Detect **embedding collapse** in an embeddings-and-retrieval pipeline, where many items unintentionally map to nearly the same vector and retrieval quality degrades. You’ll compute a simple “collapse score” based on average cosine similarity across embedding pairs.

The cosine similarity between two vectors $u$ and $v$ is:
$$
\cos(u, v) = \frac{u \cdot v}{\|u\|\|v\|}
$$

## Requirements

Implement the function
```python
def detect_embedding_collapse(embeddings, threshold=0.95):
    \"""
    Detects embedding collapse using average pairwise cosine similarity.

    Args:
        embeddings (np.ndarray): Item embeddings of shape (n_items, dim).
        threshold (float): If the average pairwise cosine similarity is >= threshold,
            report collapse.

    Returns:
        tuple[float, bool]:
            (avg_pairwise_cosine_similarity, is_collapsed)
    \"""
    # Your solution below
```

**Rules:**
- Convert the input to a NumPy array if necessary and L2-normalize each embedding vector.
- Compute the average cosine similarity over all unique pairs `(i, j)` where `i < j`.
- Return the average similarity and a boolean `is_collapsed` based on `threshold`.
- Don’t use any prebuilt similarity utilities (e.g., `sklearn.metrics.pairwise`).
- Keep it in a single Python function using only NumPy (and Python built-ins if needed).

## Example

```python
embeddings = np.array([
    [1.0, 0.0],
    [0.99, 0.01],
    [0.98, 0.02]
])

detect_embedding_collapse(embeddings, threshold=0.95)
```

Output:
```python
(0.9997, True)
```

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Embedding collapse detection

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1227. Embedding collapse detection

Examples

Constraints

Similar Questions

Argument	Type
threshold	float
embeddings	np.ndarray