Merge branch 'main' into agent_session_unit_test

docs/_static/js/keyboard_shortcuts.js

@@ -0,0 +1,14 @@
+document.addEventListener('keydown', function(event) {
+  // command+K or ctrl+K
+  if ((event.metaKey || event.ctrlKey) && event.key === 'k') {
+    event.preventDefault();
+    document.querySelector('.search-input, .search-field, input[name="q"]').focus();
+  }
+
+  // forward slash
+  if (event.key === '/' &&
+      !event.target.matches('input, textarea, select')) {
+    event.preventDefault();
+    document.querySelector('.search-input, .search-field, input[name="q"]').focus();
+  }
+});

docs/source/apis/external.md

@@ -111,7 +111,7 @@ name = "llama-stack-api-weather"
 version = "0.1.0"
 description = "Weather API for Llama Stack"
 readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.12"
 dependencies = ["llama-stack", "pydantic"]
 
 [build-system]
@@ -231,7 +231,7 @@ name = "llama-stack-provider-kaze"
 version = "0.1.0"
 description = "Kaze weather provider for Llama Stack"
 readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.12"
 dependencies = ["llama-stack", "pydantic", "aiohttp"]
 
 [build-system]

docs/source/conf.py

@@ -131,6 +131,7 @@ html_static_path = ["../_static"]
 def setup(app):
     app.add_css_file("css/my_theme.css")
     app.add_js_file("js/detect_theme.js")
+    app.add_js_file("js/keyboard_shortcuts.js")
 
     def dockerhub_role(name, rawtext, text, lineno, inliner, options={}, content=[]):
         url = f"https://hub.docker.com/r/llamastack/{text}"

docs/source/contributing/index.md

@@ -2,14 +2,28 @@
 ```{include} ../../../CONTRIBUTING.md
 ```
 
-See the [Adding a New API Provider](new_api_provider.md) which describes how to add new API providers to the Stack.
+## Testing
 
+See the [Test Page](testing.md) which describes how to test your changes.
+```{toctree}
+:maxdepth: 1
+:hidden:
+:caption: Testing
 
+testing
+```
 
+## Adding a New Provider
+
+See the [Adding a New API Provider Page](new_api_provider.md) which describes how to add new API providers to the Stack.
+
+See the [Vector Database Page](new_vector_database.md) which describes how to add a new vector databases with Llama Stack.
+
+See the [External Provider Page](../providers/external/index.md) which describes how to add external providers to the Stack.
 ```{toctree}
 :maxdepth: 1
 :hidden:
 
 new_api_provider
-testing
+new_vector_database
 ```

docs/source/contributing/new_vector_database.md

@@ -0,0 +1,75 @@
+# Adding a New Vector Database
+
+This guide will walk you through the process of adding a new vector database to Llama Stack.
+
+> **_NOTE:_** Here's an example Pull Request of the [Milvus Vector Database Provider](https://github.com/meta-llama/llama-stack/pull/1467).
+
+Vector Database providers are used to store and retrieve vector embeddings. Vector databases are not limited to vector
+search but can support keyword and hybrid search. Additionally, vector database can also support operations like
+filtering, sorting, and aggregating vectors.
+
+## Steps to Add a New Vector Database Provider
+1. **Choose the Database Type**: Determine if your vector database is a remote service, inline, or both.
+   - Remote databases make requests to external services, while inline databases execute locally. Some providers support both.
+2. **Implement the Provider**: Create a new provider class that inherits from `VectorDatabaseProvider` and implements the required methods.
+   - Implement methods for vector storage, retrieval, search, and any additional features your database supports.
+     - You will need to implement the following methods for `YourVectorIndex`:
+        - `YourVectorIndex.create()`
+        - `YourVectorIndex.initialize()`
+        - `YourVectorIndex.add_chunks()`
+        - `YourVectorIndex.delete_chunk()`
+        - `YourVectorIndex.query_vector()`
+        - `YourVectorIndex.query_keyword()`
+        - `YourVectorIndex.query_hybrid()`
+     - You will need to implement the following methods for `YourVectorIOAdapter`:
+        - `YourVectorIOAdapter.initialize()`
+        - `YourVectorIOAdapter.shutdown()`
+        - `YourVectorIOAdapter.list_vector_dbs()`
+        - `YourVectorIOAdapter.register_vector_db()`
+        - `YourVectorIOAdapter.unregister_vector_db()`
+        - `YourVectorIOAdapter.insert_chunks()`
+        - `YourVectorIOAdapter.query_chunks()`
+        - `YourVectorIOAdapter.delete_chunks()`
+3. **Add to Registry**: Register your provider in the appropriate registry file.
+   - Update {repopath}`llama_stack/providers/registry/vector_io.py` to include your new provider.
+```python
+from llama_stack.providers.registry.specs import InlineProviderSpec
+from llama_stack.providers.registry.api import Api
+
+InlineProviderSpec(
+    api=Api.vector_io,
+    provider_type="inline::milvus",
+    pip_packages=["pymilvus>=2.4.10"],
+    module="llama_stack.providers.inline.vector_io.milvus",
+    config_class="llama_stack.providers.inline.vector_io.milvus.MilvusVectorIOConfig",
+    api_dependencies=[Api.inference],
+    optional_api_dependencies=[Api.files],
+    description="",
+),
+```
+4. **Add Tests**: Create unit tests and integration tests for your provider in the `tests/` directory.
+   - Unit Tests
+     - By following the structure of the class methods, you will be able to easily run unit and integration tests for your database.
+       1. You have to configure the tests for your provide in `/tests/unit/providers/vector_io/conftest.py`.
+       2. Update the `vector_provider` fixture to include your provider if they are an inline provider.
+       3. Create a `your_vectorprovider_index` fixture that initializes your vector index.
+       4. Create a `your_vectorprovider_adapter` fixture that initializes your vector adapter.
+       5. Add your provider to the `vector_io_providers` fixture dictionary.
+         - Please follow the naming convention of `your_vectorprovider_index` and `your_vectorprovider_adapter` as the tests require this to execute properly.
+   - Integration Tests
+     - Integration tests are located in {repopath}`tests/integration`. These tests use the python client-SDK APIs (from the `llama_stack_client` package) to test functionality.
+     - The two set of integration tests are:
+       - `tests/integration/vector_io/test_vector_io.py`: This file tests registration, insertion, and retrieval.
+       - `tests/integration/vector_io/test_openai_vector_stores.py`: These tests are for OpenAI-compatible vector stores and test the OpenAI API compatibility.
+        - You will need to update `skip_if_provider_doesnt_support_openai_vector_stores` to include your provider as well as `skip_if_provider_doesnt_support_openai_vector_stores_search` to test the appropriate search functionality.
+     - Running the tests in the GitHub CI
+       - You will need to update the `.github/workflows/integration-vector-io-tests.yml` file to include your provider.
+        - If your provider is a remote provider, you will also have to add a container to spin up and run it in the action.
+   - Updating the pyproject.yml
+     - If you are adding tests for the `inline` provider you will have to update the `unit` group.
+       - `uv add new_pip_package --group unit`
+     - If you are adding tests for the `remote` provider you will have to update the `test` group, which is used in the GitHub CI for integration tests.
+       - `uv add new_pip_package --group test`
+5. **Update Documentation**: Please update the documentation for end users
+   - Generate the provider documentation by running {repopath}`./scripts/provider_codegen.py`.
+   - Update the autogenerated content in the registry/vector_io.py file with information about your provider. Please see other providers for examples.

docs/source/contributing/testing.md

@@ -1,6 +1,8 @@
-# Testing Llama Stack
+```{include} ../../../tests/README.md
+```
 
-Tests are of three different kinds:
+```{include} ../../../tests/unit/README.md
-- Unit tests
+```
-- Provider focused integration tests
+
-- Client SDK tests
+```{include} ../../../tests/integration/README.md
+```

docs/source/providers/external/external-providers-guide.md

@@ -226,7 +226,7 @@ uv init
 name = "llama-stack-provider-ollama"
 version = "0.1.0"
 description = "Ollama provider for Llama Stack"
-requires-python = ">=3.10"
+requires-python = ">=3.12"
 dependencies = ["llama-stack", "pydantic", "ollama", "aiohttp"]
 ```
 

llama_stack/apis/common/errors.py

@@ -62,3 +62,13 @@ class SessionNotFoundError(ValueError):
     def __init__(self, session_name: str) -> None:
         message = f"Session '{session_name}' not found or access denied."
         super().__init__(message)
+
+
+class ModelTypeError(TypeError):
+    """raised when a model is present but not the correct type"""
+
+    def __init__(self, model_name: str, model_type: str, expected_model_type: str) -> None:
+        message = (
+            f"Model '{model_name}' is of type '{model_type}' rather than the expected type '{expected_model_type}'"
+        )
+        super().__init__(message)

llama_stack/core/routers/inference.py

@@ -18,7 +18,7 @@ from llama_stack.apis.common.content_types import (
     InterleavedContent,
     InterleavedContentItem,
 )
-from llama_stack.apis.common.errors import ModelNotFoundError
+from llama_stack.apis.common.errors import ModelNotFoundError, ModelTypeError
 from llama_stack.apis.inference import (
     BatchChatCompletionResponse,
     BatchCompletionResponse,
@@ -177,6 +177,15 @@ class InferenceRouter(Inference):
             encoded = self.formatter.encode_content(messages)
         return len(encoded.tokens) if encoded and encoded.tokens else 0
 
+    async def _get_model(self, model_id: str, expected_model_type: str) -> Model:
+        """takes a model id and gets model after ensuring that it is accessible and of the correct type"""
+        model = await self.routing_table.get_model(model_id)
+        if model is None:
+            raise ModelNotFoundError(model_id)
+        if model.model_type != expected_model_type:
+            raise ModelTypeError(model_id, model.model_type, expected_model_type)
+        return model
+
     async def chat_completion(
         self,
         model_id: str,
@@ -195,11 +204,7 @@ class InferenceRouter(Inference):
         )
         if sampling_params is None:
             sampling_params = SamplingParams()
-        model = await self.routing_table.get_model(model_id)
+        model = await self._get_model(model_id, ModelType.llm)
-        if model is None:
-            raise ModelNotFoundError(model_id)
-        if model.model_type == ModelType.embedding:
-            raise ValueError(f"Model '{model_id}' is an embedding model and does not support chat completions")
         if tool_config:
             if tool_choice and tool_choice != tool_config.tool_choice:
                 raise ValueError("tool_choice and tool_config.tool_choice must match")
@@ -301,11 +306,7 @@ class InferenceRouter(Inference):
         logger.debug(
             f"InferenceRouter.completion: {model_id=}, {stream=}, {content=}, {sampling_params=}, {response_format=}",
         )
-        model = await self.routing_table.get_model(model_id)
+        model = await self._get_model(model_id, ModelType.llm)
-        if model is None:
-            raise ModelNotFoundError(model_id)
-        if model.model_type == ModelType.embedding:
-            raise ValueError(f"Model '{model_id}' is an embedding model and does not support chat completions")
         provider = await self.routing_table.get_provider_impl(model_id)
         params = dict(
             model_id=model_id,
@@ -355,11 +356,7 @@ class InferenceRouter(Inference):
         task_type: EmbeddingTaskType | None = None,
     ) -> EmbeddingsResponse:
         logger.debug(f"InferenceRouter.embeddings: {model_id}")
-        model = await self.routing_table.get_model(model_id)
+        await self._get_model(model_id, ModelType.embedding)
-        if model is None:
-            raise ModelNotFoundError(model_id)
-        if model.model_type == ModelType.llm:
-            raise ValueError(f"Model '{model_id}' is an LLM model and does not support embeddings")
         provider = await self.routing_table.get_provider_impl(model_id)
         return await provider.embeddings(
             model_id=model_id,
@@ -395,12 +392,7 @@ class InferenceRouter(Inference):
         logger.debug(
             f"InferenceRouter.openai_completion: {model=}, {stream=}, {prompt=}",
         )
-        model_obj = await self.routing_table.get_model(model)
+        model_obj = await self._get_model(model, ModelType.llm)
-        if model_obj is None:
-            raise ModelNotFoundError(model)
-        if model_obj.model_type == ModelType.embedding:
-            raise ValueError(f"Model '{model}' is an embedding model and does not support completions")
-
         params = dict(
             model=model_obj.identifier,
             prompt=prompt,
@@ -476,11 +468,7 @@ class InferenceRouter(Inference):
         logger.debug(
             f"InferenceRouter.openai_chat_completion: {model=}, {stream=}, {messages=}",
         )
-        model_obj = await self.routing_table.get_model(model)
+        model_obj = await self._get_model(model, ModelType.llm)
-        if model_obj is None:
-            raise ModelNotFoundError(model)
-        if model_obj.model_type == ModelType.embedding:
-            raise ValueError(f"Model '{model}' is an embedding model and does not support chat completions")
 
         # Use the OpenAI client for a bit of extra input validation without
         # exposing the OpenAI client itself as part of our API surface
@@ -567,12 +555,7 @@ class InferenceRouter(Inference):
         logger.debug(
             f"InferenceRouter.openai_embeddings: {model=}, input_type={type(input)}, {encoding_format=}, {dimensions=}",
         )
-        model_obj = await self.routing_table.get_model(model)
+        model_obj = await self._get_model(model, ModelType.embedding)
-        if model_obj is None:
-            raise ModelNotFoundError(model)
-        if model_obj.model_type != ModelType.embedding:
-            raise ValueError(f"Model '{model}' is not an embedding model")
-
         params = dict(
             model=model_obj.identifier,
             input=input,

llama_stack/core/routing_tables/toolgroups.py

@@ -124,10 +124,7 @@ class ToolGroupsRoutingTable(CommonRoutingTableImpl, ToolGroups):
         return toolgroup
 
     async def unregister_toolgroup(self, toolgroup_id: str) -> None:
-        tool_group = await self.get_tool_group(toolgroup_id)
+        await self.unregister_object(await self.get_tool_group(toolgroup_id))
-        if tool_group is None:
-            raise ToolGroupNotFoundError(toolgroup_id)
-        await self.unregister_object(tool_group)
 
     async def shutdown(self) -> None:
         pass

llama_stack/core/routing_tables/vector_dbs.py

@@ -8,7 +8,7 @@ from typing import Any
 
 from pydantic import TypeAdapter
 
-from llama_stack.apis.common.errors import ModelNotFoundError, VectorStoreNotFoundError
+from llama_stack.apis.common.errors import ModelNotFoundError, ModelTypeError, VectorStoreNotFoundError
 from llama_stack.apis.models import ModelType
 from llama_stack.apis.resource import ResourceType
 from llama_stack.apis.vector_dbs import ListVectorDBsResponse, VectorDB, VectorDBs
@@ -66,7 +66,7 @@ class VectorDBsRoutingTable(CommonRoutingTableImpl, VectorDBs):
         if model is None:
             raise ModelNotFoundError(embedding_model)
         if model.model_type != ModelType.embedding:
-            raise ValueError(f"Model {embedding_model} is not an embedding model")
+            raise ModelTypeError(embedding_model, model.model_type, ModelType.embedding)
         if "embedding_dimension" not in model.metadata:
             raise ValueError(f"Model {embedding_model} does not have an embedding dimension")
         vector_db_data = {

llama_stack/log.py

@@ -99,7 +99,8 @@ def parse_environment_config(env_config: str) -> dict[str, int]:
         Dict[str, int]: A dictionary mapping categories to their log levels.
     """
     category_levels = {}
-    for pair in env_config.split(";"):
+    delimiter = ","
+    for pair in env_config.split(delimiter):
         if not pair.strip():
             continue
 

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

@@ -15,6 +15,7 @@ from llama_stack.apis.safety import (
     RunShieldResponse,
     Safety,
     SafetyViolation,
+    ShieldStore,
     ViolationLevel,
 )
 from llama_stack.apis.shields import Shield
@@ -32,6 +33,8 @@ PROMPT_GUARD_MODEL = "Prompt-Guard-86M"
 
 
 class PromptGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
+    shield_store: ShieldStore
+
     def __init__(self, config: PromptGuardConfig, _deps) -> None:
         self.config = config
 
@@ -53,7 +56,7 @@ class PromptGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
         self,
         shield_id: str,
         messages: list[Message],
-        params: dict[str, Any] = None,
+        params: dict[str, Any],
     ) -> RunShieldResponse:
         shield = await self.shield_store.get_shield(shield_id)
         if not shield:
@@ -61,6 +64,9 @@ class PromptGuardSafetyImpl(Safety, ShieldsProtocolPrivate):
 
         return await self.shield.run(messages)
 
+    async def run_moderation(self, input: str | list[str], model: str):
+        raise NotImplementedError("run_moderation not implemented for PromptGuard")
+
 
 class PromptGuardShield:
     def __init__(
@@ -117,8 +123,10 @@ class PromptGuardShield:
         elif self.config.guard_type == PromptGuardType.jailbreak.value and score_malicious > self.threshold:
             violation = SafetyViolation(
                 violation_level=ViolationLevel.ERROR,
-                violation_type=f"prompt_injection:malicious={score_malicious}",
+                user_message="Sorry, I cannot do this.",
-                violation_return_message="Sorry, I cannot do this.",
+                metadata={
+                    "violation_type": f"prompt_injection:malicious={score_malicious}",
+                },
             )
 
         return RunShieldResponse(violation=violation)

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

@@ -457,9 +457,6 @@ class OllamaInferenceAdapter(
         user: str | None = None,
     ) -> OpenAIEmbeddingsResponse:
         model_obj = await self._get_model(model)
-        if model_obj.model_type != ModelType.embedding:
-            raise ValueError(f"Model {model} is not an embedding model")
-
         if model_obj.provider_resource_id is None:
             raise ValueError(f"Model {model} has no provider_resource_id set")
 

llama_stack/providers/utils/inference/openai_compat.py

@@ -70,7 +70,7 @@ from openai.types.chat.chat_completion_chunk import (
 from openai.types.chat.chat_completion_content_part_image_param import (
     ImageURL as OpenAIImageURL,
 )
-from openai.types.chat.chat_completion_message_tool_call_param import (
+from openai.types.chat.chat_completion_message_tool_call import (
     Function as OpenAIFunction,
 )
 from pydantic import BaseModel

llama_stack/ui/app/chat-playground/page.tsx

@@ -175,7 +175,7 @@ const handleSubmitWithContent = async (content: string) => {
   return (
     <div className="flex flex-col h-full max-w-4xl mx-auto">
       <div className="mb-4 flex justify-between items-center">
-        <h1 className="text-2xl font-bold">Chat Playground</h1>
+        <h1 className="text-2xl font-bold">Chat Playground (Completions)</h1>
         <div className="flex gap-2">
           <Select value={selectedModel} onValueChange={setSelectedModel} disabled={isModelsLoading || isGenerating}>
             <SelectTrigger className="w-[180px]">

llama_stack/ui/components/layout/app-sidebar.tsx

@@ -6,6 +6,8 @@ import {
   MoveUpRight,
   Database,
   MessageCircle,
+  Settings2,
+  Compass,
 } from "lucide-react";
 import Link from "next/link";
 import { usePathname } from "next/navigation";
@@ -22,15 +24,16 @@ import {
   SidebarMenuItem,
   SidebarHeader,
 } from "@/components/ui/sidebar";
-// Extracted Chat Playground item
-const chatPlaygroundItem = {
-  title: "Chat Playground",
-  url: "/chat-playground",
-  icon: MessageCircle,
-};
 
-// Removed Chat Playground from log items
+const createItems = [
-const logItems = [
+  {
+    title: "Chat Playground",
+    url: "/chat-playground",
+    icon: MessageCircle,
+  },
+];
+
+const manageItems = [
   {
     title: "Chat Completions",
     url: "/logs/chat-completions",
@@ -53,77 +56,96 @@ const logItems = [
   },
 ];
 
+const optimizeItems: { title: string; url: string; icon: React.ElementType }[] = [
+    {
+        title: "Evaluations",
+        url: "",
+        icon: Compass,
+    },
+    {
+        title: "Fine-tuning",
+        url: "",
+        icon: Settings2,
+    },
+];
+
+interface SidebarItem {
+  title: string;
+  url: string;
+  icon: React.ElementType;
+}
+
 export function AppSidebar() {
   const pathname = usePathname();
 
-  return (
+  const renderSidebarItems = (items: SidebarItem[]) => {
-    <Sidebar>
+    return items.map((item) => {
-      <SidebarHeader>
+      const isActive = pathname.startsWith(item.url);
-        <Link href="/">Llama Stack</Link>
+      return (
-      </SidebarHeader>
+        <SidebarMenuItem key={item.title}>
-      <SidebarContent>
+          <SidebarMenuButton
-        {/* Chat Playground as its own section */}
+            asChild
-        <SidebarGroup>
+            className={cn(
-          <SidebarGroupContent>
+              "justify-start",
-            <SidebarMenu>
+              isActive &&
-              <SidebarMenuItem>
+                "bg-gray-200 dark:bg-gray-700 hover:bg-gray-200 dark:hover:bg-gray-700 text-gray-900 dark:text-gray-100",
+            )}
+          >
+            <Link href={item.url}>
+              <item.icon
+                className={cn(
+                  isActive && "text-gray-900 dark:text-gray-100",
+                  "mr-2 h-4 w-4",
+                )}
+              />
+              <span>{item.title}</span>
+            </Link>
+          </SidebarMenuButton>
+        </SidebarMenuItem>
+      );
+    });
+  };
+
+return (
+  <Sidebar>
+    <SidebarHeader>
+      <Link href="/">Llama Stack</Link>
+    </SidebarHeader>
+    <SidebarContent>
+      <SidebarGroup>
+        <SidebarGroupLabel>Create</SidebarGroupLabel>
+        <SidebarGroupContent>
+          <SidebarMenu>{renderSidebarItems(createItems)}</SidebarMenu>
+        </SidebarGroupContent>
+      </SidebarGroup>
+
+      <SidebarGroup>
+        <SidebarGroupLabel>Manage</SidebarGroupLabel>
+        <SidebarGroupContent>
+          <SidebarMenu>{renderSidebarItems(manageItems)}</SidebarMenu>
+        </SidebarGroupContent>
+      </SidebarGroup>
+
+      <SidebarGroup>
+        <SidebarGroupLabel>Optimize</SidebarGroupLabel>
+        <SidebarGroupContent>
+          <SidebarMenu>
+            {optimizeItems.map((item) => (
+              <SidebarMenuItem key={item.title}>
                 <SidebarMenuButton
-                  asChild
+                  disabled
-                  className={cn(
+                  className="justify-start opacity-60 cursor-not-allowed"
-                    "justify-start",
-                    pathname.startsWith(chatPlaygroundItem.url) &&
-                      "bg-gray-200 dark:bg-gray-700 hover:bg-gray-200 dark:hover:bg-gray-700 text-gray-900 dark:text-gray-100",
-                  )}
                 >
-                  <Link href={chatPlaygroundItem.url}>
+                  <item.icon className="mr-2 h-4 w-4" />
-                    <chatPlaygroundItem.icon
+                  <span>{item.title}</span>
-                      className={cn(
+                  <span className="ml-2 text-xs text-gray-500">(Coming Soon)</span>
-                        pathname.startsWith(chatPlaygroundItem.url) && "text-gray-900 dark:text-gray-100",
-                        "mr-2 h-4 w-4",
-                      )}
-                    />
-                    <span>{chatPlaygroundItem.title}</span>
-                  </Link>
                 </SidebarMenuButton>
               </SidebarMenuItem>
-            </SidebarMenu>
+            ))}
-          </SidebarGroupContent>
+          </SidebarMenu>
-        </SidebarGroup>
+        </SidebarGroupContent>
-
+      </SidebarGroup>
-        {/* Logs section */}
+    </SidebarContent>
-        <SidebarGroup>
+  </Sidebar>
-          <SidebarGroupLabel>Logs</SidebarGroupLabel>
-          <SidebarGroupContent>
-            <SidebarMenu>
-              {logItems.map((item) => {
-                const isActive = pathname.startsWith(item.url);
-                return (
-                  <SidebarMenuItem key={item.title}>
-                    <SidebarMenuButton
-                      asChild
-                      className={cn(
-                        "justify-start",
-                        isActive &&
-                          "bg-gray-200 dark:bg-gray-700 hover:bg-gray-200 dark:hover:bg-gray-700 text-gray-900 dark:text-gray-100",
-                      )}
-                    >
-                      <Link href={item.url}>
-                        <item.icon
-                          className={cn(
-                            isActive && "text-gray-900 dark:text-gray-100",
-                            "mr-2 h-4 w-4",
-                          )}
-                        />
-                        <span>{item.title}</span>
-                      </Link>
-                    </SidebarMenuButton>
-                  </SidebarMenuItem>
-                );
-              })}
-            </SidebarMenu>
-          </SidebarGroupContent>
-        </SidebarGroup>
-      </SidebarContent>
-    </Sidebar>
   );
 }

pyproject.toml

@@ -33,7 +33,7 @@ dependencies = [
     "jsonschema",
     "llama-stack-client>=0.2.17",
     "llama-api-client>=0.1.2",
-    "openai>=1.66",
+    "openai>=1.99.6",
     "prompt-toolkit",
     "python-dotenv",
     "python-jose[cryptography]",
@@ -266,7 +266,6 @@ exclude = [
     "^llama_stack/providers/inline/post_training/common/validator\\.py$",
     "^llama_stack/providers/inline/safety/code_scanner/",
     "^llama_stack/providers/inline/safety/llama_guard/",
-    "^llama_stack/providers/inline/safety/prompt_guard/",
     "^llama_stack/providers/inline/scoring/basic/",
     "^llama_stack/providers/inline/scoring/braintrust/",
     "^llama_stack/providers/inline/scoring/llm_as_judge/",

tests/external/llama-stack-api-weather/pyproject.toml

@@ -3,7 +3,7 @@ name = "llama-stack-api-weather"
 version = "0.1.0"
 description = "Weather API for Llama Stack"
 readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.12"
 dependencies = ["llama-stack", "pydantic"]
 
 [build-system]

tests/external/llama-stack-provider-kaze/pyproject.toml

@@ -3,7 +3,7 @@ name = "llama-stack-provider-kaze"
 version = "0.1.0"
 description = "Kaze weather provider for Llama Stack"
 readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.12"
 dependencies = ["llama-stack", "pydantic", "aiohttp"]
 
 [build-system]

uv.lock

@@ -476,7 +476,7 @@ wheels = [
 
 [[package]]
 name = "chromadb"
-version = "1.0.15"
+version = "1.0.16"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "bcrypt" },
@@ -507,13 +507,13 @@ dependencies = [
     { name = "typing-extensions" },
     { name = "uvicorn", extra = ["standard"] },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/ad/e2/0653b2e539db5512d2200c759f1bc7f9ef5609fe47f3c7d24b82f62dc00f/chromadb-1.0.15.tar.gz", hash = "sha256:3e910da3f5414e2204f89c7beca1650847f2bf3bd71f11a2e40aad1eb31050aa", size = 1218840, upload-time = "2025-07-02T17:07:09.875Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/15/2a/5b7e793d2a27c425e9f1813e9cb965b70e9bda08b69ee15a10e07dc3e59a/chromadb-1.0.16.tar.gz", hash = "sha256:3c864b5beb5e131bdc1f83c0b63a01ec481c6ee52028f088563ffba8478478e1", size = 1241545, upload-time = "2025-08-08T00:25:41.414Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/85/5a/866c6f0c2160cbc8dca0cf77b2fb391dcf435b32a58743da1bc1a08dc442/chromadb-1.0.15-cp39-abi3-macosx_10_12_x86_64.whl", hash = "sha256:51791553014297798b53df4e043e9c30f4e8bd157647971a6bb02b04bfa65f82", size = 18838820, upload-time = "2025-07-02T17:07:07.632Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/9d/bffcc814272c9b7982551803b2d45b77f39eeea1b9e965c00c05ee81c649/chromadb-1.0.16-cp39-abi3-macosx_10_12_x86_64.whl", hash = "sha256:144163ce7ca4f4448684d5d0c13ebb37c4d68490ecb60967a95d05cea30e0d2d", size = 18942157, upload-time = "2025-08-08T00:25:38.459Z" },
-    { url = "https://files.pythonhosted.org/packages/e1/18/ff9b58ab5d334f5ecff7fdbacd6761bac467176708fa4d2500ae7c048af0/chromadb-1.0.15-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:48015803c0631c3a817befc276436dc084bb628c37fd4214047212afb2056291", size = 18057131, upload-time = "2025-07-02T17:07:05.15Z" },
+    { url = "https://files.pythonhosted.org/packages/58/4e/de0086f3cbcfd667d75d112bb546386803ab5335599bf7099272a675e98b/chromadb-1.0.16-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:4ebcc5894e6fbb6b576452bbf4659746bfe58d9daf99a18363364e9497434bd2", size = 18147831, upload-time = "2025-08-08T00:25:35.546Z" },
-    { url = "https://files.pythonhosted.org/packages/31/49/74e34cc5aeeb25aff2c0ede6790b3671e14c1b91574dd8f98d266a4c5aad/chromadb-1.0.15-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3b73cd6fb32fcdd91c577cca16ea6112b691d72b441bb3f2140426d1e79e453a", size = 18595284, upload-time = "2025-07-02T17:06:59.102Z" },
+    { url = "https://files.pythonhosted.org/packages/0e/7f/a8aff4ce96281bcb9731d10b2554f41963dd0b47acb4f90a78b2b7c4f199/chromadb-1.0.16-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:937051fc3aae94f7c171503d8f1f7662820aacc75acf45f28d3656c75c5ff1f8", size = 18682195, upload-time = "2025-08-08T00:25:29.654Z" },
-    { url = "https://files.pythonhosted.org/packages/cb/33/190df917a057067e37f8b48d082d769bed8b3c0c507edefc7b6c6bb577d0/chromadb-1.0.15-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:479f1b401af9e7c20f50642ffb3376abbfd78e2b5b170429f7c79eff52e367db", size = 19526626, upload-time = "2025-07-02T17:07:02.163Z" },
+    { url = "https://files.pythonhosted.org/packages/a3/9c/2a97d0257176aae472dff6f1ef1b7050449f384e420120e0f31d2d8f532f/chromadb-1.0.16-cp39-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c0f5c5ad0c59154a9cab1506b857bab8487b588352e668cf1222c54bb9d52daa", size = 19635695, upload-time = "2025-08-08T00:25:32.68Z" },
-    { url = "https://files.pythonhosted.org/packages/a1/30/6890da607358993f87a01e80bcce916b4d91515ce865f07dc06845cb472f/chromadb-1.0.15-cp39-abi3-win_amd64.whl", hash = "sha256:e0cb3b93fdc42b1786f151d413ef36299f30f783a30ce08bf0bfb12e552b4190", size = 19520490, upload-time = "2025-07-02T17:07:11.559Z" },
+    { url = "https://files.pythonhosted.org/packages/96/8a/f7e810f3cbdc9186ba4e649dc32711b7ab2c23aba37cf61175f731d22293/chromadb-1.0.16-cp39-abi3-win_amd64.whl", hash = "sha256:2528c01bd8b3facca9d0e1ffac866767c386b94604df484fc792ee891c86e09a", size = 19641144, upload-time = "2025-08-08T00:25:43.446Z" },
 ]
 
 [[package]]
@@ -1632,10 +1632,10 @@ test = [
     { name = "pypdf" },
     { name = "requests" },
     { name = "sqlalchemy", extra = ["asyncio"] },
-    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
+    { name = "torch", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
+    { name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
-    { name = "torchvision", version = "0.22.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
+    { name = "torchvision", version = "0.23.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
-    { name = "torchvision", version = "0.22.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "torchvision", version = "0.23.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
     { name = "transformers" },
     { name = "weaviate-client" },
 ]
@@ -1674,7 +1674,7 @@ requires-dist = [
     { name = "llama-api-client", specifier = ">=0.1.2" },
     { name = "llama-stack-client", specifier = ">=0.2.17" },
     { name = "llama-stack-client", marker = "extra == 'ui'", specifier = ">=0.2.17" },
-    { name = "openai", specifier = ">=1.66" },
+    { name = "openai", specifier = ">=1.99.6" },
     { name = "opentelemetry-exporter-otlp-proto-http", specifier = ">=1.30.0" },
     { name = "opentelemetry-sdk", specifier = ">=1.30.0" },
     { name = "pandas", marker = "extra == 'ui'" },
@@ -2301,7 +2301,7 @@ wheels = [
 
 [[package]]
 name = "openai"
-version = "1.98.0"
+version = "1.99.6"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -2313,9 +2313,9 @@ dependencies = [
     { name = "tqdm" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/d8/9d/52eadb15c92802711d6b6cf00df3a6d0d18b588f4c5ba5ff210c6419fc03/openai-1.98.0.tar.gz", hash = "sha256:3ee0fcc50ae95267fd22bd1ad095ba5402098f3df2162592e68109999f685427", size = 496695, upload-time = "2025-07-30T12:48:03.701Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/11/45/38a87bd6949236db5ae3132f41d5861824702b149f86d2627d6900919103/openai-1.99.6.tar.gz", hash = "sha256:f48f4239b938ef187062f3d5199a05b69711d8b600b9a9b6a3853cd271799183", size = 505364, upload-time = "2025-08-09T15:20:54.438Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/a8/fe/f64631075b3d63a613c0d8ab761d5941631a470f6fa87eaaee1aa2b4ec0c/openai-1.98.0-py3-none-any.whl", hash = "sha256:b99b794ef92196829120e2df37647722104772d2a74d08305df9ced5f26eae34", size = 767713, upload-time = "2025-07-30T12:48:01.264Z" },
+    { url = "https://files.pythonhosted.org/packages/d6/dd/9aa956485c2856346b3181542fbb0aea4e5b457fa7a523944726746da8da/openai-1.99.6-py3-none-any.whl", hash = "sha256:e40d44b2989588c45ce13819598788b77b8fb80ba2f7ae95ce90d14e46f1bd26", size = 786296, upload-time = "2025-08-09T15:20:51.95Z" },
 ]
 
 [[package]]
@@ -4310,7 +4310,7 @@ wheels = [
 
 [[package]]
 name = "torch"
-version = "2.7.1"
+version = "2.8.0"
 source = { registry = "https://download.pytorch.org/whl/cpu" }
 resolution-markers = [
     "python_full_version >= '3.13' and sys_platform == 'darwin'",
@@ -4326,14 +4326,14 @@ dependencies = [
     { name = "typing-extensions", marker = "sys_platform == 'darwin'" },
 ]
 wheels = [
-    { url = "https://download.pytorch.org/whl/cpu/torch-2.7.1-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:7b4f8b2b83bd08f7d399025a9a7b323bdbb53d20566f1e0d584689bb92d82f9a" },
+    { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:a47b7986bee3f61ad217d8a8ce24605809ab425baf349f97de758815edd2ef54" },
-    { url = "https://download.pytorch.org/whl/cpu/torch-2.7.1-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:95af97e7b2cecdc89edc0558962a51921bf9c61538597dbec6b7cc48d31e2e13" },
+    { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0-cp313-cp313t-macosx_14_0_arm64.whl", hash = "sha256:fbe2e149c5174ef90d29a5f84a554dfaf28e003cb4f61fa2c8c024c17ec7ca58" },
-    { url = "https://download.pytorch.org/whl/cpu/torch-2.7.1-cp313-none-macosx_11_0_arm64.whl", hash = "sha256:7ecd868a086468e1bcf74b91db425c1c2951a9cfcd0592c4c73377b7e42485ae" },
+    { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0-cp313-none-macosx_11_0_arm64.whl", hash = "sha256:057efd30a6778d2ee5e2374cd63a63f63311aa6f33321e627c655df60abdd390" },
 ]
 
 [[package]]
 name = "torch"
-version = "2.7.1+cpu"
+version = "2.8.0+cpu"
 source = { registry = "https://download.pytorch.org/whl/cpu" }
 resolution-markers = [
     "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
@@ -4351,21 +4351,24 @@ dependencies = [
     { name = "typing-extensions", marker = "sys_platform != 'darwin'" },
 ]
 wheels = [
-    { url = "https://download.pytorch.org/whl/cpu/torch-2.7.1%2Bcpu-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:3bf2db5adf77b433844f080887ade049c4705ddf9fe1a32023ff84ff735aa5ad" },
+    { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp312-cp312-linux_s390x.whl", hash = "sha256:0e34e276722ab7dd0dffa9e12fe2135a9b34a0e300c456ed7ad6430229404eb5" },
-    { url = "https://download.pytorch.org/whl/cpu/torch-2.7.1%2Bcpu-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:8f8b3cfc53010a4b4a3c7ecb88c212e9decc4f5eeb6af75c3c803937d2d60947" },
+    { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:610f600c102386e581327d5efc18c0d6edecb9820b4140d26163354a99cd800d" },
-    { url = "https://download.pytorch.org/whl/cpu/torch-2.7.1%2Bcpu-cp312-cp312-win_amd64.whl", hash = "sha256:0bc887068772233f532b51a3e8c8cfc682ae62bef74bf4e0c53526c8b9e4138f" },
+    { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:cb9a8ba8137ab24e36bf1742cb79a1294bd374db570f09fc15a5e1318160db4e" },
-    { url = "https://download.pytorch.org/whl/cpu/torch-2.7.1%2Bcpu-cp312-cp312-win_arm64.whl", hash = "sha256:a2618775f32eb4126c5b2050686da52001a08cffa331637d9cf51c8250931e00" },
+    { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp312-cp312-win_amd64.whl", hash = "sha256:2be20b2c05a0cce10430cc25f32b689259640d273232b2de357c35729132256d" },
-    { url = "https://download.pytorch.org/whl/cpu/torch-2.7.1%2Bcpu-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:eb17646792ac4374ffc87e42369f45d21eff17c790868963b90483ef0b6db4ef" },
+    { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp312-cp312-win_arm64.whl", hash = "sha256:99fc421a5d234580e45957a7b02effbf3e1c884a5dd077afc85352c77bf41434" },
-    { url = "https://download.pytorch.org/whl/cpu/torch-2.7.1%2Bcpu-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:84ea1f6a1d15663037d01b121d6e33bb9da3c90af8e069e5072c30f413455a57" },
+    { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313-linux_s390x.whl", hash = "sha256:8b5882276633cf91fe3d2d7246c743b94d44a7e660b27f1308007fdb1bb89f7d" },
-    { url = "https://download.pytorch.org/whl/cpu/torch-2.7.1%2Bcpu-cp313-cp313-win_amd64.whl", hash = "sha256:b66f77f6f67317344ee083aa7ac4751a14395fcb38060d564bf513978d267153" },
+    { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:a5064b5e23772c8d164068cc7c12e01a75faf7b948ecd95a0d4007d7487e5f25" },
-    { url = "https://download.pytorch.org/whl/cpu/torch-2.7.1%2Bcpu-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:56136a2aca6707df3c8811e46ea2d379eaafd18e656e2fd51e8e4d0ca995651b" },
+    { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:8f81dedb4c6076ec325acc3b47525f9c550e5284a18eae1d9061c543f7b6e7de" },
-    { url = "https://download.pytorch.org/whl/cpu/torch-2.7.1%2Bcpu-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:355614185a2aea7155f9c88a20bfd49de5f3063866f3cf9b2f21b6e9e59e31e0" },
+    { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313-win_amd64.whl", hash = "sha256:e1ee1b2346ade3ea90306dfbec7e8ff17bc220d344109d189ae09078333b0856" },
-    { url = "https://download.pytorch.org/whl/cpu/torch-2.7.1%2Bcpu-cp313-cp313t-win_amd64.whl", hash = "sha256:464bca1bc9452f2ccd676514688896e66b9488f2a0268ecd3ac497cf09c5aac1" },
+    { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313-win_arm64.whl", hash = "sha256:64c187345509f2b1bb334feed4666e2c781ca381874bde589182f81247e61f88" },
+    { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:af81283ac671f434b1b25c95ba295f270e72db1fad48831eb5e4748ff9840041" },
+    { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:a9dbb6f64f63258bc811e2c0c99640a81e5af93c531ad96e95c5ec777ea46dab" },
+    { url = "https://download.pytorch.org/whl/cpu/torch-2.8.0%2Bcpu-cp313-cp313t-win_amd64.whl", hash = "sha256:6d93a7165419bc4b2b907e859ccab0dea5deeab261448ae9a5ec5431f14c0e64" },
 ]
 
 [[package]]
 name = "torchvision"
-version = "0.22.1"
+version = "0.23.0"
 source = { registry = "https://download.pytorch.org/whl/cpu" }
 resolution-markers = [
     "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'",
@@ -4376,21 +4379,21 @@ resolution-markers = [
 dependencies = [
     { name = "numpy", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
     { name = "pillow", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
-    { name = "torch", version = "2.7.1", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
+    { name = "torch", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" },
+    { name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" },
 ]
 wheels = [
-    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.22.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:153f1790e505bd6da123e21eee6e83e2e155df05c0fe7d56347303067d8543c5" },
+    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.23.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:e0e2c04a91403e8dd3af9756c6a024a1d9c0ed9c0d592a8314ded8f4fe30d440" },
-    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.22.1-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:964414eef19459d55a10e886e2fca50677550e243586d1678f65e3f6f6bac47a" },
+    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.23.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:6dd7c4d329a0e03157803031bc856220c6155ef08c26d4f5bbac938acecf0948" },
-    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.22.1-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:9c3ae3319624c43cc8127020f46c14aa878406781f0899bb6283ae474afeafbf" },
+    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.23.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:1c37e325e09a184b730c3ef51424f383ec5745378dc0eca244520aca29722600" },
-    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.22.1-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:4a614a6a408d2ed74208d0ea6c28a2fbb68290e9a7df206c5fef3f0b6865d307" },
+    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.23.0-cp313-cp313-manylinux_2_28_aarch64.whl", hash = "sha256:2f7fd6c15f3697e80627b77934f77705f3bc0e98278b989b2655de01f6903e1d" },
-    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.22.1-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:043d9e35ed69c2e586aff6eb9e2887382e7863707115668ac9d140da58f42cba" },
+    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.23.0-cp313-cp313t-macosx_11_0_arm64.whl", hash = "sha256:2df618e1143805a7673aaf82cb5720dd9112d4e771983156aaf2ffff692eebf9" },
-    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.22.1-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:27142bcc8a984227a6dcf560985e83f52b82a7d3f5fe9051af586a2ccc46ef26" },
+    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.23.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:2a3299d2b1d5a7aed2d3b6ffb69c672ca8830671967eb1cee1497bacd82fe47b" },
 ]
 
 [[package]]
 name = "torchvision"
-version = "0.22.1+cpu"
+version = "0.23.0+cpu"
 source = { registry = "https://download.pytorch.org/whl/cpu" }
 resolution-markers = [
     "(python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux')",
@@ -4399,15 +4402,15 @@ resolution-markers = [
 dependencies = [
     { name = "numpy", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
     { name = "pillow", marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
-    { name = "torch", version = "2.7.1+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
 ]
 wheels = [
-    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.22.1%2Bcpu-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:b5fa7044bd82c6358e8229351c98070cf3a7bf4a6e89ea46352ae6c65745ef94" },
+    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.23.0%2Bcpu-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:ae459d4509d3b837b978dc6c66106601f916b6d2cda75c137e3f5f48324ce1da" },
-    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.22.1%2Bcpu-cp312-cp312-win_amd64.whl", hash = "sha256:433cb4dbced7291f17064cea08ac1e5aebd02ec190e1c207d117ad62a8961f2b" },
+    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.23.0%2Bcpu-cp312-cp312-win_amd64.whl", hash = "sha256:a651ccc540cf4c87eb988730c59c2220c52b57adc276f044e7efb9830fa65a1d" },
-    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.22.1%2Bcpu-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:a93c21f18c33a819616b3dda7655aa4de40b219682c654175b6bbeb65ecc2e5f" },
+    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.23.0%2Bcpu-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:dea90a67d60a5366b0358a0b8d6bf267805278697d6fd950cf0e31139e56d1be" },
-    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.22.1%2Bcpu-cp313-cp313-win_amd64.whl", hash = "sha256:34c914ad4728b81848ac802c5fc5eeb8de8ff4058cc59c1463a74ce4f4fbf0d8" },
+    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.23.0%2Bcpu-cp313-cp313-win_amd64.whl", hash = "sha256:82928788025170c62e7df1120dcdc0cd175bfc31c08374613ce6d1a040bc0cda" },
-    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.22.1%2Bcpu-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:ab7ae82529887c704c1b5d1d5198f65dc777d04fc3858b374503a6deedb82b19" },
+    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.23.0%2Bcpu-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:474d77adbbbed5166db3e5636b4b4ae3399c66ef5bfa12536e254b32259c90c0" },
-    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.22.1%2Bcpu-cp313-cp313t-win_amd64.whl", hash = "sha256:b2d1c4bdbfd8e6c779dc810a6171b56224f1332fc46986810d4081bed1633804" },
+    { url = "https://download.pytorch.org/whl/cpu/torchvision-0.23.0%2Bcpu-cp313-cp313t-win_amd64.whl", hash = "sha256:8d6a47e23d7896f0ef9aa7ea7179eb6324e82438aa66d19884c2020d0646b104" },
 ]
 
 [[package]]