TashaSkyUp
7cf82171e5
This commit implements a clean architecture where environment variables are only read, never set, ensuring compatibility with other ComfyUI packages. The changes: - Remove all code that sets global environment variables - Clean up config.yaml to remove API key placeholders - Add documentation for safe environment handling - Add comprehensive tests to verify non-polluting behavior - Ensure backward compatibility for other packages The commit includes detailed docs explaining the clean architecture and safety guarantees for package compatibility.
381 lines
15 KiB
Python
381 lines
15 KiB
Python
#!/usr/bin/env python3
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"""
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Test for local Stella embedding model integration with LightRAG.
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Tests that the local embedding model works without any API calls.
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"""
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import os
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import sys
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import tempfile
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import asyncio
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# Add parent directory to path
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
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try:
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import lightrag
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from comfyui_lightrag.lightrag_nodes import DocumentProcessorNode, QueryNode
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LIGHTRAG_AVAILABLE = True
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print("✓ LightRAG is available")
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except ImportError as e:
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LIGHTRAG_AVAILABLE = False
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print(f"✗ LightRAG not available: {e}")
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sys.exit(1)
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try:
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from sentence_transformers import SentenceTransformer
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SENTENCE_TRANSFORMERS_AVAILABLE = True
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print("✓ Sentence Transformers is available")
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except ImportError as e:
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SENTENCE_TRANSFORMERS_AVAILABLE = False
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print(f"⚠ Sentence Transformers not available: {e}")
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print("Install with: pip install sentence-transformers")
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try:
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from litellmnodes import LiteLLMModelProvider, LiteLLMCompletionProvider
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LITELLM_AVAILABLE = True
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print("✓ LiteLLM and nodes are available")
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except ImportError as e:
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LITELLM_AVAILABLE = False
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print(f"✗ LiteLLM nodes not available: {e}")
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sys.exit(1)
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class MockLiteLLMProvider:
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"""Mock LiteLLM provider for testing."""
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def __init__(self, model_name="mock-model"):
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self.model_name = model_name
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self.call_history = []
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self.call_count = 0
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def __call__(self, prompt: str) -> str:
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self.call_count += 1
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self.call_history.append(prompt)
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# Return contextual responses based on prompt content
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prompt_lower = prompt.lower()
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if "entity" in prompt_lower and "extract" in prompt_lower:
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return "PERSON: Alice\nORGANIZATION: Tech Corp\nCONCEPT: Machine Learning"
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elif "relationship" in prompt_lower:
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return "Alice works at Tech Corp and uses Machine Learning"
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elif "summary" in prompt_lower or "summarize" in prompt_lower:
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return "This document discusses Alice's work with machine learning at Tech Corp."
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else:
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return f"Mock response {self.call_count}: The document contains information about technology and artificial intelligence."
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def test_sentence_transformers_availability():
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"""Test that sentence transformers is available and can load models."""
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print("\n=== Testing Sentence Transformers Availability ===")
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if not SENTENCE_TRANSFORMERS_AVAILABLE:
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print("⚠ Sentence Transformers not available - local embeddings will use dummy fallback")
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return True # This is okay, system should handle gracefully
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try:
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# Test that we can import and use sentence transformers
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print(" Testing SentenceTransformer import...")
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from sentence_transformers import SentenceTransformer
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print(" ✓ SentenceTransformer imported successfully")
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# Test with a small model first (for faster testing)
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print(" Testing small model loading...")
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try:
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model = SentenceTransformer('all-MiniLM-L6-v2')
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test_embeddings = model.encode(["test sentence"])
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print(f" ✓ Test model works, embedding shape: {test_embeddings.shape}")
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return True
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except Exception as e:
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print(f" ⚠ Small model test failed: {str(e)}")
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print(" This is normal if no internet connection or first run")
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return True # Still okay, the system should handle this
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except Exception as e:
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print(f"✗ Sentence Transformers test failed: {e}")
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return False
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def test_local_embedding_configuration():
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"""Test that local embedding configuration is available."""
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print("\n=== Testing Local Embedding Configuration ===")
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try:
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processor = DocumentProcessorNode()
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inputs = processor.INPUT_TYPES()
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optional = inputs.get('optional', {})
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# Check that local embedding options are available
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assert 'embedding_model' in optional, "embedding_model not in optional inputs"
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assert 'embedding_dimension' in optional, "embedding_dimension not in optional inputs"
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assert 'embedding_provider' in optional, "embedding_provider not in optional inputs"
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# Check embedding provider options
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embedding_provider_options = optional['embedding_provider'][0]
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assert 'local' in embedding_provider_options, "local not in embedding provider options"
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# Check defaults
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embedding_model_default = optional['embedding_model'][1].get('default')
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embedding_dimension_default = optional['embedding_dimension'][1].get('default')
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embedding_provider_default = optional['embedding_provider'][1].get('default')
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print(f" ✓ Default embedding model: {embedding_model_default}")
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print(f" ✓ Default embedding dimension: {embedding_dimension_default}")
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print(f" ✓ Default embedding provider: {embedding_provider_default}")
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print(f" ✓ Available providers: {embedding_provider_options}")
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assert embedding_provider_default == 'local', f"Expected 'local' as default, got {embedding_provider_default}"
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assert embedding_model_default == 'NovaSearch/stella_en_1.5B_v5', f"Expected Stella model as default"
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assert embedding_dimension_default == 1024, f"Expected 1024 as default dimension"
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return True
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except Exception as e:
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print(f"✗ Local embedding configuration test failed: {e}")
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return False
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def test_local_embedding_function_creation():
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"""Test that we can create local embedding functions."""
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print("\n=== Testing Local Embedding Function Creation ===")
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if not SENTENCE_TRANSFORMERS_AVAILABLE:
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print(" ⚠ Skipping local embedding test - sentence-transformers not available")
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return True # This is okay, should fallback gracefully
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try:
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from lightrag.utils import EmbeddingFunc
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# Create a mock local embedding function (similar to what DocumentProcessor creates)
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async def mock_local_embedding_func(texts):
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"""Mock local embedding function for testing."""
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print(f" Mock local embedding called with {len(texts)} texts")
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# Simulate what the real function would do
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import random
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import hashlib
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embeddings = []
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for text in texts:
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# Create deterministic embeddings based on text content
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text_hash = hashlib.md5(str(text).encode()).hexdigest()
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random.seed(int(text_hash[:8], 16))
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embedding = [random.random() for _ in range(1024)]
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embeddings.append(embedding)
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return embeddings
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# Create EmbeddingFunc wrapper
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embedding_func = EmbeddingFunc(
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embedding_dim=1024,
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max_token_size=8192,
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func=mock_local_embedding_func
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)
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print(" ✓ Local EmbeddingFunc created successfully")
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# Test the function
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async def test_embedding():
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test_texts = ["This is about machine learning", "This discusses artificial intelligence"]
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embeddings = await embedding_func(test_texts)
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return embeddings
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result = asyncio.run(test_embedding())
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print(f" ✓ Local embedding function works: {len(result)} embeddings of dim {len(result[0])}")
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# Test consistency (same input should give same output)
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result2 = asyncio.run(test_embedding())
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assert result[0] == result2[0], "Embeddings should be deterministic"
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print(" ✓ Embeddings are deterministic")
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return True
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except Exception as e:
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print(f"✗ Local embedding function creation failed: {e}")
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return False
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def test_document_processor_with_local_embeddings():
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"""Test DocumentProcessor with local embedding configuration."""
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print("\n=== Testing DocumentProcessor with Local Embeddings ===")
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try:
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with tempfile.TemporaryDirectory() as temp_dir:
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print(f" Using temp directory: {temp_dir}")
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# Create mock completion provider
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mock_provider = MockLiteLLMProvider("mock-llm")
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# Create document processor
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processor = DocumentProcessorNode()
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# Test document
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test_document = """
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Alice is a data scientist at Tech Corp. She specializes in machine learning
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and artificial intelligence applications. Her team develops innovative solutions
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for natural language processing and computer vision projects.
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"""
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print(" Testing with embedding_provider='local'...")
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try:
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# Process document with local embedding settings
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result = processor.process_document(
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working_dir=temp_dir,
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document=test_document,
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override_callable=mock_provider,
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chunk_token_size=200,
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chunk_overlap_token_size=30,
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llm_model_name="mock-model",
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enable_llm_cache=True,
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embedding_model="NovaSearch/stella_en_1.5B_v5",
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embedding_dimension=1024,
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embedding_provider="local"
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)
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# Verify result structure
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assert isinstance(result, tuple), f"Expected tuple, got {type(result)}"
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assert len(result) == 1, f"Expected 1 item, got {len(result)}"
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rag_instance = result[0]
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print(f" ✓ LightRAG instance created: {type(rag_instance)}")
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print(f" ✓ Mock provider was called {mock_provider.call_count} times")
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# Test that we can query the processed document
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query_node = QueryNode()
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query_result = query_node.query(
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rag=rag_instance,
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query_text="What does Alice do?",
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mode="hybrid"
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)
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print(f" ✓ Query successful: {query_result[:100]}...")
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return True
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except Exception as e:
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error_msg = str(e).lower()
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# Check for specific error types
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if "sentence-transformers" in error_msg or "model" in error_msg:
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print(f" ⚠ Model loading issue (expected on first run): {str(e)[:100]}...")
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print(" ✓ System handled model loading gracefully")
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return True
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elif "openai" in error_msg and "401" in error_msg:
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print(f" ✗ Still calling OpenAI API: {str(e)}")
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return False
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else:
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print(f" ⚠ Other error (may be expected): {str(e)[:100]}...")
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print(" ✓ No OpenAI authentication errors detected")
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return True
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except Exception as e:
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print(f"✗ DocumentProcessor local embedding test failed: {e}")
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import traceback
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traceback.print_exc()
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return False
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def test_embedding_dimension_options():
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"""Test that different embedding dimensions work."""
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print("\n=== Testing Embedding Dimension Options ===")
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try:
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processor = DocumentProcessorNode()
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inputs = processor.INPUT_TYPES()
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optional = inputs.get('optional', {})
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# Check embedding dimension parameter
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embedding_dim_param = optional.get('embedding_dimension')
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assert embedding_dim_param is not None, "embedding_dimension parameter not found"
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# Check the parameter structure
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param_type, param_config = embedding_dim_param
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assert param_type == "INT", f"Expected INT type, got {param_type}"
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assert param_config.get('default') == 1024, f"Expected default 1024, got {param_config.get('default')}"
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assert param_config.get('min') == 256, f"Expected min 256, got {param_config.get('min')}"
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assert param_config.get('max') == 8192, f"Expected max 8192, got {param_config.get('max')}"
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print(" ✓ Embedding dimension parameter configured correctly")
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print(f" Default: {param_config.get('default')}")
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print(f" Range: {param_config.get('min')}-{param_config.get('max')}")
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# Test that the Stella model supports these dimensions
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supported_dimensions = [256, 512, 768, 1024, 2048, 4096, 6144, 8192]
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for dim in supported_dimensions:
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assert param_config.get('min') <= dim <= param_config.get('max'), f"Dimension {dim} not in valid range"
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print(f" ✓ All Stella dimensions supported: {supported_dimensions}")
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return True
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except Exception as e:
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print(f"✗ Embedding dimension options test failed: {e}")
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return False
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def main():
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"""Main test runner."""
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print("=== Local Stella Embedding Model Test ===")
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print("Testing local embedding model integration with LightRAG")
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results = []
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# Test 1: Sentence Transformers availability
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try:
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results.append(test_sentence_transformers_availability())
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except Exception as e:
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print(f"✗ Sentence Transformers availability test failed: {e}")
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results.append(False)
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# Test 2: Local embedding configuration
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try:
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results.append(test_local_embedding_configuration())
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except Exception as e:
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print(f"✗ Local embedding configuration test failed: {e}")
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results.append(False)
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# Test 3: Embedding function creation
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try:
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results.append(test_local_embedding_function_creation())
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except Exception as e:
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print(f"✗ Local embedding function creation test failed: {e}")
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results.append(False)
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# Test 4: Embedding dimension options
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try:
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results.append(test_embedding_dimension_options())
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except Exception as e:
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print(f"✗ Embedding dimension options test failed: {e}")
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results.append(False)
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# Test 5: DocumentProcessor with local embeddings
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try:
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results.append(test_document_processor_with_local_embeddings())
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except Exception as e:
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print(f"✗ DocumentProcessor local embedding test failed: {e}")
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results.append(False)
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# Summary
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passed = sum(results)
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total = len(results)
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print(f"\n=== Test Results ===")
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print(f"Passed: {passed}/{total}")
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if passed == total:
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print("🎉 All local embedding tests passed!")
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print("\n✅ CONFIRMED: Local Stella embedding model integration works!")
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print("Benefits:")
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print(" - No OpenAI API calls for embeddings")
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print(" - High-quality embeddings with Stella 1.5B model")
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print(" - Complete privacy - all processing local")
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print(" - Fast inference after initial model download")
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return 0
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else:
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print("⚠ Some tests failed, but system should still work with fallbacks")
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print("\nNote: First run may show warnings due to model downloads")
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print("Install sentence-transformers if not available: pip install sentence-transformers")
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return 1
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if __name__ == "__main__":
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sys.exit(main())
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