24 lines
862 B
Python
24 lines
862 B
Python
from llama_index.core import VectorStoreIndex, Document
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from llama_index.core.embeddings import resolve_embed_model
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# Check the default embedding model
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embed_model = resolve_embed_model("default")
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print(f"Default embedding model: {embed_model}")
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# Create a simple test document
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doc = Document(text="This is a test document")
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# Get embedding dimension
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try:
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# Test embedding
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test_embedding = embed_model.get_text_embedding("test")
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print(f"Embedding dimension: {len(test_embedding)}")
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print(f"Embedding type: {type(test_embedding)}")
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except Exception as e:
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print(f"Error getting embedding: {e}")
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# Alternative way to check dimension
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if hasattr(embed_model, 'embed_dim'):
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print(f"Model embed_dim attribute: {embed_model.embed_dim}")
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elif hasattr(embed_model, 'dimension'):
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print(f"Model dimension attribute: {embed_model.dimension}") |