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Searches for nearest neighbors in the encrypted index using vector similarity search. Supports single vector queries, batch queries, and semantic search with text content. 
# Single vector query
index.query(query_vector=vector, top_k=10)

# Batch vector queries
index.query(query_vectors=vectors, top_k=10)

# Semantic search with text
index.query(query_contents=text, top_k=10)

​
Parameters

ParameterTypeDefaultDescription
query_vectorList[float] or np.ndarrayNoneSingle query vector
query_vectorsList[List[float]] or np.ndarrayNoneMultiple query vectors for batch search
query_contentsstrNoneText content for semantic search
top_kint100Number of results to return per query
n_probesint1Number of clusters to probe for search
filtersDictNone(Optional) Metadata filters to apply
includeList[str]["distance", "metadata"]Fields to include in results
greedyboolFalseUse greedy search algorithm

​
Returns

List[List[Dict]] - List of result lists. For single queries, returns a list with one result list. For batch queries, returns multiple result lists.

​
Result Format

[
  [
    {
      "id": str, # Vector identifier
      "distance": float, # Similarity distance
      "metadata": Dict, # Vector metadata (if included)
      "contents": str, # Vector contents (if included)
      "vector": List[float] # Vector data (if included)
    },
    ...
  ],
  ...
]

​
Exceptions

  • Throws if the API request fails due to network connectivity issues.
  • Throws if authentication fails (invalid API key).
  • Throws if the encryption key is invalid for the specified index.
  • Throws if there are internal server errors during the search.
  • Throws if no query vector is provided.
  • Throws if vector dimensions don’t match the index configuration.
  • Throws if parameter values are out of valid ranges.
  • Throws if the include parameter contains invalid field names.

​
Example Usage

​
Single Vector Query

# Basic similarity search
query_vector = [0.1, 0.2, 0.3, 0.4]
results = index.query(query_vector=query_vector, top_k=5)

# Access results (single query returns list with one result list)
for result in results[0]:
    print(f"ID: {result['id']}, Distance: {result['distance']}")
Batch vector queries: 
import numpy as np

# Query multiple vectors at once
query_vectors = [
    [0.1, 0.2, 0.3, 0.4],
    [0.5, 0.6, 0.7, 0.8],
    [0.9, 1.0, 1.1, 1.2]
]

batch_results = index.query(query_vectors=query_vectors, top_k=3)

# Process each query's results
for i, query_results in enumerate(batch_results):
    print(f"Results for query {i+1}:")
    for result in query_results:
        print(f"  ID: {result['id']}, Distance: {result['distance']}")

​
Semantic Search with Text

# Search using text content
results = index.query(
    query_contents="machine learning healthcare applications",
    top_k=10
)

for result in results[0]:
    print(f"Found: {result['id']}")
    if 'contents' in result:
        print(f"Content: {result['contents'][:100]}...")
Advanced filtering and options: 
# Query with metadata filters and custom options
results = index.query(
    query_vector=[0.1] * 384,
    top_k=20,
    n_probes=5,  # Search more clusters for better recall
    filters={'category': 'healthcare', 'priority': 'high'},
    include=['distance', 'metadata', 'contents'],  # Include content in results
    greedy=True  # Use greedy search for potentially better results
)

for result in results[0]:
    print(f"ID: {result['id']}")
    print(f"Distance: {result['distance']:.4f}")
    print(f"Category: {result['metadata']['category']}")
    print(f"Content preview: {result['contents'][:50]}...")
    print("---")