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():

# 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), ...]

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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).

# 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), ...]

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Batched Queries

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

# 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)

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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 guide.

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Note on Trained vs. Untrained Queries

For the embedded lib 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.

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API Reference

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