# What is the typical code snippet to compute the cosine similarity between two sentence embeddings using the library?

Source: https://zilliz.com/ai-faq/what-is-the-typical-code-snippet-to-compute-the-cosine-similarity-between-two-sentence-embeddings-using-the-library
Author: zilliz

To compute cosine similarity between two sentence embeddings using Python, you typically use libraries like NumPy or scikit-learn. Here's a straightforward example:

**Using NumPy** 
Cosine similarity measures the cosine of the angle between two vectors. The formula is the dot product of the vectors divided by the product of their magnitudes. For two embeddings `emb1` and `emb2` (1D arrays), the code is:

```python
import numpy as np

def cosine_similarity(emb1, emb2):
 dot_product = np.dot(emb1, emb2)
 norm_emb1 = np.linalg.norm(emb1)
 norm_emb2 = np.linalg.norm(emb2)
 return dot_product / (norm_emb1 * norm_emb2)
```

This works for single vectors. If your embeddings are 2D (e.g., from a batch process), flatten them first: `emb1.flatten()`.

**Using scikit-learn** 
For batch operations or pairwise comparisons, `sklearn.metrics.pairwise.cosine_similarity` is efficient. For two embeddings stored as 2D arrays (shape `(1, embedding_size)`):

```python
from sklearn.metrics.pairwise import cosine_similarity

similarity = cosine_similarity(emb1.reshape(1, -1), emb2.reshape(1, -1))[0][0]
```

This handles reshaping to ensure compatibility and returns a scalar value.

**Key Considerations** 
- **Normalization**: If embeddings are pre-normalized (common in libraries like Sentence Transformers), cosine similarity simplifies to `np.dot(emb1, emb2.T)`. 
- **Batch Processing**: For multiple embeddings, pass a matrix to `cosine_similarity` to get pairwise results. 
- **Performance**: NumPy is sufficient for small-scale tasks; scikit-learn optimizes batch operations. 

Example with actual values:
```python
emb1 = np.array([0.2, 0.5, 0.8])
emb2 = np.array([0.3, 0.4, 0.7])
print(cosine_similarity(emb1, emb2)) # Output: ~0.992
``` 

This approach works with any embedding format (PyTorch, TensorFlow) by converting them to NumPy arrays first.