Search Operations

Perform powerful similarity searches across your vector data using text queries or vector inputs for intelligent content discovery.

Search Your Vectors

CLI
Python SDK
JavaScript SDK

gravixlayer vectors vector search-text <index-id> --query "sample document" --model "baai/bge-large-en-v1.5" --top-k 5

Example Output:

Text search in index: a6012e38-8742-463b-bf1d-96f46a583923
SUCCESS: Text search completed in 59ms
   Usage: {'prompt_tokens': 2, 'total_tokens': 2}
   Found 1 result(s):

1. Vector ID: sample-doc-1
   Score: 0.953193

import os
from gravixlayer import GravixLayer

client = GravixLayer()

index_id = "your-api-key"
vectors = client.vectors.index(index_id)

# Search with vector embedding
query_embedding = [0.1, 0.2, 0.3] * 341 + [0.1]  # 1024 dimensions
results = vectors.search(
    vector=query_embedding,
    top_k=5
)

print(f"Found {len(results.hits)} results")
for hit in results.hits:
    print(f"ID: {hit.id}, Score: {hit.score:.6f}")
    if hit.metadata:
        print(f"Metadata: {hit.metadata}")
    print("---")

import { GravixLayer } from 'gravixlayer';

const client = new GravixLayer({
  apiKey: process.env.GRAVIXLAYER_API_KEY,
});

const indexId = "your-index-id";
const vectors = client.vectors.index(indexId);
const results = await vectors.searchText(
  "sample document",
  "baai/bge-large-en-v1.5",
  5
);

console.log(`Found ${results.hits.length} results in ${results.query_time_ms}ms`);
if (results.hits.length > 0) {
  console.log(`First result: ID=${results.hits[0].id}, Score=${results.hits[0].score.toFixed(6)}`);
}

Search with Vector Embeddings

CLI
Python SDK
JavaScript SDK

# Search with vector
gravixlayer vectors vector search <index-id> --embedding "[0.1,0.2,0.3,0.4,0.5]" --top-k 3

import os
from gravixlayer import GravixLayer

client = GravixLayer()

index_id = "your-index-id"
vectors = client.vectors.index(index_id)

# Search with vector embedding
query_embedding = [0.1, 0.2, 0.3] * 341 + [0.1]  # 1024 dimensions
results = vectors.search(
    embedding=query_embedding,
    top_k=5
)

print(f"Found {len(results.hits)} results")
for hit in results.hits:
    print(f"ID: {hit.id}, Score: {hit.score:.6f}")
    if hit.metadata:
        print(f"Metadata: {hit.metadata}")
    print("---")

import { GravixLayer } from 'gravixlayer';

const client = new GravixLayer({
  apiKey: process.env.GRAVIXLAYER_API_KEY,
});

const indexId = "your-index-id";
const vectors = client.vectors.index(indexId);

// Search with vector embedding
const queryEmbedding = Array.from({length: 1024}, () => Math.random() * 0.1);
const results = await vectors.search(queryEmbedding, 5);

console.log(`Found ${results.hits.length} results`);
results.hits.forEach(hit => {
  console.log(`ID: ${hit.id}, Score: ${hit.score.toFixed(6)}`);
  if (hit.metadata) {
    console.log(`Metadata: ${JSON.stringify(hit.metadata)}`);
  }
  console.log("---");
});

Advanced Search with Filters

Python SDK
JavaScript SDK

import os
from gravixlayer import GravixLayer

client = GravixLayer()

index_id = "your-index-id"
vectors = client.vectors.index(index_id)

# Search with metadata filters
results = vectors.search_text(
    query="machine learning",
    model="baai/bge-large-en-v1.5",
    top_k=10,
    filter={
        "category": "technology",
        "difficulty": "beginner"
    }
)

print(f"Filtered search found {len(results.hits)} results")
for hit in results.hits:
    print(f"ID: {hit.id}")
    print(f"Score: {hit.score:.6f}")
    print(f"Metadata: {hit.metadata}")
    print("---")

import { GravixLayer } from 'gravixlayer';

const client = new GravixLayer({
  apiKey: process.env.GRAVIXLAYER_API_KEY,
});

const indexId = "your-index-id";
const vectors = client.vectors.index(indexId);

// Search with metadata filters
const results = await vectors.searchText(
  "machine learning",
  "baai/bge-large-en-v1.5",
  10,
  {
    category: "technology",
    difficulty: "beginner"
  }
);

console.log(`Filtered search found ${results.hits.length} results`);
results.hits.forEach(hit => {
  console.log(`ID: ${hit.id}`);
  console.log(`Score: ${hit.score.toFixed(6)}`);
  console.log(`Metadata: ${JSON.stringify(hit.metadata)}`);
  console.log("---");
});

Batch Search Operations

Python SDK
JavaScript SDK

import os
from gravixlayer import GravixLayer

client = GravixLayer()

index_id = "your-index-id"
vectors = client.vectors.index(index_id)

# Multiple text searches
queries = [
    "machine learning algorithms",
    "artificial intelligence",
    "deep learning networks"
]

for i, query in enumerate(queries):
    results = vectors.search_text(
        query=query,
        model="baai/bge-large-en-v1.5",
        top_k=3
    )
    print(f"Query {i+1}: '{query}'")
    print(f"Found {len(results.hits)} results")
    
    for hit in results.hits:
        print(f"  - {hit.id}: {hit.score:.4f}")
    print()

import { GravixLayer } from 'gravixlayer';

const client = new GravixLayer({
  apiKey: process.env.GRAVIXLAYER_API_KEY,
});

const indexId = "your-index-id";
const vectors = client.vectors.index(indexId);

// Multiple text searches
const queries = [
    "machine learning algorithms",
    "artificial intelligence", 
    "deep learning networks"
];

for (let i = 0; i < queries.length; i++) {
    const query = queries[i];
    const results = await vectors.searchText(
        query,
        "baai/bge-large-en-v1.5",
        3
    );
    console.log(`Query ${i+1}: '${query}'`);
    console.log(`Found ${results.hits.length} results`);
    
    results.hits.forEach(hit => {
        console.log(`  - ${hit.id}: ${hit.score.toFixed(4)}`);
    });
    console.log();
}

Search Performance Tips

Optimize Query Performance

Python SDK
JavaScript SDK

import os
from gravixlayer import GravixLayer
import time

client = GravixLayer()

index_id = "your-index-id"
vectors = client.vectors.index(index_id)

def benchmark_search(query, iterations=5):
    """Benchmark search performance"""
    times = []
    
    for i in range(iterations):
        start_time = time.time()
        results = vectors.search_text(
            query=query,
            model="baai/bge-large-en-v1.5",
            top_k=10
        )
        end_time = time.time()
        
        search_time = (end_time - start_time) * 1000  # Convert to ms
        times.append(search_time)
        
        print(f"Iteration {i+1}: {search_time:.2f}ms, {len(results.hits)} results")
    
    avg_time = sum(times) / len(times)
    print(f"Average search time: {avg_time:.2f}ms")
    
    return avg_time

# Benchmark your searches
benchmark_search("artificial intelligence")

import { GravixLayer } from 'gravixlayer';

const client = new GravixLayer({
  apiKey: process.env.GRAVIXLAYER_API_KEY,
});

const indexId = "your-index-id";
const vectors = client.vectors.index(indexId);

async function benchmarkSearch(query, iterations = 5) {
    const times = [];
    
    for (let i = 0; i < iterations; i++) {
        const startTime = Date.now();
        const results = await vectors.searchText(
            query,
            "baai/bge-large-en-v1.5",
            10
        );
        const endTime = Date.now();
        
        const searchTime = endTime - startTime;
        times.push(searchTime);
        
        console.log(`Iteration ${i+1}: ${searchTime}ms, ${results.hits.length} results`);
    }
    
    const avgTime = times.reduce((a, b) => a + b, 0) / times.length;
    console.log(`Average search time: ${avgTime.toFixed(2)}ms`);
    
    return avgTime;
}

// Benchmark your searches
await benchmarkSearch("artificial intelligence");

Getting Started

Core Features

Advanced Usage

Resources

Search Operations

Search Your Vectors

Search with Vector Embeddings

Advanced Search with Filters

Batch Search Operations

Search Performance Tips

Optimize Query Performance

Getting Started

Core Features

Advanced Usage

Resources

​Search Your Vectors

​Search with Vector Embeddings

​Advanced Search with Filters

​Batch Search Operations

​Search Performance Tips

​Optimize Query Performance

Search Your Vectors

Search with Vector Embeddings

Advanced Search with Filters

Batch Search Operations

Search Performance Tips

Optimize Query Performance