> ## 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.

# Query Encrypted Index

Once you've added items to an encrypted index, you can query the index to retrieve the items that match a given query. This is done via `query()`:

<CodeGroup>
  ```python Python theme={null}
  # Example query
  query_vector = [0.5, 0.9, 0.2, 0.7]
  top_k = 10

  # Perform query
  results = index.query(query_vector=query_vector, top_k=top_k)

  print(results)
  # Example results (IDs and distances)
  # [("id": 12, "distance": 0.01), ("id": 7, "distance": 0.04), ...]
  ```

  ```cpp C++ theme={null}
  // Example query
  std::vector<float> query_vector = {0.5, 0.9, 0.2, 0.7};
  size_t top_k = 10;

  // Perform query
  cyborg::QueryParams query_params(top_k);
  cyborg::QueryResults results = index.Query(query_vector, query_params);

  // Print the results
  for (const auto& result : results) {
      std::cout << "ID: " << result.id << ", Distance: " << result.distance << std::endl;
  }
  ```
</CodeGroup>

## Query Parameters

You can specify additional parameters for the query, such as:

* `top_k`: the number of results to return.
* `n_probes`: the number of clusters to search for each query vector.
* `return_distances`: whether to return distances with the IDs.
* `greedy`: whether to perform a greedy search (higher recall but slower).

<CodeGroup>
  ```python Python theme={null}
  # Example query
  query_vector = [0.5, 0.9, 0.2, 0.7]
  top_k = 10
  n_probes = 5
  return_distances = True
  greedy = False

  # Perform query
  results = index.query(query_vector=query_vector, top_k=top_k, n_probes=n_probes, return_distances=return_distances, greedy=greedy)

  print(results)
  # Example results (IDs and distances)
  # [("id": 12, "distance": 0.01), ("id": 7, "distance": 0.04), ...]
  ```

  ```cpp C++ theme={null}
  // Example query
  std::vector<float> query_vector = {0.5, 0.9, 0.2, 0.7};
  size_t top_k = 10;
  size_t n_probes = 5;
  bool return_distances = true;
  bool greedy = false;

  // Perform query
  cyborg::QueryParams query_params(top_k, n_probes, return_distances, greedy);
  cyborg::QueryResults results = index.Query(query_vector, query_params);

  // Print the results
  for (const auto& result : results) {
      std::cout << "ID: " << result.id << ", Distance: " << result.distance << std::endl;
  }
  ```
</CodeGroup>

## Batched Queries

It's also possible to perform batch queries by passing a list of query vectors to `query()`:

<CodeGroup>
  ```python Python theme={null}
  # Example batch query
  query_vectors = [[0.5, 0.9, 0.2, 0.7], [0.1, 0.3, 0.8, 0.6]]
  top_k = 10

  # Perform batch query
  results = index.query(query_vectors=query_vectors, top_k=top_k)
  ```

  ```cpp C++ theme={null}
  // Example batch query
  std::vector<std::vector<float>> query_vectors = {{0.5, 0.9, 0.2, 0.7}, {0.1, 0.3, 0.8, 0.6}};
  size_t top_k = 10;

  // Perform batch query
  cyborg::QueryParams query_params(top_k);
  cyborg::QueryResults results = index.Query(query_vectors, query_params);
  ```
</CodeGroup>

## Retrieving Items Post-Query

In certain applications, such as RAG, it may be desirable to retrieve matching items after a query. This is possible via `get_item`, which retrieves and decrypts items contents (if they were added in `upsert`). For more details, see the [Get Items](./get-items) guide.

## Note on Trained vs. Untrained Queries

For the [embedded lib](../get-started/about#deployment-models) version of Cyborg Vector Search, queries will initially default to 'untrained' queries, which use an exhaustive search algorithm. This is fine for small datasets, but once you have more than `50,000` vectors in your index, you should train the index. Without doing so, queries will run slower. For more details, see [Training an Encrypted Index](./train-index).

## API Reference

For more information on querying encrypted indexes, refer to the API reference:

<CardGroup cols={2}>
  <Card title="Python API Reference" href="../../api-reference/python/encrypted-index/query" icon="python">
    API reference for `query()` in Python
  </Card>

  <Card title="C++ API Reference" href="../../api-reference/cpp/encrypted-index/query" icon="brackets-curly">
    API reference for `Query()` in C++
  </Card>
</CardGroup>