
+                    tool_call_id=input_item.call_id,
                 )
+
+        for input_item in input:
+            if isinstance(input_item, OpenAIResponseInputFunctionToolCallOutput):
+                # skip as these have been extracted and inserted in order
+                pass
             elif isinstance(input_item, OpenAIResponseOutputMessageFunctionToolCall):
                 tool_call = OpenAIChatCompletionToolCall(
                     index=0,
@@ -117,6 +127,28 @@ async def convert_response_input_to_chat_messages(
                     ),
                 )
                 messages.append(OpenAIAssistantMessageParam(tool_calls=[tool_call]))
+                if input_item.call_id in tool_call_results:
+                    messages.append(tool_call_results[input_item.call_id])
+                    del tool_call_results[input_item.call_id]
+            elif isinstance(input_item, OpenAIResponseOutputMessageMCPCall):
+                tool_call = OpenAIChatCompletionToolCall(
+                    index=0,
+                    id=input_item.id,
+                    function=OpenAIChatCompletionToolCallFunction(
+                        name=input_item.name,
+                        arguments=input_item.arguments,
+                    ),
+                )
+                messages.append(OpenAIAssistantMessageParam(tool_calls=[tool_call]))
+                messages.append(
+                    OpenAIToolMessageParam(
+                        content=input_item.output,
+                        tool_call_id=input_item.id,
+                    )
+                )
+            elif isinstance(input_item, OpenAIResponseOutputMessageMCPListTools):
+                # the tool list will be handled separately
+                pass
             else:
                 content = await convert_response_content_to_chat_content(input_item.content)
                 message_type = await get_message_type_by_role(input_item.role)
@@ -125,6 +157,10 @@ async def convert_response_input_to_chat_messages(
                         f"Llama Stack OpenAI Responses does not yet support message role '{input_item.role}' in this context"
                     )
                 messages.append(message_type(content=content))
+        if len(tool_call_results):
+            raise ValueError(
+                f"Received function_call_output(s) with call_id(s) {tool_call_results.keys()}, but no corresponding function_call"
+            )
     else:
         messages.append(OpenAIUserMessageParam(content=input))
     return messages

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

@@ -5,13 +5,13 @@
 # the root directory of this source tree.
 
 import asyncio
-import logging
 
 from llama_stack.apis.inference import Message
 from llama_stack.apis.safety import Safety, SafetyViolation, ViolationLevel
+from llama_stack.log import get_logger
 from llama_stack.providers.utils.telemetry import tracing
 
-log = logging.getLogger(__name__)
+log = get_logger(name=__name__, category="agents::meta_reference")
 
 
 class SafetyException(Exception):  # noqa: N818

llama_stack/providers/inline/batches/reference/batches.py

@@ -5,6 +5,7 @@
 # the root directory of this source tree.
 
 import asyncio
+import hashlib
 import itertools
 import json
 import time
@@ -136,28 +137,45 @@ class ReferenceBatchesImpl(Batches):
         endpoint: str,
         completion_window: Literal["24h"],
         metadata: dict[str, str] | None = None,
+        idempotency_key: str | None = None,
     ) -> BatchObject:
         """
         Create a new batch for processing multiple API requests.
 
-        Error handling by levels -
-         0. Input param handling, results in 40x errors before processing, e.g.
-           - Wrong completion_window
-           - Invalid metadata types
-           - Unknown endpoint
-          -> no batch created
-         1. Errors preventing processing, result in BatchErrors aggregated in process_batch, e.g.
-           - input_file_id missing
-           - invalid json in file
-           - missing custom_id, method, url, body
-           - invalid model
-           - streaming
-          -> batch created, validation sends to failed status
-         2. Processing errors, result in error_file_id entries, e.g.
-           - Any error returned from inference endpoint
-          -> batch created, goes to completed status
+        This implementation provides optional idempotency: when an idempotency key
+        (idempotency_key) is provided, a deterministic ID is generated based on the input
+        parameters. If a batch with the same parameters already exists, it will be
+        returned instead of creating a duplicate. Without an idempotency key,
+        each request creates a new batch with a unique ID.
+
+        Args:
+            input_file_id: The ID of an uploaded file containing requests for the batch.
+            endpoint: The endpoint to be used for all requests in the batch.
+            completion_window: The time window within which the batch should be processed.
+            metadata: Optional metadata for the batch.
+            idempotency_key: Optional idempotency key for enabling idempotent behavior.
+
+        Returns:
+            The created or existing batch object.
         """
 
+        # Error handling by levels -
+        #  0. Input param handling, results in 40x errors before processing, e.g.
+        #    - Wrong completion_window
+        #    - Invalid metadata types
+        #    - Unknown endpoint
+        #   -> no batch created
+        #  1. Errors preventing processing, result in BatchErrors aggregated in process_batch, e.g.
+        #    - input_file_id missing
+        #    - invalid json in file
+        #    - missing custom_id, method, url, body
+        #    - invalid model
+        #    - streaming
+        #   -> batch created, validation sends to failed status
+        #  2. Processing errors, result in error_file_id entries, e.g.
+        #    - Any error returned from inference endpoint
+        #   -> batch created, goes to completed status
+
         # TODO: set expiration time for garbage collection
 
         if endpoint not in ["/v1/chat/completions"]:
@@ -171,6 +189,35 @@ class ReferenceBatchesImpl(Batches):
             )
 
         batch_id = f"batch_{uuid.uuid4().hex[:16]}"
+
+        # For idempotent requests, use the idempotency key for the batch ID
+        # This ensures the same key always maps to the same batch ID,
+        # allowing us to detect parameter conflicts
+        if idempotency_key is not None:
+            hash_input = idempotency_key.encode("utf-8")
+            hash_digest = hashlib.sha256(hash_input).hexdigest()[:24]
+            batch_id = f"batch_{hash_digest}"
+
+            try:
+                existing_batch = await self.retrieve_batch(batch_id)
+
+                if (
+                    existing_batch.input_file_id != input_file_id
+                    or existing_batch.endpoint != endpoint
+                    or existing_batch.completion_window != completion_window
+                    or existing_batch.metadata != metadata
+                ):
+                    raise ConflictError(
+                        f"Idempotency key '{idempotency_key}' was previously used with different parameters. "
+                        "Either use a new idempotency key or ensure all parameters match the original request."
+                    )
+
+                logger.info(f"Returning existing batch with ID: {batch_id}")
+                return existing_batch
+            except ResourceNotFoundError:
+                # Batch doesn't exist, continue with creation
+                pass
+
         current_time = int(time.time())
 
         batch = BatchObject(
@@ -185,6 +232,7 @@ class ReferenceBatchesImpl(Batches):
         )
 
         await self.kvstore.set(f"batch:{batch_id}", batch.to_json())
+        logger.info(f"Created new batch with ID: {batch_id}")
 
         if self.process_batches:
             task = asyncio.create_task(self._process_batch(batch_id))

llama_stack/providers/inline/files/localfs/files.py

@@ -11,6 +11,7 @@ from typing import Annotated
 
 from fastapi import File, Form, Response, UploadFile
 
+from llama_stack.apis.common.errors import ResourceNotFoundError
 from llama_stack.apis.common.responses import Order
 from llama_stack.apis.files import (
     Files,
@@ -20,12 +21,15 @@ from llama_stack.apis.files import (
     OpenAIFilePurpose,
 )
 from llama_stack.core.datatypes import AccessRule
+from llama_stack.log import get_logger
 from llama_stack.providers.utils.sqlstore.api import ColumnDefinition, ColumnType
 from llama_stack.providers.utils.sqlstore.authorized_sqlstore import AuthorizedSqlStore
 from llama_stack.providers.utils.sqlstore.sqlstore import sqlstore_impl
 
 from .config import LocalfsFilesImplConfig
 
+logger = get_logger(name=__name__, category="files")
+
 
 class LocalfsFilesImpl(Files):
     def __init__(self, config: LocalfsFilesImplConfig, policy: list[AccessRule]) -> None:
@@ -65,6 +69,18 @@ class LocalfsFilesImpl(Files):
         """Get the filesystem path for a file ID."""
         return Path(self.config.storage_dir) / file_id
 
+    async def _lookup_file_id(self, file_id: str) -> tuple[OpenAIFileObject, Path]:
+        """Look up a OpenAIFileObject and filesystem path from its ID."""
+        if not self.sql_store:
+            raise RuntimeError("Files provider not initialized")
+
+        row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id})
+        if not row:
+            raise ResourceNotFoundError(file_id, "File", "client.files.list()")
+
+        file_path = Path(row.pop("file_path"))
+        return OpenAIFileObject(**row), file_path
+
     # OpenAI Files API Implementation
     async def openai_upload_file(
         self,
@@ -157,37 +173,19 @@ class LocalfsFilesImpl(Files):
 
     async def openai_retrieve_file(self, file_id: str) -> OpenAIFileObject:
         """Returns information about a specific file."""
-        if not self.sql_store:
-            raise RuntimeError("Files provider not initialized")
+        file_obj, _ = await self._lookup_file_id(file_id)
 
-        row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id})
-        if not row:
-            raise ValueError(f"File with id {file_id} not found")
-
-        return OpenAIFileObject(
-            id=row["id"],
-            filename=row["filename"],
-            purpose=OpenAIFilePurpose(row["purpose"]),
-            bytes=row["bytes"],
-            created_at=row["created_at"],
-            expires_at=row["expires_at"],
-        )
+        return file_obj
 
     async def openai_delete_file(self, file_id: str) -> OpenAIFileDeleteResponse:
         """Delete a file."""
-        if not self.sql_store:
-            raise RuntimeError("Files provider not initialized")
-
-        row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id})
-        if not row:
-            raise ValueError(f"File with id {file_id} not found")
-
         # Delete physical file
-        file_path = Path(row["file_path"])
+        _, file_path = await self._lookup_file_id(file_id)
         if file_path.exists():
             file_path.unlink()
 
         # Delete metadata from database
+        assert self.sql_store is not None, "Files provider not initialized"
         await self.sql_store.delete("openai_files", where={"id": file_id})
 
         return OpenAIFileDeleteResponse(
@@ -197,25 +195,17 @@ class LocalfsFilesImpl(Files):
 
     async def openai_retrieve_file_content(self, file_id: str) -> Response:
         """Returns the contents of the specified file."""
-        if not self.sql_store:
-            raise RuntimeError("Files provider not initialized")
-
-        # Get file metadata
-        row = await self.sql_store.fetch_one("openai_files", policy=self.policy, where={"id": file_id})
-        if not row:
-            raise ValueError(f"File with id {file_id} not found")
-
         # Read file content
-        file_path = Path(row["file_path"])
-        if not file_path.exists():
-            raise ValueError(f"File content not found on disk: {file_path}")
+        file_obj, file_path = await self._lookup_file_id(file_id)
 
-        with open(file_path, "rb") as f:
-            content = f.read()
+        if not file_path.exists():
+            logger.warning(f"File '{file_id}'s underlying '{file_path}' is missing, deleting metadata.")
+            await self.openai_delete_file(file_id)
+            raise ResourceNotFoundError(file_id, "File", "client.files.list()")
 
         # Return as binary response with appropriate content type
         return Response(
-            content=content,
+            content=file_path.read_bytes(),
             media_type="application/octet-stream",
-            headers={"Content-Disposition": f'attachment; filename="{row["filename"]}"'},
+            headers={"Content-Disposition": f'attachment; filename="{file_obj.filename}"'},
         )

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

@@ -12,7 +12,6 @@
 
 import copy
 import json
-import logging
 import multiprocessing
 import os
 import tempfile
@@ -32,13 +31,14 @@ from fairscale.nn.model_parallel.initialize import (
 from pydantic import BaseModel, Field
 from torch.distributed.launcher.api import LaunchConfig, elastic_launch
 
+from llama_stack.log import get_logger
 from llama_stack.models.llama.datatypes import GenerationResult
 from llama_stack.providers.utils.inference.prompt_adapter import (
     ChatCompletionRequestWithRawContent,
     CompletionRequestWithRawContent,
 )
 
-log = logging.getLogger(__name__)
+log = get_logger(name=__name__, category="inference")
 
 
 class ProcessingMessageName(str, Enum):

llama_stack/providers/inline/inference/sentence_transformers/sentence_transformers.py

@@ -4,13 +4,11 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import logging
 from collections.abc import AsyncGenerator
 
 from llama_stack.apis.inference import (
     CompletionResponse,
     InferenceProvider,
-    InterleavedContent,
     LogProbConfig,
     Message,
     ResponseFormat,
@@ -21,6 +19,7 @@ from llama_stack.apis.inference import (
     ToolPromptFormat,
 )
 from llama_stack.apis.models import ModelType
+from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import Model, ModelsProtocolPrivate
 from llama_stack.providers.utils.inference.embedding_mixin import (
     SentenceTransformerEmbeddingMixin,
@@ -32,7 +31,7 @@ from llama_stack.providers.utils.inference.openai_compat import (
 
 from .config import SentenceTransformersInferenceConfig
 
-log = logging.getLogger(__name__)
+log = get_logger(name=__name__, category="inference")
 
 
 class SentenceTransformersInferenceImpl(
@@ -100,25 +99,3 @@ class SentenceTransformersInferenceImpl(
         tool_config: ToolConfig | None = None,
     ) -> AsyncGenerator:
         raise ValueError("Sentence transformers don't support chat completion")
-
-    async def batch_completion(
-        self,
-        model_id: str,
-        content_batch: list[InterleavedContent],
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        logprobs: LogProbConfig | None = None,
-    ):
-        raise NotImplementedError("Batch completion is not supported for Sentence Transformers")
-
-    async def batch_chat_completion(
-        self,
-        model_id: str,
-        messages_batch: list[list[Message]],
-        sampling_params: SamplingParams | None = None,
-        tools: list[ToolDefinition] | None = None,
-        tool_config: ToolConfig | None = None,
-        response_format: ResponseFormat | None = None,
-        logprobs: LogProbConfig | None = None,
-    ):
-        raise NotImplementedError("Batch chat completion is not supported for Sentence Transformers")

llama_stack/providers/inline/post_training/huggingface/recipes/finetune_single_device.py

@@ -6,7 +6,6 @@
 
 import gc
 import json
-import logging
 import multiprocessing
 from pathlib import Path
 from typing import Any
@@ -28,6 +27,7 @@ from llama_stack.apis.post_training import (
     LoraFinetuningConfig,
     TrainingConfig,
 )
+from llama_stack.log import get_logger
 from llama_stack.providers.inline.post_training.common.utils import evacuate_model_from_device
 
 from ..config import HuggingFacePostTrainingConfig
@@ -44,7 +44,7 @@ from ..utils import (
     split_dataset,
 )
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="post_training")
 
 
 class HFFinetuningSingleDevice:

llama_stack/providers/inline/post_training/huggingface/recipes/finetune_single_device_dpo.py

@@ -5,7 +5,6 @@
 # the root directory of this source tree.
 
 import gc
-import logging
 import multiprocessing
 from pathlib import Path
 from typing import Any
@@ -24,6 +23,7 @@ from llama_stack.apis.post_training import (
     DPOAlignmentConfig,
     TrainingConfig,
 )
+from llama_stack.log import get_logger
 from llama_stack.providers.inline.post_training.common.utils import evacuate_model_from_device
 
 from ..config import HuggingFacePostTrainingConfig
@@ -40,7 +40,7 @@ from ..utils import (
     split_dataset,
 )
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="post_training")
 
 
 class HFDPOAlignmentSingleDevice:

llama_stack/providers/inline/post_training/huggingface/utils.py

@@ -4,7 +4,6 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import logging
 import os
 import signal
 import sys
@@ -19,10 +18,11 @@ from transformers import AutoConfig, AutoModelForCausalLM
 
 from llama_stack.apis.datasetio import DatasetIO
 from llama_stack.apis.post_training import Checkpoint, TrainingConfig
+from llama_stack.log import get_logger
 
 from .config import HuggingFacePostTrainingConfig
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="post_training")
 
 
 def setup_environment():

llama_stack/providers/inline/post_training/torchtune/recipes/lora_finetuning_single_device.py

@@ -4,7 +4,6 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import logging
 import os
 import time
 from datetime import UTC, datetime
@@ -19,6 +18,7 @@ from torch.utils.data import DataLoader, DistributedSampler
 from torchtune import modules, training
 from torchtune import utils as torchtune_utils
 from torchtune.data import padded_collate_sft
+from torchtune.models.llama3._tokenizer import Llama3Tokenizer
 from torchtune.modules.loss import CEWithChunkedOutputLoss
 from torchtune.modules.peft import (
     get_adapter_params,
@@ -45,6 +45,7 @@ from llama_stack.apis.post_training import (
 )
 from llama_stack.core.utils.config_dirs import DEFAULT_CHECKPOINT_DIR
 from llama_stack.core.utils.model_utils import model_local_dir
+from llama_stack.log import get_logger
 from llama_stack.models.llama.sku_list import resolve_model
 from llama_stack.providers.inline.post_training.common.utils import evacuate_model_from_device
 from llama_stack.providers.inline.post_training.torchtune.common import utils
@@ -56,9 +57,7 @@ from llama_stack.providers.inline.post_training.torchtune.config import (
 )
 from llama_stack.providers.inline.post_training.torchtune.datasets.sft import SFTDataset
 
-log = logging.getLogger(__name__)
-
-from torchtune.models.llama3._tokenizer import Llama3Tokenizer
+log = get_logger(name=__name__, category="post_training")
 
 
 class LoraFinetuningSingleDevice:

llama_stack/providers/inline/safety/code_scanner/code_scanner.py

@@ -4,7 +4,6 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import logging
 import uuid
 from typing import TYPE_CHECKING, Any
 
@@ -20,13 +19,14 @@ from llama_stack.apis.safety import (
 )
 from llama_stack.apis.safety.safety import ModerationObject, ModerationObjectResults
 from llama_stack.apis.shields import Shield
+from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.prompt_adapter import (
     interleaved_content_as_str,
 )
 
 from .config import CodeScannerConfig
 
-log = logging.getLogger(__name__)
+log = get_logger(name=__name__, category="safety")
 
 ALLOWED_CODE_SCANNER_MODEL_IDS = [
     "code-scanner",

llama_stack/providers/inline/safety/llama_guard/llama_guard.py

@@ -4,7 +4,6 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import logging
 import re
 import uuid
 from string import Template
@@ -21,6 +20,7 @@ from llama_stack.apis.safety import (
 from llama_stack.apis.safety.safety import ModerationObject, ModerationObjectResults
 from llama_stack.apis.shields import Shield
 from llama_stack.core.datatypes import Api
+from llama_stack.log import get_logger
 from llama_stack.models.llama.datatypes import Role
 from llama_stack.models.llama.sku_types import CoreModelId
 from llama_stack.providers.datatypes import ShieldsProtocolPrivate
@@ -132,6 +132,8 @@ Provide your safety assessment for ONLY THE LAST $agent_type message in the abov
 
 PROMPT_TEMPLATE = Template(f"{PROMPT_TASK}{SAFETY_CATEGORIES}{PROMPT_CONVERSATION}{PROMPT_INSTRUCTIONS}")
 
+logger = get_logger(name=__name__, category="safety")
+
 
 class LlamaGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
     def __init__(self, config: LlamaGuardConfig, deps) -> None:
@@ -407,7 +409,7 @@ class LlamaGuardShield:
             unsafe_code_list = [code.strip() for code in unsafe_code.split(",")]
             invalid_codes = [code for code in unsafe_code_list if code not in SAFETY_CODE_TO_CATEGORIES_MAP]
             if invalid_codes:
-                logging.warning(f"Invalid safety codes returned: {invalid_codes}")
+                logger.warning(f"Invalid safety codes returned: {invalid_codes}")
                 # just returning safe object, as we don't know what the invalid codes can map to
                 return ModerationObject(
                     id=f"modr-{uuid.uuid4()}",

llama_stack/providers/inline/safety/prompt_guard/prompt_guard.py

@@ -4,7 +4,6 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import logging
 from typing import Any
 
 import torch
@@ -21,6 +20,7 @@ from llama_stack.apis.safety import (
 from llama_stack.apis.safety.safety import ModerationObject
 from llama_stack.apis.shields import Shield
 from llama_stack.core.utils.model_utils import model_local_dir
+from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import ShieldsProtocolPrivate
 from llama_stack.providers.utils.inference.prompt_adapter import (
     interleaved_content_as_str,
@@ -28,7 +28,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 
 from .config import PromptGuardConfig, PromptGuardType
 
-log = logging.getLogger(__name__)
+log = get_logger(name=__name__, category="safety")
 
 PROMPT_GUARD_MODEL = "Prompt-Guard-86M"
 

llama_stack/providers/inline/scoring/basic/utils/ifeval_utils.py

@@ -7,7 +7,6 @@
 import collections
 import functools
 import json
-import logging
 import random
 import re
 import string
@@ -20,7 +19,9 @@ import nltk
 from pythainlp.tokenize import sent_tokenize as sent_tokenize_thai
 from pythainlp.tokenize import word_tokenize as word_tokenize_thai
 
-logger = logging.getLogger()
+from llama_stack.log import get_logger
+
+logger = get_logger(name=__name__, category="scoring")
 
 WORD_LIST = [
     "western",

llama_stack/providers/inline/telemetry/meta_reference/telemetry.py

@@ -4,13 +4,11 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import logging
+import datetime
 import threading
 from typing import Any
 
 from opentelemetry import metrics, trace
-
-logger = logging.getLogger(__name__)
 from opentelemetry.exporter.otlp.proto.http.metric_exporter import OTLPMetricExporter
 from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
 from opentelemetry.sdk.metrics import MeterProvider
@@ -40,6 +38,7 @@ from llama_stack.apis.telemetry import (
     UnstructuredLogEvent,
 )
 from llama_stack.core.datatypes import Api
+from llama_stack.log import get_logger
 from llama_stack.providers.inline.telemetry.meta_reference.console_span_processor import (
     ConsoleSpanProcessor,
 )
@@ -61,6 +60,8 @@ _GLOBAL_STORAGE: dict[str, dict[str | int, Any]] = {
 _global_lock = threading.Lock()
 _TRACER_PROVIDER = None
 
+logger = get_logger(name=__name__, category="telemetry")
+
 
 def is_tracing_enabled(tracer):
     with tracer.start_as_current_span("check_tracing") as span:
@@ -145,11 +146,41 @@ class TelemetryAdapter(TelemetryDatasetMixin, Telemetry):
         metric_name: str,
         start_time: int,
         end_time: int | None = None,
-        granularity: str | None = "1d",
+        granularity: str | None = None,
         query_type: MetricQueryType = MetricQueryType.RANGE,
         label_matchers: list[MetricLabelMatcher] | None = None,
     ) -> QueryMetricsResponse:
-        raise NotImplementedError("Querying metrics is not implemented")
+        """Query metrics from the telemetry store.
+
+        Args:
+            metric_name: The name of the metric to query (e.g., "prompt_tokens")
+            start_time: Start time as Unix timestamp
+            end_time: End time as Unix timestamp (defaults to now if None)
+            granularity: Time granularity for aggregation
+            query_type: Type of query (RANGE or INSTANT)
+            label_matchers: Label filters to apply
+
+        Returns:
+            QueryMetricsResponse with metric time series data
+        """
+        # Convert timestamps to datetime objects
+        start_dt = datetime.datetime.fromtimestamp(start_time, datetime.UTC)
+        end_dt = datetime.datetime.fromtimestamp(end_time, datetime.UTC) if end_time else None
+
+        # Use SQLite trace store if available
+        if hasattr(self, "trace_store") and self.trace_store:
+            return await self.trace_store.query_metrics(
+                metric_name=metric_name,
+                start_time=start_dt,
+                end_time=end_dt,
+                granularity=granularity,
+                query_type=query_type,
+                label_matchers=label_matchers,
+            )
+        else:
+            raise ValueError(
+                f"In order to query_metrics, you must have {TelemetrySink.SQLITE} set in your telemetry sinks"
+            )
 
     def _log_unstructured(self, event: UnstructuredLogEvent, ttl_seconds: int) -> None:
         with self._lock:

llama_stack/providers/inline/tool_runtime/rag/memory.py

@@ -5,7 +5,6 @@
 # the root directory of this source tree.
 
 import asyncio
-import logging
 import secrets
 import string
 from typing import Any
@@ -32,6 +31,7 @@ from llama_stack.apis.tools import (
     ToolRuntime,
 )
 from llama_stack.apis.vector_io import QueryChunksResponse, VectorIO
+from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import ToolGroupsProtocolPrivate
 from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str
 from llama_stack.providers.utils.memory.vector_store import (
@@ -42,7 +42,7 @@ from llama_stack.providers.utils.memory.vector_store import (
 from .config import RagToolRuntimeConfig
 from .context_retriever import generate_rag_query
 
-log = logging.getLogger(__name__)
+log = get_logger(name=__name__, category="tool_runtime")
 
 
 def make_random_string(length: int = 8):

llama_stack/providers/inline/vector_io/faiss/faiss.py

@@ -8,7 +8,6 @@ import asyncio
 import base64
 import io
 import json
-import logging
 from typing import Any
 
 import faiss
@@ -24,6 +23,7 @@ from llama_stack.apis.vector_io import (
     QueryChunksResponse,
     VectorIO,
 )
+from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import (
     HealthResponse,
     HealthStatus,
@@ -40,7 +40,7 @@ from llama_stack.providers.utils.memory.vector_store import (
 
 from .config import FaissVectorIOConfig
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="vector_io")
 
 VERSION = "v3"
 VECTOR_DBS_PREFIX = f"vector_dbs:{VERSION}::"

llama_stack/providers/inline/vector_io/sqlite_vec/sqlite_vec.py

@@ -5,7 +5,6 @@
 # the root directory of this source tree.
 
 import asyncio
-import logging
 import re
 import sqlite3
 import struct
@@ -24,6 +23,7 @@ from llama_stack.apis.vector_io import (
     QueryChunksResponse,
     VectorIO,
 )
+from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import VectorDBsProtocolPrivate
 from llama_stack.providers.utils.kvstore import kvstore_impl
 from llama_stack.providers.utils.kvstore.api import KVStore
@@ -36,7 +36,7 @@ from llama_stack.providers.utils.memory.vector_store import (
     VectorDBWithIndex,
 )
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="vector_io")
 
 # Specifying search mode is dependent on the VectorIO provider.
 VECTOR_SEARCH = "vector"

llama_stack/providers/registry/files.py

@@ -5,9 +5,11 @@
 # the root directory of this source tree.
 
 from llama_stack.providers.datatypes import (
+    AdapterSpec,
     Api,
     InlineProviderSpec,
     ProviderSpec,
+    remote_provider_spec,
 )
 from llama_stack.providers.utils.sqlstore.sqlstore import sql_store_pip_packages
 
@@ -23,4 +25,14 @@ def available_providers() -> list[ProviderSpec]:
             config_class="llama_stack.providers.inline.files.localfs.config.LocalfsFilesImplConfig",
             description="Local filesystem-based file storage provider for managing files and documents locally.",
         ),
+        remote_provider_spec(
+            api=Api.files,
+            adapter=AdapterSpec(
+                adapter_type="s3",
+                pip_packages=["boto3"] + sql_store_pip_packages,
+                module="llama_stack.providers.remote.files.s3",
+                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.",
+            ),
+        ),
     ]

llama_stack/providers/registry/post_training.py

@@ -5,34 +5,74 @@
 # the root directory of this source tree.
 
 
+from typing import cast
+
 from llama_stack.providers.datatypes import AdapterSpec, Api, InlineProviderSpec, ProviderSpec, remote_provider_spec
 
+# 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.
+torchtune_def = dict(
+    api=Api.post_training,
+    pip_packages=["torchtune==0.5.0", "torchao==0.8.0", "numpy"],
+    module="llama_stack.providers.inline.post_training.torchtune",
+    config_class="llama_stack.providers.inline.post_training.torchtune.TorchtunePostTrainingConfig",
+    api_dependencies=[
+        Api.datasetio,
+        Api.datasets,
+    ],
+    description="TorchTune-based post-training provider for fine-tuning and optimizing models using Meta's TorchTune framework.",
+)
+
+huggingface_def = dict(
+    api=Api.post_training,
+    pip_packages=["trl", "transformers", "peft", "datasets"],
+    module="llama_stack.providers.inline.post_training.huggingface",
+    config_class="llama_stack.providers.inline.post_training.huggingface.HuggingFacePostTrainingConfig",
+    api_dependencies=[
+        Api.datasetio,
+        Api.datasets,
+    ],
+    description="HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem.",
+)
+
 
 def available_providers() -> list[ProviderSpec]:
     return [
         InlineProviderSpec(
-            api=Api.post_training,
-            provider_type="inline::torchtune",
-            pip_packages=["torch", "torchtune==0.5.0", "torchao==0.8.0", "numpy"],
-            module="llama_stack.providers.inline.post_training.torchtune",
-            config_class="llama_stack.providers.inline.post_training.torchtune.TorchtunePostTrainingConfig",
-            api_dependencies=[
-                Api.datasetio,
-                Api.datasets,
-            ],
-            description="TorchTune-based post-training provider for fine-tuning and optimizing models using Meta's TorchTune framework.",
+            **{
+                **torchtune_def,
+                "provider_type": "inline::torchtune-cpu",
+                "pip_packages": (
+                    cast(list[str], torchtune_def["pip_packages"])
+                    + ["torch torchtune==0.5.0 torchao==0.8.0 --index-url https://download.pytorch.org/whl/cpu"]
+                ),
+            },
         ),
         InlineProviderSpec(
-            api=Api.post_training,
-            provider_type="inline::huggingface",
-            pip_packages=["torch", "trl", "transformers", "peft", "datasets"],
-            module="llama_stack.providers.inline.post_training.huggingface",
-            config_class="llama_stack.providers.inline.post_training.huggingface.HuggingFacePostTrainingConfig",
-            api_dependencies=[
-                Api.datasetio,
-                Api.datasets,
-            ],
-            description="HuggingFace-based post-training provider for fine-tuning models using the HuggingFace ecosystem.",
+            **{
+                **huggingface_def,
+                "provider_type": "inline::huggingface-cpu",
+                "pip_packages": (
+                    cast(list[str], huggingface_def["pip_packages"])
+                    + ["torch --index-url https://download.pytorch.org/whl/cpu"]
+                ),
+            },
+        ),
+        InlineProviderSpec(
+            **{
+                **torchtune_def,
+                "provider_type": "inline::torchtune-gpu",
+                "pip_packages": (
+                    cast(list[str], torchtune_def["pip_packages"]) + ["torch torchtune==0.5.0 torchao==0.8.0"]
+                ),
+            },
+        ),
+        InlineProviderSpec(
+            **{
+                **huggingface_def,
+                "provider_type": "inline::huggingface-gpu",
+                "pip_packages": (cast(list[str], huggingface_def["pip_packages"]) + ["torch"]),
+            },
         ),
         remote_provider_spec(
             api=Api.post_training,

llama_stack/providers/remote/files/s3/README.md

@@ -0,0 +1,237 @@
+# S3 Files Provider
+
+A remote S3-based implementation of the Llama Stack Files API that provides scalable cloud file storage with metadata persistence.
+
+## Features
+
+- **AWS S3 Storage**: Store files in AWS S3 buckets for scalable, durable storage
+- **Metadata Management**: Uses SQL database for efficient file metadata queries
+- **OpenAI API Compatibility**: Full compatibility with OpenAI Files API endpoints
+- **Flexible Authentication**: Support for IAM roles and access keys
+- **Custom S3 Endpoints**: Support for MinIO and other S3-compatible services
+
+## Configuration
+
+### Basic Configuration
+
+```yaml
+api: files
+provider_type: remote::s3
+config:
+  bucket_name: my-llama-stack-files
+  region: us-east-1
+  metadata_store:
+    type: sqlite
+    db_path: ./s3_files_metadata.db
+```
+
+### Advanced Configuration
+
+```yaml
+api: files
+provider_type: remote::s3
+config:
+  bucket_name: my-llama-stack-files
+  region: us-east-1
+  aws_access_key_id: YOUR_ACCESS_KEY
+  aws_secret_access_key: YOUR_SECRET_KEY
+  endpoint_url: https://s3.amazonaws.com  # Optional for custom endpoints
+  metadata_store:
+    type: sqlite
+    db_path: ./s3_files_metadata.db
+```
+
+### Environment Variables
+
+The configuration supports environment variable substitution:
+
+```yaml
+config:
+  bucket_name: "${env.S3_BUCKET_NAME}"
+  region: "${env.AWS_REGION:=us-east-1}"
+  aws_access_key_id: "${env.AWS_ACCESS_KEY_ID:=}"
+  aws_secret_access_key: "${env.AWS_SECRET_ACCESS_KEY:=}"
+  endpoint_url: "${env.S3_ENDPOINT_URL:=}"
+```
+
+Note: `S3_BUCKET_NAME` has no default value since S3 bucket names must be globally unique.
+
+## Authentication
+
+### IAM Roles (Recommended)
+
+For production deployments, use IAM roles:
+
+```yaml
+config:
+  bucket_name: my-bucket
+  region: us-east-1
+  # No credentials needed - will use IAM role
+```
+
+### Access Keys
+
+For development or specific use cases:
+
+```yaml
+config:
+  bucket_name: my-bucket
+  region: us-east-1
+  aws_access_key_id: AKIAIOSFODNN7EXAMPLE
+  aws_secret_access_key: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
+```
+
+## S3 Bucket Setup
+
+### Required Permissions
+
+The S3 provider requires the following permissions:
+
+```json
+{
+  "Version": "2012-10-17",
+  "Statement": [
+    {
+      "Effect": "Allow",
+      "Action": [
+        "s3:GetObject",
+        "s3:PutObject",
+        "s3:DeleteObject",
+        "s3:ListBucket"
+      ],
+      "Resource": [
+        "arn:aws:s3:::your-bucket-name",
+        "arn:aws:s3:::your-bucket-name/*"
+      ]
+    }
+  ]
+}
+```
+
+### Automatic Bucket Creation
+
+By default, the S3 provider expects the bucket to already exist. If you want the provider to automatically create the bucket when it doesn't exist, set `auto_create_bucket: true` in your configuration:
+
+```yaml
+config:
+  bucket_name: my-bucket
+  auto_create_bucket: true  # Will create bucket if it doesn't exist
+  region: us-east-1
+```
+
+**Note**: When `auto_create_bucket` is enabled, the provider will need additional permissions:
+
+```json
+{
+  "Version": "2012-10-17",
+  "Statement": [
+    {
+      "Effect": "Allow",
+      "Action": [
+        "s3:GetObject",
+        "s3:PutObject",
+        "s3:DeleteObject",
+        "s3:ListBucket",
+        "s3:CreateBucket"
+      ],
+      "Resource": [
+        "arn:aws:s3:::your-bucket-name",
+        "arn:aws:s3:::your-bucket-name/*"
+      ]
+    }
+  ]
+}
+```
+
+### Bucket Policy (Optional)
+
+For additional security, you can add a bucket policy:
+
+```json
+{
+  "Version": "2012-10-17",
+  "Statement": [
+    {
+      "Sid": "LlamaStackAccess",
+      "Effect": "Allow",
+      "Principal": {
+        "AWS": "arn:aws:iam::YOUR-ACCOUNT:role/LlamaStackRole"
+      },
+      "Action": [
+        "s3:GetObject",
+        "s3:PutObject",
+        "s3:DeleteObject"
+      ],
+      "Resource": "arn:aws:s3:::your-bucket-name/*"
+    },
+    {
+      "Sid": "LlamaStackBucketAccess",
+      "Effect": "Allow",
+      "Principal": {
+        "AWS": "arn:aws:iam::YOUR-ACCOUNT:role/LlamaStackRole"
+      },
+      "Action": [
+        "s3:ListBucket"
+      ],
+      "Resource": "arn:aws:s3:::your-bucket-name"
+    }
+  ]
+}
+```
+
+## Features
+
+### Metadata Persistence
+
+File metadata is stored in a SQL database for fast queries and OpenAI API compatibility. The metadata includes:
+
+- File ID
+- Original filename
+- Purpose (assistants, batch, etc.)
+- File size in bytes
+- Created and expiration timestamps
+
+### TTL and Cleanup
+
+Files currently have a fixed long expiration time (100 years).
+
+## Development and Testing
+
+### Using MinIO
+
+For self-hosted S3-compatible storage:
+
+```yaml
+config:
+  bucket_name: test-bucket
+  region: us-east-1
+  endpoint_url: http://localhost:9000
+  aws_access_key_id: minioadmin
+  aws_secret_access_key: minioadmin
+```
+
+## Monitoring and Logging
+
+The provider logs important operations and errors. For production deployments, consider:
+
+- CloudWatch monitoring for S3 operations
+- Custom metrics for file upload/download rates
+- Error rate monitoring
+- Performance metrics tracking
+
+## Error Handling
+
+The provider handles various error scenarios:
+
+- S3 connectivity issues
+- Bucket access permissions
+- File not found errors
+- Metadata consistency checks
+
+## Known Limitations
+
+- Fixed long TTL (100 years) instead of configurable expiration
+- No server-side encryption enabled by default
+- No support for AWS session tokens
+- No S3 key prefix organization support
+- No multipart upload support (all files uploaded as single objects)

llama_stack/providers/remote/files/s3/__init__.py

@@ -0,0 +1,20 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Any
+
+from llama_stack.core.datatypes import Api
+
+from .config import S3FilesImplConfig
+
+
+async def get_adapter_impl(config: S3FilesImplConfig, deps: dict[Api, Any]):
+    from .files import S3FilesImpl
+
+    # TODO: authorization policies and user separation
+    impl = S3FilesImpl(config)
+    await impl.initialize()
+    return impl

llama_stack/providers/remote/files/s3/config.py

@@ -0,0 +1,42 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig
+
+
+class S3FilesImplConfig(BaseModel):
+    """Configuration for S3-based files provider."""
+
+    bucket_name: str = Field(description="S3 bucket name to store files")
+    region: str = Field(default="us-east-1", description="AWS region where the bucket is located")
+    aws_access_key_id: str | None = Field(default=None, description="AWS access key ID (optional if using IAM roles)")
+    aws_secret_access_key: str | None = Field(
+        default=None, description="AWS secret access key (optional if using IAM roles)"
+    )
+    endpoint_url: str | None = Field(default=None, description="Custom S3 endpoint URL (for MinIO, LocalStack, etc.)")
+    auto_create_bucket: bool = Field(
+        default=False, description="Automatically create the S3 bucket if it doesn't exist"
+    )
+    metadata_store: SqlStoreConfig = Field(description="SQL store configuration for file metadata")
+
+    @classmethod
+    def sample_run_config(cls, __distro_dir__: str) -> dict[str, Any]:
+        return {
+            "bucket_name": "${env.S3_BUCKET_NAME}",  # no default, buckets must be globally unique
+            "region": "${env.AWS_REGION:=us-east-1}",
+            "aws_access_key_id": "${env.AWS_ACCESS_KEY_ID:=}",
+            "aws_secret_access_key": "${env.AWS_SECRET_ACCESS_KEY:=}",
+            "endpoint_url": "${env.S3_ENDPOINT_URL:=}",
+            "auto_create_bucket": "${env.S3_AUTO_CREATE_BUCKET:=false}",
+            "metadata_store": SqliteSqlStoreConfig.sample_run_config(
+                __distro_dir__=__distro_dir__,
+                db_name="s3_files_metadata.db",
+            ),
+        }

llama_stack/providers/remote/files/s3/files.py

@@ -0,0 +1,272 @@
+# 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 time
+import uuid
+from typing import Annotated
+
+import boto3
+from botocore.exceptions import BotoCoreError, ClientError, NoCredentialsError
+from fastapi import File, Form, Response, UploadFile
+
+from llama_stack.apis.common.errors import ResourceNotFoundError
+from llama_stack.apis.common.responses import Order
+from llama_stack.apis.files import (
+    Files,
+    ListOpenAIFileResponse,
+    OpenAIFileDeleteResponse,
+    OpenAIFileObject,
+    OpenAIFilePurpose,
+)
+from llama_stack.providers.utils.sqlstore.api import ColumnDefinition, ColumnType
+from llama_stack.providers.utils.sqlstore.sqlstore import SqlStore, sqlstore_impl
+
+from .config import S3FilesImplConfig
+
+# TODO: provider data for S3 credentials
+
+
+def _create_s3_client(config: S3FilesImplConfig) -> boto3.client:
+    try:
+        s3_config = {
+            "region_name": config.region,
+        }
+
+        # endpoint URL if specified (for MinIO, LocalStack, etc.)
+        if config.endpoint_url:
+            s3_config["endpoint_url"] = config.endpoint_url
+
+        if config.aws_access_key_id and config.aws_secret_access_key:
+            s3_config.update(
+                {
+                    "aws_access_key_id": config.aws_access_key_id,
+                    "aws_secret_access_key": config.aws_secret_access_key,
+                }
+            )
+
+        return boto3.client("s3", **s3_config)
+
+    except (BotoCoreError, NoCredentialsError) as e:
+        raise RuntimeError(f"Failed to initialize S3 client: {e}") from e
+
+
+async def _create_bucket_if_not_exists(client: boto3.client, config: S3FilesImplConfig) -> None:
+    try:
+        client.head_bucket(Bucket=config.bucket_name)
+    except ClientError as e:
+        error_code = e.response["Error"]["Code"]
+        if error_code == "404":
+            if not config.auto_create_bucket:
+                raise RuntimeError(
+                    f"S3 bucket '{config.bucket_name}' does not exist. "
+                    f"Either create the bucket manually or set 'auto_create_bucket: true' in your configuration."
+                ) from e
+            try:
+                # For us-east-1, we can't specify LocationConstraint
+                if config.region == "us-east-1":
+                    client.create_bucket(Bucket=config.bucket_name)
+                else:
+                    client.create_bucket(
+                        Bucket=config.bucket_name,
+                        CreateBucketConfiguration={"LocationConstraint": config.region},
+                    )
+            except ClientError as create_error:
+                raise RuntimeError(
+                    f"Failed to create S3 bucket '{config.bucket_name}': {create_error}"
+                ) from create_error
+        elif error_code == "403":
+            raise RuntimeError(f"Access denied to S3 bucket '{config.bucket_name}'") from e
+        else:
+            raise RuntimeError(f"Failed to access S3 bucket '{config.bucket_name}': {e}") from e
+
+
+class S3FilesImpl(Files):
+    """S3-based implementation of the Files API."""
+
+    # TODO: implement expiration, for now a silly offset
+    _SILLY_EXPIRATION_OFFSET = 100 * 365 * 24 * 60 * 60
+
+    def __init__(self, config: S3FilesImplConfig) -> None:
+        self._config = config
+        self._client: boto3.client | None = None
+        self._sql_store: SqlStore | None = None
+
+    async def initialize(self) -> None:
+        self._client = _create_s3_client(self._config)
+        await _create_bucket_if_not_exists(self._client, self._config)
+
+        self._sql_store = sqlstore_impl(self._config.metadata_store)
+        await self._sql_store.create_table(
+            "openai_files",
+            {
+                "id": ColumnDefinition(type=ColumnType.STRING, primary_key=True),
+                "filename": ColumnType.STRING,
+                "purpose": ColumnType.STRING,
+                "bytes": ColumnType.INTEGER,
+                "created_at": ColumnType.INTEGER,
+                "expires_at": ColumnType.INTEGER,
+                # TODO: add s3_etag field for integrity checking
+            },
+        )
+
+    async def shutdown(self) -> None:
+        pass
+
+    @property
+    def client(self) -> boto3.client:
+        assert self._client is not None, "Provider not initialized"
+        return self._client
+
+    @property
+    def sql_store(self) -> SqlStore:
+        assert self._sql_store is not None, "Provider not initialized"
+        return self._sql_store
+
+    async def openai_upload_file(
+        self,
+        file: Annotated[UploadFile, File()],
+        purpose: Annotated[OpenAIFilePurpose, Form()],
+    ) -> OpenAIFileObject:
+        file_id = f"file-{uuid.uuid4().hex}"
+
+        filename = getattr(file, "filename", None) or "uploaded_file"
+
+        created_at = int(time.time())
+        expires_at = created_at + self._SILLY_EXPIRATION_OFFSET
+        content = await file.read()
+        file_size = len(content)
+
+        await self.sql_store.insert(
+            "openai_files",
+            {
+                "id": file_id,
+                "filename": filename,
+                "purpose": purpose.value,
+                "bytes": file_size,
+                "created_at": created_at,
+                "expires_at": expires_at,
+            },
+        )
+
+        try:
+            self.client.put_object(
+                Bucket=self._config.bucket_name,
+                Key=file_id,
+                Body=content,
+                # TODO: enable server-side encryption
+            )
+        except ClientError as e:
+            await self.sql_store.delete("openai_files", where={"id": file_id})
+
+            raise RuntimeError(f"Failed to upload file to S3: {e}") from e
+
+        return OpenAIFileObject(
+            id=file_id,
+            filename=filename,
+            purpose=purpose,
+            bytes=file_size,
+            created_at=created_at,
+            expires_at=expires_at,
+        )
+
+    async def openai_list_files(
+        self,
+        after: str | None = None,
+        limit: int | None = 10000,
+        order: Order | None = Order.desc,
+        purpose: OpenAIFilePurpose | None = None,
+    ) -> ListOpenAIFileResponse:
+        # this purely defensive. it should not happen because the router also default to Order.desc.
+        if not order:
+            order = Order.desc
+
+        where_conditions = {}
+        if purpose:
+            where_conditions["purpose"] = purpose.value
+
+        paginated_result = await self.sql_store.fetch_all(
+            table="openai_files",
+            where=where_conditions if where_conditions else None,
+            order_by=[("created_at", order.value)],
+            cursor=("id", after) if after else None,
+            limit=limit,
+        )
+
+        files = [
+            OpenAIFileObject(
+                id=row["id"],
+                filename=row["filename"],
+                purpose=OpenAIFilePurpose(row["purpose"]),
+                bytes=row["bytes"],
+                created_at=row["created_at"],
+                expires_at=row["expires_at"],
+            )
+            for row in paginated_result.data
+        ]
+
+        return ListOpenAIFileResponse(
+            data=files,
+            has_more=paginated_result.has_more,
+            # empty string or None? spec says str, ref impl returns str | None, we go with spec
+            first_id=files[0].id if files else "",
+            last_id=files[-1].id if files else "",
+        )
+
+    async def openai_retrieve_file(self, file_id: str) -> OpenAIFileObject:
+        row = await self.sql_store.fetch_one("openai_files", where={"id": file_id})
+        if not row:
+            raise ResourceNotFoundError(file_id, "File", "files.list()")
+
+        return OpenAIFileObject(
+            id=row["id"],
+            filename=row["filename"],
+            purpose=OpenAIFilePurpose(row["purpose"]),
+            bytes=row["bytes"],
+            created_at=row["created_at"],
+            expires_at=row["expires_at"],
+        )
+
+    async def openai_delete_file(self, file_id: str) -> OpenAIFileDeleteResponse:
+        row = await self.sql_store.fetch_one("openai_files", where={"id": file_id})
+        if not row:
+            raise ResourceNotFoundError(file_id, "File", "files.list()")
+
+        try:
+            self.client.delete_object(
+                Bucket=self._config.bucket_name,
+                Key=row["id"],
+            )
+        except ClientError as e:
+            if e.response["Error"]["Code"] != "NoSuchKey":
+                raise RuntimeError(f"Failed to delete file from S3: {e}") from e
+
+        await self.sql_store.delete("openai_files", where={"id": file_id})
+
+        return OpenAIFileDeleteResponse(id=file_id, deleted=True)
+
+    async def openai_retrieve_file_content(self, file_id: str) -> Response:
+        row = await self.sql_store.fetch_one("openai_files", where={"id": file_id})
+        if not row:
+            raise ResourceNotFoundError(file_id, "File", "files.list()")
+
+        try:
+            response = self.client.get_object(
+                Bucket=self._config.bucket_name,
+                Key=row["id"],
+            )
+            # TODO: can we stream this instead of loading it into memory
+            content = response["Body"].read()
+        except ClientError as e:
+            if e.response["Error"]["Code"] == "NoSuchKey":
+                await self.sql_store.delete("openai_files", where={"id": file_id})
+                raise ResourceNotFoundError(file_id, "File", "files.list()") from e
+            raise RuntimeError(f"Failed to download file from S3: {e}") from e
+
+        return Response(
+            content=content,
+            media_type="application/octet-stream",
+            headers={"Content-Disposition": f'attachment; filename="{row["filename"]}"'},
+        )

llama_stack/providers/remote/inference/fireworks/fireworks.py

@@ -65,7 +65,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 from .config import FireworksImplConfig
 from .models import MODEL_ENTRIES
 
-logger = get_logger(name=__name__, category="inference")
+logger = get_logger(name=__name__, category="inference::fireworks")
 
 
 class FireworksInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProviderData):

llama_stack/providers/remote/inference/llama_openai_compat/llama.py

@@ -3,15 +3,14 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
-import logging
-
+from llama_stack.log import get_logger
 from llama_stack.providers.remote.inference.llama_openai_compat.config import LlamaCompatConfig
 from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .models import MODEL_ENTRIES
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="inference::llama_openai_compat")
 
 
 class LlamaCompatInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin):

llama_stack/providers/remote/inference/nvidia/NVIDIA.md

@@ -41,6 +41,11 @@ client.initialize()
 
 ### Create Completion
 
+> Note on Completion API
+>
+> The hosted NVIDIA Llama NIMs (e.g., `meta-llama/Llama-3.1-8B-Instruct`) with ```NVIDIA_BASE_URL="https://integrate.api.nvidia.com"``` does not support the ```completion``` method, while the locally deployed NIM does.
+
+
 ```python
 response = client.inference.completion(
     model_id="meta-llama/Llama-3.1-8B-Instruct",
@@ -76,7 +81,78 @@ response = client.inference.chat_completion(
 print(f"Response: {response.completion_message.content}")
 ```
 
+### Tool Calling Example ###
+```python
+from llama_stack.models.llama.datatypes import ToolDefinition, ToolParamDefinition
+
+tool_definition = ToolDefinition(
+    tool_name="get_weather",
+    description="Get current weather information for a location",
+    parameters={
+        "location": ToolParamDefinition(
+            param_type="string",
+            description="The city and state, e.g. San Francisco, CA",
+            required=True,
+        ),
+        "unit": ToolParamDefinition(
+            param_type="string",
+            description="Temperature unit (celsius or fahrenheit)",
+            required=False,
+            default="celsius",
+        ),
+    },
+)
+
+tool_response = client.inference.chat_completion(
+    model_id="meta-llama/Llama-3.1-8B-Instruct",
+    messages=[{"role": "user", "content": "What's the weather like in San Francisco?"}],
+    tools=[tool_definition],
+)
+
+print(f"Tool Response: {tool_response.completion_message.content}")
+if tool_response.completion_message.tool_calls:
+    for tool_call in tool_response.completion_message.tool_calls:
+        print(f"Tool Called: {tool_call.tool_name}")
+        print(f"Arguments: {tool_call.arguments}")
+```
+
+### Structured Output Example
+```python
+from llama_stack.apis.inference import JsonSchemaResponseFormat, ResponseFormatType
+
+person_schema = {
+    "type": "object",
+    "properties": {
+        "name": {"type": "string"},
+        "age": {"type": "integer"},
+        "occupation": {"type": "string"},
+    },
+    "required": ["name", "age", "occupation"],
+}
+
+response_format = JsonSchemaResponseFormat(
+    type=ResponseFormatType.json_schema, json_schema=person_schema
+)
+
+structured_response = client.inference.chat_completion(
+    model_id="meta-llama/Llama-3.1-8B-Instruct",
+    messages=[
+        {
+            "role": "user",
+            "content": "Create a profile for a fictional person named Alice who is 30 years old and is a software engineer. ",
+        }
+    ],
+    response_format=response_format,
+)
+
+print(f"Structured Response: {structured_response.completion_message.content}")
+```
+
 ### Create Embeddings
+> Note on OpenAI embeddings compatibility
+>
+> NVIDIA asymmetric embedding models (e.g., `nvidia/llama-3.2-nv-embedqa-1b-v2`) require an `input_type` parameter not present in the standard OpenAI embeddings API. The NVIDIA Inference Adapter automatically sets `input_type="query"` when using the OpenAI-compatible embeddings endpoint for NVIDIA. For passage embeddings, use the `embeddings` API with `task_type="document"`.
+
 ```python
 response = client.inference.embeddings(
     model_id="nvidia/llama-3.2-nv-embedqa-1b-v2",

llama_stack/providers/remote/inference/nvidia/nvidia.py

@@ -4,11 +4,10 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import logging
 import warnings
 from collections.abc import AsyncIterator
 
-from openai import APIConnectionError, BadRequestError
+from openai import NOT_GIVEN, APIConnectionError
 
 from llama_stack.apis.common.content_types import (
     InterleavedContent,
@@ -27,12 +26,16 @@ from llama_stack.apis.inference import (
     Inference,
     LogProbConfig,
     Message,
+    OpenAIEmbeddingData,
+    OpenAIEmbeddingsResponse,
+    OpenAIEmbeddingUsage,
     ResponseFormat,
     SamplingParams,
     TextTruncation,
     ToolChoice,
     ToolConfig,
 )
+from llama_stack.log import get_logger
 from llama_stack.models.llama.datatypes import ToolDefinition, ToolPromptFormat
 from llama_stack.providers.utils.inference.model_registry import (
     ModelRegistryHelper,
@@ -54,7 +57,7 @@ from .openai_utils import (
 )
 from .utils import _is_nvidia_hosted
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="inference::nvidia")
 
 
 class NVIDIAInferenceAdapter(OpenAIMixin, Inference, ModelRegistryHelper):
@@ -194,15 +197,11 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference, ModelRegistryHelper):
             }
             extra_body["input_type"] = task_type_options[task_type]
 
-        try:
-            response = await self.client.embeddings.create(
-                model=provider_model_id,
-                input=input,
-                extra_body=extra_body,
-            )
-        except BadRequestError as e:
-            raise ValueError(f"Failed to get embeddings: {e}") from e
-
+        response = await self.client.embeddings.create(
+            model=provider_model_id,
+            input=input,
+            extra_body=extra_body,
+        )
         #
         # OpenAI: CreateEmbeddingResponse(data=[Embedding(embedding=list[float], ...)], ...)
         #  ->
@@ -210,6 +209,57 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference, ModelRegistryHelper):
         #
         return EmbeddingsResponse(embeddings=[embedding.embedding for embedding in response.data])
 
+    async def openai_embeddings(
+        self,
+        model: str,
+        input: str | list[str],
+        encoding_format: str | None = "float",
+        dimensions: int | None = None,
+        user: str | None = None,
+    ) -> OpenAIEmbeddingsResponse:
+        """
+        OpenAI-compatible embeddings for NVIDIA NIM.
+
+        Note: NVIDIA NIM asymmetric embedding models require an "input_type" field not present in the standard OpenAI embeddings API.
+        We default this to "query" to ensure requests succeed when using the
+        OpenAI-compatible endpoint. For passage embeddings, use the embeddings API with
+        `task_type='document'`.
+        """
+        extra_body: dict[str, object] = {"input_type": "query"}
+        logger.warning(
+            "NVIDIA OpenAI-compatible embeddings: defaulting to input_type='query'. "
+            "For passage embeddings, use the embeddings API with task_type='document'."
+        )
+
+        response = await self.client.embeddings.create(
+            model=await self._get_provider_model_id(model),
+            input=input,
+            encoding_format=encoding_format if encoding_format is not None else NOT_GIVEN,
+            dimensions=dimensions if dimensions is not None else NOT_GIVEN,
+            user=user if user is not None else NOT_GIVEN,
+            extra_body=extra_body,
+        )
+
+        data = []
+        for i, embedding_data in enumerate(response.data):
+            data.append(
+                OpenAIEmbeddingData(
+                    embedding=embedding_data.embedding,
+                    index=i,
+                )
+            )
+
+        usage = OpenAIEmbeddingUsage(
+            prompt_tokens=response.usage.prompt_tokens,
+            total_tokens=response.usage.total_tokens,
+        )
+
+        return OpenAIEmbeddingsResponse(
+            data=data,
+            model=response.model,
+            usage=usage,
+        )
+
     async def chat_completion(
         self,
         model_id: str,

llama_stack/providers/remote/inference/nvidia/utils.py

@@ -4,13 +4,13 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import logging
-
 import httpx
 
+from llama_stack.log import get_logger
+
 from . import NVIDIAConfig
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="inference::nvidia")
 
 
 def _is_nvidia_hosted(config: NVIDIAConfig) -> bool:

llama_stack/providers/remote/inference/ollama/ollama.py

@@ -85,7 +85,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 
 from .models import MODEL_ENTRIES
 
-logger = get_logger(name=__name__, category="inference")
+logger = get_logger(name=__name__, category="inference::ollama")
 
 
 class OllamaInferenceAdapter(
@@ -619,28 +619,6 @@ class OllamaInferenceAdapter(
             response.id = id
             return response
 
-    async def batch_completion(
-        self,
-        model_id: str,
-        content_batch: list[InterleavedContent],
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        logprobs: LogProbConfig | None = None,
-    ):
-        raise NotImplementedError("Batch completion is not supported for Ollama")
-
-    async def batch_chat_completion(
-        self,
-        model_id: str,
-        messages_batch: list[list[Message]],
-        sampling_params: SamplingParams | None = None,
-        tools: list[ToolDefinition] | None = None,
-        tool_config: ToolConfig | None = None,
-        response_format: ResponseFormat | None = None,
-        logprobs: LogProbConfig | None = None,
-    ):
-        raise NotImplementedError("Batch chat completion is not supported for Ollama")
-
 
 async def convert_message_to_openai_dict_for_ollama(message: Message) -> list[dict]:
     async def _convert_content(content) -> dict:

llama_stack/providers/remote/inference/openai/openai.py

@@ -4,15 +4,14 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import logging
-
+from llama_stack.log import get_logger
 from llama_stack.providers.utils.inference.litellm_openai_mixin import LiteLLMOpenAIMixin
 from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
 
 from .config import OpenAIConfig
 from .models import MODEL_ENTRIES
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="inference::openai")
 
 
 #

llama_stack/providers/remote/inference/tgi/tgi.py

@@ -5,7 +5,6 @@
 # the root directory of this source tree.
 
 
-import logging
 from collections.abc import AsyncGenerator
 
 from huggingface_hub import AsyncInferenceClient, HfApi
@@ -34,6 +33,7 @@ from llama_stack.apis.inference import (
     ToolPromptFormat,
 )
 from llama_stack.apis.models import Model
+from llama_stack.log import get_logger
 from llama_stack.models.llama.sku_list import all_registered_models
 from llama_stack.providers.datatypes import ModelsProtocolPrivate
 from llama_stack.providers.utils.inference.model_registry import (
@@ -58,7 +58,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 
 from .config import InferenceAPIImplConfig, InferenceEndpointImplConfig, TGIImplConfig
 
-log = logging.getLogger(__name__)
+log = get_logger(name=__name__, category="inference::tgi")
 
 
 def build_hf_repo_model_entries():

llama_stack/providers/remote/inference/together/together.py

@@ -61,7 +61,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 from .config import TogetherImplConfig
 from .models import MODEL_ENTRIES
 
-logger = get_logger(name=__name__, category="inference")
+logger = get_logger(name=__name__, category="inference::together")
 
 
 class TogetherInferenceAdapter(ModelRegistryHelper, Inference, NeedsRequestProviderData):

llama_stack/providers/remote/inference/vllm/vllm.py

@@ -85,7 +85,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
 
 from .config import VLLMInferenceAdapterConfig
 
-log = get_logger(name=__name__, category="inference")
+log = get_logger(name=__name__, category="inference::vllm")
 
 
 def build_hf_repo_model_entries():
@@ -711,25 +711,3 @@ class VLLMInferenceAdapter(Inference, ModelsProtocolPrivate):
             user=user,
         )
         return await self.client.chat.completions.create(**params)  # type: ignore
-
-    async def batch_completion(
-        self,
-        model_id: str,
-        content_batch: list[InterleavedContent],
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        logprobs: LogProbConfig | None = None,
-    ):
-        raise NotImplementedError("Batch completion is not supported for Ollama")
-
-    async def batch_chat_completion(
-        self,
-        model_id: str,
-        messages_batch: list[list[Message]],
-        sampling_params: SamplingParams | None = None,
-        tools: list[ToolDefinition] | None = None,
-        tool_config: ToolConfig | None = None,
-        response_format: ResponseFormat | None = None,
-        logprobs: LogProbConfig | None = None,
-    ):
-        raise NotImplementedError("Batch chat completion is not supported for Ollama")

llama_stack/providers/remote/post_training/nvidia/utils.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.
 
-import logging
 import warnings
 from typing import Any
 
 from pydantic import BaseModel
 
 from llama_stack.apis.post_training import TrainingConfig
+from llama_stack.log import get_logger
 from llama_stack.providers.remote.post_training.nvidia.config import SFTLoRADefaultConfig
 
 from .config import NvidiaPostTrainingConfig
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="post_training::nvidia")
 
 
 def warn_unsupported_params(config_dict: Any, supported_keys: set[str], config_name: str) -> None:

llama_stack/providers/remote/safety/bedrock/bedrock.py

@@ -5,7 +5,6 @@
 # the root directory of this source tree.
 
 import json
-import logging
 from typing import Any
 
 from llama_stack.apis.inference import Message
@@ -16,12 +15,13 @@ from llama_stack.apis.safety import (
     ViolationLevel,
 )
 from llama_stack.apis.shields import Shield
+from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import ShieldsProtocolPrivate
 from llama_stack.providers.utils.bedrock.client import create_bedrock_client
 
 from .config import BedrockSafetyConfig
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="safety::bedrock")
 
 
 class BedrockSafetyAdapter(Safety, ShieldsProtocolPrivate):

llama_stack/providers/remote/safety/nvidia/nvidia.py

@@ -4,20 +4,20 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import logging
 from typing import Any
 
 import requests
 
 from llama_stack.apis.inference import Message
-from llama_stack.apis.safety import RunShieldResponse, Safety, SafetyViolation, ViolationLevel
+from llama_stack.apis.safety import ModerationObject, RunShieldResponse, Safety, SafetyViolation, ViolationLevel
 from llama_stack.apis.shields import Shield
+from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import ShieldsProtocolPrivate
 from llama_stack.providers.utils.inference.openai_compat import convert_message_to_openai_dict_new
 
 from .config import NVIDIASafetyConfig
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="safety::nvidia")
 
 
 class NVIDIASafetyAdapter(Safety, ShieldsProtocolPrivate):
@@ -67,6 +67,9 @@ class NVIDIASafetyAdapter(Safety, ShieldsProtocolPrivate):
         self.shield = NeMoGuardrails(self.config, shield.shield_id)
         return await self.shield.run(messages)
 
+    async def run_moderation(self, input: str | list[str], model: str) -> ModerationObject:
+        raise NotImplementedError("NVIDIA safety provider currently does not implement run_moderation")
+
 
 class NeMoGuardrails:
     """

llama_stack/providers/remote/safety/sambanova/sambanova.py

@@ -5,7 +5,6 @@
 # the root directory of this source tree.
 
 import json
-import logging
 from typing import Any
 
 import litellm
@@ -20,12 +19,13 @@ from llama_stack.apis.safety import (
 )
 from llama_stack.apis.shields import Shield
 from llama_stack.core.request_headers import NeedsRequestProviderData
+from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import ShieldsProtocolPrivate
 from llama_stack.providers.utils.inference.openai_compat import convert_message_to_openai_dict_new
 
 from .config import SambaNovaSafetyConfig
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="safety::sambanova")
 
 CANNED_RESPONSE_TEXT = "I can't answer that. Can I help with something else?"
 

llama_stack/providers/remote/vector_io/chroma/chroma.py

@@ -5,7 +5,6 @@
 # the root directory of this source tree.
 import asyncio
 import json
-import logging
 from typing import Any
 from urllib.parse import urlparse
 
@@ -20,6 +19,7 @@ from llama_stack.apis.vector_io import (
     QueryChunksResponse,
     VectorIO,
 )
+from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
 from llama_stack.providers.inline.vector_io.chroma import ChromaVectorIOConfig as InlineChromaVectorIOConfig
 from llama_stack.providers.utils.kvstore import kvstore_impl
@@ -33,7 +33,7 @@ from llama_stack.providers.utils.memory.vector_store import (
 
 from .config import ChromaVectorIOConfig as RemoteChromaVectorIOConfig
 
-log = logging.getLogger(__name__)
+log = get_logger(name=__name__, category="vector_io::chroma")
 
 ChromaClientType = chromadb.api.AsyncClientAPI | chromadb.api.ClientAPI
 

llama_stack/providers/remote/vector_io/milvus/milvus.py

@@ -5,7 +5,6 @@
 # the root directory of this source tree.
 
 import asyncio
-import logging
 import os
 from typing import Any
 
@@ -21,6 +20,7 @@ from llama_stack.apis.vector_io import (
     QueryChunksResponse,
     VectorIO,
 )
+from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import VectorDBsProtocolPrivate
 from llama_stack.providers.inline.vector_io.milvus import MilvusVectorIOConfig as InlineMilvusVectorIOConfig
 from llama_stack.providers.utils.kvstore import kvstore_impl
@@ -36,7 +36,7 @@ from llama_stack.providers.utils.vector_io.vector_utils import sanitize_collecti
 
 from .config import MilvusVectorIOConfig as RemoteMilvusVectorIOConfig
 
-logger = logging.getLogger(__name__)
+logger = get_logger(name=__name__, category="vector_io::milvus")
 
 VERSION = "v3"
 VECTOR_DBS_PREFIX = f"vector_dbs:milvus:{VERSION}::"

llama_stack/providers/remote/vector_io/pgvector/pgvector.py

@@ -4,7 +4,6 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import logging
 from typing import Any
 
 import psycopg2
@@ -22,6 +21,7 @@ from llama_stack.apis.vector_io import (
     QueryChunksResponse,
     VectorIO,
 )
+from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
 from llama_stack.providers.utils.kvstore import kvstore_impl
 from llama_stack.providers.utils.kvstore.api import KVStore
@@ -34,7 +34,7 @@ from llama_stack.providers.utils.memory.vector_store import (
 
 from .config import PGVectorVectorIOConfig
 
-log = logging.getLogger(__name__)
+log = get_logger(name=__name__, category="vector_io::pgvector")
 
 VERSION = "v3"
 VECTOR_DBS_PREFIX = f"vector_dbs:pgvector:{VERSION}::"

llama_stack/providers/remote/vector_io/qdrant/qdrant.py

@@ -5,7 +5,6 @@
 # the root directory of this source tree.
 
 import asyncio
-import logging
 import uuid
 from typing import Any
 
@@ -24,6 +23,7 @@ from llama_stack.apis.vector_io import (
     VectorStoreChunkingStrategy,
     VectorStoreFileObject,
 )
+from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
 from llama_stack.providers.inline.vector_io.qdrant import QdrantVectorIOConfig as InlineQdrantVectorIOConfig
 from llama_stack.providers.utils.kvstore import KVStore, kvstore_impl
@@ -36,7 +36,7 @@ from llama_stack.providers.utils.memory.vector_store import (
 
 from .config import QdrantVectorIOConfig as RemoteQdrantVectorIOConfig
 
-log = logging.getLogger(__name__)
+log = get_logger(name=__name__, category="vector_io::qdrant")
 CHUNK_ID_KEY = "_chunk_id"
 
 # KV store prefixes for vector databases

llama_stack/providers/remote/vector_io/weaviate/weaviate.py

@@ -4,7 +4,6 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 import json
-import logging
 from typing import Any
 
 import weaviate
@@ -19,6 +18,7 @@ from llama_stack.apis.files.files import Files
 from llama_stack.apis.vector_dbs import VectorDB
 from llama_stack.apis.vector_io import Chunk, QueryChunksResponse, VectorIO
 from llama_stack.core.request_headers import NeedsRequestProviderData
+from llama_stack.log import get_logger
 from llama_stack.providers.datatypes import Api, VectorDBsProtocolPrivate
 from llama_stack.providers.utils.kvstore import kvstore_impl
 from llama_stack.providers.utils.kvstore.api import KVStore
@@ -34,7 +34,7 @@ from llama_stack.providers.utils.vector_io.vector_utils import sanitize_collecti
 
 from .config import WeaviateVectorIOConfig
 
-log = logging.getLogger(__name__)
+log = get_logger(name=__name__, category="vector_io::weaviate")
 
 VERSION = "v3"
 VECTOR_DBS_PREFIX = f"vector_dbs:weaviate:{VERSION}::"

llama_stack/providers/utils/inference/embedding_mixin.py

@@ -5,10 +5,11 @@
 # the root directory of this source tree.
 
 import base64
-import logging
 import struct
 from typing import TYPE_CHECKING
 
+from llama_stack.log import get_logger
+
 if TYPE_CHECKING:
     from sentence_transformers import SentenceTransformer
 
@@ -27,7 +28,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import interleaved_con
 EMBEDDING_MODELS = {}
 
 
-log = logging.getLogger(__name__)
+log = get_logger(name=__name__, category="providers::utils")
 
 
 class SentenceTransformerEmbeddingMixin:

llama_stack/providers/utils/inference/litellm_openai_mixin.py

@@ -54,7 +54,7 @@ from llama_stack.providers.utils.inference.prompt_adapter import (
     interleaved_content_as_str,
 )
 
-logger = get_logger(name=__name__, category="inference")
+logger = get_logger(name=__name__, category="providers::utils")
 
 
 class LiteLLMOpenAIMixin(
@@ -429,28 +429,6 @@ class LiteLLMOpenAIMixin(
         )
         return await litellm.acompletion(**params)
 
-    async def batch_completion(
-        self,
-        model_id: str,
-        content_batch: list[InterleavedContent],
-        sampling_params: SamplingParams | None = None,
-        response_format: ResponseFormat | None = None,
-        logprobs: LogProbConfig | None = None,
-    ):
-        raise NotImplementedError("Batch completion is not supported for OpenAI Compat")
-
-    async def batch_chat_completion(
-        self,
-        model_id: str,
-        messages_batch: list[list[Message]],
-        sampling_params: SamplingParams | None = None,
-        tools: list[ToolDefinition] | None = None,
-        tool_config: ToolConfig | None = None,
-        response_format: ResponseFormat | None = None,
-        logprobs: LogProbConfig | None = None,
-    ):
-        raise NotImplementedError("Batch chat completion is not supported for OpenAI Compat")
-
     async def check_model_availability(self, model: str) -> bool:
         """
         Check if a specific model is available via LiteLLM for the current