> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cyborg.co/llms.txt
> Use this file to discover all available pages before exploring further.

# _similarity_search_with_relevance_scores

Returns documents with relevance scores in the range \[0, 1], with optional score filtering.

```python theme={null}
_similarity_search_with_relevance_scores(
    query: str,
    k: int,
    **kwargs
) -> List[Tuple[Document, float]]
```

### Parameters

| Parameter  | Type  | Description                                                   |
| ---------- | ----- | ------------------------------------------------------------- |
| `query`    | `str` | Query text to search for                                      |
| `k`        | `int` | Number of documents to return                                 |
| `**kwargs` | `Any` | Additional arguments including `filter` and `score_threshold` |

### Keyword Arguments

| Parameter         | Type             | Description                                      |
| ----------------- | ---------------- | ------------------------------------------------ |
| `filter`          | `Dict[str, Any]` | *(Optional)* Metadata filters to apply           |
| `score_threshold` | `float`          | *(Optional)* Minimum score threshold for results |

### Returns

`List[Tuple[Document, float]]`: List of (Document, score) tuples with scores in \[0, 1]

### Example Usage

```python theme={null}
# Search with relevance scores and threshold
results = store._similarity_search_with_relevance_scores(
    "machine learning",
    k=10,
    score_threshold=0.5
)

print(f"Found {len(results)} documents above threshold")
for doc, score in results:
    print(f"Relevance: {score:.2%} - {doc.page_content[:50]}...")
```