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Merge remote-tracking branch 'origin/main' into migrate-safety-to-openai

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This commit is contained in:
Ashwin Bharambe 2025-09-29 13:10:39 -07:00
commit 9d27f214d7
31 changed files with 247 additions and 720 deletions
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2
.github/workflows/conformance.yml vendored
View file

@ -43,7 +43,7 @@ jobs:
# Cache oasdiff to avoid checksum failures and speed up builds
- name: Cache oasdiff
id: cache-oasdiff
uses: actions/cache@0400d5f644dc74513175e3cd8d07132dd4860809
uses: actions/cache@0057852bfaa89a56745cba8c7296529d2fc39830
with:
path: ~/oasdiff
key: oasdiff-${{ runner.os }}

120
docs/static/llama-stack-spec.html vendored
View file

@ -1035,50 +1035,6 @@
]
}
},
"/v1/inference/embeddings": {
"post": {
"responses": {
"200": {
"description": "An array of embeddings, one for each content. Each embedding is a list of floats. The dimensionality of the embedding is model-specific; you can check model metadata using /models/{model_id}.",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/EmbeddingsResponse"
}
}
}
},
"400": {
"$ref": "#/components/responses/BadRequest400"
},
"429": {
"$ref": "#/components/responses/TooManyRequests429"
},
"500": {
"$ref": "#/components/responses/InternalServerError500"
},
"default": {
"$ref": "#/components/responses/DefaultError"
}
},
"tags": [
"Inference"
],
"summary": "Generate embeddings for content pieces using the specified model.",
"description": "Generate embeddings for content pieces using the specified model.",
"parameters": [],
"requestBody": {
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/EmbeddingsRequest"
}
}
},
"required": true
}
}
},
"/v1alpha/eval/benchmarks/{benchmark_id}/evaluations": {
"post": {
"responses": {
@ -5475,7 +5431,7 @@
}
}
},
"/v1/inference/rerank": {
"/v1alpha/inference/rerank": {
"post": {
"responses": {
"200": {
@ -10547,80 +10503,6 @@
"title": "OpenAIDeleteResponseObject",
"description": "Response object confirming deletion of an OpenAI response."
},
"EmbeddingsRequest": {
"type": "object",
"properties": {
"model_id": {
"type": "string",
"description": "The identifier of the model to use. The model must be an embedding model registered with Llama Stack and available via the /models endpoint."
},
"contents": {
"oneOf": [
{
"type": "array",
"items": {
"type": "string"
}
},
{
"type": "array",
"items": {
"$ref": "#/components/schemas/InterleavedContentItem"
}
}
],
"description": "List of contents to generate embeddings for. Each content can be a string or an InterleavedContentItem (and hence can be multimodal). The behavior depends on the model and provider. Some models may only support text."
},
"text_truncation": {
"type": "string",
"enum": [
"none",
"start",
"end"
],
"description": "(Optional) Config for how to truncate text for embedding when text is longer than the model's max sequence length."
},
"output_dimension": {
"type": "integer",
"description": "(Optional) Output dimensionality for the embeddings. Only supported by Matryoshka models."
},
"task_type": {
"type": "string",
"enum": [
"query",
"document"
],
"description": "(Optional) How is the embedding being used? This is only supported by asymmetric embedding models."
}
},
"additionalProperties": false,
"required": [
"model_id",
"contents"
],
"title": "EmbeddingsRequest"
},
"EmbeddingsResponse": {
"type": "object",
"properties": {
"embeddings": {
"type": "array",
"items": {
"type": "array",
"items": {
"type": "number"
}
},
"description": "List of embedding vectors, one per input content. Each embedding is a list of floats. The dimensionality of the embedding is model-specific; you can check model metadata using /models/{model_id}"
}
},
"additionalProperties": false,
"required": [
"embeddings"
],
"title": "EmbeddingsResponse",
"description": "Response containing generated embeddings."
},
"AgentCandidate": {
"type": "object",
"properties": {

103
docs/static/llama-stack-spec.yaml vendored
View file

@ -720,41 +720,6 @@ paths:
required: true
schema:
type: string
/v1/inference/embeddings:
post:
responses:
'200':
description: >-
An array of embeddings, one for each content. Each embedding is a list
of floats. The dimensionality of the embedding is model-specific; you
can check model metadata using /models/{model_id}.
content:
application/json:
schema:
$ref: '#/components/schemas/EmbeddingsResponse'
'400':
$ref: '#/components/responses/BadRequest400'
'429':
$ref: >-
#/components/responses/TooManyRequests429
'500':
$ref: >-
#/components/responses/InternalServerError500
default:
$ref: '#/components/responses/DefaultError'
tags:
- Inference
summary: >-
Generate embeddings for content pieces using the specified model.
description: >-
Generate embeddings for content pieces using the specified model.
parameters: []
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/EmbeddingsRequest'
required: true
/v1alpha/eval/benchmarks/{benchmark_id}/evaluations:
post:
responses:
@ -3930,7 +3895,7 @@ paths:
schema:
$ref: '#/components/schemas/QueryTracesRequest'
required: true
/v1/inference/rerank:
/v1alpha/inference/rerank:
post:
responses:
'200':
@ -7795,72 +7760,6 @@ components:
title: OpenAIDeleteResponseObject
description: >-
Response object confirming deletion of an OpenAI response.
EmbeddingsRequest:
type: object
properties:
model_id:
type: string
description: >-
The identifier of the model to use. The model must be an embedding model
registered with Llama Stack and available via the /models endpoint.
contents:
oneOf:
- type: array
items:
type: string
- type: array
items:
$ref: '#/components/schemas/InterleavedContentItem'
description: >-
List of contents to generate embeddings for. Each content can be a string
or an InterleavedContentItem (and hence can be multimodal). The behavior
depends on the model and provider. Some models may only support text.
text_truncation:
type: string
enum:
- none
- start
- end
description: >-
(Optional) Config for how to truncate text for embedding when text is
longer than the model's max sequence length.
output_dimension:
type: integer
description: >-
(Optional) Output dimensionality for the embeddings. Only supported by
Matryoshka models.
task_type:
type: string
enum:
- query
- document
description: >-
(Optional) How is the embedding being used? This is only supported by
asymmetric embedding models.
additionalProperties: false
required:
- model_id
- contents
title: EmbeddingsRequest
EmbeddingsResponse:
type: object
properties:
embeddings:
type: array
items:
type: array
items:
type: number
description: >-
List of embedding vectors, one per input content. Each embedding is a
list of floats. The dimensionality of the embedding is model-specific;
you can check model metadata using /models/{model_id}
additionalProperties: false
required:
- embeddings
title: EmbeddingsResponse
description: >-
Response containing generated embeddings.
AgentCandidate:
type: object
properties:

26
llama_stack/apis/inference/inference.py
View file

@ -17,11 +17,11 @@ from typing import (
from pydantic import BaseModel, Field, field_validator
from typing_extensions import TypedDict
from llama_stack.apis.common.content_types import ContentDelta, InterleavedContent, InterleavedContentItem
from llama_stack.apis.common.content_types import ContentDelta, InterleavedContent
from llama_stack.apis.common.responses import Order
from llama_stack.apis.models import Model
from llama_stack.apis.telemetry import MetricResponseMixin
from llama_stack.apis.version import LLAMA_STACK_API_V1
from llama_stack.apis.version import LLAMA_STACK_API_V1, LLAMA_STACK_API_V1ALPHA
from llama_stack.models.llama.datatypes import (
BuiltinTool,
StopReason,
@ -1070,27 +1070,7 @@ class InferenceProvider(Protocol):
"""
...
@webmethod(route="/inference/embeddings", method="POST", level=LLAMA_STACK_API_V1)
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
"""Generate embeddings for content pieces using the specified model.
:param model_id: The identifier of the model to use. The model must be an embedding model registered with Llama Stack and available via the /models endpoint.
:param contents: List of contents to generate embeddings for. Each content can be a string or an InterleavedContentItem (and hence can be multimodal). The behavior depends on the model and provider. Some models may only support text.
:param output_dimension: (Optional) Output dimensionality for the embeddings. Only supported by Matryoshka models.
:param text_truncation: (Optional) Config for how to truncate text for embedding when text is longer than the model's max sequence length.
:param task_type: (Optional) How is the embedding being used? This is only supported by asymmetric embedding models.
:returns: An array of embeddings, one for each content. Each embedding is a list of floats. The dimensionality of the embedding is model-specific; you can check model metadata using /models/{model_id}.
"""
...
@webmethod(route="/inference/rerank", method="POST", experimental=True, level=LLAMA_STACK_API_V1)
@webmethod(route="/inference/rerank", method="POST", level=LLAMA_STACK_API_V1ALPHA)
async def rerank(
self,
model: str,

6
llama_stack/core/datatypes.py
View file

@ -433,6 +433,12 @@ class InferenceStoreConfig(BaseModel):
num_writers: int = Field(default=4, description="Number of concurrent background writers")
class ResponsesStoreConfig(BaseModel):
sql_store_config: SqlStoreConfig
max_write_queue_size: int = Field(default=10000, description="Max queued writes for responses store")
num_writers: int = Field(default=4, description="Number of concurrent background writers")
class StackRunConfig(BaseModel):
version: int = LLAMA_STACK_RUN_CONFIG_VERSION

11
llama_stack/core/resolver.py
View file

@ -29,6 +29,7 @@ from llama_stack.apis.telemetry import Telemetry
from llama_stack.apis.tools import ToolGroups, ToolRuntime
from llama_stack.apis.vector_dbs import VectorDBs
from llama_stack.apis.vector_io import VectorIO
from llama_stack.apis.version import LLAMA_STACK_API_V1ALPHA
from llama_stack.core.client import get_client_impl
from llama_stack.core.datatypes import (
AccessRule,
@ -412,8 +413,14 @@ def check_protocol_compliance(obj: Any, protocol: Any) -> None:
mro = type(obj).__mro__
for name, value in inspect.getmembers(protocol):
if inspect.isfunction(value) and hasattr(value, "__webmethod__"):
if value.__webmethod__.experimental:
if inspect.isfunction(value) and hasattr(value, "__webmethods__"):
has_alpha_api = False
for webmethod in value.__webmethods__:
if webmethod.level == LLAMA_STACK_API_V1ALPHA:
has_alpha_api = True
break
# if this API has multiple webmethods, and one of them is an alpha API, this API should be skipped when checking for missing or not callable routes
if has_alpha_api:
continue
if not hasattr(obj, name):
missing_methods.append((name, "missing"))

23
llama_stack/core/routers/inference.py
View file

@ -16,7 +16,6 @@ from pydantic import Field, TypeAdapter
from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
)
from llama_stack.apis.common.errors import ModelNotFoundError, ModelTypeError
from llama_stack.apis.inference import (
@ -26,8 +25,6 @@ from llama_stack.apis.inference import (
CompletionMessage,
CompletionResponse,
CompletionResponseStreamChunk,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
ListOpenAIChatCompletionResponse,
LogProbConfig,
@ -48,7 +45,6 @@ from llama_stack.apis.inference import (
ResponseFormat,
SamplingParams,
StopReason,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -312,25 +308,6 @@ class InferenceRouter(Inference):
return response
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
logger.debug(f"InferenceRouter.embeddings: {model_id}")
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,
contents=contents,
text_truncation=text_truncation,
output_dimension=output_dimension,
task_type=task_type,
)
async def openai_completion(
self,
model: str,

2
llama_stack/providers/inline/agents/meta_reference/agent_instance.py
View file

@ -924,7 +924,7 @@ async def get_raw_document_text(document: Document) -> str:
DeprecationWarning,
stacklevel=2,
)
elif not (document.mime_type.startswith("text/") or document.mime_type == "application/yaml"):
elif not (document.mime_type.startswith("text/") or document.mime_type in ("application/yaml", "application/json")):
raise ValueError(f"Unexpected document mime type: {document.mime_type}")
if isinstance(document.content, URL):

36
llama_stack/providers/remote/inference/bedrock/bedrock.py
View file

@ -11,21 +11,17 @@ from botocore.client import BaseClient
from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
)
from llama_stack.apis.inference import (
ChatCompletionRequest,
ChatCompletionResponse,
ChatCompletionResponseStreamChunk,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
LogProbConfig,
Message,
OpenAIEmbeddingsResponse,
ResponseFormat,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -47,8 +43,6 @@ from llama_stack.providers.utils.inference.openai_compat import (
)
from llama_stack.providers.utils.inference.prompt_adapter import (
chat_completion_request_to_prompt,
content_has_media,
interleaved_content_as_str,
)
from .models import MODEL_ENTRIES
@ -218,36 +212,6 @@ class BedrockInferenceAdapter(
),
}
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
model = await self.model_store.get_model(model_id)
# Convert foundation model ID to inference profile ID
region_name = self.client.meta.region_name
inference_profile_id = _to_inference_profile_id(model.provider_resource_id, region_name)
embeddings = []
for content in contents:
assert not content_has_media(content), "Bedrock does not support media for embeddings"
input_text = interleaved_content_as_str(content)
input_body = {"inputText": input_text}
body = json.dumps(input_body)
response = self.client.invoke_model(
body=body,
modelId=inference_profile_id,
accept="application/json",
contentType="application/json",
)
response_body = json.loads(response.get("body").read())
embeddings.append(response_body.get("embedding"))
return EmbeddingsResponse(embeddings=embeddings)
async def openai_embeddings(
self,
model: str,

14
llama_stack/providers/remote/inference/cerebras/cerebras.py
View file

@ -11,21 +11,17 @@ from cerebras.cloud.sdk import AsyncCerebras
from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
)
from llama_stack.apis.inference import (
ChatCompletionRequest,
CompletionRequest,
CompletionResponse,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
LogProbConfig,
Message,
OpenAIEmbeddingsResponse,
ResponseFormat,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -187,16 +183,6 @@ class CerebrasInferenceAdapter(
**get_sampling_options(request.sampling_params),
}
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
raise NotImplementedError()
async def openai_embeddings(
self,
model: str,

14
llama_stack/providers/remote/inference/databricks/databricks.py
View file

@ -11,15 +11,12 @@ from databricks.sdk import WorkspaceClient
from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
)
from llama_stack.apis.inference import (
ChatCompletionResponse,
ChatCompletionResponseStreamChunk,
CompletionResponse,
CompletionResponseStreamChunk,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
LogProbConfig,
Message,
@ -27,7 +24,6 @@ from llama_stack.apis.inference import (
OpenAICompletion,
ResponseFormat,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -118,16 +114,6 @@ class DatabricksInferenceAdapter(
) -> ChatCompletionResponse | AsyncIterator[ChatCompletionResponseStreamChunk]:
raise NotImplementedError()
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
raise NotImplementedError()
async def list_models(self) -> list[Model] | None:
self._model_cache = {} # from OpenAIMixin
ws_client = WorkspaceClient(host=self.config.url, token=self.get_api_key()) # TODO: this is not async

31
llama_stack/providers/remote/inference/fireworks/fireworks.py
View file

@ -10,22 +10,18 @@ from fireworks.client import Fireworks
from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
)
from llama_stack.apis.inference import (
ChatCompletionRequest,
ChatCompletionResponse,
CompletionRequest,
CompletionResponse,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
LogProbConfig,
Message,
ResponseFormat,
ResponseFormatType,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -48,8 +44,6 @@ from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
from llama_stack.providers.utils.inference.prompt_adapter import (
chat_completion_request_to_prompt,
completion_request_to_prompt,
content_has_media,
interleaved_content_as_str,
request_has_media,
)
@ -259,28 +253,3 @@ class FireworksInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, Nee
logger.debug(f"params to fireworks: {params}")
return params
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
model = await self.model_store.get_model(model_id)
kwargs = {}
if model.metadata.get("embedding_dimension"):
kwargs["dimensions"] = model.metadata.get("embedding_dimension")
assert all(not content_has_media(content) for content in contents), (
"Fireworks does not support media for embeddings"
)
response = self._get_client().embeddings.create(
model=model.provider_resource_id,
input=[interleaved_content_as_str(content) for content in contents],
**kwargs,
)
embeddings = [data.embedding for data in response.data]
return EmbeddingsResponse(embeddings=embeddings)

59
llama_stack/providers/remote/inference/nvidia/nvidia.py
View file

@ -11,8 +11,6 @@ from openai import NOT_GIVEN, APIConnectionError
from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
TextContentItem,
)
from llama_stack.apis.inference import (
ChatCompletionRequest,
@ -21,8 +19,6 @@ from llama_stack.apis.inference import (
CompletionRequest,
CompletionResponse,
CompletionResponseStreamChunk,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
LogProbConfig,
Message,
@ -31,7 +27,6 @@ from llama_stack.apis.inference import (
OpenAIEmbeddingUsage,
ResponseFormat,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
)
@ -156,60 +151,6 @@ class NVIDIAInferenceAdapter(OpenAIMixin, Inference):
# we pass n=1 to get only one completion
return convert_openai_completion_choice(response.choices[0])
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
if any(content_has_media(content) for content in contents):
raise NotImplementedError("Media is not supported")
#
# Llama Stack: contents = list[str] | list[InterleavedContentItem]
# ->
# OpenAI: input = str | list[str]
#
# we can ignore str and always pass list[str] to OpenAI
#
flat_contents = [content.text if isinstance(content, TextContentItem) else content for content in contents]
input = [content.text if isinstance(content, TextContentItem) else content for content in flat_contents]
provider_model_id = await self._get_provider_model_id(model_id)
extra_body = {}
if text_truncation is not None:
text_truncation_options = {
TextTruncation.none: "NONE",
TextTruncation.end: "END",
TextTruncation.start: "START",
}
extra_body["truncate"] = text_truncation_options[text_truncation]
if output_dimension is not None:
extra_body["dimensions"] = output_dimension
if task_type is not None:
task_type_options = {
EmbeddingTaskType.document: "passage",
EmbeddingTaskType.query: "query",
}
extra_body["input_type"] = task_type_options[task_type]
response = await self.client.embeddings.create(
model=provider_model_id,
input=input,
extra_body=extra_body,
)
#
# OpenAI: CreateEmbeddingResponse(data=[Embedding(embedding=list[float], ...)], ...)
# ->
# Llama Stack: EmbeddingsResponse(embeddings=list[list[float]])
#
return EmbeddingsResponse(embeddings=[embedding.embedding for embedding in response.data])
async def openai_embeddings(
self,
model: str,

27
llama_stack/providers/remote/inference/ollama/ollama.py
View file

@ -14,7 +14,6 @@ from ollama import AsyncClient as AsyncOllamaClient
from llama_stack.apis.common.content_types import (
ImageContentItem,
InterleavedContent,
InterleavedContentItem,
TextContentItem,
)
from llama_stack.apis.common.errors import UnsupportedModelError
@ -25,8 +24,6 @@ from llama_stack.apis.inference import (
CompletionRequest,
CompletionResponse,
CompletionResponseStreamChunk,
EmbeddingsResponse,
EmbeddingTaskType,
GrammarResponseFormat,
InferenceProvider,
JsonSchemaResponseFormat,
@ -34,7 +31,6 @@ from llama_stack.apis.inference import (
Message,
ResponseFormat,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -66,9 +62,7 @@ from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
from llama_stack.providers.utils.inference.prompt_adapter import (
chat_completion_request_to_prompt,
completion_request_to_prompt,
content_has_media,
convert_image_content_to_url,
interleaved_content_as_str,
request_has_media,
)
@ -363,27 +357,6 @@ class OllamaInferenceAdapter(
async for chunk in process_chat_completion_stream_response(stream, request):
yield chunk
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
model = await self._get_model(model_id)
assert all(not content_has_media(content) for content in contents), (
"Ollama does not support media for embeddings"
)
response = await self.ollama_client.embed(
model=model.provider_resource_id,
input=[interleaved_content_as_str(content) for content in contents],
)
embeddings = response["embeddings"]
return EmbeddingsResponse(embeddings=embeddings)
async def register_model(self, model: Model) -> Model:
if await self.check_model_availability(model.provider_model_id):
return model

22
llama_stack/providers/remote/inference/passthrough/passthrough.py
View file

@ -14,8 +14,6 @@ from llama_stack.apis.inference import (
ChatCompletionResponse,
ChatCompletionResponseStreamChunk,
CompletionMessage,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
LogProbConfig,
Message,
@ -27,7 +25,6 @@ from llama_stack.apis.inference import (
OpenAIResponseFormatParam,
ResponseFormat,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -190,25 +187,6 @@ class PassthroughInferenceAdapter(Inference):
chunk = convert_to_pydantic(ChatCompletionResponseStreamChunk, chunk)
yield chunk
async def embeddings(
self,
model_id: str,
contents: list[InterleavedContent],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
client = self._get_client()
model = await self.model_store.get_model(model_id)
return await client.inference.embeddings(
model_id=model.provider_resource_id,
contents=contents,
text_truncation=text_truncation,
output_dimension=output_dimension,
task_type=task_type,
)
async def openai_embeddings(
self,
model: str,

10
llama_stack/providers/remote/inference/runpod/runpod.py
View file

@ -136,16 +136,6 @@ class RunpodInferenceAdapter(
**get_sampling_options(request.sampling_params),
}
async def embeddings(
self,
model: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
raise NotImplementedError()
async def openai_embeddings(
self,
model: str,

14
llama_stack/providers/remote/inference/tgi/tgi.py
View file

@ -12,14 +12,11 @@ from pydantic import SecretStr
from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
)
from llama_stack.apis.inference import (
ChatCompletionRequest,
ChatCompletionResponse,
CompletionRequest,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
LogProbConfig,
Message,
@ -27,7 +24,6 @@ from llama_stack.apis.inference import (
ResponseFormat,
ResponseFormatType,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -306,16 +302,6 @@ class _HfAdapter(
**self._build_options(request.sampling_params, request.response_format),
)
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
raise NotImplementedError()
async def openai_embeddings(
self,
model: str,

26
llama_stack/providers/remote/inference/together/together.py
View file

@ -12,14 +12,11 @@ from together.constants import BASE_URL
from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
)
from llama_stack.apis.inference import (
ChatCompletionRequest,
ChatCompletionResponse,
CompletionRequest,
EmbeddingsResponse,
EmbeddingTaskType,
Inference,
LogProbConfig,
Message,
@ -27,7 +24,6 @@ from llama_stack.apis.inference import (
ResponseFormat,
ResponseFormatType,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -50,8 +46,6 @@ from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
from llama_stack.providers.utils.inference.prompt_adapter import (
chat_completion_request_to_prompt,
completion_request_to_prompt,
content_has_media,
interleaved_content_as_str,
request_has_media,
)
@ -247,26 +241,6 @@ class TogetherInferenceAdapter(OpenAIMixin, ModelRegistryHelper, Inference, Need
logger.debug(f"params to together: {params}")
return params
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
model = await self.model_store.get_model(model_id)
assert all(not content_has_media(content) for content in contents), (
"Together does not support media for embeddings"
)
client = self._get_client()
r = await client.embeddings.create(
model=model.provider_resource_id,
input=[interleaved_content_as_str(content) for content in contents],
)
embeddings = [item.embedding for item in r.data]
return EmbeddingsResponse(embeddings=embeddings)
async def list_models(self) -> list[Model] | None:
self._model_cache = {}
# Together's /v1/models is not compatible with OpenAI's /v1/models. Together support ticket #13355 -> will not fix, use Together's own client

30
llama_stack/providers/remote/inference/vllm/vllm.py
View file

@ -16,7 +16,6 @@ from openai.types.chat.chat_completion_chunk import (
from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
TextDelta,
ToolCallDelta,
ToolCallParseStatus,
@ -31,8 +30,6 @@ from llama_stack.apis.inference import (
CompletionRequest,
CompletionResponse,
CompletionResponseStreamChunk,
EmbeddingsResponse,
EmbeddingTaskType,
GrammarResponseFormat,
Inference,
JsonSchemaResponseFormat,
@ -41,7 +38,6 @@ from llama_stack.apis.inference import (
ModelStore,
ResponseFormat,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -74,8 +70,6 @@ from llama_stack.providers.utils.inference.openai_compat import (
from llama_stack.providers.utils.inference.openai_mixin import OpenAIMixin
from llama_stack.providers.utils.inference.prompt_adapter import (
completion_request_to_prompt,
content_has_media,
interleaved_content_as_str,
request_has_media,
)
@ -550,27 +544,3 @@ class VLLMInferenceAdapter(OpenAIMixin, LiteLLMOpenAIMixin, Inference, ModelsPro
"stream": request.stream,
**options,
}
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
model = await self._get_model(model_id)
kwargs = {}
assert model.model_type == ModelType.embedding
assert model.metadata.get("embedding_dimension")
kwargs["dimensions"] = model.metadata.get("embedding_dimension")
assert all(not content_has_media(content) for content in contents), "VLLM does not support media for embeddings"
response = await self.client.embeddings.create(
model=model.provider_resource_id,
input=[interleaved_content_as_str(content) for content in contents],
**kwargs,
)
embeddings = [data.embedding for data in response.data]
return EmbeddingsResponse(embeddings=embeddings)

15
llama_stack/providers/remote/inference/watsonx/watsonx.py
View file

@ -11,13 +11,11 @@ from ibm_watsonx_ai.foundation_models import Model
from ibm_watsonx_ai.metanames import GenTextParamsMetaNames as GenParams
from openai import AsyncOpenAI
from llama_stack.apis.common.content_types import InterleavedContent, InterleavedContentItem
from llama_stack.apis.common.content_types import InterleavedContent
from llama_stack.apis.inference import (
ChatCompletionRequest,
ChatCompletionResponse,
CompletionRequest,
EmbeddingsResponse,
EmbeddingTaskType,
GreedySamplingStrategy,
Inference,
LogProbConfig,
@ -30,7 +28,6 @@ from llama_stack.apis.inference import (
OpenAIResponseFormatParam,
ResponseFormat,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -265,16 +262,6 @@ class WatsonXInferenceAdapter(Inference, ModelRegistryHelper):
}
return params
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
raise NotImplementedError("embedding is not supported for watsonx")
async def openai_embeddings(
self,
model: str,

22
llama_stack/providers/utils/inference/embedding_mixin.py
View file

@ -15,16 +15,11 @@ if TYPE_CHECKING:
from sentence_transformers import SentenceTransformer
from llama_stack.apis.inference import (
EmbeddingsResponse,
EmbeddingTaskType,
InterleavedContentItem,
ModelStore,
OpenAIEmbeddingData,
OpenAIEmbeddingsResponse,
OpenAIEmbeddingUsage,
TextTruncation,
)
from llama_stack.providers.utils.inference.prompt_adapter import interleaved_content_as_str
EMBEDDING_MODELS = {}
@ -35,23 +30,6 @@ log = get_logger(name=__name__, category="providers::utils")
class SentenceTransformerEmbeddingMixin:
model_store: ModelStore
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
model = await self.model_store.get_model(model_id)
embedding_model = await self._load_sentence_transformer_model(model.provider_resource_id)
embeddings = await asyncio.to_thread(
embedding_model.encode,
[interleaved_content_as_str(content) for content in contents],
show_progress_bar=False,
)
return EmbeddingsResponse(embeddings=embeddings)
async def openai_embeddings(
self,
model: str,

25
llama_stack/providers/utils/inference/litellm_openai_mixin.py
View file

@ -11,14 +11,11 @@ import litellm
from llama_stack.apis.common.content_types import (
InterleavedContent,
InterleavedContentItem,
)
from llama_stack.apis.inference import (
ChatCompletionRequest,
ChatCompletionResponse,
ChatCompletionResponseStreamChunk,
EmbeddingsResponse,
EmbeddingTaskType,
InferenceProvider,
JsonSchemaResponseFormat,
LogProbConfig,
@ -32,7 +29,6 @@ from llama_stack.apis.inference import (
OpenAIResponseFormatParam,
ResponseFormat,
SamplingParams,
TextTruncation,
ToolChoice,
ToolConfig,
ToolDefinition,
@ -50,9 +46,6 @@ from llama_stack.providers.utils.inference.openai_compat import (
get_sampling_options,
prepare_openai_completion_params,
)
from llama_stack.providers.utils.inference.prompt_adapter import (
interleaved_content_as_str,
)
logger = get_logger(name=__name__, category="providers::utils")
@ -269,24 +262,6 @@ class LiteLLMOpenAIMixin(
)
return api_key
async def embeddings(
self,
model_id: str,
contents: list[str] | list[InterleavedContentItem],
text_truncation: TextTruncation | None = TextTruncation.none,
output_dimension: int | None = None,
task_type: EmbeddingTaskType | None = None,
) -> EmbeddingsResponse:
model = await self.model_store.get_model(model_id)
response = litellm.embedding(
model=self.get_litellm_model_name(model.provider_resource_id),
input=[interleaved_content_as_str(content) for content in contents],
)
embeddings = [data["embedding"] for data in response["data"]]
return EmbeddingsResponse(embeddings=embeddings)
async def openai_embeddings(
self,
model: str,

110
llama_stack/providers/utils/responses/responses_store.py
View file

@ -3,6 +3,9 @@
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import asyncio
from typing import Any
from llama_stack.apis.agents import (
Order,
)
@ -14,24 +17,51 @@ from llama_stack.apis.agents.openai_responses import (
OpenAIResponseObject,
OpenAIResponseObjectWithInput,
)
from llama_stack.core.datatypes import AccessRule
from llama_stack.core.datatypes import AccessRule, ResponsesStoreConfig
from llama_stack.core.utils.config_dirs import RUNTIME_BASE_DIR
from llama_stack.log import get_logger
from ..sqlstore.api import ColumnDefinition, ColumnType
from ..sqlstore.authorized_sqlstore import AuthorizedSqlStore
from ..sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig, sqlstore_impl
from ..sqlstore.sqlstore import SqliteSqlStoreConfig, SqlStoreConfig, SqlStoreType, sqlstore_impl
logger = get_logger(name=__name__, category="responses_store")
class ResponsesStore:
def __init__(self, sql_store_config: SqlStoreConfig, policy: list[AccessRule]):
if not sql_store_config:
sql_store_config = SqliteSqlStoreConfig(
def __init__(
self,
config: ResponsesStoreConfig | SqlStoreConfig,
policy: list[AccessRule],
):
# Handle backward compatibility
if not isinstance(config, ResponsesStoreConfig):
# Legacy: SqlStoreConfig passed directly as config
config = ResponsesStoreConfig(
sql_store_config=config,
)
self.config = config
self.sql_store_config = config.sql_store_config
if not self.sql_store_config:
self.sql_store_config = SqliteSqlStoreConfig(
db_path=(RUNTIME_BASE_DIR / "sqlstore.db").as_posix(),
)
self.sql_store = AuthorizedSqlStore(sqlstore_impl(sql_store_config), policy)
self.sql_store = None
self.policy = policy
# Disable write queue for SQLite to avoid concurrency issues
self.enable_write_queue = self.sql_store_config.type != SqlStoreType.sqlite
# Async write queue and worker control
self._queue: asyncio.Queue[tuple[OpenAIResponseObject, list[OpenAIResponseInput]]] | None = None
self._worker_tasks: list[asyncio.Task[Any]] = []
self._max_write_queue_size: int = config.max_write_queue_size
self._num_writers: int = max(1, config.num_writers)
async def initialize(self):
"""Create the necessary tables if they don't exist."""
self.sql_store = AuthorizedSqlStore(sqlstore_impl(self.sql_store_config), self.policy)
await self.sql_store.create_table(
"openai_responses",
{
@ -42,9 +72,68 @@ class ResponsesStore:
},
)
if self.enable_write_queue:
self._queue = asyncio.Queue(maxsize=self._max_write_queue_size)
for _ in range(self._num_writers):
self._worker_tasks.append(asyncio.create_task(self._worker_loop()))
else:
logger.info("Write queue disabled for SQLite to avoid concurrency issues")
async def shutdown(self) -> None:
if not self._worker_tasks:
return
if self._queue is not None:
await self._queue.join()
for t in self._worker_tasks:
if not t.done():
t.cancel()
for t in self._worker_tasks:
try:
await t
except asyncio.CancelledError:
pass
self._worker_tasks.clear()
async def flush(self) -> None:
"""Wait for all queued writes to complete. Useful for testing."""
if self.enable_write_queue and self._queue is not None:
await self._queue.join()
async def store_response_object(
self, response_object: OpenAIResponseObject, input: list[OpenAIResponseInput]
) -> None:
if self.enable_write_queue:
if self._queue is None:
raise ValueError("Responses store is not initialized")
try:
self._queue.put_nowait((response_object, input))
except asyncio.QueueFull:
logger.warning(f"Write queue full; adding response id={getattr(response_object, 'id', '<unknown>')}")
await self._queue.put((response_object, input))
else:
await self._write_response_object(response_object, input)
async def _worker_loop(self) -> None:
assert self._queue is not None
while True:
try:
item = await self._queue.get()
except asyncio.CancelledError:
break
response_object, input = item
try:
await self._write_response_object(response_object, input)
except Exception as e: # noqa: BLE001
logger.error(f"Error writing response object: {e}")
finally:
self._queue.task_done()
async def _write_response_object(
self, response_object: OpenAIResponseObject, input: list[OpenAIResponseInput]
) -> None:
if self.sql_store is None:
raise ValueError("Responses store is not initialized")
data = response_object.model_dump()
data["input"] = [input_item.model_dump() for input_item in input]
@ -73,6 +162,9 @@ class ResponsesStore:
:param model: The model to filter by.
:param order: The order to sort the responses by.
"""
if not self.sql_store:
raise ValueError("Responses store is not initialized")
if not order:
order = Order.desc
@ -100,6 +192,9 @@ class ResponsesStore:
"""
Get a response object with automatic access control checking.
"""
if not self.sql_store:
raise ValueError("Responses store is not initialized")
row = await self.sql_store.fetch_one(
"openai_responses",
where={"id": response_id},
@ -113,6 +208,9 @@ class ResponsesStore:
return OpenAIResponseObjectWithInput(**row["response_object"])
async def delete_response_object(self, response_id: str) -> OpenAIDeleteResponseObject:
if not self.sql_store:
raise ValueError("Responses store is not initialized")
row = await self.sql_store.fetch_one("openai_responses", where={"id": response_id})
if not row:
raise ValueError(f"Response with id {response_id} not found")

4
llama_stack/schema_utils.py
View file

@ -22,7 +22,6 @@ class WebMethod:
raw_bytes_request_body: bool | None = False
# A descriptive name of the corresponding span created by tracing
descriptive_name: str | None = None
experimental: bool | None = False
required_scope: str | None = None
deprecated: bool | None = False
@ -39,7 +38,6 @@ def webmethod(
response_examples: list[Any] | None = None,
raw_bytes_request_body: bool | None = False,
descriptive_name: str | None = None,
experimental: bool | None = False,
required_scope: str | None = None,
deprecated: bool | None = False,
) -> Callable[[T], T]:
@ -50,7 +48,6 @@ def webmethod(
:param public: True if the operation can be invoked without prior authentication.
:param request_examples: Sample requests that the operation might take. Pass a list of objects, not JSON.
:param response_examples: Sample responses that the operation might produce. Pass a list of objects, not JSON.
:param experimental: True if the operation is experimental and subject to change.
:param required_scope: Required scope for this endpoint (e.g., 'monitoring.viewer').
"""
@ -64,7 +61,6 @@ def webmethod(
response_examples=response_examples,
raw_bytes_request_body=raw_bytes_request_body,
descriptive_name=descriptive_name,
experimental=experimental,
required_scope=required_scope,
deprecated=deprecated,
)

127
llama_stack/ui/package-lock.json generated
View file

@ -28,7 +28,7 @@
"react-markdown": "^10.1.0",
"remark-gfm": "^4.0.1",
"remeda": "^2.32.0",
"shiki": "^1.29.2",
"shiki": "^3.13.0",
"sonner": "^2.0.7",
"tailwind-merge": "^3.3.1"
},
@ -51,7 +51,7 @@
"prettier": "3.6.2",
"tailwindcss": "^4",
"ts-node": "^10.9.2",
"tw-animate-css": "^1.2.9",
"tw-animate-css": "^1.4.0",
"typescript": "^5"
}
},
@ -3250,65 +3250,63 @@
"license": "MIT"
},
"node_modules/@shikijs/core": {
"version": "1.29.2",
"resolved": "https://registry.npmjs.org/@shikijs/core/-/core-1.29.2.tgz",
"integrity": "sha512-vju0lY9r27jJfOY4Z7+Rt/nIOjzJpZ3y+nYpqtUZInVoXQ/TJZcfGnNOGnKjFdVZb8qexiCuSlZRKcGfhhTTZQ==",
"version": "3.13.0",
"resolved": "https://registry.npmjs.org/@shikijs/core/-/core-3.13.0.tgz",
"integrity": "sha512-3P8rGsg2Eh2qIHekwuQjzWhKI4jV97PhvYjYUzGqjvJfqdQPz+nMlfWahU24GZAyW1FxFI1sYjyhfh5CoLmIUA==",
"license": "MIT",
"dependencies": {
"@shikijs/engine-javascript": "1.29.2",
"@shikijs/engine-oniguruma": "1.29.2",
"@shikijs/types": "1.29.2",
"@shikijs/vscode-textmate": "^10.0.1",
"@shikijs/types": "3.13.0",
"@shikijs/vscode-textmate": "^10.0.2",
"@types/hast": "^3.0.4",
"hast-util-to-html": "^9.0.4"
"hast-util-to-html": "^9.0.5"
}
},
"node_modules/@shikijs/engine-javascript": {
"version": "1.29.2",
"resolved": "https://registry.npmjs.org/@shikijs/engine-javascript/-/engine-javascript-1.29.2.tgz",
"integrity": "sha512-iNEZv4IrLYPv64Q6k7EPpOCE/nuvGiKl7zxdq0WFuRPF5PAE9PRo2JGq/d8crLusM59BRemJ4eOqrFrC4wiQ+A==",
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"license": "MIT",
"dependencies": {
"@shikijs/types": "1.29.2",
"@shikijs/vscode-textmate": "^10.0.1",
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"@shikijs/types": "3.13.0",
"@shikijs/vscode-textmate": "^10.0.2",
"oniguruma-to-es": "^4.3.3"
}
},
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"version": "1.29.2",
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"dependencies": {
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"@shikijs/vscode-textmate": "^10.0.1"
"@shikijs/types": "3.13.0",
"@shikijs/vscode-textmate": "^10.0.2"
}
},
"node_modules/@shikijs/langs": {
"version": "1.29.2",
"resolved": "https://registry.npmjs.org/@shikijs/langs/-/langs-1.29.2.tgz",
"integrity": "sha512-FIBA7N3LZ+223U7cJDUYd5shmciFQlYkFXlkKVaHsCPgfVLiO+e12FmQE6Tf9vuyEsFe3dIl8qGWKXgEHL9wmQ==",
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"license": "MIT",
"dependencies": {
"@shikijs/types": "1.29.2"
"@shikijs/types": "3.13.0"
}
},
"node_modules/@shikijs/themes": {
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"resolved": "https://registry.npmjs.org/@shikijs/themes/-/themes-3.13.0.tgz",
"integrity": "sha512-Vxw1Nm1/Od8jyA7QuAenaV78BG2nSr3/gCGdBkLpfLscddCkzkL36Q5b67SrLLfvAJTOUzW39x4FHVCFriPVgg==",
"license": "MIT",
"dependencies": {
"@shikijs/types": "1.29.2"
"@shikijs/types": "3.13.0"
}
},
"node_modules/@shikijs/types": {
"version": "1.29.2",
"resolved": "https://registry.npmjs.org/@shikijs/types/-/types-1.29.2.tgz",
"integrity": "sha512-VJjK0eIijTZf0QSTODEXCqinjBn0joAHQ+aPSBzrv4O2d/QSbsMw+ZeSRx03kV34Hy7NzUvV/7NqfYGRLrASmw==",
"version": "3.13.0",
"resolved": "https://registry.npmjs.org/@shikijs/types/-/types-3.13.0.tgz",
"integrity": "sha512-oM9P+NCFri/mmQ8LoFGVfVyemm5Hi27330zuOBp0annwJdKH1kOLndw3zCtAVDehPLg9fKqoEx3Ht/wNZxolfw==",
"license": "MIT",
"dependencies": {
"@shikijs/vscode-textmate": "^10.0.1",
"@shikijs/vscode-textmate": "^10.0.2",
"@types/hast": "^3.0.4"
}
},
@ -6084,12 +6082,6 @@
"dev": true,
"license": "MIT"
},
"node_modules/emoji-regex-xs": {
"version": "1.0.0",
"resolved": "https://registry.npmjs.org/emoji-regex-xs/-/emoji-regex-xs-1.0.0.tgz",
"integrity": "sha512-LRlerrMYoIDrT6jgpeZ2YYl/L8EulRTt5hQcYjy5AInh7HWXKimpqx68aknBFpGL2+/IcogTcaydJEgaTmOpDg==",
"license": "MIT"
},
"node_modules/encodeurl": {
"version": "2.0.0",
"resolved": "https://registry.npmjs.org/encodeurl/-/encodeurl-2.0.0.tgz",
@ -11813,15 +11805,21 @@
"url": "https://github.com/sponsors/sindresorhus"
}
},
"node_modules/oniguruma-parser": {
"version": "0.12.1",
"resolved": "https://registry.npmjs.org/oniguruma-parser/-/oniguruma-parser-0.12.1.tgz",
"integrity": "sha512-8Unqkvk1RYc6yq2WBYRj4hdnsAxVze8i7iPfQr8e4uSP3tRv0rpZcbGUDvxfQQcdwHt/e9PrMvGCsa8OqG9X3w==",
"license": "MIT"
},
"node_modules/oniguruma-to-es": {
"version": "2.3.0",
"resolved": "https://registry.npmjs.org/oniguruma-to-es/-/oniguruma-to-es-2.3.0.tgz",
"integrity": "sha512-bwALDxriqfKGfUufKGGepCzu9x7nJQuoRoAFp4AnwehhC2crqrDIAP/uN2qdlsAvSMpeRC3+Yzhqc7hLmle5+g==",
"version": "4.3.3",
"resolved": "https://registry.npmjs.org/oniguruma-to-es/-/oniguruma-to-es-4.3.3.tgz",
"integrity": "sha512-rPiZhzC3wXwE59YQMRDodUwwT9FZ9nNBwQQfsd1wfdtlKEyCdRV0avrTcSZ5xlIvGRVPd/cx6ZN45ECmS39xvg==",
"license": "MIT",
"dependencies": {
"emoji-regex-xs": "^1.0.0",
"regex": "^5.1.1",
"regex-recursion": "^5.1.1"
"oniguruma-parser": "^0.12.1",
"regex": "^6.0.1",
"regex-recursion": "^6.0.2"
}
},
"node_modules/openid-client": {
@ -12613,21 +12611,20 @@
}
},
"node_modules/regex": {
"version": "5.1.1",
"resolved": "https://registry.npmjs.org/regex/-/regex-5.1.1.tgz",
"integrity": "sha512-dN5I359AVGPnwzJm2jN1k0W9LPZ+ePvoOeVMMfqIMFz53sSwXkxaJoxr50ptnsC771lK95BnTrVSZxq0b9yCGw==",
"version": "6.0.1",
"resolved": "https://registry.npmjs.org/regex/-/regex-6.0.1.tgz",
"integrity": "sha512-uorlqlzAKjKQZ5P+kTJr3eeJGSVroLKoHmquUj4zHWuR+hEyNqlXsSKlYYF5F4NI6nl7tWCs0apKJ0lmfsXAPA==",
"license": "MIT",
"dependencies": {
"regex-utilities": "^2.3.0"
}
},
"node_modules/regex-recursion": {
"version": "5.1.1",
"resolved": "https://registry.npmjs.org/regex-recursion/-/regex-recursion-5.1.1.tgz",
"integrity": "sha512-ae7SBCbzVNrIjgSbh7wMznPcQel1DNlDtzensnFxpiNpXt1U2ju/bHugH422r+4LAVS1FpW1YCwilmnNsjum9w==",
"version": "6.0.2",
"resolved": "https://registry.npmjs.org/regex-recursion/-/regex-recursion-6.0.2.tgz",
"integrity": "sha512-0YCaSCq2VRIebiaUviZNs0cBz1kg5kVS2UKUfNIx8YVs1cN3AV7NTctO5FOKBA+UT2BPJIWZauYHPqJODG50cg==",
"license": "MIT",
"dependencies": {
"regex": "^5.1.1",
"regex-utilities": "^2.3.0"
}
},
@ -13165,18 +13162,18 @@
}
},
"node_modules/shiki": {
"version": "1.29.2",
"resolved": "https://registry.npmjs.org/shiki/-/shiki-1.29.2.tgz",
"integrity": "sha512-njXuliz/cP+67jU2hukkxCNuH1yUi4QfdZZY+sMr5PPrIyXSu5iTb/qYC4BiWWB0vZ+7TbdvYUCeL23zpwCfbg==",
"version": "3.13.0",
"resolved": "https://registry.npmjs.org/shiki/-/shiki-3.13.0.tgz",
"integrity": "sha512-aZW4l8Og16CokuCLf8CF8kq+KK2yOygapU5m3+hoGw0Mdosc6fPitjM+ujYarppj5ZIKGyPDPP1vqmQhr+5/0g==",
"license": "MIT",
"dependencies": {
"@shikijs/core": "1.29.2",
"@shikijs/engine-javascript": "1.29.2",
"@shikijs/engine-oniguruma": "1.29.2",
"@shikijs/langs": "1.29.2",
"@shikijs/themes": "1.29.2",
"@shikijs/types": "1.29.2",
"@shikijs/vscode-textmate": "^10.0.1",
"@shikijs/core": "3.13.0",
"@shikijs/engine-javascript": "3.13.0",
"@shikijs/engine-oniguruma": "3.13.0",
"@shikijs/langs": "3.13.0",
"@shikijs/themes": "3.13.0",
"@shikijs/types": "3.13.0",
"@shikijs/vscode-textmate": "^10.0.2",
"@types/hast": "^3.0.4"
}
},
@ -13970,9 +13967,9 @@
"license": "0BSD"
},
"node_modules/tw-animate-css": {
"version": "1.2.9",
"resolved": "https://registry.npmjs.org/tw-animate-css/-/tw-animate-css-1.2.9.tgz",
"integrity": "sha512-9O4k1at9pMQff9EAcCEuy1UNO43JmaPQvq+0lwza9Y0BQ6LB38NiMj+qHqjoQf40355MX+gs6wtlR6H9WsSXFg==",
"version": "1.4.0",
"resolved": "https://registry.npmjs.org/tw-animate-css/-/tw-animate-css-1.4.0.tgz",
"integrity": "sha512-7bziOlRqH0hJx80h/3mbicLW7o8qLsH5+RaLR2t+OHM3D0JlWGODQKQ4cxbK7WlvmUxpcj6Kgu6EKqjrGFe3QQ==",
"dev": true,
"license": "MIT",
"funding": {

4
llama_stack/ui/package.json
View file

@ -33,7 +33,7 @@
"react-markdown": "^10.1.0",
"remark-gfm": "^4.0.1",
"remeda": "^2.32.0",
"shiki": "^1.29.2",
"shiki": "^3.13.0",
"sonner": "^2.0.7",
"tailwind-merge": "^3.3.1"
},
@ -56,7 +56,7 @@
"prettier": "3.6.2",
"tailwindcss": "^4",
"ts-node": "^10.9.2",
"tw-animate-css": "^1.2.9",
"tw-animate-css": "^1.4.0",
"typescript": "^5"
}
}

7
tests/integration/providers/nvidia/test_datastore.py
View file

@ -14,6 +14,13 @@ from . import skip_in_github_actions
# LLAMA_STACK_CONFIG="nvidia" pytest -v tests/integration/providers/nvidia/test_datastore.py
@pytest.fixture(autouse=True)
def skip_if_no_nvidia_provider(llama_stack_client):
provider_types = {p.provider_type for p in llama_stack_client.providers.list() if p.api == "datasetio"}
if "remote::nvidia" not in provider_types:
pytest.skip("datasetio=remote::nvidia provider not configured, skipping")
# nvidia provider only
@skip_in_github_actions
@pytest.mark.parametrize(

24
tests/unit/providers/agent/test_get_raw_document_text.py
View file

@ -107,14 +107,34 @@ async def test_get_raw_document_text_deprecated_text_yaml_with_text_content_item
assert "text/yaml" in str(w[0].message)
async def test_get_raw_document_text_supports_json_mime_type():
"""Test that the function accepts application/json mime type."""
json_content = '{"name": "test", "version": "1.0", "items": ["item1", "item2"]}'
document = Document(content=json_content, mime_type="application/json")
result = await get_raw_document_text(document)
assert result == json_content
async def test_get_raw_document_text_with_json_text_content_item():
"""Test that the function handles JSON TextContentItem correctly."""
json_content = '{"key": "value", "nested": {"array": [1, 2, 3]}}'
document = Document(content=TextContentItem(text=json_content), mime_type="application/json")
result = await get_raw_document_text(document)
assert result == json_content
async def test_get_raw_document_text_rejects_unsupported_mime_types():
"""Test that the function rejects unsupported mime types."""
document = Document(
content="Some content",
mime_type="application/json", # Not supported
mime_type="application/pdf", # Not supported
)
with pytest.raises(ValueError, match="Unexpected document mime type: application/json"):
with pytest.raises(ValueError, match="Unexpected document mime type: application/pdf"):
await get_raw_document_text(document)

6
tests/unit/providers/agents/meta_reference/test_openai_responses.py
View file

@ -42,10 +42,12 @@ from llama_stack.apis.inference import (
)
from llama_stack.apis.tools.tools import Tool, ToolGroups, ToolInvocationResult, ToolParameter, ToolRuntime
from llama_stack.core.access_control.access_control import default_policy
from llama_stack.core.datatypes import ResponsesStoreConfig
from llama_stack.providers.inline.agents.meta_reference.responses.openai_responses import (
OpenAIResponsesImpl,
)
from llama_stack.providers.utils.responses.responses_store import ResponsesStore
from llama_stack.providers.utils.sqlstore.sqlstore import SqliteSqlStoreConfig
from tests.unit.providers.agents.meta_reference.fixtures import load_chat_completion_fixture
@ -677,7 +679,9 @@ async def test_responses_store_list_input_items_logic():
# Create mock store and response store
mock_sql_store = AsyncMock()
responses_store = ResponsesStore(sql_store_config=None, policy=default_policy())
responses_store = ResponsesStore(
ResponsesStoreConfig(sql_store_config=SqliteSqlStoreConfig(db_path="mock_db_path")), policy=default_policy()
)
responses_store.sql_store = mock_sql_store
# Setup test data - multiple input items

26
tests/unit/providers/vector_io/test_faiss.py
View file

@ -5,13 +5,12 @@
# the root directory of this source tree.
import asyncio
from unittest.mock import AsyncMock, MagicMock, patch
from unittest.mock import MagicMock, patch
import numpy as np
import pytest
from llama_stack.apis.files import Files
from llama_stack.apis.inference import EmbeddingsResponse, Inference
from llama_stack.apis.vector_dbs import VectorDB
from llama_stack.apis.vector_io import Chunk, QueryChunksResponse
from llama_stack.providers.datatypes import HealthStatus
@ -70,13 +69,6 @@ def mock_vector_db(vector_db_id, embedding_dimension) -> MagicMock:
return mock_vector_db
@pytest.fixture
def mock_inference_api(sample_embeddings):
mock_api = MagicMock(spec=Inference)
mock_api.embeddings = AsyncMock(return_value=EmbeddingsResponse(embeddings=sample_embeddings))
return mock_api
@pytest.fixture
def mock_files_api():
mock_api = MagicMock(spec=Files)
@ -96,22 +88,6 @@ async def faiss_index(embedding_dimension):
yield index
@pytest.fixture
async def faiss_adapter(faiss_config, mock_inference_api, mock_files_api) -> FaissVectorIOAdapter:
# Create the adapter
adapter = FaissVectorIOAdapter(config=faiss_config, inference_api=mock_inference_api, files_api=mock_files_api)
# Create a mock KVStore
mock_kvstore = MagicMock()
mock_kvstore.values_in_range = AsyncMock(return_value=[])
# Patch the initialize method to avoid the kvstore_impl call
with patch.object(FaissVectorIOAdapter, "initialize"):
# Set the kvstore directly
adapter.kvstore = mock_kvstore
yield adapter
async def test_faiss_query_vector_returns_infinity_when_query_and_embedding_are_identical(
faiss_index, sample_chunks, sample_embeddings, embedding_dimension
):

21
tests/unit/utils/responses/test_responses_store.py
View file

@ -67,6 +67,9 @@ async def test_responses_store_pagination_basic():
input_list = [create_test_response_input(f"Input for {response_id}", f"input-{response_id}")]
await store.store_response_object(response, input_list)
# Wait for all queued writes to complete
await store.flush()
# Test 1: First page with limit=2, descending order (default)
result = await store.list_responses(limit=2, order=Order.desc)
assert len(result.data) == 2
@ -110,6 +113,9 @@ async def test_responses_store_pagination_ascending():
input_list = [create_test_response_input(f"Input for {response_id}", f"input-{response_id}")]
await store.store_response_object(response, input_list)
# Wait for all queued writes to complete
await store.flush()
# Test ascending order pagination
result = await store.list_responses(limit=1, order=Order.asc)
assert len(result.data) == 1
@ -145,6 +151,9 @@ async def test_responses_store_pagination_with_model_filter():
input_list = [create_test_response_input(f"Input for {response_id}", f"input-{response_id}")]
await store.store_response_object(response, input_list)
# Wait for all queued writes to complete
await store.flush()
# Test pagination with model filter
result = await store.list_responses(limit=1, model="model-a", order=Order.desc)
assert len(result.data) == 1
@ -192,6 +201,9 @@ async def test_responses_store_pagination_no_limit():
input_list = [create_test_response_input(f"Input for {response_id}", f"input-{response_id}")]
await store.store_response_object(response, input_list)
# Wait for all queued writes to complete
await store.flush()
# Test without limit (should use default of 50)
result = await store.list_responses(order=Order.desc)
assert len(result.data) == 2
@ -212,6 +224,9 @@ async def test_responses_store_get_response_object():
input_list = [create_test_response_input("Test input content", "input-test-resp")]
await store.store_response_object(response, input_list)
# Wait for all queued writes to complete
await store.flush()
# Retrieve the response
retrieved = await store.get_response_object("test-resp")
assert retrieved.id == "test-resp"
@ -242,6 +257,9 @@ async def test_responses_store_input_items_pagination():
]
await store.store_response_object(response, input_list)
# Wait for all queued writes to complete
await store.flush()
# Verify all items are stored correctly with explicit IDs
all_items = await store.list_response_input_items("test-resp", order=Order.desc)
assert len(all_items.data) == 5
@ -319,6 +337,9 @@ async def test_responses_store_input_items_before_pagination():
]
await store.store_response_object(response, input_list)
# Wait for all queued writes to complete
await store.flush()
# Test before pagination with descending order
# In desc order: [Fifth, Fourth, Third, Second, First]
# before="before-3" should return [Fifth, Fourth]