# Searching

The main functionality of deglib is to search for similar feature vectors.

```python
import numpy as np
import deglib

# build the graph ...

# generate query
query = np.random.random(DIMS).astype(np.float32)

# search query
indices, distances = graph.search(query, eps=0.1, k=16)

print(indices)    # data[result_indices] will show the 16 closest datapoints to "query"
print(distances)  # numpy array with 16 distances to the results
```

This will give the 16 indices of the datapoints closest to the query as well as their distances to the query.

## Multiple queries at once

It is also possible to search for multiple queries at once:

```python
N_QUERIES = 10
query = np.random.random(N_QUERIES, DIMS).astype(np.float32)

indices, distances = graph.search(query, eps=0.1, k=16)
```

In this case indices and distances will have shape (10, 16) (16 indices for 10 queries).

## Parameters

There are a lot of parameters to control the search speed and recall. See [SearchGraph](../api/graphs/search_graph.md#search-graph) for more details.

## Filters

Sometimes you want to exclude some samples of your dataset from search. This can be done using filters:

```python
valid_labels = np.array([0, 3, 34, 52, 108])
query = np.random.random(dims).astype(np.float32)

results, distances = graph.search(query, filter_labels=valid_labels, eps=0.01, k=4)
```

In this case results will only include values in valid_labels.