mirror of
https://github.com/trustgraph-ai/trustgraph.git
synced 2026-07-21 19:21:03 +02:00
Added more LLMs
This commit is contained in:
Cyber MacGeddon
parent
ada7d96b4c
commit
50a4549c02
2 changed files with 559 additions and 1 deletions
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@ -392,4 +392,108 @@ def mock_vllm_error_response():
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"""Mock vLLM error response"""
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mock_response = MagicMock()
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mock_response.status = 500
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return mock_response
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return mock_response
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# === Cohere Specific Fixtures ===
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@pytest.fixture
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def cohere_processor_config(base_processor_config):
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"""Default configuration for Cohere processor"""
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config = base_processor_config.copy()
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config.update({
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'model': 'c4ai-aya-23-8b',
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'api_key': 'test-api-key',
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'temperature': 0.0
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})
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return config
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@pytest.fixture
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def mock_cohere_client():
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"""Mock Cohere client"""
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mock_client = MagicMock()
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# Mock the response structure
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mock_output = MagicMock()
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mock_output.text = "Test response from Cohere"
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mock_output.meta.billed_units.input_tokens = 18
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mock_output.meta.billed_units.output_tokens = 10
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mock_client.chat.return_value = mock_output
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return mock_client
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@pytest.fixture
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def mock_cohere_rate_limit_error():
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"""Mock Cohere rate limit error"""
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import cohere
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return cohere.TooManyRequestsError("Rate limit exceeded")
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# === Google AI Studio Specific Fixtures ===
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@pytest.fixture
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def googleaistudio_processor_config(base_processor_config):
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"""Default configuration for Google AI Studio processor"""
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config = base_processor_config.copy()
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config.update({
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'model': 'gemini-2.0-flash-001',
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'api_key': 'test-api-key',
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'temperature': 0.0,
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'max_output': 8192
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})
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return config
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@pytest.fixture
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def mock_googleaistudio_client():
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"""Mock Google AI Studio client"""
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mock_client = MagicMock()
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# Mock the response structure
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mock_response = MagicMock()
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mock_response.text = "Test response from Google AI Studio"
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mock_response.usage_metadata.prompt_token_count = 20
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mock_response.usage_metadata.candidates_token_count = 12
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mock_client.models.generate_content.return_value = mock_response
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return mock_client
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@pytest.fixture
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def mock_googleaistudio_rate_limit_error():
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"""Mock Google AI Studio rate limit error"""
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from google.api_core.exceptions import ResourceExhausted
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return ResourceExhausted("Rate limit exceeded")
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# === LlamaFile Specific Fixtures ===
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@pytest.fixture
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def llamafile_processor_config(base_processor_config):
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"""Default configuration for LlamaFile processor"""
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config = base_processor_config.copy()
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config.update({
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'model': 'LLaMA_CPP',
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'llamafile': 'http://localhost:8080/v1',
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'temperature': 0.0,
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'max_output': 4096
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})
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return config
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@pytest.fixture
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def mock_llamafile_client():
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"""Mock OpenAI client for LlamaFile"""
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mock_client = MagicMock()
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# Mock the response structure
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mock_response = MagicMock()
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mock_response.choices = [MagicMock()]
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mock_response.choices[0].message.content = "Test response from LlamaFile"
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mock_response.usage.prompt_tokens = 14
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mock_response.usage.completion_tokens = 8
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mock_client.chat.completions.create.return_value = mock_response
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return mock_client
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454
tests/unit/test_text_completion/test_llamafile_processor.py
Normal file
454
tests/unit/test_text_completion/test_llamafile_processor.py
Normal file
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@ -0,0 +1,454 @@
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"""
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Unit tests for trustgraph.model.text_completion.llamafile
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Following the same successful pattern as previous tests
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"""
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import pytest
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from unittest.mock import AsyncMock, MagicMock, patch
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from unittest import IsolatedAsyncioTestCase
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# Import the service under test
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from trustgraph.model.text_completion.llamafile.llm import Processor
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from trustgraph.base import LlmResult
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from trustgraph.exceptions import TooManyRequests
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class TestLlamaFileProcessorSimple(IsolatedAsyncioTestCase):
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"""Test LlamaFile processor functionality"""
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@patch('trustgraph.model.text_completion.llamafile.llm.OpenAI')
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@patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
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@patch('trustgraph.base.llm_service.LlmService.__init__')
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async def test_processor_initialization_basic(self, mock_llm_init, mock_async_init, mock_openai_class):
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"""Test basic processor initialization"""
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# Arrange
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mock_openai_client = MagicMock()
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mock_openai_class.return_value = mock_openai_client
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mock_async_init.return_value = None
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mock_llm_init.return_value = None
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config = {
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'model': 'LLaMA_CPP',
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'llamafile': 'http://localhost:8080/v1',
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'temperature': 0.0,
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'max_output': 4096,
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'concurrency': 1,
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'taskgroup': AsyncMock(),
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'id': 'test-processor'
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}
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# Act
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processor = Processor(**config)
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# Assert
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assert processor.model == 'LLaMA_CPP'
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assert processor.llamafile == 'http://localhost:8080/v1'
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assert processor.temperature == 0.0
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assert processor.max_output == 4096
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assert hasattr(processor, 'openai')
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mock_openai_class.assert_called_once_with(
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base_url='http://localhost:8080/v1',
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api_key='sk-no-key-required'
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)
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@patch('trustgraph.model.text_completion.llamafile.llm.OpenAI')
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@patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
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@patch('trustgraph.base.llm_service.LlmService.__init__')
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async def test_generate_content_success(self, mock_llm_init, mock_async_init, mock_openai_class):
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"""Test successful content generation"""
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# Arrange
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mock_openai_client = MagicMock()
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mock_response = MagicMock()
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mock_response.choices = [MagicMock()]
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mock_response.choices[0].message.content = "Generated response from LlamaFile"
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mock_response.usage.prompt_tokens = 20
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mock_response.usage.completion_tokens = 12
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mock_openai_client.chat.completions.create.return_value = mock_response
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mock_openai_class.return_value = mock_openai_client
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mock_async_init.return_value = None
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mock_llm_init.return_value = None
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config = {
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'model': 'LLaMA_CPP',
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'llamafile': 'http://localhost:8080/v1',
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'temperature': 0.0,
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'max_output': 4096,
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'concurrency': 1,
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'taskgroup': AsyncMock(),
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'id': 'test-processor'
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}
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processor = Processor(**config)
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# Act
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result = await processor.generate_content("System prompt", "User prompt")
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# Assert
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assert isinstance(result, LlmResult)
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assert result.text == "Generated response from LlamaFile"
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assert result.in_token == 20
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assert result.out_token == 12
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assert result.model == 'llama.cpp' # Note: model in result is hardcoded to 'llama.cpp'
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# Verify the OpenAI API call structure
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mock_openai_client.chat.completions.create.assert_called_once_with(
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model='LLaMA_CPP',
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messages=[{
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"role": "user",
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"content": "System prompt\n\nUser prompt"
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}]
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)
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@patch('trustgraph.model.text_completion.llamafile.llm.OpenAI')
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@patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
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@patch('trustgraph.base.llm_service.LlmService.__init__')
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async def test_generate_content_generic_exception(self, mock_llm_init, mock_async_init, mock_openai_class):
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"""Test handling of generic exceptions"""
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# Arrange
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mock_openai_client = MagicMock()
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mock_openai_client.chat.completions.create.side_effect = Exception("Connection error")
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mock_openai_class.return_value = mock_openai_client
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mock_async_init.return_value = None
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mock_llm_init.return_value = None
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config = {
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'model': 'LLaMA_CPP',
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'llamafile': 'http://localhost:8080/v1',
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'temperature': 0.0,
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'max_output': 4096,
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'concurrency': 1,
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'taskgroup': AsyncMock(),
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'id': 'test-processor'
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}
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processor = Processor(**config)
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# Act & Assert
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with pytest.raises(Exception, match="Connection error"):
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await processor.generate_content("System prompt", "User prompt")
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@patch('trustgraph.model.text_completion.llamafile.llm.OpenAI')
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@patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
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@patch('trustgraph.base.llm_service.LlmService.__init__')
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async def test_processor_initialization_with_custom_parameters(self, mock_llm_init, mock_async_init, mock_openai_class):
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"""Test processor initialization with custom parameters"""
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# Arrange
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mock_openai_client = MagicMock()
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mock_openai_class.return_value = mock_openai_client
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mock_async_init.return_value = None
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mock_llm_init.return_value = None
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config = {
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'model': 'custom-llama',
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'llamafile': 'http://custom-host:8080/v1',
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'temperature': 0.7,
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'max_output': 2048,
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'concurrency': 1,
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'taskgroup': AsyncMock(),
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'id': 'test-processor'
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}
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# Act
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processor = Processor(**config)
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# Assert
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assert processor.model == 'custom-llama'
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assert processor.llamafile == 'http://custom-host:8080/v1'
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assert processor.temperature == 0.7
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assert processor.max_output == 2048
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mock_openai_class.assert_called_once_with(
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base_url='http://custom-host:8080/v1',
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api_key='sk-no-key-required'
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)
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@patch('trustgraph.model.text_completion.llamafile.llm.OpenAI')
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@patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
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@patch('trustgraph.base.llm_service.LlmService.__init__')
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async def test_processor_initialization_with_defaults(self, mock_llm_init, mock_async_init, mock_openai_class):
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"""Test processor initialization with default values"""
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# Arrange
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mock_openai_client = MagicMock()
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mock_openai_class.return_value = mock_openai_client
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mock_async_init.return_value = None
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mock_llm_init.return_value = None
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# Only provide required fields, should use defaults
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config = {
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'concurrency': 1,
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'taskgroup': AsyncMock(),
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'id': 'test-processor'
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}
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# Act
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processor = Processor(**config)
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# Assert
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assert processor.model == 'LLaMA_CPP' # default_model
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assert processor.llamafile == 'http://localhost:8080/v1' # default_llamafile
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assert processor.temperature == 0.0 # default_temperature
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assert processor.max_output == 4096 # default_max_output
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mock_openai_class.assert_called_once_with(
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base_url='http://localhost:8080/v1',
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api_key='sk-no-key-required'
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)
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@patch('trustgraph.model.text_completion.llamafile.llm.OpenAI')
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@patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
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@patch('trustgraph.base.llm_service.LlmService.__init__')
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async def test_generate_content_empty_prompts(self, mock_llm_init, mock_async_init, mock_openai_class):
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"""Test content generation with empty prompts"""
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# Arrange
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mock_openai_client = MagicMock()
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mock_response = MagicMock()
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mock_response.choices = [MagicMock()]
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mock_response.choices[0].message.content = "Default response"
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mock_response.usage.prompt_tokens = 2
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mock_response.usage.completion_tokens = 3
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mock_openai_client.chat.completions.create.return_value = mock_response
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mock_openai_class.return_value = mock_openai_client
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mock_async_init.return_value = None
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mock_llm_init.return_value = None
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config = {
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'model': 'LLaMA_CPP',
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'llamafile': 'http://localhost:8080/v1',
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'temperature': 0.0,
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'max_output': 4096,
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'concurrency': 1,
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'taskgroup': AsyncMock(),
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'id': 'test-processor'
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}
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processor = Processor(**config)
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# Act
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result = await processor.generate_content("", "")
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# Assert
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assert isinstance(result, LlmResult)
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assert result.text == "Default response"
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assert result.in_token == 2
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assert result.out_token == 3
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assert result.model == 'llama.cpp'
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# Verify the combined prompt is sent correctly
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call_args = mock_openai_client.chat.completions.create.call_args
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expected_prompt = "\n\n" # Empty system + "\n\n" + empty user
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assert call_args[1]['messages'][0]['content'] == expected_prompt
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@patch('trustgraph.model.text_completion.llamafile.llm.OpenAI')
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@patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
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@patch('trustgraph.base.llm_service.LlmService.__init__')
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async def test_generate_content_message_structure(self, mock_llm_init, mock_async_init, mock_openai_class):
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"""Test that LlamaFile messages are structured correctly"""
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# Arrange
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mock_openai_client = MagicMock()
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mock_response = MagicMock()
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mock_response.choices = [MagicMock()]
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mock_response.choices[0].message.content = "Response with proper structure"
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mock_response.usage.prompt_tokens = 25
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mock_response.usage.completion_tokens = 15
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mock_openai_client.chat.completions.create.return_value = mock_response
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mock_openai_class.return_value = mock_openai_client
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mock_async_init.return_value = None
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mock_llm_init.return_value = None
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config = {
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'model': 'LLaMA_CPP',
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'llamafile': 'http://localhost:8080/v1',
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'temperature': 0.0,
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'max_output': 4096,
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'concurrency': 1,
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'taskgroup': AsyncMock(),
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'id': 'test-processor'
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}
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processor = Processor(**config)
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# Act
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result = await processor.generate_content("You are a helpful assistant", "What is AI?")
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# Assert
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assert result.text == "Response with proper structure"
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assert result.in_token == 25
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assert result.out_token == 15
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# Verify the message structure
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call_args = mock_openai_client.chat.completions.create.call_args
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messages = call_args[1]['messages']
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assert len(messages) == 1
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assert messages[0]['role'] == 'user'
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assert messages[0]['content'] == "You are a helpful assistant\n\nWhat is AI?"
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# Verify model parameter
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assert call_args[1]['model'] == 'LLaMA_CPP'
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@patch('trustgraph.model.text_completion.llamafile.llm.OpenAI')
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@patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
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@patch('trustgraph.base.llm_service.LlmService.__init__')
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async def test_openai_client_initialization(self, mock_llm_init, mock_async_init, mock_openai_class):
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"""Test that OpenAI client is initialized correctly for LlamaFile"""
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# Arrange
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mock_openai_client = MagicMock()
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mock_openai_class.return_value = mock_openai_client
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mock_async_init.return_value = None
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mock_llm_init.return_value = None
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config = {
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'model': 'llama-custom',
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'llamafile': 'http://llamafile-server:8080/v1',
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'temperature': 0.0,
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'max_output': 4096,
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'concurrency': 1,
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'taskgroup': AsyncMock(),
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'id': 'test-processor'
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}
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# Act
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processor = Processor(**config)
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# Assert
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# Verify OpenAI client was called with correct parameters
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mock_openai_class.assert_called_once_with(
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base_url='http://llamafile-server:8080/v1',
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api_key='sk-no-key-required'
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)
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# Verify processor has the client
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assert processor.openai == mock_openai_client
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@patch('trustgraph.model.text_completion.llamafile.llm.OpenAI')
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@patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
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@patch('trustgraph.base.llm_service.LlmService.__init__')
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async def test_generate_content_prompt_construction(self, mock_llm_init, mock_async_init, mock_openai_class):
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"""Test prompt construction with system and user prompts"""
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# Arrange
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mock_openai_client = MagicMock()
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mock_response = MagicMock()
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mock_response.choices = [MagicMock()]
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mock_response.choices[0].message.content = "Response with system instructions"
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mock_response.usage.prompt_tokens = 30
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mock_response.usage.completion_tokens = 20
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mock_openai_client.chat.completions.create.return_value = mock_response
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mock_openai_class.return_value = mock_openai_client
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mock_async_init.return_value = None
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mock_llm_init.return_value = None
|
||||
|
||||
config = {
|
||||
'model': 'LLaMA_CPP',
|
||||
'llamafile': 'http://localhost:8080/v1',
|
||||
'temperature': 0.0,
|
||||
'max_output': 4096,
|
||||
'concurrency': 1,
|
||||
'taskgroup': AsyncMock(),
|
||||
'id': 'test-processor'
|
||||
}
|
||||
|
||||
processor = Processor(**config)
|
||||
|
||||
# Act
|
||||
result = await processor.generate_content("You are a helpful assistant", "What is machine learning?")
|
||||
|
||||
# Assert
|
||||
assert result.text == "Response with system instructions"
|
||||
assert result.in_token == 30
|
||||
assert result.out_token == 20
|
||||
|
||||
# Verify the combined prompt
|
||||
call_args = mock_openai_client.chat.completions.create.call_args
|
||||
expected_prompt = "You are a helpful assistant\n\nWhat is machine learning?"
|
||||
assert call_args[1]['messages'][0]['content'] == expected_prompt
|
||||
|
||||
@patch('trustgraph.model.text_completion.llamafile.llm.OpenAI')
|
||||
@patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
|
||||
@patch('trustgraph.base.llm_service.LlmService.__init__')
|
||||
async def test_generate_content_hardcoded_model_response(self, mock_llm_init, mock_async_init, mock_openai_class):
|
||||
"""Test that response model is hardcoded to 'llama.cpp'"""
|
||||
# Arrange
|
||||
mock_openai_client = MagicMock()
|
||||
mock_response = MagicMock()
|
||||
mock_response.choices = [MagicMock()]
|
||||
mock_response.choices[0].message.content = "Test response"
|
||||
mock_response.usage.prompt_tokens = 15
|
||||
mock_response.usage.completion_tokens = 10
|
||||
|
||||
mock_openai_client.chat.completions.create.return_value = mock_response
|
||||
mock_openai_class.return_value = mock_openai_client
|
||||
|
||||
mock_async_init.return_value = None
|
||||
mock_llm_init.return_value = None
|
||||
|
||||
config = {
|
||||
'model': 'custom-model-name', # This should be ignored in response
|
||||
'llamafile': 'http://localhost:8080/v1',
|
||||
'temperature': 0.0,
|
||||
'max_output': 4096,
|
||||
'concurrency': 1,
|
||||
'taskgroup': AsyncMock(),
|
||||
'id': 'test-processor'
|
||||
}
|
||||
|
||||
processor = Processor(**config)
|
||||
|
||||
# Act
|
||||
result = await processor.generate_content("System", "User")
|
||||
|
||||
# Assert
|
||||
assert result.model == 'llama.cpp' # Should always be 'llama.cpp', not 'custom-model-name'
|
||||
assert processor.model == 'custom-model-name' # But processor.model should still be custom
|
||||
|
||||
@patch('trustgraph.model.text_completion.llamafile.llm.OpenAI')
|
||||
@patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
|
||||
@patch('trustgraph.base.llm_service.LlmService.__init__')
|
||||
async def test_generate_content_no_rate_limiting(self, mock_llm_init, mock_async_init, mock_openai_class):
|
||||
"""Test that no rate limiting is implemented (SLM assumption)"""
|
||||
# Arrange
|
||||
mock_openai_client = MagicMock()
|
||||
mock_response = MagicMock()
|
||||
mock_response.choices = [MagicMock()]
|
||||
mock_response.choices[0].message.content = "No rate limiting test"
|
||||
mock_response.usage.prompt_tokens = 10
|
||||
mock_response.usage.completion_tokens = 5
|
||||
|
||||
mock_openai_client.chat.completions.create.return_value = mock_response
|
||||
mock_openai_class.return_value = mock_openai_client
|
||||
|
||||
mock_async_init.return_value = None
|
||||
mock_llm_init.return_value = None
|
||||
|
||||
config = {
|
||||
'model': 'LLaMA_CPP',
|
||||
'llamafile': 'http://localhost:8080/v1',
|
||||
'temperature': 0.0,
|
||||
'max_output': 4096,
|
||||
'concurrency': 1,
|
||||
'taskgroup': AsyncMock(),
|
||||
'id': 'test-processor'
|
||||
}
|
||||
|
||||
processor = Processor(**config)
|
||||
|
||||
# Act
|
||||
result = await processor.generate_content("System", "User")
|
||||
|
||||
# Assert
|
||||
assert result.text == "No rate limiting test"
|
||||
# No specific rate limit error handling tested since SLM presumably has no rate limits
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
pytest.main([__file__])
|
||||
Loading…
Add table
Add a link
Reference in a new issue