refactor(logging): rename llama_stack logger categories

This PR renames logging categores of llama_stack logger across the codebase according to #3065 (comment).

Signed-off-by: Mustafa Elbehery <[email protected]>

Author: Elbehery

docs/source/providers/agents/index.md

@@ -4,12 +4,12 @@
 
 Agents API for creating and interacting with agentic systems.
 
-    Main functionalities provided by this API:
-    - Create agents with specific instructions and ability to use tools.
-    - Interactions with agents are grouped into sessions ("threads"), and each interaction is called a "turn".
-    - Agents can be provided with various tools (see the ToolGroups and ToolRuntime APIs for more details).
-    - Agents can be provided with various shields (see the Safety API for more details).
-    - Agents can also use Memory to retrieve information from knowledge bases. See the RAG Tool and Vector IO APIs for more details.
+Main functionalities provided by this API:
+- Create agents with specific instructions and ability to use tools.
+- Interactions with agents are grouped into sessions ("threads"), and each interaction is called a "turn".
+- Agents can be provided with various tools (see the ToolGroups and ToolRuntime APIs for more details).
+- Agents can be provided with various shields (see the Safety API for more details).
+- Agents can also use Memory to retrieve information from knowledge bases. See the RAG Tool and Vector IO APIs for more details.
 
 This section contains documentation for all available providers for the **agents** API.
 

docs/source/providers/batches/index.md

@@ -4,11 +4,11 @@
 
 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 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.
 
-    Note: This API is currently under active development and may undergo changes.
+Note: This API is currently under active development and may undergo changes.
 
 This section contains documentation for all available providers for the **batches** API.
 

docs/source/providers/inference/index.md

@@ -4,9 +4,9 @@
 
 Llama Stack Inference API for generating completions, chat completions, and embeddings.
 
-    This API provides the raw interface to the underlying models. Two kinds of models are supported:
-    - LLM models: these models generate "raw" and "chat" (conversational) completions.
-    - Embedding models: these models generate embeddings to be used for semantic search.
+This API provides the raw interface to the underlying models. Two kinds of models are supported:
+- LLM models: these models generate "raw" and "chat" (conversational) completions.
+- Embedding models: these models generate embeddings to be used for semantic search.
 
 This section contains documentation for all available providers for the **inference** API.
 

llama_stack/cli/stack/run.py

@@ -15,7 +15,7 @@
 
 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/routers/datasets.py

@@ -12,7 +12,7 @@
 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.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.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.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 ..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.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):