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Merge branch 'main' into agent_session_unit_test

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Francisco Arceo 2025-08-12 10:51:00 -06:00 • committed by GitHub
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14
docs/_static/js/keyboard_shortcuts.js vendored Normal file
View file

@ -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();
}
});

4
docs/source/apis/external.md
View file

@ -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]

1
docs/source/conf.py
View file

@ -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}"

18
docs/source/contributing/index.md
View file

@ -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
```

75
docs/source/contributing/new_vector_database.md Normal file
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@ -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.

12
docs/source/contributing/testing.md
View file

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

2
docs/source/providers/external/external-providers-guide.md vendored
View file

@ -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"]
```

10
llama_stack/apis/common/errors.py
View file

@ -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)

49
llama_stack/core/routers/inference.py
View file

@ -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)
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")
model = await self._get_model(model_id, ModelType.llm)
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)
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")
model = await self._get_model(model_id, ModelType.llm)
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)
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")
await self._get_model(model_id, ModelType.embedding)
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)
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")
model_obj = await self._get_model(model, ModelType.llm)
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)
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")
model_obj = await self._get_model(model, ModelType.llm)
# 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)
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")
model_obj = await self._get_model(model, ModelType.embedding)
params = dict(
model=model_obj.identifier,
input=input,

5
llama_stack/core/routing_tables/toolgroups.py
View file

@ -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)
if tool_group is None:
raise ToolGroupNotFoundError(toolgroup_id)
await self.unregister_object(tool_group)
await self.unregister_object(await self.get_tool_group(toolgroup_id))
async def shutdown(self) -> None:
pass

4
llama_stack/core/routing_tables/vector_dbs.py
View file

@ -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 = {

3
llama_stack/log.py
View file

@ -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

14
llama_stack/providers/inline/safety/prompt_guard/prompt_guard.py
View file

@ -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}",
violation_return_message="Sorry, I cannot do this.",
user_message="Sorry, I cannot do this.",
metadata={
"violation_type": f"prompt_injection:malicious={score_malicious}",
},
)
return RunShieldResponse(violation=violation)

3
llama_stack/providers/remote/inference/ollama/ollama.py
View file

@ -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")

2
llama_stack/providers/utils/inference/openai_compat.py
View file

@ -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

2
llama_stack/ui/app/chat-playground/page.tsx
View file

@ -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]">

114
llama_stack/ui/components/layout/app-sidebar.tsx
View file

@ -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 = {
const createItems = [
{
title: "Chat Playground",
url: "/chat-playground",
icon: MessageCircle,
};
},
];
// Removed Chat Playground from log items
const logItems = [
const manageItems = [
{
title: "Chat Completions",
url: "/logs/chat-completions",
@ -53,49 +56,30 @@ 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 (
<Sidebar>
<SidebarHeader>
<Link href="/">Llama Stack</Link>
</SidebarHeader>
<SidebarContent>
{/* Chat Playground as its own section */}
<SidebarGroup>
<SidebarGroupContent>
<SidebarMenu>
<SidebarMenuItem>
<SidebarMenuButton
asChild
className={cn(
"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}>
<chatPlaygroundItem.icon
className={cn(
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>
</SidebarGroup>
{/* Logs section */}
<SidebarGroup>
<SidebarGroupLabel>Logs</SidebarGroupLabel>
<SidebarGroupContent>
<SidebarMenu>
{logItems.map((item) => {
const renderSidebarItems = (items: SidebarItem[]) => {
return items.map((item) => {
const isActive = pathname.startsWith(item.url);
return (
<SidebarMenuItem key={item.title}>
@ -119,7 +103,45 @@ export function AppSidebar() {
</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
disabled
className="justify-start opacity-60 cursor-not-allowed"
>
<item.icon className="mr-2 h-4 w-4" />
<span>{item.title}</span>
<span className="ml-2 text-xs text-gray-500">(Coming Soon)</span>
</SidebarMenuButton>
</SidebarMenuItem>
))}
</SidebarMenu>
</SidebarGroupContent>
</SidebarGroup>

3
pyproject.toml
View file

@ -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/",

2
tests/external/llama-stack-api-weather/pyproject.toml vendored
View file

@ -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]

2
tests/external/llama-stack-provider-kaze/pyproject.toml vendored
View file

@ -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]

97
uv.lock generated
View file

@ -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" }
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