docs: fix semantic similarity description (cross-encoder -> bi-encoder) (#1910)

This PR updates the documentation to correctly describe the Semantic
similarity.

### Issue
The documentation previously stated that a **cross-encoder** was used
for computing the semantic similarity score. However, after reviewing
the implementation, it is clear that the current approach follows a
**bi-encoder** strategy:

- The ground truth and response are encoded independently
- Their embeddings are then compared using cosine similarity

A cross-encoder would typically process both texts together in a single
forward pass (e.g., concatenating them before encoding), which is not
the case in the current implementation.

### Current Implementation
For example, in the current implementation:

```python
embedding_1 = np.array(await self.embeddings.embed_text(ground_truth))
embedding_2 = np.array(await self.embeddings.embed_text(answer))
# Normalization factors of the above embeddings
norms_1 = np.linalg.norm(embedding_1, keepdims=True)
norms_2 = np.linalg.norm(embedding_2, keepdims=True)
embedding_1_normalized = embedding_1 / norms_1
embedding_2_normalized = embedding_2 / norms_2
similarity = embedding_1_normalized @ embedding_2_normalized.T
score = similarity.flatten()
```

This code shows that the ground truth and response are encoded
separately, and their similarity is computed using cosine similarity,
which is characteristic of a **bi-encoder** approach.

### Fix
The term "cross-encoder" has been corrected to "bi-encoder" in the
documentation to ensure consistency with the actual implementation.

Author: Ayaka-mogumogu

docs/concepts/metrics/available_metrics/semantic_similarity.md

@@ -2,7 +2,7 @@
 
 The concept of Answer Semantic Similarity pertains to the assessment of the semantic resemblance between the generated answer and the ground truth. This evaluation is based on the `ground truth` and the `answer`, with values falling within the range of 0 to 1. A higher score signifies a better alignment between the generated answer and the ground truth.
 
-Measuring the semantic similarity between answers can offer valuable insights into the quality of the generated response. This evaluation utilizes a cross-encoder model to calculate the semantic similarity score.
+Measuring the semantic similarity between answers can offer valuable insights into the quality of the generated response. This evaluation utilizes a bi-encoder model to calculate the semantic similarity score.
 
 
 ### Example