Query

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

Parameter	Type	Default	Description
`query_vector`	`List[float]` or `np.ndarray`	`None`	Single query vector
`query_vectors`	`List[List[float]]` or `np.ndarray`	`None`	Multiple query vectors for batch search
`query_contents`	`str`	`None`	Text content for semantic search
`top_k`	`int`	`100`	Number of results to return per query
`n_probes`	`int`	`1`	Number of clusters to probe for search
`filters`	`Dict`	`None`	(Optional) Metadata filters to apply
`include`	`List[str]`	`["distance", "metadata"]`	Fields to include in results
`greedy`	`bool`	`False`	Use 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

Error

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.

Validation Errors

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("---")

Introduction

Client

Encrypted Index

Index Configuration

Parameters

Returns

Result Format

Exceptions

Example Usage

Single Vector Query

Semantic Search with Text

​Parameters

​Returns

​Result Format

​Exceptions

​Example Usage

​Single Vector Query

​Semantic Search with Text

Parameters

Returns

Result Format

Exceptions

Example Usage

Single Vector Query

Semantic Search with Text