fix: rename llama_stack_api dir (#4155)

# What does this PR do?

the directory structure was src/llama-stack-api/llama_stack_api

instead it should just be src/llama_stack_api to match the other
packages.

update the structure and pyproject/linting config

---------

Signed-off-by: Charlie Doern <cdoern@redhat.com>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>

src/llama_stack_api/common/__init__.py

@@ -0,0 +1,5 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.

src/llama_stack_api/common/content_types.py

@@ -0,0 +1,101 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Annotated, Literal
+
+from pydantic import BaseModel, Field, model_validator
+
+from llama_stack_api.schema_utils import json_schema_type, register_schema
+
+
+@json_schema_type
+class URL(BaseModel):
+    """A URL reference to external content.
+
+    :param uri: The URL string pointing to the resource
+    """
+
+    uri: str
+
+
+class _URLOrData(BaseModel):
+    """
+    A URL or a base64 encoded string
+
+    :param url: A URL of the image or data URL in the format of data:image/{type};base64,{data}. Note that URL could have length limits.
+    :param data: base64 encoded image data as string
+    """
+
+    url: URL | None = None
+    # data is a base64 encoded string, hint with contentEncoding=base64
+    data: str | None = Field(default=None, json_schema_extra={"contentEncoding": "base64"})
+
+    @model_validator(mode="before")
+    @classmethod
+    def validator(cls, values):
+        if isinstance(values, dict):
+            return values
+        return {"url": values}
+
+
+@json_schema_type
+class ImageContentItem(BaseModel):
+    """A image content item
+
+    :param type: Discriminator type of the content item. Always "image"
+    :param image: Image as a base64 encoded string or an URL
+    """
+
+    type: Literal["image"] = "image"
+    image: _URLOrData
+
+
+@json_schema_type
+class TextContentItem(BaseModel):
+    """A text content item
+
+    :param type: Discriminator type of the content item. Always "text"
+    :param text: Text content
+    """
+
+    type: Literal["text"] = "text"
+    text: str
+
+
+# other modalities can be added here
+InterleavedContentItem = Annotated[
+    ImageContentItem | TextContentItem,
+    Field(discriminator="type"),
+]
+register_schema(InterleavedContentItem, name="InterleavedContentItem")
+
+# accept a single "str" as a special case since it is common
+InterleavedContent = str | InterleavedContentItem | list[InterleavedContentItem]
+register_schema(InterleavedContent, name="InterleavedContent")
+
+
+@json_schema_type
+class TextDelta(BaseModel):
+    """A text content delta for streaming responses.
+
+    :param type: Discriminator type of the delta. Always "text"
+    :param text: The incremental text content
+    """
+
+    type: Literal["text"] = "text"
+    text: str
+
+
+@json_schema_type
+class ImageDelta(BaseModel):
+    """An image content delta for streaming responses.
+
+    :param type: Discriminator type of the delta. Always "image"
+    :param image: The incremental image data as bytes
+    """
+
+    type: Literal["image"] = "image"
+    image: bytes

src/llama_stack_api/common/errors.py

@@ -0,0 +1,95 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+# Custom Llama Stack Exception classes should follow the following schema
+#   1. All classes should inherit from an existing Built-In Exception class: https://docs.python.org/3/library/exceptions.html
+#   2. All classes should have a custom error message with the goal of informing the Llama Stack user specifically
+#   3. All classes should propogate the inherited __init__ function otherwise via 'super().__init__(message)'
+
+
+class ResourceNotFoundError(ValueError):
+    """generic exception for a missing Llama Stack resource"""
+
+    def __init__(self, resource_name: str, resource_type: str, client_list: str) -> None:
+        message = (
+            f"{resource_type} '{resource_name}' not found. Use '{client_list}' to list available {resource_type}s."
+        )
+        super().__init__(message)
+
+
+class UnsupportedModelError(ValueError):
+    """raised when model is not present in the list of supported models"""
+
+    def __init__(self, model_name: str, supported_models_list: list[str]):
+        message = f"'{model_name}' model is not supported. Supported models are: {', '.join(supported_models_list)}"
+        super().__init__(message)
+
+
+class ModelNotFoundError(ResourceNotFoundError):
+    """raised when Llama Stack cannot find a referenced model"""
+
+    def __init__(self, model_name: str) -> None:
+        super().__init__(model_name, "Model", "client.models.list()")
+
+
+class VectorStoreNotFoundError(ResourceNotFoundError):
+    """raised when Llama Stack cannot find a referenced vector store"""
+
+    def __init__(self, vector_store_name: str) -> None:
+        super().__init__(vector_store_name, "Vector Store", "client.vector_dbs.list()")
+
+
+class DatasetNotFoundError(ResourceNotFoundError):
+    """raised when Llama Stack cannot find a referenced dataset"""
+
+    def __init__(self, dataset_name: str) -> None:
+        super().__init__(dataset_name, "Dataset", "client.datasets.list()")
+
+
+class ToolGroupNotFoundError(ResourceNotFoundError):
+    """raised when Llama Stack cannot find a referenced tool group"""
+
+    def __init__(self, toolgroup_name: str) -> None:
+        super().__init__(toolgroup_name, "Tool Group", "client.toolgroups.list()")
+
+
+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)
+
+
+class ConflictError(ValueError):
+    """raised when an operation cannot be performed due to a conflict with the current state"""
+
+    def __init__(self, message: str) -> None:
+        super().__init__(message)
+
+
+class TokenValidationError(ValueError):
+    """raised when token validation fails during authentication"""
+
+    def __init__(self, message: str) -> None:
+        super().__init__(message)
+
+
+class ConversationNotFoundError(ResourceNotFoundError):
+    """raised when Llama Stack cannot find a referenced conversation"""
+
+    def __init__(self, conversation_id: str) -> None:
+        super().__init__(conversation_id, "Conversation", "client.conversations.list()")
+
+
+class InvalidConversationIdError(ValueError):
+    """raised when a conversation ID has an invalid format"""
+
+    def __init__(self, conversation_id: str) -> None:
+        message = f"Invalid conversation ID '{conversation_id}'. Expected an ID that begins with 'conv_'."
+        super().__init__(message)

src/llama_stack_api/common/job_types.py

@@ -0,0 +1,38 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+from enum import Enum
+
+from pydantic import BaseModel
+
+from llama_stack_api.schema_utils import json_schema_type
+
+
+class JobStatus(Enum):
+    """Status of a job execution.
+    :cvar completed: Job has finished successfully
+    :cvar in_progress: Job is currently running
+    :cvar failed: Job has failed during execution
+    :cvar scheduled: Job is scheduled but not yet started
+    :cvar cancelled: Job was cancelled before completion
+    """
+
+    completed = "completed"
+    in_progress = "in_progress"
+    failed = "failed"
+    scheduled = "scheduled"
+    cancelled = "cancelled"
+
+
+@json_schema_type
+class Job(BaseModel):
+    """A job execution instance with status tracking.
+
+    :param job_id: Unique identifier for the job
+    :param status: Current execution status of the job
+    """
+
+    job_id: str
+    status: JobStatus

src/llama_stack_api/common/responses.py

@@ -0,0 +1,77 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from enum import Enum
+from typing import Any
+
+from pydantic import BaseModel
+
+from llama_stack_api.schema_utils import json_schema_type
+
+
+class Order(Enum):
+    """Sort order for paginated responses.
+    :cvar asc: Ascending order
+    :cvar desc: Descending order
+    """
+
+    asc = "asc"
+    desc = "desc"
+
+
+@json_schema_type
+class PaginatedResponse(BaseModel):
+    """A generic paginated response that follows a simple format.
+
+    :param data: The list of items for the current page
+    :param has_more: Whether there are more items available after this set
+    :param url: The URL for accessing this list
+    """
+
+    data: list[dict[str, Any]]
+    has_more: bool
+    url: str | None = None
+
+
+# This is a short term solution to allow inference API to return metrics
+# The ideal way to do this is to have a way for all response types to include metrics
+# and all metric events logged to the telemetry API to be included with the response
+# To do this, we will need to augment all response types with a metrics field.
+# We have hit a blocker from stainless SDK that prevents us from doing this.
+# The blocker is that if we were to augment the response types that have a data field
+# in them like so
+# class ListModelsResponse(BaseModel):
+# metrics: Optional[List[MetricEvent]] = None
+# data: List[Models]
+# ...
+# The client SDK will need to access the data by using a .data field, which is not
+# ergonomic. Stainless SDK does support unwrapping the response type, but it
+# requires that the response type to only have a single field.
+
+# We will need a way in the client SDK to signal that the metrics are needed
+# and if they are needed, the client SDK has to return the full response type
+# without unwrapping it.
+
+
+@json_schema_type
+class MetricInResponse(BaseModel):
+    """A metric value included in API responses.
+    :param metric: The name of the metric
+    :param value: The numeric value of the metric
+    :param unit: (Optional) The unit of measurement for the metric value
+    """
+
+    metric: str
+    value: int | float
+    unit: str | None = None
+
+
+class MetricResponseMixin(BaseModel):
+    """Mixin class for API responses that can include metrics.
+    :param metrics: (Optional) List of metrics associated with the API response
+    """
+
+    metrics: list[MetricInResponse] | None = None

src/llama_stack_api/common/tracing.py

@@ -0,0 +1,22 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+
+def telemetry_traceable(cls):
+    """
+    Mark a protocol for automatic tracing when telemetry is enabled.
+
+    This is a metadata-only decorator with no dependencies on core.
+    Actual tracing is applied by core routers at runtime if telemetry is enabled.
+
+    Usage:
+        @runtime_checkable
+        @telemetry_traceable
+        class MyProtocol(Protocol):
+            ...
+    """
+    cls.__marked_for_tracing__ = True
+    return cls

src/llama_stack_api/common/training_types.py

@@ -0,0 +1,47 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from datetime import datetime
+
+from pydantic import BaseModel
+
+from llama_stack_api.schema_utils import json_schema_type
+
+
+@json_schema_type
+class PostTrainingMetric(BaseModel):
+    """Training metrics captured during post-training jobs.
+
+    :param epoch: Training epoch number
+    :param train_loss: Loss value on the training dataset
+    :param validation_loss: Loss value on the validation dataset
+    :param perplexity: Perplexity metric indicating model confidence
+    """
+
+    epoch: int
+    train_loss: float
+    validation_loss: float
+    perplexity: float
+
+
+@json_schema_type
+class Checkpoint(BaseModel):
+    """Checkpoint created during training runs.
+
+    :param identifier: Unique identifier for the checkpoint
+    :param created_at: Timestamp when the checkpoint was created
+    :param epoch: Training epoch when the checkpoint was saved
+    :param post_training_job_id: Identifier of the training job that created this checkpoint
+    :param path: File system path where the checkpoint is stored
+    :param training_metrics: (Optional) Training metrics associated with this checkpoint
+    """
+
+    identifier: str
+    created_at: datetime
+    epoch: int
+    post_training_job_id: str
+    path: str
+    training_metrics: PostTrainingMetric | None = None

src/llama_stack_api/common/type_system.py

@@ -0,0 +1,146 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the terms described in the LICENSE file in
+# the root directory of this source tree.
+
+from typing import Annotated, Literal
+
+from pydantic import BaseModel, Field
+
+from llama_stack_api.schema_utils import json_schema_type, register_schema
+
+
+@json_schema_type
+class StringType(BaseModel):
+    """Parameter type for string values.
+
+    :param type: Discriminator type. Always "string"
+    """
+
+    type: Literal["string"] = "string"
+
+
+@json_schema_type
+class NumberType(BaseModel):
+    """Parameter type for numeric values.
+
+    :param type: Discriminator type. Always "number"
+    """
+
+    type: Literal["number"] = "number"
+
+
+@json_schema_type
+class BooleanType(BaseModel):
+    """Parameter type for boolean values.
+
+    :param type: Discriminator type. Always "boolean"
+    """
+
+    type: Literal["boolean"] = "boolean"
+
+
+@json_schema_type
+class ArrayType(BaseModel):
+    """Parameter type for array values.
+
+    :param type: Discriminator type. Always "array"
+    """
+
+    type: Literal["array"] = "array"
+
+
+@json_schema_type
+class ObjectType(BaseModel):
+    """Parameter type for object values.
+
+    :param type: Discriminator type. Always "object"
+    """
+
+    type: Literal["object"] = "object"
+
+
+@json_schema_type
+class JsonType(BaseModel):
+    """Parameter type for JSON values.
+
+    :param type: Discriminator type. Always "json"
+    """
+
+    type: Literal["json"] = "json"
+
+
+@json_schema_type
+class UnionType(BaseModel):
+    """Parameter type for union values.
+
+    :param type: Discriminator type. Always "union"
+    """
+
+    type: Literal["union"] = "union"
+
+
+@json_schema_type
+class ChatCompletionInputType(BaseModel):
+    """Parameter type for chat completion input.
+
+    :param type: Discriminator type. Always "chat_completion_input"
+    """
+
+    # expects List[Message] for messages
+    type: Literal["chat_completion_input"] = "chat_completion_input"
+
+
+@json_schema_type
+class CompletionInputType(BaseModel):
+    """Parameter type for completion input.
+
+    :param type: Discriminator type. Always "completion_input"
+    """
+
+    # expects InterleavedTextMedia for content
+    type: Literal["completion_input"] = "completion_input"
+
+
+@json_schema_type
+class DialogType(BaseModel):
+    """Parameter type for dialog data with semantic output labels.
+
+    :param type: Discriminator type. Always "dialog"
+    """
+
+    # expects List[Message] for messages
+    # this type semantically contains the output label whereas ChatCompletionInputType does not
+    type: Literal["dialog"] = "dialog"
+
+
+ParamType = Annotated[
+    StringType
+    | NumberType
+    | BooleanType
+    | ArrayType
+    | ObjectType
+    | JsonType
+    | UnionType
+    | ChatCompletionInputType
+    | CompletionInputType,
+    Field(discriminator="type"),
+]
+register_schema(ParamType, name="ParamType")
+
+"""
+# TODO: recursive definition of ParamType in these containers
+# will cause infinite recursion in OpenAPI generation script
+# since we are going with ChatCompletionInputType and CompletionInputType
+# we don't need to worry about ArrayType/ObjectType/UnionType for now
+ArrayType.model_rebuild()
+ObjectType.model_rebuild()
+UnionType.model_rebuild()
+
+
+class CustomType(BaseModel):
+pylint: disable=syntax-error
+    type: Literal["custom"] = "custom"
+    validator_class: str
+"""