Merge branch 'main' into add-mongodb-vector_io

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Young Han 2025-11-11 11:13:23 -08:00 committed by GitHub
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@ -7,18 +7,11 @@
from llama_stack.apis.conversations.conversations import (
Conversation,
ConversationCreateRequest,
ConversationItem,
ConversationItemList,
)
def test_conversation_create_request_defaults():
request = ConversationCreateRequest()
assert request.items == []
assert request.metadata == {}
def test_conversation_model_defaults():
conversation = Conversation(
id="conv_123456789",

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@ -0,0 +1,130 @@
# 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.
"""Tests for the llama stack list command."""
import argparse
from unittest.mock import MagicMock, patch
import pytest
from llama_stack.cli.stack.list_stacks import StackListBuilds
@pytest.fixture
def list_stacks_command():
"""Create a StackListBuilds instance for testing."""
parser = argparse.ArgumentParser()
subparsers = parser.add_subparsers()
return StackListBuilds(subparsers)
@pytest.fixture
def mock_distribs_base_dir(tmp_path):
"""Create a mock DISTRIBS_BASE_DIR with some custom distributions."""
custom_dir = tmp_path / "distributions"
custom_dir.mkdir(parents=True, exist_ok=True)
# Create a custom distribution
starter_custom = custom_dir / "starter"
starter_custom.mkdir()
(starter_custom / "starter-build.yaml").write_text("# build config")
(starter_custom / "starter-run.yaml").write_text("# run config")
return custom_dir
@pytest.fixture
def mock_distro_dir(tmp_path):
"""Create a mock distributions directory with built-in distributions."""
distro_dir = tmp_path / "src" / "llama_stack" / "distributions"
distro_dir.mkdir(parents=True, exist_ok=True)
# Create some built-in distributions
for distro_name in ["starter", "nvidia", "dell"]:
distro_path = distro_dir / distro_name
distro_path.mkdir()
(distro_path / "build.yaml").write_text("# build config")
(distro_path / "run.yaml").write_text("# run config")
return distro_dir
def create_path_mock(builtin_dist_dir):
"""Create a properly mocked Path object that returns builtin_dist_dir for the distributions path."""
mock_parent_parent_parent = MagicMock()
mock_parent_parent_parent.__truediv__ = (
lambda self, other: builtin_dist_dir if other == "distributions" else MagicMock()
)
mock_path = MagicMock()
mock_path.parent.parent.parent = mock_parent_parent_parent
return mock_path
class TestStackList:
"""Test suite for llama stack list command."""
def test_builtin_distros_shown_without_running(self, list_stacks_command, mock_distro_dir, tmp_path):
"""Test that built-in distributions are shown even before running them."""
mock_path = create_path_mock(mock_distro_dir)
# Mock DISTRIBS_BASE_DIR to be a non-existent directory (no custom distributions)
with patch("llama_stack.cli.stack.list_stacks.DISTRIBS_BASE_DIR", tmp_path / "nonexistent"):
with patch("llama_stack.cli.stack.list_stacks.Path") as mock_path_class:
mock_path_class.return_value = mock_path
distributions = list_stacks_command._get_distribution_dirs()
# Verify built-in distributions are found
assert len(distributions) > 0, "Should find built-in distributions"
assert all(source_type == "built-in" for _, source_type in distributions.values()), (
"All should be built-in"
)
# Check specific distributions we created
assert "starter" in distributions
assert "nvidia" in distributions
assert "dell" in distributions
def test_custom_distribution_overrides_builtin(self, list_stacks_command, mock_distro_dir, mock_distribs_base_dir):
"""Test that custom distributions override built-in ones with the same name."""
mock_path = create_path_mock(mock_distro_dir)
with patch("llama_stack.cli.stack.list_stacks.DISTRIBS_BASE_DIR", mock_distribs_base_dir):
with patch("llama_stack.cli.stack.list_stacks.Path") as mock_path_class:
mock_path_class.return_value = mock_path
distributions = list_stacks_command._get_distribution_dirs()
# "starter" should exist and be marked as "custom" (not "built-in")
# because the custom version overrides the built-in one
assert "starter" in distributions
_, source_type = distributions["starter"]
assert source_type == "custom", "Custom distribution should override built-in"
def test_hidden_directories_ignored(self, list_stacks_command, mock_distro_dir, tmp_path):
"""Test that hidden directories (starting with .) are ignored."""
# Add a hidden directory
hidden_dir = mock_distro_dir / ".hidden"
hidden_dir.mkdir()
(hidden_dir / "build.yaml").write_text("# build")
# Add a __pycache__ directory
pycache_dir = mock_distro_dir / "__pycache__"
pycache_dir.mkdir()
mock_path = create_path_mock(mock_distro_dir)
with patch("llama_stack.cli.stack.list_stacks.DISTRIBS_BASE_DIR", tmp_path / "nonexistent"):
with patch("llama_stack.cli.stack.list_stacks.Path") as mock_path_class:
mock_path_class.return_value = mock_path
distributions = list_stacks_command._get_distribution_dirs()
assert ".hidden" not in distributions
assert "__pycache__" not in distributions

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@ -1,303 +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 llama_stack.apis.inference import (
ChatCompletionRequest,
CompletionMessage,
StopReason,
SystemMessage,
SystemMessageBehavior,
ToolCall,
ToolConfig,
UserMessage,
)
from llama_stack.models.llama.datatypes import (
BuiltinTool,
ToolDefinition,
ToolPromptFormat,
)
from llama_stack.providers.utils.inference.prompt_adapter import (
chat_completion_request_to_messages,
chat_completion_request_to_prompt,
interleaved_content_as_str,
)
MODEL = "Llama3.1-8B-Instruct"
MODEL3_2 = "Llama3.2-3B-Instruct"
async def test_system_default():
content = "Hello !"
request = ChatCompletionRequest(
model=MODEL,
messages=[
UserMessage(content=content),
],
)
messages = chat_completion_request_to_messages(request, MODEL)
assert len(messages) == 2
assert messages[-1].content == content
assert "Cutting Knowledge Date: December 2023" in interleaved_content_as_str(messages[0].content)
async def test_system_builtin_only():
content = "Hello !"
request = ChatCompletionRequest(
model=MODEL,
messages=[
UserMessage(content=content),
],
tools=[
ToolDefinition(tool_name=BuiltinTool.code_interpreter),
ToolDefinition(tool_name=BuiltinTool.brave_search),
],
)
messages = chat_completion_request_to_messages(request, MODEL)
assert len(messages) == 2
assert messages[-1].content == content
assert "Cutting Knowledge Date: December 2023" in interleaved_content_as_str(messages[0].content)
assert "Tools: brave_search" in interleaved_content_as_str(messages[0].content)
async def test_system_custom_only():
content = "Hello !"
request = ChatCompletionRequest(
model=MODEL,
messages=[
UserMessage(content=content),
],
tools=[
ToolDefinition(
tool_name="custom1",
description="custom1 tool",
input_schema={
"type": "object",
"properties": {
"param1": {
"type": "str",
"description": "param1 description",
},
},
"required": ["param1"],
},
)
],
tool_config=ToolConfig(tool_prompt_format=ToolPromptFormat.json),
)
messages = chat_completion_request_to_messages(request, MODEL)
assert len(messages) == 3
assert "Environment: ipython" in interleaved_content_as_str(messages[0].content)
assert "Return function calls in JSON format" in interleaved_content_as_str(messages[1].content)
assert messages[-1].content == content
async def test_system_custom_and_builtin():
content = "Hello !"
request = ChatCompletionRequest(
model=MODEL,
messages=[
UserMessage(content=content),
],
tools=[
ToolDefinition(tool_name=BuiltinTool.code_interpreter),
ToolDefinition(tool_name=BuiltinTool.brave_search),
ToolDefinition(
tool_name="custom1",
description="custom1 tool",
input_schema={
"type": "object",
"properties": {
"param1": {
"type": "str",
"description": "param1 description",
},
},
"required": ["param1"],
},
),
],
)
messages = chat_completion_request_to_messages(request, MODEL)
assert len(messages) == 3
assert "Environment: ipython" in interleaved_content_as_str(messages[0].content)
assert "Tools: brave_search" in interleaved_content_as_str(messages[0].content)
assert "Return function calls in JSON format" in interleaved_content_as_str(messages[1].content)
assert messages[-1].content == content
async def test_completion_message_encoding():
request = ChatCompletionRequest(
model=MODEL3_2,
messages=[
UserMessage(content="hello"),
CompletionMessage(
content="",
stop_reason=StopReason.end_of_turn,
tool_calls=[
ToolCall(
tool_name="custom1",
arguments='{"param1": "value1"}', # arguments must be a JSON string
call_id="123",
)
],
),
],
tools=[
ToolDefinition(
tool_name="custom1",
description="custom1 tool",
input_schema={
"type": "object",
"properties": {
"param1": {
"type": "str",
"description": "param1 description",
},
},
"required": ["param1"],
},
),
],
tool_config=ToolConfig(tool_prompt_format=ToolPromptFormat.python_list),
)
prompt = await chat_completion_request_to_prompt(request, request.model)
assert '[custom1(param1="value1")]' in prompt
request.model = MODEL
request.tool_config = ToolConfig(tool_prompt_format=ToolPromptFormat.json)
prompt = await chat_completion_request_to_prompt(request, request.model)
assert '{"type": "function", "name": "custom1", "parameters": {"param1": "value1"}}' in prompt
async def test_user_provided_system_message():
content = "Hello !"
system_prompt = "You are a pirate"
request = ChatCompletionRequest(
model=MODEL,
messages=[
SystemMessage(content=system_prompt),
UserMessage(content=content),
],
tools=[
ToolDefinition(tool_name=BuiltinTool.code_interpreter),
],
)
messages = chat_completion_request_to_messages(request, MODEL)
assert len(messages) == 2
assert interleaved_content_as_str(messages[0].content).endswith(system_prompt)
assert messages[-1].content == content
async def test_replace_system_message_behavior_builtin_tools():
content = "Hello !"
system_prompt = "You are a pirate"
request = ChatCompletionRequest(
model=MODEL,
messages=[
SystemMessage(content=system_prompt),
UserMessage(content=content),
],
tools=[
ToolDefinition(tool_name=BuiltinTool.code_interpreter),
],
tool_config=ToolConfig(
tool_choice="auto",
tool_prompt_format=ToolPromptFormat.python_list,
system_message_behavior=SystemMessageBehavior.replace,
),
)
messages = chat_completion_request_to_messages(request, MODEL3_2)
assert len(messages) == 2
assert interleaved_content_as_str(messages[0].content).endswith(system_prompt)
assert "Environment: ipython" in interleaved_content_as_str(messages[0].content)
assert messages[-1].content == content
async def test_replace_system_message_behavior_custom_tools():
content = "Hello !"
system_prompt = "You are a pirate"
request = ChatCompletionRequest(
model=MODEL,
messages=[
SystemMessage(content=system_prompt),
UserMessage(content=content),
],
tools=[
ToolDefinition(tool_name=BuiltinTool.code_interpreter),
ToolDefinition(
tool_name="custom1",
description="custom1 tool",
input_schema={
"type": "object",
"properties": {
"param1": {
"type": "str",
"description": "param1 description",
},
},
"required": ["param1"],
},
),
],
tool_config=ToolConfig(
tool_choice="auto",
tool_prompt_format=ToolPromptFormat.python_list,
system_message_behavior=SystemMessageBehavior.replace,
),
)
messages = chat_completion_request_to_messages(request, MODEL3_2)
assert len(messages) == 2
assert interleaved_content_as_str(messages[0].content).endswith(system_prompt)
assert "Environment: ipython" in interleaved_content_as_str(messages[0].content)
assert messages[-1].content == content
async def test_replace_system_message_behavior_custom_tools_with_template():
content = "Hello !"
system_prompt = "You are a pirate {{ function_description }}"
request = ChatCompletionRequest(
model=MODEL,
messages=[
SystemMessage(content=system_prompt),
UserMessage(content=content),
],
tools=[
ToolDefinition(tool_name=BuiltinTool.code_interpreter),
ToolDefinition(
tool_name="custom1",
description="custom1 tool",
input_schema={
"type": "object",
"properties": {
"param1": {
"type": "str",
"description": "param1 description",
},
},
"required": ["param1"],
},
),
],
tool_config=ToolConfig(
tool_choice="auto",
tool_prompt_format=ToolPromptFormat.python_list,
system_message_behavior=SystemMessageBehavior.replace,
),
)
messages = chat_completion_request_to_messages(request, MODEL3_2)
assert len(messages) == 2
assert "Environment: ipython" in interleaved_content_as_str(messages[0].content)
assert "You are a pirate" in interleaved_content_as_str(messages[0].content)
# function description is present in the system prompt
assert '"name": "custom1"' in interleaved_content_as_str(messages[0].content)
assert messages[-1].content == content

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@ -0,0 +1,78 @@
# 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 types import SimpleNamespace
from unittest.mock import AsyncMock, MagicMock
import pytest
from openai import AuthenticationError
from llama_stack.apis.inference import OpenAIChatCompletionRequestWithExtraBody
from llama_stack.providers.remote.inference.bedrock.bedrock import BedrockInferenceAdapter
from llama_stack.providers.remote.inference.bedrock.config import BedrockConfig
def test_adapter_initialization():
config = BedrockConfig(api_key="test-key", region_name="us-east-1")
adapter = BedrockInferenceAdapter(config=config)
assert adapter.config.auth_credential.get_secret_value() == "test-key"
assert adapter.config.region_name == "us-east-1"
def test_client_url_construction():
config = BedrockConfig(api_key="test-key", region_name="us-west-2")
adapter = BedrockInferenceAdapter(config=config)
assert adapter.get_base_url() == "https://bedrock-runtime.us-west-2.amazonaws.com/openai/v1"
def test_api_key_from_config():
config = BedrockConfig(api_key="config-key", region_name="us-east-1")
adapter = BedrockInferenceAdapter(config=config)
assert adapter.config.auth_credential.get_secret_value() == "config-key"
def test_api_key_from_header_overrides_config():
"""Test API key from request header overrides config via client property"""
config = BedrockConfig(api_key="config-key", region_name="us-east-1")
adapter = BedrockInferenceAdapter(config=config)
adapter.provider_data_api_key_field = "aws_bedrock_api_key"
adapter.get_request_provider_data = MagicMock(return_value=SimpleNamespace(aws_bedrock_api_key="header-key"))
# The client property is where header override happens (in OpenAIMixin)
assert adapter.client.api_key == "header-key"
async def test_authentication_error_handling():
"""Test that AuthenticationError from OpenAI client is converted to ValueError with helpful message"""
config = BedrockConfig(api_key="invalid-key", region_name="us-east-1")
adapter = BedrockInferenceAdapter(config=config)
# Mock the parent class method to raise AuthenticationError
mock_response = MagicMock()
mock_response.message = "Invalid authentication credentials"
auth_error = AuthenticationError(message="Invalid authentication credentials", response=mock_response, body=None)
# Create a mock that raises the error
mock_super = AsyncMock(side_effect=auth_error)
# Patch the parent class method
original_method = BedrockInferenceAdapter.__bases__[0].openai_chat_completion
BedrockInferenceAdapter.__bases__[0].openai_chat_completion = mock_super
try:
with pytest.raises(ValueError) as exc_info:
params = OpenAIChatCompletionRequestWithExtraBody(
model="test-model", messages=[{"role": "user", "content": "test"}]
)
await adapter.openai_chat_completion(params=params)
assert "AWS Bedrock authentication failed" in str(exc_info.value)
assert "Please verify your API key" in str(exc_info.value)
finally:
# Restore original method
BedrockInferenceAdapter.__bases__[0].openai_chat_completion = original_method

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@ -0,0 +1,39 @@
# 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 llama_stack.providers.remote.inference.bedrock.config import BedrockConfig
def test_bedrock_config_defaults_no_env(monkeypatch):
"""Test BedrockConfig defaults when env vars are not set"""
monkeypatch.delenv("AWS_BEDROCK_API_KEY", raising=False)
monkeypatch.delenv("AWS_DEFAULT_REGION", raising=False)
config = BedrockConfig()
assert config.auth_credential is None
assert config.region_name == "us-east-2"
def test_bedrock_config_reads_from_env(monkeypatch):
"""Test BedrockConfig field initialization reads from environment variables"""
monkeypatch.setenv("AWS_DEFAULT_REGION", "eu-west-1")
config = BedrockConfig()
assert config.region_name == "eu-west-1"
def test_bedrock_config_with_values():
"""Test BedrockConfig accepts explicit values via alias"""
config = BedrockConfig(api_key="test-key", region_name="us-west-2")
assert config.auth_credential.get_secret_value() == "test-key"
assert config.region_name == "us-west-2"
def test_bedrock_config_sample():
"""Test BedrockConfig sample_run_config returns correct format"""
sample = BedrockConfig.sample_run_config()
assert "api_key" in sample
assert "region_name" in sample
assert sample["api_key"] == "${env.AWS_BEDROCK_API_KEY:=}"
assert sample["region_name"] == "${env.AWS_DEFAULT_REGION:=us-east-2}"

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@ -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.

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@ -0,0 +1,44 @@
# 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 unittest.mock import Mock
import pytest
from llama_stack.providers.inline.inference.meta_reference.model_parallel import (
ModelRunner,
)
class TestModelRunner:
"""Test ModelRunner task dispatching for model-parallel inference."""
def test_chat_completion_task_dispatch(self):
"""Verify ModelRunner correctly dispatches chat_completion tasks."""
# Create a mock generator
mock_generator = Mock()
mock_generator.chat_completion = Mock(return_value=iter([]))
runner = ModelRunner(mock_generator)
# Create a chat_completion task
fake_params = {"model": "test"}
fake_messages = [{"role": "user", "content": "test"}]
task = ("chat_completion", [fake_params, fake_messages])
# Execute task
runner(task)
# Verify chat_completion was called with correct arguments
mock_generator.chat_completion.assert_called_once_with(fake_params, fake_messages)
def test_invalid_task_type_raises_error(self):
"""Verify ModelRunner rejects invalid task types."""
mock_generator = Mock()
runner = ModelRunner(mock_generator)
with pytest.raises(ValueError, match="Unexpected task type"):
runner(("invalid_task", []))

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@ -10,11 +10,13 @@ from unittest.mock import AsyncMock, MagicMock, patch
import pytest
from llama_stack.apis.inference import CompletionMessage, UserMessage
from llama_stack.apis.inference import (
OpenAIAssistantMessageParam,
OpenAIUserMessageParam,
)
from llama_stack.apis.resource import ResourceType
from llama_stack.apis.safety import RunShieldResponse, ViolationLevel
from llama_stack.apis.shields import Shield
from llama_stack.models.llama.datatypes import StopReason
from llama_stack.providers.remote.safety.nvidia.config import NVIDIASafetyConfig
from llama_stack.providers.remote.safety.nvidia.nvidia import NVIDIASafetyAdapter
@ -136,11 +138,9 @@ async def test_run_shield_allowed(nvidia_adapter, mock_guardrails_post):
# Run the shield
messages = [
UserMessage(role="user", content="Hello, how are you?"),
CompletionMessage(
role="assistant",
OpenAIUserMessageParam(content="Hello, how are you?"),
OpenAIAssistantMessageParam(
content="I'm doing well, thank you for asking!",
stop_reason=StopReason.end_of_message,
tool_calls=[],
),
]
@ -191,13 +191,10 @@ async def test_run_shield_blocked(nvidia_adapter, mock_guardrails_post):
# Mock Guardrails API response
mock_guardrails_post.return_value = {"status": "blocked", "rails_status": {"reason": "harmful_content"}}
# Run the shield
messages = [
UserMessage(role="user", content="Hello, how are you?"),
CompletionMessage(
role="assistant",
OpenAIUserMessageParam(content="Hello, how are you?"),
OpenAIAssistantMessageParam(
content="I'm doing well, thank you for asking!",
stop_reason=StopReason.end_of_message,
tool_calls=[],
),
]
@ -243,7 +240,7 @@ async def test_run_shield_not_found(nvidia_adapter, mock_guardrails_post):
adapter.shield_store.get_shield.return_value = None
messages = [
UserMessage(role="user", content="Hello, how are you?"),
OpenAIUserMessageParam(content="Hello, how are you?"),
]
with pytest.raises(ValueError):
@ -274,11 +271,9 @@ async def test_run_shield_http_error(nvidia_adapter, mock_guardrails_post):
# Running the shield should raise an exception
messages = [
UserMessage(role="user", content="Hello, how are you?"),
CompletionMessage(
role="assistant",
OpenAIUserMessageParam(content="Hello, how are you?"),
OpenAIAssistantMessageParam(
content="I'm doing well, thank you for asking!",
stop_reason=StopReason.end_of_message,
tool_calls=[],
),
]

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@ -4,50 +4,66 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from llama_stack.providers.remote.inference.bedrock.bedrock import (
_get_region_prefix,
_to_inference_profile_id,
)
from types import SimpleNamespace
from unittest.mock import AsyncMock, PropertyMock, patch
from llama_stack.apis.inference import OpenAIChatCompletionRequestWithExtraBody
from llama_stack.providers.remote.inference.bedrock.bedrock import BedrockInferenceAdapter
from llama_stack.providers.remote.inference.bedrock.config import BedrockConfig
def test_region_prefixes():
assert _get_region_prefix("us-east-1") == "us."
assert _get_region_prefix("eu-west-1") == "eu."
assert _get_region_prefix("ap-south-1") == "ap."
assert _get_region_prefix("ca-central-1") == "us."
def test_can_create_adapter():
config = BedrockConfig(api_key="test-key", region_name="us-east-1")
adapter = BedrockInferenceAdapter(config=config)
# Test case insensitive
assert _get_region_prefix("US-EAST-1") == "us."
assert _get_region_prefix("EU-WEST-1") == "eu."
assert _get_region_prefix("Ap-South-1") == "ap."
# Test None region
assert _get_region_prefix(None) == "us."
assert adapter is not None
assert adapter.config.region_name == "us-east-1"
assert adapter.get_api_key() == "test-key"
def test_model_id_conversion():
# Basic conversion
assert (
_to_inference_profile_id("meta.llama3-1-70b-instruct-v1:0", "us-east-1") == "us.meta.llama3-1-70b-instruct-v1:0"
def test_different_aws_regions():
# just check a couple regions to verify URL construction works
config = BedrockConfig(api_key="key", region_name="us-east-1")
adapter = BedrockInferenceAdapter(config=config)
assert adapter.get_base_url() == "https://bedrock-runtime.us-east-1.amazonaws.com/openai/v1"
config = BedrockConfig(api_key="key", region_name="eu-west-1")
adapter = BedrockInferenceAdapter(config=config)
assert adapter.get_base_url() == "https://bedrock-runtime.eu-west-1.amazonaws.com/openai/v1"
async def test_basic_chat_completion():
"""Test basic chat completion works with OpenAIMixin"""
config = BedrockConfig(api_key="k", region_name="us-east-1")
adapter = BedrockInferenceAdapter(config=config)
class FakeModelStore:
async def has_model(self, model_id):
return True
async def get_model(self, model_id):
return SimpleNamespace(provider_resource_id="meta.llama3-1-8b-instruct-v1:0")
adapter.model_store = FakeModelStore()
fake_response = SimpleNamespace(
id="chatcmpl-123",
choices=[SimpleNamespace(message=SimpleNamespace(content="Hello!", role="assistant"), finish_reason="stop")],
)
# Already has prefix
assert (
_to_inference_profile_id("us.meta.llama3-1-70b-instruct-v1:0", "us-east-1")
== "us.meta.llama3-1-70b-instruct-v1:0"
)
mock_create = AsyncMock(return_value=fake_response)
# ARN should be returned unchanged
arn = "arn:aws:bedrock:us-east-1:123456789012:inference-profile/us.meta.llama3-1-70b-instruct-v1:0"
assert _to_inference_profile_id(arn, "us-east-1") == arn
class FakeClient:
def __init__(self):
self.chat = SimpleNamespace(completions=SimpleNamespace(create=mock_create))
# ARN should be returned unchanged even without region
assert _to_inference_profile_id(arn) == arn
with patch.object(type(adapter), "client", new_callable=PropertyMock, return_value=FakeClient()):
params = OpenAIChatCompletionRequestWithExtraBody(
model="llama3-1-8b",
messages=[{"role": "user", "content": "hello"}],
stream=False,
)
response = await adapter.openai_chat_completion(params=params)
# Optional region parameter defaults to us-east-1
assert _to_inference_profile_id("meta.llama3-1-70b-instruct-v1:0") == "us.meta.llama3-1-70b-instruct-v1:0"
# Different regions work with optional parameter
assert (
_to_inference_profile_id("meta.llama3-1-70b-instruct-v1:0", "eu-west-1") == "eu.meta.llama3-1-70b-instruct-v1:0"
)
assert response.id == "chatcmpl-123"
assert mock_create.await_count == 1

View file

@ -1,220 +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.
import pytest
from pydantic import ValidationError
from llama_stack.apis.common.content_types import TextContentItem
from llama_stack.apis.inference import (
CompletionMessage,
OpenAIAssistantMessageParam,
OpenAIChatCompletionContentPartImageParam,
OpenAIChatCompletionContentPartTextParam,
OpenAIDeveloperMessageParam,
OpenAIImageURL,
OpenAISystemMessageParam,
OpenAIToolMessageParam,
OpenAIUserMessageParam,
SystemMessage,
UserMessage,
)
from llama_stack.models.llama.datatypes import BuiltinTool, StopReason, ToolCall
from llama_stack.providers.utils.inference.openai_compat import (
convert_message_to_openai_dict,
convert_message_to_openai_dict_new,
openai_messages_to_messages,
)
async def test_convert_message_to_openai_dict():
message = UserMessage(content=[TextContentItem(text="Hello, world!")], role="user")
assert await convert_message_to_openai_dict(message) == {
"role": "user",
"content": [{"type": "text", "text": "Hello, world!"}],
}
# Test convert_message_to_openai_dict with a tool call
async def test_convert_message_to_openai_dict_with_tool_call():
message = CompletionMessage(
content="",
tool_calls=[ToolCall(call_id="123", tool_name="test_tool", arguments='{"foo": "bar"}')],
stop_reason=StopReason.end_of_turn,
)
openai_dict = await convert_message_to_openai_dict(message)
assert openai_dict == {
"role": "assistant",
"content": [{"type": "text", "text": ""}],
"tool_calls": [
{"id": "123", "type": "function", "function": {"name": "test_tool", "arguments": '{"foo": "bar"}'}}
],
}
async def test_convert_message_to_openai_dict_with_builtin_tool_call():
message = CompletionMessage(
content="",
tool_calls=[
ToolCall(
call_id="123",
tool_name=BuiltinTool.brave_search,
arguments='{"foo": "bar"}',
)
],
stop_reason=StopReason.end_of_turn,
)
openai_dict = await convert_message_to_openai_dict(message)
assert openai_dict == {
"role": "assistant",
"content": [{"type": "text", "text": ""}],
"tool_calls": [
{"id": "123", "type": "function", "function": {"name": "brave_search", "arguments": '{"foo": "bar"}'}}
],
}
async def test_openai_messages_to_messages_with_content_str():
openai_messages = [
OpenAISystemMessageParam(content="system message"),
OpenAIUserMessageParam(content="user message"),
OpenAIAssistantMessageParam(content="assistant message"),
]
llama_messages = openai_messages_to_messages(openai_messages)
assert len(llama_messages) == 3
assert isinstance(llama_messages[0], SystemMessage)
assert isinstance(llama_messages[1], UserMessage)
assert isinstance(llama_messages[2], CompletionMessage)
assert llama_messages[0].content == "system message"
assert llama_messages[1].content == "user message"
assert llama_messages[2].content == "assistant message"
async def test_openai_messages_to_messages_with_content_list():
openai_messages = [
OpenAISystemMessageParam(content=[OpenAIChatCompletionContentPartTextParam(text="system message")]),
OpenAIUserMessageParam(content=[OpenAIChatCompletionContentPartTextParam(text="user message")]),
OpenAIAssistantMessageParam(content=[OpenAIChatCompletionContentPartTextParam(text="assistant message")]),
]
llama_messages = openai_messages_to_messages(openai_messages)
assert len(llama_messages) == 3
assert isinstance(llama_messages[0], SystemMessage)
assert isinstance(llama_messages[1], UserMessage)
assert isinstance(llama_messages[2], CompletionMessage)
assert llama_messages[0].content[0].text == "system message"
assert llama_messages[1].content[0].text == "user message"
assert llama_messages[2].content[0].text == "assistant message"
@pytest.mark.parametrize(
"message_class,kwargs",
[
(OpenAISystemMessageParam, {}),
(OpenAIAssistantMessageParam, {}),
(OpenAIDeveloperMessageParam, {}),
(OpenAIUserMessageParam, {}),
(OpenAIToolMessageParam, {"tool_call_id": "call_123"}),
],
)
def test_message_accepts_text_string(message_class, kwargs):
"""Test that messages accept string text content."""
msg = message_class(content="Test message", **kwargs)
assert msg.content == "Test message"
@pytest.mark.parametrize(
"message_class,kwargs",
[
(OpenAISystemMessageParam, {}),
(OpenAIAssistantMessageParam, {}),
(OpenAIDeveloperMessageParam, {}),
(OpenAIUserMessageParam, {}),
(OpenAIToolMessageParam, {"tool_call_id": "call_123"}),
],
)
def test_message_accepts_text_list(message_class, kwargs):
"""Test that messages accept list of text content parts."""
content_list = [OpenAIChatCompletionContentPartTextParam(text="Test message")]
msg = message_class(content=content_list, **kwargs)
assert len(msg.content) == 1
assert msg.content[0].text == "Test message"
@pytest.mark.parametrize(
"message_class,kwargs",
[
(OpenAISystemMessageParam, {}),
(OpenAIAssistantMessageParam, {}),
(OpenAIDeveloperMessageParam, {}),
(OpenAIToolMessageParam, {"tool_call_id": "call_123"}),
],
)
def test_message_rejects_images(message_class, kwargs):
"""Test that system, assistant, developer, and tool messages reject image content."""
with pytest.raises(ValidationError):
message_class(
content=[
OpenAIChatCompletionContentPartImageParam(image_url=OpenAIImageURL(url="http://example.com/image.jpg"))
],
**kwargs,
)
def test_user_message_accepts_images():
"""Test that user messages accept image content (unlike other message types)."""
# List with images should work
msg = OpenAIUserMessageParam(
content=[
OpenAIChatCompletionContentPartTextParam(text="Describe this image:"),
OpenAIChatCompletionContentPartImageParam(image_url=OpenAIImageURL(url="http://example.com/image.jpg")),
]
)
assert len(msg.content) == 2
assert msg.content[0].text == "Describe this image:"
assert msg.content[1].image_url.url == "http://example.com/image.jpg"
async def test_convert_message_to_openai_dict_new_user_message():
"""Test convert_message_to_openai_dict_new with UserMessage."""
message = UserMessage(content="Hello, world!", role="user")
result = await convert_message_to_openai_dict_new(message)
assert result["role"] == "user"
assert result["content"] == "Hello, world!"
async def test_convert_message_to_openai_dict_new_completion_message_with_tool_calls():
"""Test convert_message_to_openai_dict_new with CompletionMessage containing tool calls."""
message = CompletionMessage(
content="I'll help you find the weather.",
tool_calls=[
ToolCall(
call_id="call_123",
tool_name="get_weather",
arguments='{"city": "Sligo"}',
)
],
stop_reason=StopReason.end_of_turn,
)
result = await convert_message_to_openai_dict_new(message)
# This would have failed with "Cannot instantiate typing.Union" before the fix
assert result["role"] == "assistant"
assert result["content"] == "I'll help you find the weather."
assert "tool_calls" in result
assert result["tool_calls"] is not None
assert len(result["tool_calls"]) == 1
tool_call = result["tool_calls"][0]
assert tool_call.id == "call_123"
assert tool_call.type == "function"
assert tool_call.function.name == "get_weather"
assert tool_call.function.arguments == '{"city": "Sligo"}'

View file

@ -0,0 +1,35 @@
# 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 llama_stack.apis.inference import (
OpenAIAssistantMessageParam,
OpenAIUserMessageParam,
)
from llama_stack.models.llama.datatypes import RawTextItem
from llama_stack.providers.utils.inference.prompt_adapter import (
convert_openai_message_to_raw_message,
)
class TestConvertOpenAIMessageToRawMessage:
"""Test conversion of OpenAI message types to RawMessage format."""
async def test_user_message_conversion(self):
msg = OpenAIUserMessageParam(role="user", content="Hello world")
raw_msg = await convert_openai_message_to_raw_message(msg)
assert raw_msg.role == "user"
assert isinstance(raw_msg.content, RawTextItem)
assert raw_msg.content.text == "Hello world"
async def test_assistant_message_conversion(self):
msg = OpenAIAssistantMessageParam(role="assistant", content="Hi there!")
raw_msg = await convert_openai_message_to_raw_message(msg)
assert raw_msg.role == "assistant"
assert isinstance(raw_msg.content, RawTextItem)
assert raw_msg.content.text == "Hi there!"
assert raw_msg.tool_calls == []

View file

@ -92,6 +92,99 @@ async def test_persistence_across_adapter_restarts(vector_io_adapter):
await vector_io_adapter.shutdown()
async def test_vector_store_lazy_loading_from_kvstore(vector_io_adapter):
"""
Test that vector stores can be lazy-loaded from KV store when not in cache.
Verifies that clearing the cache doesn't break vector store access - they
can be loaded on-demand from persistent storage.
"""
await vector_io_adapter.initialize()
vector_store_id = f"lazy_load_test_{np.random.randint(1e6)}"
vector_store = VectorStore(
identifier=vector_store_id,
provider_id="test_provider",
embedding_model="test_model",
embedding_dimension=128,
)
await vector_io_adapter.register_vector_store(vector_store)
assert vector_store_id in vector_io_adapter.cache
vector_io_adapter.cache.clear()
assert vector_store_id not in vector_io_adapter.cache
loaded_index = await vector_io_adapter._get_and_cache_vector_store_index(vector_store_id)
assert loaded_index is not None
assert loaded_index.vector_store.identifier == vector_store_id
assert vector_store_id in vector_io_adapter.cache
cached_index = await vector_io_adapter._get_and_cache_vector_store_index(vector_store_id)
assert cached_index is loaded_index
await vector_io_adapter.shutdown()
async def test_vector_store_preloading_on_initialization(vector_io_adapter):
"""
Test that vector stores are preloaded from KV store during initialization.
Verifies that after restart, all vector stores are automatically loaded into
cache and immediately accessible without requiring lazy loading.
"""
await vector_io_adapter.initialize()
vector_store_ids = [f"preload_test_{i}_{np.random.randint(1e6)}" for i in range(3)]
for vs_id in vector_store_ids:
vector_store = VectorStore(
identifier=vs_id,
provider_id="test_provider",
embedding_model="test_model",
embedding_dimension=128,
)
await vector_io_adapter.register_vector_store(vector_store)
for vs_id in vector_store_ids:
assert vs_id in vector_io_adapter.cache
await vector_io_adapter.shutdown()
await vector_io_adapter.initialize()
for vs_id in vector_store_ids:
assert vs_id in vector_io_adapter.cache
for vs_id in vector_store_ids:
loaded_index = await vector_io_adapter._get_and_cache_vector_store_index(vs_id)
assert loaded_index is not None
assert loaded_index.vector_store.identifier == vs_id
await vector_io_adapter.shutdown()
async def test_kvstore_none_raises_runtime_error(vector_io_adapter):
"""
Test that accessing vector stores with uninitialized kvstore raises RuntimeError.
Verifies proper RuntimeError is raised instead of assertions when kvstore is None.
"""
await vector_io_adapter.initialize()
vector_store_id = f"kvstore_none_test_{np.random.randint(1e6)}"
vector_store = VectorStore(
identifier=vector_store_id,
provider_id="test_provider",
embedding_model="test_model",
embedding_dimension=128,
)
await vector_io_adapter.register_vector_store(vector_store)
vector_io_adapter.cache.clear()
vector_io_adapter.kvstore = None
with pytest.raises(RuntimeError, match="KVStore not initialized"):
await vector_io_adapter._get_and_cache_vector_store_index(vector_store_id)
async def test_register_and_unregister_vector_store(vector_io_adapter):
unique_id = f"foo_db_{np.random.randint(1e6)}"
dummy = VectorStore(