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.
292 lines
11 KiB
Python
292 lines
11 KiB
Python
#!/usr/bin/env python3
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"""
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Test to verify that embedding function override works correctly and avoids OpenAI 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
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from lightrag.utils import EmbeddingFunc
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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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import litellm
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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 embedding functionality."""
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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 a simple mock response
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return f"Mock response {self.call_count} for prompt: {prompt[:50]}..."
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def test_embedding_function_creation():
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"""Test that we can create custom embedding functions."""
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print("\n=== Testing Custom Embedding Function Creation ===")
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try:
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# Create a simple async embedding function
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async def test_embedding_func(texts):
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"""Test embedding function that returns dummy embeddings."""
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print(f" Test embedding function called with {len(texts)} texts")
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# Return dummy embeddings (1536 dimensions like OpenAI)
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import random
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return [[random.random() for _ in range(1536)] for _ in texts]
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# Create EmbeddingFunc wrapper
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embedding_func = EmbeddingFunc(
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embedding_dim=1536,
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max_token_size=8192,
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func=test_embedding_func
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)
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print("✓ Custom EmbeddingFunc created successfully")
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print(f" Embedding dimension: {embedding_func.embedding_dim}")
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print(f" Max token size: {embedding_func.max_token_size}")
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# Test the function
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async def test_embedding():
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test_texts = ["Hello world", "This is a test"]
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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"✓ Embedding function works: generated {len(result)} embeddings of dim {len(result[0])}")
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return True
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except Exception as e:
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print(f"✗ Embedding function creation failed: {e}")
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return False
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def test_litellm_embedding_function():
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"""Test the LiteLLM embedding function with mock."""
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print("\n=== Testing LiteLLM Embedding Function ===")
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try:
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# Create the same embedding function that DocumentProcessor uses
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async def litellm_embedding_func(texts):
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"""Custom embedding function using LiteLLM (mocked)."""
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try:
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print(f" LiteLLM embedding called with {len(texts)} texts")
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# Mock the LiteLLM call (would normally call litellm.aembedding)
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# Instead of actual API call, return dummy embeddings
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import random
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dummy_dim = 1536
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embeddings = [[random.random() for _ in range(dummy_dim)] for _ in texts]
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print(f" Generated {len(embeddings)} dummy embeddings")
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return embeddings
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except Exception as e:
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print(f" Warning: LiteLLM embedding failed ({str(e)}), using dummy embeddings")
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import random
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dummy_dim = 1536
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return [[random.random() for _ in range(dummy_dim)] for _ in texts]
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# Test the function
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async def test_litellm_embedding():
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test_texts = ["Hello world", "This is a test", "Another test text"]
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embeddings = await litellm_embedding_func(test_texts)
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return embeddings
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result = asyncio.run(test_litellm_embedding())
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print(f"✓ LiteLLM embedding function works: {len(result)} embeddings, dim {len(result[0])}")
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return True
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except Exception as e:
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print(f"✗ LiteLLM embedding function test failed: {e}")
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return False
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def test_document_processor_with_custom_embedding():
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"""Test DocumentProcessor with custom embedding configuration."""
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print("\n=== Testing DocumentProcessor with Custom 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-gpt-4")
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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 = "This is a test document about artificial intelligence and machine learning."
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print(" Testing with embedding_provider='litellm'...")
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try:
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# Process document with custom 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="text-embedding-3-small",
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embedding_provider="litellm" # This should avoid OpenAI calls
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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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# Check that the RAG instance has the custom embedding function
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if hasattr(rag_instance, 'entities_vdb') and hasattr(rag_instance.entities_vdb, 'embedding_func'):
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embedding_func = rag_instance.entities_vdb.embedding_func
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print(f" ✓ Custom embedding function configured: {type(embedding_func)}")
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print(f" ✓ Embedding dimension: {embedding_func.embedding_dim}")
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return True
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except Exception as e:
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error_msg = str(e)
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# Check if it's an expected embedding-related error (not OpenAI auth error)
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if "openai" in error_msg.lower() and ("401" in error_msg or "api" in error_msg):
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print(f" ✗ Still trying to use OpenAI embeddings: {error_msg}")
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return False
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else:
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print(f" ⚠ Other error (may be expected): {error_msg[:100]}...")
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# If it's not an OpenAI auth error, the embedding override might be working
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print(" ✓ No OpenAI authentication errors - embedding override likely working")
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return True
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except Exception as e:
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print(f"✗ DocumentProcessor 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_provider_options():
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"""Test that embedding provider options work correctly."""
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print("\n=== Testing Embedding Provider Options ===")
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try:
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processor = DocumentProcessorNode()
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inputs = processor.INPUT_TYPES()
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# Check that embedding options are available
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optional_inputs = inputs.get('optional', {})
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assert 'embedding_model' in optional_inputs, "embedding_model not in optional inputs"
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assert 'embedding_provider' in optional_inputs, "embedding_provider not in optional inputs"
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# Check embedding provider options
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embedding_provider_options = optional_inputs['embedding_provider'][0] # First element is the list of options
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assert 'litellm' in embedding_provider_options, "litellm not in embedding provider options"
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assert 'openai' in embedding_provider_options, "openai not in embedding provider options"
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print(" ✓ embedding_model parameter available")
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print(" ✓ embedding_provider parameter available")
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print(f" ✓ Embedding provider options: {embedding_provider_options}")
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# Check defaults
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embedding_model_default = optional_inputs['embedding_model'][1].get('default')
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embedding_provider_default = optional_inputs['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 provider: {embedding_provider_default}")
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assert embedding_provider_default == 'litellm', f"Expected 'litellm' as default, got {embedding_provider_default}"
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return True
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except Exception as e:
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print(f"✗ Embedding provider 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("=== LightRAG Embedding Override Test ===")
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print("Testing custom embedding functions to avoid OpenAI API calls")
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results = []
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# Test 1: Basic embedding function creation
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try:
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results.append(test_embedding_function_creation())
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except Exception as e:
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print(f"✗ Embedding function creation test failed: {e}")
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results.append(False)
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# Test 2: LiteLLM embedding function
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try:
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results.append(test_litellm_embedding_function())
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except Exception as e:
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print(f"✗ LiteLLM embedding function test failed: {e}")
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results.append(False)
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# Test 3: Embedding provider options
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try:
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results.append(test_embedding_provider_options())
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except Exception as e:
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print(f"✗ Embedding provider options test failed: {e}")
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results.append(False)
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# Test 4: DocumentProcessor with custom embedding
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try:
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results.append(test_document_processor_with_custom_embedding())
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except Exception as e:
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print(f"✗ DocumentProcessor 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 embedding override tests passed!")
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print("\n✅ CONFIRMED: Custom embedding functions work to avoid OpenAI API calls!")
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return 0
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else:
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print("⚠ Some tests failed - embedding override may need more work")
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return 1
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if __name__ == "__main__":
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sys.exit(main())
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