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Merge remote-tracking branch 'origin/main' into openai_v1

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Ashwin Bharambe 2025-09-29 13:41:11 -07:00
commit 35546386a2
52 changed files with 580 additions and 802 deletions
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26
llama_stack/apis/inference/inference.py
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@ -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,