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feat: split API and provider specs into separate llama-stack-api pkg (#3895)

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# What does this PR do?

Extract API definitions and provider specifications into a standalone
llama-stack-api package that can be published to PyPI independently of
the main llama-stack server.


see: https://github.com/llamastack/llama-stack/pull/2978 and
https://github.com/llamastack/llama-stack/pull/2978#issuecomment-3145115942

Motivation

External providers currently import from llama-stack, which overrides
the installed version and causes dependency conflicts. This separation
allows external providers to:

- Install only the type definitions they need without server
dependencies
- Avoid version conflicts with the installed llama-stack package
- Be versioned and released independently

This enables us to re-enable external provider module tests that were
previously blocked by these import conflicts.

Changes

- Created llama-stack-api package with minimal dependencies (pydantic,
jsonschema)
- Moved APIs, providers datatypes, strong_typing, and schema_utils
- Updated all imports from llama_stack.* to llama_stack_api.*
- Configured local editable install for development workflow
- Updated linting and type-checking configuration for both packages

Next Steps

- Publish llama-stack-api to PyPI
- Update external provider dependencies
- Re-enable external provider module tests


Pre-cursor PRs to this one:

- #4093 
- #3954 
- #4064 

These PRs moved key pieces _out_ of the Api pkg, limiting the scope of
change here.


relates to #3237 

## Test Plan

Package builds successfully and can be imported independently. All
pre-commit hooks pass with expected exclusions maintained.

---------

Signed-off-by: Charlie Doern <cdoern@redhat.com>
This commit is contained in:
Charlie Doern 2025-11-13 14:51:17 -05:00 • committed by GitHub
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272
src/llama_stack/apis/conversations/conversations.py
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@ -1,272 +0,0 @@
# 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 StrEnum
from typing import Annotated, Literal, Protocol, runtime_checkable
from pydantic import BaseModel, Field
from llama_stack.apis.agents.openai_responses import (
OpenAIResponseInputFunctionToolCallOutput,
OpenAIResponseMCPApprovalRequest,
OpenAIResponseMCPApprovalResponse,
OpenAIResponseMessage,
OpenAIResponseOutputMessageFileSearchToolCall,
OpenAIResponseOutputMessageFunctionToolCall,
OpenAIResponseOutputMessageMCPCall,
OpenAIResponseOutputMessageMCPListTools,
OpenAIResponseOutputMessageWebSearchToolCall,
)
from llama_stack.apis.common.tracing import telemetry_traceable
from llama_stack.apis.version import LLAMA_STACK_API_V1
from llama_stack.schema_utils import json_schema_type, register_schema, webmethod
Metadata = dict[str, str]
@json_schema_type
class Conversation(BaseModel):
"""OpenAI-compatible conversation object."""
id: str = Field(..., description="The unique ID of the conversation.")
object: Literal["conversation"] = Field(
default="conversation", description="The object type, which is always conversation."
)
created_at: int = Field(
..., description="The time at which the conversation was created, measured in seconds since the Unix epoch."
)
metadata: Metadata | None = Field(
default=None,
description="Set of 16 key-value pairs that can be attached to an object. This can be useful for storing additional information about the object in a structured format, and querying for objects via API or the dashboard.",
)
items: list[dict] | None = Field(
default=None,
description="Initial items to include in the conversation context. You may add up to 20 items at a time.",
)
@json_schema_type
class ConversationMessage(BaseModel):
"""OpenAI-compatible message item for conversations."""
id: str = Field(..., description="unique identifier for this message")
content: list[dict] = Field(..., description="message content")
role: str = Field(..., description="message role")
status: str = Field(..., description="message status")
type: Literal["message"] = "message"
object: Literal["message"] = "message"
ConversationItem = Annotated[
OpenAIResponseMessage
| OpenAIResponseOutputMessageWebSearchToolCall
| OpenAIResponseOutputMessageFileSearchToolCall
| OpenAIResponseOutputMessageFunctionToolCall
| OpenAIResponseInputFunctionToolCallOutput
| OpenAIResponseMCPApprovalRequest
| OpenAIResponseMCPApprovalResponse
| OpenAIResponseOutputMessageMCPCall
| OpenAIResponseOutputMessageMCPListTools
| OpenAIResponseOutputMessageMCPCall
| OpenAIResponseOutputMessageMCPListTools,
Field(discriminator="type"),
]
register_schema(ConversationItem, name="ConversationItem")
# Using OpenAI types directly caused issues but some notes for reference:
# Note that ConversationItem is a Annotated Union of the types below:
# from openai.types.responses import *
# from openai.types.responses.response_item import *
# from openai.types.conversations import ConversationItem
# f = [
# ResponseFunctionToolCallItem,
# ResponseFunctionToolCallOutputItem,
# ResponseFileSearchToolCall,
# ResponseFunctionWebSearch,
# ImageGenerationCall,
# ResponseComputerToolCall,
# ResponseComputerToolCallOutputItem,
# ResponseReasoningItem,
# ResponseCodeInterpreterToolCall,
# LocalShellCall,
# LocalShellCallOutput,
# McpListTools,
# McpApprovalRequest,
# McpApprovalResponse,
# McpCall,
# ResponseCustomToolCall,
# ResponseCustomToolCallOutput
# ]
@json_schema_type
class ConversationDeletedResource(BaseModel):
"""Response for deleted conversation."""
id: str = Field(..., description="The deleted conversation identifier")
object: str = Field(default="conversation.deleted", description="Object type")
deleted: bool = Field(default=True, description="Whether the object was deleted")
@json_schema_type
class ConversationItemCreateRequest(BaseModel):
"""Request body for creating conversation items."""
items: list[ConversationItem] = Field(
...,
description="Items to include in the conversation context. You may add up to 20 items at a time.",
max_length=20,
)
class ConversationItemInclude(StrEnum):
"""
Specify additional output data to include in the model response.
"""
web_search_call_action_sources = "web_search_call.action.sources"
code_interpreter_call_outputs = "code_interpreter_call.outputs"
computer_call_output_output_image_url = "computer_call_output.output.image_url"
file_search_call_results = "file_search_call.results"
message_input_image_image_url = "message.input_image.image_url"
message_output_text_logprobs = "message.output_text.logprobs"
reasoning_encrypted_content = "reasoning.encrypted_content"
@json_schema_type
class ConversationItemList(BaseModel):
"""List of conversation items with pagination."""
object: str = Field(default="list", description="Object type")
data: list[ConversationItem] = Field(..., description="List of conversation items")
first_id: str | None = Field(default=None, description="The ID of the first item in the list")
last_id: str | None = Field(default=None, description="The ID of the last item in the list")
has_more: bool = Field(default=False, description="Whether there are more items available")
@json_schema_type
class ConversationItemDeletedResource(BaseModel):
"""Response for deleted conversation item."""
id: str = Field(..., description="The deleted item identifier")
object: str = Field(default="conversation.item.deleted", description="Object type")
deleted: bool = Field(default=True, description="Whether the object was deleted")
@runtime_checkable
@telemetry_traceable
class Conversations(Protocol):
"""Conversations
Protocol for conversation management operations."""
@webmethod(route="/conversations", method="POST", level=LLAMA_STACK_API_V1)
async def create_conversation(
self, items: list[ConversationItem] | None = None, metadata: Metadata | None = None
) -> Conversation:
"""Create a conversation.
Create a conversation.
:param items: Initial items to include in the conversation context.
:param metadata: Set of key-value pairs that can be attached to an object.
:returns: The created conversation object.
"""
...
@webmethod(route="/conversations/{conversation_id}", method="GET", level=LLAMA_STACK_API_V1)
async def get_conversation(self, conversation_id: str) -> Conversation:
"""Retrieve a conversation.
Get a conversation with the given ID.
:param conversation_id: The conversation identifier.
:returns: The conversation object.
"""
...
@webmethod(route="/conversations/{conversation_id}", method="POST", level=LLAMA_STACK_API_V1)
async def update_conversation(self, conversation_id: str, metadata: Metadata) -> Conversation:
"""Update a conversation.
Update a conversation's metadata with the given ID.
:param conversation_id: The conversation identifier.
:param metadata: Set of key-value pairs that can be attached to an object.
:returns: The updated conversation object.
"""
...
@webmethod(route="/conversations/{conversation_id}", method="DELETE", level=LLAMA_STACK_API_V1)
async def openai_delete_conversation(self, conversation_id: str) -> ConversationDeletedResource:
"""Delete a conversation.
Delete a conversation with the given ID.
:param conversation_id: The conversation identifier.
:returns: The deleted conversation resource.
"""
...
@webmethod(route="/conversations/{conversation_id}/items", method="POST", level=LLAMA_STACK_API_V1)
async def add_items(self, conversation_id: str, items: list[ConversationItem]) -> ConversationItemList:
"""Create items.
Create items in the conversation.
:param conversation_id: The conversation identifier.
:param items: Items to include in the conversation context.
:returns: List of created items.
"""
...
@webmethod(route="/conversations/{conversation_id}/items/{item_id}", method="GET", level=LLAMA_STACK_API_V1)
async def retrieve(self, conversation_id: str, item_id: str) -> ConversationItem:
"""Retrieve an item.
Retrieve a conversation item.
:param conversation_id: The conversation identifier.
:param item_id: The item identifier.
:returns: The conversation item.
"""
...
@webmethod(route="/conversations/{conversation_id}/items", method="GET", level=LLAMA_STACK_API_V1)
async def list_items(
self,
conversation_id: str,
after: str | None = None,
include: list[ConversationItemInclude] | None = None,
limit: int | None = None,
order: Literal["asc", "desc"] | None = None,
) -> ConversationItemList:
"""List items.
List items in the conversation.
:param conversation_id: The conversation identifier.
:param after: An item ID to list items after, used in pagination.
:param include: Specify additional output data to include in the response.
:param limit: A limit on the number of objects to be returned (1-100, default 20).
:param order: The order to return items in (asc or desc, default desc).
:returns: List of conversation items.
"""
...
@webmethod(route="/conversations/{conversation_id}/items/{item_id}", method="DELETE", level=LLAMA_STACK_API_V1)
async def openai_delete_conversation_item(
self, conversation_id: str, item_id: str
) -> ConversationItemDeletedResource:
"""Delete an item.
Delete a conversation item.
:param conversation_id: The conversation identifier.
:param item_id: The item identifier.
:returns: The deleted item resource.
"""
...