> ## Documentation Index
> Fetch the complete documentation index at: https://cyborg-encryption-copy.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Query
## Single Query
Retrieves the nearest neighbors for a given query vector.
```python theme={null}
def query(self,
query_vector: List[float] = None,
query_contents: Union[str, Image] = None,
top_k: int = 100,
n_probes: int = 1,
filters: Dict[str, Any] = None,
include: List[str] = ["distance"],
greedy: bool = False)
```
### Parameters
| Parameter | Type | Default | Description |
| -------------- | ---------------- | ------------ | ----------------------------------------------------------------------------------------------------------------------------------- |
| `query_vector` | `List[float]` | - | A single query vector as a list of floats (for single query). |
| `top_k` | `int` | `100` | *(Optional)* Number of nearest neighbors to return. |
| `n_probes` | `int` | `1` | *(Optional)* Number of lists probed during the query; higher values may increase recall but can also reduce performance. |
| `filters` | `Dict[str, Any]` | `None` | *(Optional)* A dictionary of filters to apply to vector metadata, limiting search scope to these vectors. |
| `include` | `List[str]` | `"distance"` | *(Optional)* List of item fields to return. Can include `distance`, `metadata`, and `vector` (`vector` only for `IVFFlat` indexes). |
| `greedy` | `bool` | `False` | *(Optional)* Whether to use greedy search (higher recall with same `n_probes`). |
If embedding auto-generation is enabled (by setting the `embedding_model` parameter in [`create_index()`](../client/create-index), then the `query_string` can be used instead of `query_vector`.
`filters` use a subset of the [MongoDB Query and Projection Operators](https://www.mongodb.com/docs/manual/reference/operator/query/).
For instance: `filters: { "$and": [ { "label": "cat" }, { "confidence": { "$gte": 0.9 } } ] }` means that only vectors where `label == "cat"` and `confidence >= 0.9` will be considered for encrypted vector search.
For more info on metadata, see [Metadata Filtering](../../../guides/data-operations/metadata-filtering).
### Returns
`List[Dict[str, Union[int, float, Dict[]]]]`: List of results for the query vector. Each result is a list of `top_k` dictionaries, each containing `id`, as well as `distance`, `metadata`, and `vector` based on `include`.
### Exceptions
* Throws if the query vector has incompatible dimensions with the index.
* Throws if the index was not created or loaded yet.
* Throws if the query could not be executed.
### Example Usage
*Single Query with Distances*:
```python theme={null}
# Load index
index = client.load_index(index_name=index_name, index_key=index_key)
# Define a single query vector
query_vector = [0.1, 0.2, 0.3] # Single query vector of dimension 3
# Perform a single query with distances
results = index.query(query_vector=query_vector, top_k=2, n_probes=5, include=["distance"])
# Example output:
# [{"id": "101", "distance": 0.05}, {"id": "102", "distance": 0.1}]
```
*Single Query without Distances*:
```python theme={null}
results = index.query(query_vector=query_vector, top_k=10, n_probes=5, include=[])
# Example output:
# [{"id": "101"}, {"id": "102"}]
```
*Single Query with Metadata*:
```python theme={null}
results = index.query(query_vector=query_vector, top_k=10, n_probes=5, filters={"label": "dog"}, include=["metadata"])
# Example output:
# [{"id": "101", "metadata": {"label": "dog", "temperament": "good boy"}},
# {"id": "102", "metadata": {"label": "dog", "temperament": "hyper"})]]
```
## Batched Queries
Retrieves the nearest neighbors for one or more query vectors.
```python theme={null}
def query(self,
query_vectors: Union[np.ndarray, List[List[float]]],
top_k: int = 100,
n_probes: int = 1,
filters: Dict[str, Any] = None,
include: List[str] = ["distance"],
greedy: bool = False)
```
### Parameters
| Parameter | Type | Default | Description |
| --------------- | ----------------------------------- | ------------ | ----------------------------------------------------------------------------------------------------------------------------------- |
| `query_vectors` | `np.ndarray` or `List[List[float]]` | - | A 2D NumPy array or list of lists, where each inner list represents a query vector (for batch query). |
| `top_k` | `int` | `100` | *(Optional)* Number of nearest neighbors to return. |
| `n_probes` | `int` | `1` | *(Optional)* Number of lists probed during the query; higher values may increase recall but can also reduce performance. |
| `filters` | `Dict[str, Any]` | `None` | *(Optional)* A dictionary of filters to apply to vector metadata, limiting search scope to these vectors. |
| `include` | `List[str]` | `"distance"` | *(Optional)* List of item fields to return. Can include `distance`, `metadata`, and `vector` (`vector` only for `IVFFlat` indexes). |
| `greedy` | `bool` | `False` | *(Optional)* Whether to use greedy search (higher recall with same `n_probes`). |
`filters` use a subset of the [MongoDB Query and Projection Operators](https://www.mongodb.com/docs/manual/reference/operator/query/).
For instance: `filters: { "$and": [ { "label": "cat" }, { "confidence": { "$gte": 0.9 } } ] }` means that only vectors where `label == "cat"` and `confidence >= 0.9` will be considered for encrypted vector search.
For more info on metadata, see [Metadata Filtering](../../../guides/data-operations/metadata-filtering).
### Returns
`List[List[Dict[str, Union[int, float, Dict[]]]]]`: List of results for each query vector. Each result is a list of `top_k` dictionaries, each containing `id`, as well as `distance`, `metadata`, and `vector` based on `include`.
### Exceptions
* Throws if the query vectors have incompatible dimensions with the index.
* Throws if the index was not created or loaded yet.
* Throws if the query could not be executed.
### Example Usage
*Batch Query with Distances*:
```python theme={null}
import numpy as np
# Multiple query vectors
query_vectors = np.array([
[0.1, 0.2, 0.3],
[0.4, 0.5, 0.6]
])
# Query using default parameters and distances
results = index.query(query_vectors=query_vectors)
# Example output:
# [
# [{"id": "item_101", "distance": 0.05}, {"id": "item_202", "distance": 0.1}],
# [{"id": "item_202", "distance": 0.2}, {"id": "item_303", "distance": 0.3}]
# ]
```