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5 Commits
codex/requ
...
stack/inte
| Author | SHA1 | Date | |
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ed91c760b2 | ||
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23e0cc2f9e | ||
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efcf169be4 | ||
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3e2f839da9 | ||
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e2c9ee04a5 |
@@ -1,127 +0,0 @@
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"""
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Unit tests for App description validation functions.
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This test module validates the 400-character limit enforcement
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for App descriptions across all creation and editing endpoints.
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"""
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import sys
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import pytest
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class TestAppDescriptionValidationUnit:
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"""Unit tests for description validation function"""
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def test_validate_description_length_function(self):
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"""Test the validate_description_length function directly"""
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from libs.validators import validate_description_length
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# Test valid descriptions
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assert validate_description_length("") == ""
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assert validate_description_length("x" * 400) == "x" * 400
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assert validate_description_length(None) is None
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# Test invalid descriptions
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with pytest.raises(ValueError) as exc_info:
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validate_description_length("x" * 401)
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assert "Description cannot exceed 400 characters." in str(exc_info.value)
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with pytest.raises(ValueError) as exc_info:
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validate_description_length("x" * 500)
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assert "Description cannot exceed 400 characters." in str(exc_info.value)
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with pytest.raises(ValueError) as exc_info:
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validate_description_length("x" * 1000)
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assert "Description cannot exceed 400 characters." in str(exc_info.value)
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def test_boundary_values(self):
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"""Test boundary values for description validation"""
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from libs.validators import validate_description_length
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# Test exact boundary
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exactly_400 = "x" * 400
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assert validate_description_length(exactly_400) == exactly_400
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# Test just over boundary
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just_over_400 = "x" * 401
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with pytest.raises(ValueError):
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validate_description_length(just_over_400)
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# Test just under boundary
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just_under_400 = "x" * 399
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assert validate_description_length(just_under_400) == just_under_400
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def test_edge_cases(self):
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"""Test edge cases for description validation"""
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from libs.validators import validate_description_length
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# Test None input
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assert validate_description_length(None) is None
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# Test empty string
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assert validate_description_length("") == ""
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# Test single character
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assert validate_description_length("a") == "a"
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# Test unicode characters
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unicode_desc = "测试" * 200 # 400 characters in Chinese
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assert validate_description_length(unicode_desc) == unicode_desc
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# Test unicode over limit
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unicode_over = "测试" * 201 # 402 characters
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with pytest.raises(ValueError):
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validate_description_length(unicode_over)
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def test_whitespace_handling(self):
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"""Test how validation handles whitespace"""
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from libs.validators import validate_description_length
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# Test description with spaces
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spaces_400 = " " * 400
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assert validate_description_length(spaces_400) == spaces_400
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# Test description with spaces over limit
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spaces_401 = " " * 401
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with pytest.raises(ValueError):
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validate_description_length(spaces_401)
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# Test mixed content
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mixed_400 = "a" * 200 + " " * 200
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assert validate_description_length(mixed_400) == mixed_400
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# Test mixed over limit
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mixed_401 = "a" * 200 + " " * 201
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with pytest.raises(ValueError):
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validate_description_length(mixed_401)
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if __name__ == "__main__":
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# Run tests directly
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import traceback
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test_instance = TestAppDescriptionValidationUnit()
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test_methods = [method for method in dir(test_instance) if method.startswith("test_")]
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passed = 0
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failed = 0
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for test_method in test_methods:
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try:
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print(f"Running {test_method}...")
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getattr(test_instance, test_method)()
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print(f"✅ {test_method} PASSED")
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passed += 1
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except Exception as e:
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print(f"❌ {test_method} FAILED: {str(e)}")
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traceback.print_exc()
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failed += 1
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print(f"\n📊 Test Results: {passed} passed, {failed} failed")
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if failed == 0:
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print("🎉 All tests passed!")
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else:
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print("💥 Some tests failed!")
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sys.exit(1)
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@@ -1,253 +1,5 @@
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import datetime
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import uuid
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from collections.abc import Generator, Sequence
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from decimal import Decimal
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from json import dumps
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"""Compatibility import for unit tests that predate the integration-test migration."""
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from core.plugin.entities.plugin import PluginInstallationSource
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from core.plugin.entities.plugin_daemon import PluginModelProviderEntity
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from core.plugin.impl.model import PluginModelClient
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from tests.unit_tests.core.workflow.nodes.parameter_extractor.fixtures.plugin_model import MockModelClass
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# import monkeypatch
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from graphon.model_runtime.entities.common_entities import I18nObject
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from graphon.model_runtime.entities.llm_entities import (
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LLMMode,
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LLMResult,
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LLMResultChunk,
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LLMResultChunkDelta,
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LLMUsage,
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)
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from graphon.model_runtime.entities.message_entities import AssistantPromptMessage, PromptMessage, PromptMessageTool
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from graphon.model_runtime.entities.model_entities import (
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AIModelEntity,
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FetchFrom,
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ModelFeature,
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ModelPropertyKey,
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ModelType,
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)
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from graphon.model_runtime.entities.provider_entities import ConfigurateMethod, ProviderEntity
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class MockModelClass(PluginModelClient):
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def fetch_model_providers(self, tenant_id: str) -> Sequence[PluginModelProviderEntity]:
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"""
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Fetch model providers for the given tenant.
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"""
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return [
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PluginModelProviderEntity(
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id=uuid.uuid4().hex,
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created_at=datetime.datetime.now(),
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updated_at=datetime.datetime.now(),
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provider="openai",
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tenant_id=tenant_id,
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plugin_unique_identifier="langgenius/openai/openai",
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plugin_id="langgenius/openai",
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installation_source=PluginInstallationSource.Marketplace,
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declaration=ProviderEntity(
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provider="openai",
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label=I18nObject(
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en_US="OpenAI",
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zh_Hans="OpenAI",
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),
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description=I18nObject(
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en_US="OpenAI",
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zh_Hans="OpenAI",
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),
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icon_small=I18nObject(
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en_US="https://example.com/icon_small.png",
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zh_Hans="https://example.com/icon_small.png",
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),
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supported_model_types=[ModelType.LLM],
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configurate_methods=[ConfigurateMethod.PREDEFINED_MODEL],
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models=[
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AIModelEntity(
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model="gpt-3.5-turbo",
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label=I18nObject(
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en_US="gpt-3.5-turbo",
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zh_Hans="gpt-3.5-turbo",
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),
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model_type=ModelType.LLM,
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fetch_from=FetchFrom.PREDEFINED_MODEL,
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model_properties={},
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features=[ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL],
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),
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AIModelEntity(
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model="gpt-3.5-turbo-instruct",
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label=I18nObject(
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en_US="gpt-3.5-turbo-instruct",
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zh_Hans="gpt-3.5-turbo-instruct",
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),
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model_type=ModelType.LLM,
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fetch_from=FetchFrom.PREDEFINED_MODEL,
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model_properties={
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ModelPropertyKey.MODE: LLMMode.COMPLETION,
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},
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features=[],
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),
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||||
],
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||||
),
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||||
)
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]
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def get_model_schema(
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self,
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tenant_id: str,
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user_id: str,
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plugin_id: str,
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||||
provider: str,
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||||
model_type: str,
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||||
model: str,
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||||
credentials: dict,
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||||
) -> AIModelEntity | None:
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"""
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Get model schema
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||||
"""
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return AIModelEntity(
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model=model,
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label=I18nObject(
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en_US="OpenAI",
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||||
zh_Hans="OpenAI",
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||||
),
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||||
model_type=ModelType(model_type),
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||||
fetch_from=FetchFrom.PREDEFINED_MODEL,
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||||
model_properties={},
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||||
features=[ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL] if model == "gpt-3.5-turbo" else [],
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||||
)
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||||
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||||
@staticmethod
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||||
def generate_function_call(
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||||
tools: list[PromptMessageTool] | None,
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||||
) -> AssistantPromptMessage.ToolCall | None:
|
||||
if not tools or len(tools) == 0:
|
||||
return None
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||||
function: PromptMessageTool = tools[0]
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||||
function_name = function.name
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||||
function_parameters = function.parameters
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||||
function_parameters_type = function_parameters["type"]
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||||
if function_parameters_type != "object":
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||||
return None
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||||
function_parameters_properties = function_parameters["properties"]
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||||
function_parameters_required = function_parameters["required"]
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||||
parameters = {}
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||||
for parameter_name, parameter in function_parameters_properties.items():
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||||
if parameter_name not in function_parameters_required:
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||||
continue
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||||
parameter_type = parameter["type"]
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||||
if parameter_type == "string":
|
||||
if "enum" in parameter:
|
||||
if len(parameter["enum"]) == 0:
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||||
continue
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||||
parameters[parameter_name] = parameter["enum"][0]
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||||
else:
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||||
parameters[parameter_name] = "kawaii"
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||||
elif parameter_type == "integer":
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||||
parameters[parameter_name] = 114514
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||||
elif parameter_type == "number":
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||||
parameters[parameter_name] = 1919810.0
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||||
elif parameter_type == "boolean":
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||||
parameters[parameter_name] = True
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||||
return AssistantPromptMessage.ToolCall(
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||||
id=str(uuid.uuid4()),
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||||
type="function",
|
||||
function=AssistantPromptMessage.ToolCall.ToolCallFunction(
|
||||
name=function_name,
|
||||
arguments=dumps(parameters),
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||||
),
|
||||
)
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||||
|
||||
@staticmethod
|
||||
def mocked_chat_create_sync(
|
||||
model: str,
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||||
prompt_messages: list[PromptMessage],
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||||
tools: list[PromptMessageTool] | None = None,
|
||||
) -> LLMResult:
|
||||
tool_call = MockModelClass.generate_function_call(tools=tools)
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||||
|
||||
return LLMResult(
|
||||
id=str(uuid.uuid4()),
|
||||
model=model,
|
||||
prompt_messages=prompt_messages,
|
||||
message=AssistantPromptMessage(content="elaina", tool_calls=[tool_call] if tool_call else []),
|
||||
usage=LLMUsage(
|
||||
prompt_tokens=2,
|
||||
completion_tokens=1,
|
||||
total_tokens=3,
|
||||
prompt_unit_price=Decimal(0.0001),
|
||||
completion_unit_price=Decimal(0.0002),
|
||||
prompt_price_unit=Decimal(1),
|
||||
prompt_price=Decimal(0.0001),
|
||||
completion_price_unit=Decimal(1),
|
||||
completion_price=Decimal(0.0002),
|
||||
total_price=Decimal(0.0003),
|
||||
currency="USD",
|
||||
latency=0.001,
|
||||
),
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def mocked_chat_create_stream(
|
||||
model: str,
|
||||
prompt_messages: list[PromptMessage],
|
||||
tools: list[PromptMessageTool] | None = None,
|
||||
) -> Generator[LLMResultChunk, None, None]:
|
||||
tool_call = MockModelClass.generate_function_call(tools=tools)
|
||||
|
||||
full_text = "Hello, world!\n\n```python\nprint('Hello, world!')\n```"
|
||||
for i in range(0, len(full_text) + 1):
|
||||
if i == len(full_text):
|
||||
yield LLMResultChunk(
|
||||
model=model,
|
||||
prompt_messages=prompt_messages,
|
||||
delta=LLMResultChunkDelta(
|
||||
index=0,
|
||||
message=AssistantPromptMessage(
|
||||
content="",
|
||||
tool_calls=[tool_call] if tool_call else [],
|
||||
),
|
||||
),
|
||||
)
|
||||
else:
|
||||
yield LLMResultChunk(
|
||||
model=model,
|
||||
prompt_messages=prompt_messages,
|
||||
delta=LLMResultChunkDelta(
|
||||
index=0,
|
||||
message=AssistantPromptMessage(
|
||||
content=full_text[i],
|
||||
tool_calls=[tool_call] if tool_call else [],
|
||||
),
|
||||
usage=LLMUsage(
|
||||
prompt_tokens=2,
|
||||
completion_tokens=17,
|
||||
total_tokens=19,
|
||||
prompt_unit_price=Decimal(0.0001),
|
||||
completion_unit_price=Decimal(0.0002),
|
||||
prompt_price_unit=Decimal(1),
|
||||
prompt_price=Decimal(0.0001),
|
||||
completion_price_unit=Decimal(1),
|
||||
completion_price=Decimal(0.0002),
|
||||
total_price=Decimal(0.0003),
|
||||
currency="USD",
|
||||
latency=0.001,
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
def invoke_llm(
|
||||
self: PluginModelClient,
|
||||
*,
|
||||
tenant_id: str,
|
||||
user_id: str,
|
||||
plugin_id: str,
|
||||
provider: str,
|
||||
model: str,
|
||||
credentials: dict,
|
||||
prompt_messages: list[PromptMessage],
|
||||
model_parameters: dict | None = None,
|
||||
tools: list[PromptMessageTool] | None = None,
|
||||
stop: list[str] | None = None,
|
||||
stream: bool = True,
|
||||
app_id: str | None = None,
|
||||
):
|
||||
return MockModelClass.mocked_chat_create_stream(model=model, prompt_messages=prompt_messages, tools=tools)
|
||||
__all__ = ["MockModelClass"]
|
||||
|
||||
@@ -1,67 +0,0 @@
|
||||
import os
|
||||
from typing import Literal
|
||||
|
||||
import httpx
|
||||
import pytest
|
||||
|
||||
from core.plugin.entities.plugin_daemon import PluginDaemonBasicResponse, PluginToolProviderEntity
|
||||
from core.tools.entities.common_entities import I18nObject
|
||||
from core.tools.entities.tool_entities import ToolProviderEntityWithPlugin, ToolProviderIdentity
|
||||
|
||||
|
||||
class MockedHttp:
|
||||
@classmethod
|
||||
def list_tools(cls) -> list[PluginToolProviderEntity]:
|
||||
return [
|
||||
PluginToolProviderEntity(
|
||||
provider="Yeuoly",
|
||||
plugin_unique_identifier="langgenius/yeuoly:0.0.1@mock",
|
||||
plugin_id="mock-plugin",
|
||||
declaration=ToolProviderEntityWithPlugin(
|
||||
identity=ToolProviderIdentity(
|
||||
author="Yeuoly",
|
||||
name="Yeuoly",
|
||||
description=I18nObject(en_US="Yeuoly"),
|
||||
icon="ssss.svg",
|
||||
label=I18nObject(en_US="Yeuoly"),
|
||||
)
|
||||
),
|
||||
)
|
||||
]
|
||||
|
||||
@classmethod
|
||||
def requests_request(
|
||||
cls, method: Literal["GET", "POST", "PUT", "DELETE", "PATCH", "HEAD"], url: str, **kwargs
|
||||
) -> httpx.Response:
|
||||
"""
|
||||
Mocked httpx.request
|
||||
"""
|
||||
request = httpx.Request(method, url)
|
||||
if url.endswith("/tools"):
|
||||
content = PluginDaemonBasicResponse[list[PluginToolProviderEntity]](
|
||||
code=0, message="success", data=cls.list_tools()
|
||||
).model_dump_json()
|
||||
else:
|
||||
raise ValueError("")
|
||||
|
||||
response = httpx.Response(status_code=200)
|
||||
response.request = request
|
||||
response._content = content.encode("utf-8")
|
||||
return response
|
||||
|
||||
|
||||
MOCK_SWITCH = os.getenv("MOCK_SWITCH", "false").lower() == "true"
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def setup_http_mock(request, monkeypatch: pytest.MonkeyPatch):
|
||||
if MOCK_SWITCH:
|
||||
monkeypatch.setattr(httpx, "request", MockedHttp.requests_request)
|
||||
|
||||
def unpatch():
|
||||
monkeypatch.undo()
|
||||
|
||||
yield
|
||||
|
||||
if MOCK_SWITCH:
|
||||
unpatch()
|
||||
@@ -1,9 +0,0 @@
|
||||
from core.plugin.impl.tool import PluginToolManager
|
||||
|
||||
pytest_plugins = ("tests.integration_tests.plugin.__mock.http",)
|
||||
|
||||
|
||||
def test_fetch_all_plugin_tools(setup_http_mock):
|
||||
manager = PluginToolManager()
|
||||
tools = manager.fetch_tool_providers(tenant_id="test-tenant")
|
||||
assert len(tools) >= 1
|
||||
@@ -1,3 +1,5 @@
|
||||
"""Model-instance builders for parameter-extractor unit tests."""
|
||||
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
|
||||
@@ -13,8 +15,8 @@ def get_mocked_fetch_model_config(
|
||||
provider: str,
|
||||
model: str,
|
||||
mode: str,
|
||||
credentials: dict,
|
||||
):
|
||||
credentials: dict[str, object],
|
||||
) -> MagicMock:
|
||||
model_assembly = create_plugin_model_assembly(tenant_id="9d2074fc-6f86-45a9-b09d-6ecc63b9056b")
|
||||
model_provider_factory = model_assembly.model_provider_factory
|
||||
model_type_instance = model_assembly.create_model_type_instance(provider=provider, model_type=ModelType.LLM)
|
||||
@@ -55,8 +57,8 @@ def get_mocked_fetch_model_instance(
|
||||
provider: str,
|
||||
model: str,
|
||||
mode: str,
|
||||
credentials: dict,
|
||||
):
|
||||
credentials: dict[str, object],
|
||||
) -> MagicMock:
|
||||
mock_fetch_model_config = get_mocked_fetch_model_config(
|
||||
provider=provider,
|
||||
model=model,
|
||||
@@ -1,10 +1,9 @@
|
||||
import os
|
||||
from collections.abc import Callable
|
||||
from collections.abc import Callable, Generator
|
||||
|
||||
import pytest
|
||||
|
||||
from core.plugin.impl.model import PluginModelClient
|
||||
from tests.integration_tests.model_runtime.__mock.plugin_model import MockModelClass
|
||||
from tests.unit_tests.core.workflow.nodes.parameter_extractor.fixtures.plugin_model import MockModelClass
|
||||
|
||||
|
||||
def mock_plugin_daemon(
|
||||
@@ -17,7 +16,7 @@ def mock_plugin_daemon(
|
||||
:return: unpatch function
|
||||
"""
|
||||
|
||||
def unpatch():
|
||||
def unpatch() -> None:
|
||||
monkeypatch.undo()
|
||||
|
||||
monkeypatch.setattr(PluginModelClient, "invoke_llm", MockModelClass.invoke_llm)
|
||||
@@ -27,15 +26,8 @@ def mock_plugin_daemon(
|
||||
return unpatch
|
||||
|
||||
|
||||
MOCK = os.getenv("MOCK_SWITCH", "false").lower() == "true"
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def setup_model_mock(monkeypatch: pytest.MonkeyPatch):
|
||||
if MOCK:
|
||||
unpatch = mock_plugin_daemon(monkeypatch)
|
||||
|
||||
def setup_model_mock(monkeypatch: pytest.MonkeyPatch) -> Generator[None, None, None]:
|
||||
unpatch = mock_plugin_daemon(monkeypatch)
|
||||
yield
|
||||
|
||||
if MOCK:
|
||||
unpatch()
|
||||
unpatch()
|
||||
@@ -0,0 +1,258 @@
|
||||
"""Deterministic plugin-model double for parameter-extractor unit tests."""
|
||||
|
||||
import datetime
|
||||
import uuid
|
||||
from collections.abc import Generator, Sequence
|
||||
from decimal import Decimal
|
||||
from json import dumps
|
||||
from typing import override
|
||||
|
||||
from core.plugin.entities.plugin import PluginInstallationSource
|
||||
from core.plugin.entities.plugin_daemon import PluginModelProviderEntity
|
||||
from core.plugin.impl.model import PluginModelClient
|
||||
|
||||
# import monkeypatch
|
||||
from graphon.model_runtime.entities.common_entities import I18nObject
|
||||
from graphon.model_runtime.entities.llm_entities import (
|
||||
LLMMode,
|
||||
LLMResult,
|
||||
LLMResultChunk,
|
||||
LLMResultChunkDelta,
|
||||
LLMUsage,
|
||||
)
|
||||
from graphon.model_runtime.entities.message_entities import AssistantPromptMessage, PromptMessage, PromptMessageTool
|
||||
from graphon.model_runtime.entities.model_entities import (
|
||||
AIModelEntity,
|
||||
FetchFrom,
|
||||
ModelFeature,
|
||||
ModelPropertyKey,
|
||||
ModelType,
|
||||
)
|
||||
from graphon.model_runtime.entities.provider_entities import ConfigurateMethod, ProviderEntity
|
||||
|
||||
|
||||
class MockModelClass(PluginModelClient):
|
||||
@override
|
||||
def fetch_model_providers(self, tenant_id: str) -> Sequence[PluginModelProviderEntity]:
|
||||
"""
|
||||
Fetch model providers for the given tenant.
|
||||
"""
|
||||
return [
|
||||
PluginModelProviderEntity(
|
||||
id=uuid.uuid4().hex,
|
||||
created_at=datetime.datetime.now(),
|
||||
updated_at=datetime.datetime.now(),
|
||||
provider="openai",
|
||||
tenant_id=tenant_id,
|
||||
plugin_unique_identifier="langgenius/openai/openai",
|
||||
plugin_id="langgenius/openai",
|
||||
installation_source=PluginInstallationSource.Marketplace,
|
||||
declaration=ProviderEntity(
|
||||
provider="openai",
|
||||
label=I18nObject(
|
||||
en_US="OpenAI",
|
||||
zh_Hans="OpenAI",
|
||||
),
|
||||
description=I18nObject(
|
||||
en_US="OpenAI",
|
||||
zh_Hans="OpenAI",
|
||||
),
|
||||
icon_small=I18nObject(
|
||||
en_US="https://example.com/icon_small.png",
|
||||
zh_Hans="https://example.com/icon_small.png",
|
||||
),
|
||||
supported_model_types=[ModelType.LLM],
|
||||
configurate_methods=[ConfigurateMethod.PREDEFINED_MODEL],
|
||||
models=[
|
||||
AIModelEntity(
|
||||
model="gpt-3.5-turbo",
|
||||
label=I18nObject(
|
||||
en_US="gpt-3.5-turbo",
|
||||
zh_Hans="gpt-3.5-turbo",
|
||||
),
|
||||
model_type=ModelType.LLM,
|
||||
fetch_from=FetchFrom.PREDEFINED_MODEL,
|
||||
model_properties={},
|
||||
features=[ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL],
|
||||
),
|
||||
AIModelEntity(
|
||||
model="gpt-3.5-turbo-instruct",
|
||||
label=I18nObject(
|
||||
en_US="gpt-3.5-turbo-instruct",
|
||||
zh_Hans="gpt-3.5-turbo-instruct",
|
||||
),
|
||||
model_type=ModelType.LLM,
|
||||
fetch_from=FetchFrom.PREDEFINED_MODEL,
|
||||
model_properties={
|
||||
ModelPropertyKey.MODE: LLMMode.COMPLETION,
|
||||
},
|
||||
features=[],
|
||||
),
|
||||
],
|
||||
),
|
||||
)
|
||||
]
|
||||
|
||||
@override
|
||||
def get_model_schema(
|
||||
self,
|
||||
tenant_id: str,
|
||||
user_id: str | None,
|
||||
plugin_id: str,
|
||||
provider: str,
|
||||
model_type: str,
|
||||
model: str,
|
||||
credentials: dict[str, object],
|
||||
) -> AIModelEntity | None:
|
||||
"""
|
||||
Get model schema
|
||||
"""
|
||||
return AIModelEntity(
|
||||
model=model,
|
||||
label=I18nObject(
|
||||
en_US="OpenAI",
|
||||
zh_Hans="OpenAI",
|
||||
),
|
||||
model_type=ModelType(model_type),
|
||||
fetch_from=FetchFrom.PREDEFINED_MODEL,
|
||||
model_properties={},
|
||||
features=[ModelFeature.TOOL_CALL, ModelFeature.MULTI_TOOL_CALL] if model == "gpt-3.5-turbo" else [],
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def generate_function_call(
|
||||
tools: list[PromptMessageTool] | None,
|
||||
) -> AssistantPromptMessage.ToolCall | None:
|
||||
if not tools or len(tools) == 0:
|
||||
return None
|
||||
function: PromptMessageTool = tools[0]
|
||||
function_name = function.name
|
||||
function_parameters = function.parameters
|
||||
function_parameters_type = function_parameters["type"]
|
||||
if function_parameters_type != "object":
|
||||
return None
|
||||
function_parameters_properties = function_parameters["properties"]
|
||||
function_parameters_required = function_parameters["required"]
|
||||
parameters = {}
|
||||
for parameter_name, parameter in function_parameters_properties.items():
|
||||
if parameter_name not in function_parameters_required:
|
||||
continue
|
||||
parameter_type = parameter["type"]
|
||||
if parameter_type == "string":
|
||||
if "enum" in parameter:
|
||||
if len(parameter["enum"]) == 0:
|
||||
continue
|
||||
parameters[parameter_name] = parameter["enum"][0]
|
||||
else:
|
||||
parameters[parameter_name] = "kawaii"
|
||||
elif parameter_type == "integer":
|
||||
parameters[parameter_name] = 114514
|
||||
elif parameter_type == "number":
|
||||
parameters[parameter_name] = 1919810.0
|
||||
elif parameter_type == "boolean":
|
||||
parameters[parameter_name] = True
|
||||
|
||||
return AssistantPromptMessage.ToolCall(
|
||||
id=str(uuid.uuid4()),
|
||||
type="function",
|
||||
function=AssistantPromptMessage.ToolCall.ToolCallFunction(
|
||||
name=function_name,
|
||||
arguments=dumps(parameters),
|
||||
),
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def mocked_chat_create_sync(
|
||||
model: str,
|
||||
prompt_messages: list[PromptMessage],
|
||||
tools: list[PromptMessageTool] | None = None,
|
||||
) -> LLMResult:
|
||||
tool_call = MockModelClass.generate_function_call(tools=tools)
|
||||
|
||||
return LLMResult(
|
||||
id=str(uuid.uuid4()),
|
||||
model=model,
|
||||
prompt_messages=prompt_messages,
|
||||
message=AssistantPromptMessage(content="elaina", tool_calls=[tool_call] if tool_call else []),
|
||||
usage=LLMUsage(
|
||||
prompt_tokens=2,
|
||||
completion_tokens=1,
|
||||
total_tokens=3,
|
||||
prompt_unit_price=Decimal(0.0001),
|
||||
completion_unit_price=Decimal(0.0002),
|
||||
prompt_price_unit=Decimal(1),
|
||||
prompt_price=Decimal(0.0001),
|
||||
completion_price_unit=Decimal(1),
|
||||
completion_price=Decimal(0.0002),
|
||||
total_price=Decimal(0.0003),
|
||||
currency="USD",
|
||||
latency=0.001,
|
||||
),
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def mocked_chat_create_stream(
|
||||
model: str,
|
||||
prompt_messages: list[PromptMessage],
|
||||
tools: list[PromptMessageTool] | None = None,
|
||||
) -> Generator[LLMResultChunk, None, None]:
|
||||
tool_call = MockModelClass.generate_function_call(tools=tools)
|
||||
|
||||
full_text = "Hello, world!\n\n```python\nprint('Hello, world!')\n```"
|
||||
for i in range(0, len(full_text) + 1):
|
||||
if i == len(full_text):
|
||||
yield LLMResultChunk(
|
||||
model=model,
|
||||
prompt_messages=prompt_messages,
|
||||
delta=LLMResultChunkDelta(
|
||||
index=0,
|
||||
message=AssistantPromptMessage(
|
||||
content="",
|
||||
tool_calls=[tool_call] if tool_call else [],
|
||||
),
|
||||
),
|
||||
)
|
||||
else:
|
||||
yield LLMResultChunk(
|
||||
model=model,
|
||||
prompt_messages=prompt_messages,
|
||||
delta=LLMResultChunkDelta(
|
||||
index=0,
|
||||
message=AssistantPromptMessage(
|
||||
content=full_text[i],
|
||||
tool_calls=[tool_call] if tool_call else [],
|
||||
),
|
||||
usage=LLMUsage(
|
||||
prompt_tokens=2,
|
||||
completion_tokens=17,
|
||||
total_tokens=19,
|
||||
prompt_unit_price=Decimal(0.0001),
|
||||
completion_unit_price=Decimal(0.0002),
|
||||
prompt_price_unit=Decimal(1),
|
||||
prompt_price=Decimal(0.0001),
|
||||
completion_price_unit=Decimal(1),
|
||||
completion_price=Decimal(0.0002),
|
||||
total_price=Decimal(0.0003),
|
||||
currency="USD",
|
||||
latency=0.001,
|
||||
),
|
||||
),
|
||||
)
|
||||
|
||||
@override
|
||||
def invoke_llm(
|
||||
self: PluginModelClient,
|
||||
tenant_id: str,
|
||||
user_id: str | None,
|
||||
plugin_id: str,
|
||||
provider: str,
|
||||
model: str,
|
||||
credentials: dict[str, object],
|
||||
prompt_messages: list[PromptMessage],
|
||||
model_parameters: dict[str, object] | None = None,
|
||||
tools: list[PromptMessageTool] | None = None,
|
||||
stop: list[str] | None = None,
|
||||
stream: bool = True,
|
||||
app_id: str | None = None,
|
||||
) -> Generator[LLMResultChunk, None, None]:
|
||||
return MockModelClass.mocked_chat_create_stream(model=model, prompt_messages=prompt_messages, tools=tools)
|
||||
@@ -1,6 +1,9 @@
|
||||
"""Unit coverage for parameter extraction with the plugin-model boundary replaced."""
|
||||
|
||||
import os
|
||||
import time
|
||||
import uuid
|
||||
from typing import override
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
import pytest
|
||||
@@ -12,29 +15,34 @@ from core.workflow.system_variables import build_system_variables
|
||||
from extensions.ext_database import db
|
||||
from graphon.enums import WorkflowNodeExecutionStatus
|
||||
from graphon.model_runtime.entities import AssistantPromptMessage, UserPromptMessage
|
||||
from graphon.model_runtime.entities.message_entities import PromptMessage
|
||||
from graphon.model_runtime.memory.prompt_message_memory import PromptMessageMemory
|
||||
from graphon.nodes.llm.protocols import CredentialsProvider, ModelFactory
|
||||
from graphon.nodes.parameter_extractor.entities import ParameterExtractorNodeData
|
||||
from graphon.nodes.parameter_extractor.parameter_extractor_node import ParameterExtractorNode
|
||||
from graphon.runtime import GraphRuntimeState, VariablePool
|
||||
from tests.integration_tests.workflow.nodes.__mock.model import get_mocked_fetch_model_instance
|
||||
from tests.unit_tests.core.workflow.nodes.parameter_extractor.fixtures.model import get_mocked_fetch_model_instance
|
||||
from tests.workflow_test_utils import build_test_graph_init_params
|
||||
|
||||
pytest_plugins = ("tests.integration_tests.model_runtime.__mock.plugin_daemon",)
|
||||
pytest_plugins = ("tests.unit_tests.core.workflow.nodes.parameter_extractor.fixtures.plugin_daemon",)
|
||||
|
||||
|
||||
def get_mocked_fetch_memory(memory_text: str):
|
||||
class MemoryMock:
|
||||
def get_mocked_fetch_memory(memory_text: str) -> MagicMock:
|
||||
class MemoryMock(PromptMessageMemory):
|
||||
@override
|
||||
def get_history_prompt_messages(
|
||||
self,
|
||||
max_token_limit: int = 2000,
|
||||
message_limit: int | None = None,
|
||||
):
|
||||
) -> list[PromptMessage]:
|
||||
return [UserPromptMessage(content=memory_text), AssistantPromptMessage(content="mocked answer")]
|
||||
|
||||
return MagicMock(return_value=MemoryMock())
|
||||
|
||||
|
||||
def init_parameter_extractor_node(config: dict, memory=None):
|
||||
def init_parameter_extractor_node(
|
||||
config: dict[str, object], memory: PromptMessageMemory | None = None
|
||||
) -> ParameterExtractorNode:
|
||||
graph_config = {
|
||||
"edges": [
|
||||
{
|
||||
@@ -89,7 +97,8 @@ def _mock_db_session_close(monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
monkeypatch.setattr(db.session, "close", MagicMock())
|
||||
|
||||
|
||||
def test_function_calling_parameter_extractor(setup_model_mock, monkeypatch: pytest.MonkeyPatch):
|
||||
@pytest.mark.usefixtures("setup_model_mock")
|
||||
def test_function_calling_parameter_extractor(monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
"""
|
||||
Test function calling for parameter extractor.
|
||||
"""
|
||||
@@ -130,7 +139,8 @@ def test_function_calling_parameter_extractor(setup_model_mock, monkeypatch: pyt
|
||||
assert result.outputs.get("__reason") == None
|
||||
|
||||
|
||||
def test_instructions(setup_model_mock, monkeypatch: pytest.MonkeyPatch):
|
||||
@pytest.mark.usefixtures("setup_model_mock")
|
||||
def test_instructions(monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
"""
|
||||
Test chat parameter extractor.
|
||||
"""
|
||||
@@ -180,7 +190,8 @@ def test_instructions(setup_model_mock, monkeypatch: pytest.MonkeyPatch):
|
||||
assert "what's the weather in SF" in prompt.get("text")
|
||||
|
||||
|
||||
def test_chat_parameter_extractor(setup_model_mock, monkeypatch: pytest.MonkeyPatch):
|
||||
@pytest.mark.usefixtures("setup_model_mock")
|
||||
def test_chat_parameter_extractor(monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
"""
|
||||
Test chat parameter extractor.
|
||||
"""
|
||||
@@ -231,7 +242,8 @@ def test_chat_parameter_extractor(setup_model_mock, monkeypatch: pytest.MonkeyPa
|
||||
assert '<structure>\n{"type": "object"' in prompt.get("text")
|
||||
|
||||
|
||||
def test_completion_parameter_extractor(setup_model_mock, monkeypatch: pytest.MonkeyPatch):
|
||||
@pytest.mark.usefixtures("setup_model_mock")
|
||||
def test_completion_parameter_extractor(monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
"""
|
||||
Test completion parameter extractor.
|
||||
"""
|
||||
@@ -278,7 +290,7 @@ def test_completion_parameter_extractor(setup_model_mock, monkeypatch: pytest.Mo
|
||||
assert "SF" in result.process_data.get("prompts", [])[0].get("text")
|
||||
|
||||
|
||||
def test_extract_json_response():
|
||||
def test_extract_json_response() -> None:
|
||||
"""
|
||||
Test extract json response.
|
||||
"""
|
||||
@@ -316,7 +328,7 @@ def test_extract_json_response():
|
||||
assert result["location"] == "kawaii"
|
||||
|
||||
|
||||
def test_extract_json_from_tool_call():
|
||||
def test_extract_json_from_tool_call() -> None:
|
||||
"""
|
||||
Test extract json response.
|
||||
"""
|
||||
@@ -356,7 +368,8 @@ def test_extract_json_from_tool_call():
|
||||
assert result["location"] == "kawaii"
|
||||
|
||||
|
||||
def test_chat_parameter_extractor_with_memory(setup_model_mock, monkeypatch: pytest.MonkeyPatch):
|
||||
@pytest.mark.usefixtures("setup_model_mock")
|
||||
def test_chat_parameter_extractor_with_memory(monkeypatch: pytest.MonkeyPatch) -> None:
|
||||
"""
|
||||
Test chat parameter extractor with memory.
|
||||
"""
|
||||
Reference in New Issue
Block a user