mirror of
https://github.com/semantica-agi/semantica.git
synced 2026-08-29 04:26:20 +00:00
KaifAhmad1
58707ff721
- Robust ID extraction in CentralityCalculator, CommunityDetector, and ConnectivityAnalyzer - Support for direct Entity objects and dictionaries as node identifiers - Improved Entity hashability in utils/types.py - Added integration test to verify fix and prevent regression
88 lines
3.5 KiB
Python
88 lines
3.5 KiB
Python
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import sys
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import os
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import unittest
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import numpy as np
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# Add project root to path
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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from semantica.embeddings import TextEmbedder, EmbeddingGenerator
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class TestEmbeddingProviders(unittest.TestCase):
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def test_sentence_transformers_default(self):
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print("\nTesting Sentence Transformers (Default)...")
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embedder = TextEmbedder(method="sentence_transformers")
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text = "This is a test sentence."
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embedding = embedder.embed_text(text)
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self.assertIsInstance(embedding, np.ndarray)
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print(f"Embedding shape: {embedding.shape}")
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# Default model is all-MiniLM-L6-v2 which is 384 dim
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self.assertEqual(len(embedding), 384)
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def test_sentence_transformers_custom_model(self):
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print("\nTesting Sentence Transformers (Custom Model: all-mpnet-base-v2)...")
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# all-mpnet-base-v2 produces 768 dim embeddings
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try:
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embedder = TextEmbedder(
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method="sentence_transformers",
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model_name="all-mpnet-base-v2"
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)
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text = "This is a test sentence."
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embedding = embedder.embed_text(text)
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self.assertIsInstance(embedding, np.ndarray)
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print(f"Embedding shape: {embedding.shape}")
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self.assertEqual(len(embedding), 768)
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except Exception as e:
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print(f"Skipping custom model test if download fails: {e}")
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def test_fastembed_default(self):
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print("\nTesting FastEmbed (Default)...")
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try:
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embedder = TextEmbedder(method="fastembed")
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text = "This is a test sentence."
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embedding = embedder.embed_text(text)
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self.assertIsInstance(embedding, np.ndarray)
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print(f"Embedding shape: {embedding.shape}")
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# FastEmbed default is usually BAAI/bge-small-en-v1.5 (384 dim) or similar
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self.assertTrue(len(embedding) > 0)
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except ImportError:
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print("FastEmbed not installed, skipping.")
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def test_fastembed_custom_model(self):
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print("\nTesting FastEmbed (Custom Model: BAAI/bge-small-en-v1.5)...")
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try:
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embedder = TextEmbedder(
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method="fastembed",
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model_name="BAAI/bge-small-en-v1.5"
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)
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text = "This is a test sentence."
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embedding = embedder.embed_text(text)
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self.assertIsInstance(embedding, np.ndarray)
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print(f"Embedding shape: {embedding.shape}")
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self.assertEqual(len(embedding), 384)
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except ImportError:
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print("FastEmbed not installed, skipping.")
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except Exception as e:
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print(f"FastEmbed custom model error: {e}")
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def test_embedding_generator_config(self):
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print("\nTesting EmbeddingGenerator with config...")
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# Configure to use fastembed via EmbeddingGenerator
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config = {
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"text": {
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"method": "fastembed",
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"model_name": "BAAI/bge-small-en-v1.5"
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}
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}
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generator = EmbeddingGenerator(config=config)
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embeddings = generator.generate_embeddings(["Test text"], data_type="text")
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self.assertEqual(embeddings.shape[1], 384)
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print("EmbeddingGenerator config test passed.")
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if __name__ == '__main__':
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with open("test_results.txt", "w") as f:
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runner = unittest.TextTestRunner(stream=f, verbosity=2)
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unittest.main(testRunner=runner, exit=False)
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