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Getting StartedExamples & Tutorials

Examples & Tutorials

This guide provides comprehensive examples and tutorials for common use cases.

Basic CRUD Operations

Complete Example

import { MilvusClient, DataType } from '@zilliz/milvus2-sdk-node';
 
const client = new MilvusClient({
  address: 'localhost:19530',
});
 
await client.connectPromise;
 
// Create collection
const schema = [
  {
    name: 'id',
    data_type: DataType.Int64,
    is_primary_key: true,
    autoID: true,
  },
  {
    name: 'vector',
    data_type: DataType.FloatVector,
    dim: 128,
  },
  {
    name: 'text',
    data_type: DataType.VarChar,
    max_length: 256,
  },
];
 
await client.createCollection({
  collection_name: 'my_collection',
  fields: schema,
});
 
// Insert data
const insertResult = await client.insert({
  collection_name: 'my_collection',
  data: [
    {
      vector: Array.from({ length: 128 }, () => Math.random()),
      text: 'Hello Milvus',
    },
    {
      vector: Array.from({ length: 128 }, () => Math.random()),
      text: 'Vector database',
    },
  ],
});
 
console.log('Inserted IDs:', insertResult.IDs);
 
// Create index
await client.createIndex({
  collection_name: 'my_collection',
  field_name: 'vector',
  index_type: 'HNSW',
  metric_type: 'L2',
});
 
// Load collection
await client.loadCollectionSync({
  collection_name: 'my_collection',
});
 
// Search
const searchResults = await client.search({
  collection_name: 'my_collection',
  data: [Array.from({ length: 128 }, () => Math.random())],
  limit: 5,
  output_fields: ['text'],
});
 
console.log('Search results:', searchResults.results);
 
// Query
const queryResults = await client.query({
  collection_name: 'my_collection',
  expr: 'text == "Hello Milvus"',
  output_fields: ['id', 'text'],
});
 
console.log('Query results:', queryResults.data);
 
// Delete
await client.delete({
  collection_name: 'my_collection',
  ids: [insertResult.IDs[0]],
});
 
// Drop collection
await client.dropCollection({
  collection_name: 'my_collection',
});

Image Search Example

import { MilvusClient, DataType } from '@zilliz/milvus2-sdk-node';
 
const client = new MilvusClient({
  address: 'localhost:19530',
});
 
await client.connectPromise;
 
// Create collection for image embeddings
await client.createCollection({
  collection_name: 'image_collection',
  fields: [
    {
      name: 'id',
      data_type: DataType.Int64,
      is_primary_key: true,
      autoID: true,
    },
    {
      name: 'image_embedding',
      data_type: DataType.FloatVector,
      dim: 512,
    },
    {
      name: 'image_url',
      data_type: DataType.VarChar,
      max_length: 512,
    },
  ],
});
 
// Insert image embeddings
await client.insert({
  collection_name: 'image_collection',
  data: [
    {
      image_embedding: Array.from({ length: 512 }, () => Math.random()),
      image_url: 'https://example.com/image1.jpg',
    },
    // ... more images
  ],
});
 
// Create index
await client.createIndex({
  collection_name: 'image_collection',
  field_name: 'image_embedding',
  index_type: 'HNSW',
  metric_type: 'COSINE',
});
 
// Load and search
await client.loadCollectionSync({
  collection_name: 'image_collection',
});
 
const queryEmbedding = Array.from({ length: 512 }, () => Math.random());
 
const results = await client.search({
  collection_name: 'image_collection',
  data: [queryEmbedding],
  limit: 10,
  output_fields: ['image_url'],
});
 
console.log('Similar images:', results.results);
// Create collection with multiple vector fields
await client.createCollection({
  collection_name: 'hybrid_collection',
  fields: [
    {
      name: 'id',
      data_type: DataType.Int64,
      is_primary_key: true,
      autoID: true,
    },
    {
      name: 'text_vector',
      data_type: DataType.FloatVector,
      dim: 768,
    },
    {
      name: 'image_vector',
      data_type: DataType.FloatVector,
      dim: 512,
    },
  ],
});
 
// Create indexes for both vectors
await client.createIndex({
  collection_name: 'hybrid_collection',
  field_name: 'text_vector',
  index_type: 'HNSW',
  metric_type: 'L2',
});
 
await client.createIndex({
  collection_name: 'hybrid_collection',
  field_name: 'image_vector',
  index_type: 'HNSW',
  metric_type: 'L2',
});
 
// Load collection
await client.loadCollectionSync({
  collection_name: 'hybrid_collection',
});
 
// Hybrid search
const results = await client.hybridSearch({
  collection_name: 'hybrid_collection',
  reqs: [
    {
      data: [Array.from({ length: 768 }, () => Math.random())],
      anns_field: 'text_vector',
      param: { nprobe: 64 },
      limit: 10,
    },
    {
      data: [Array.from({ length: 512 }, () => Math.random())],
      anns_field: 'image_vector',
      param: { nprobe: 64 },
      limit: 10,
    },
  ],
  ranker: 'rrf',
  limit: 10,
});

Dynamic Schema Usage

Flexible Schema Example

// Create collection with dynamic fields enabled
await client.createCollection({
  collection_name: 'dynamic_collection',
  fields: [
    {
      name: 'id',
      data_type: DataType.Int64,
      is_primary_key: true,
      autoID: true,
    },
    {
      name: 'vector',
      data_type: DataType.FloatVector,
      dim: 128,
    },
  ],
  enable_dynamic_field: true,
});
 
// Insert data with dynamic fields
await client.insert({
  collection_name: 'dynamic_collection',
  data: [
    {
      vector: Array.from({ length: 128 }, () => Math.random()),
      category: 'tech',        // Dynamic field
      tags: ['ai', 'ml'],      // Dynamic field
      metadata: {               // Dynamic field
        author: 'John Doe',
        date: '2024-01-01',
      },
    },
  ],
});

Partition Key Usage

Time-Based Partitioning

// Create collection with partition key
await client.createCollection({
  collection_name: 'time_partitioned',
  fields: [
    {
      name: 'id',
      data_type: DataType.Int64,
      is_primary_key: true,
      autoID: true,
    },
    {
      name: 'vector',
      data_type: DataType.FloatVector,
      dim: 128,
    },
    {
      name: 'timestamp',
      data_type: DataType.Int64,
      is_partition_key: true, // Partition key
    },
  ],
});
 
// Insert data (automatically partitioned by timestamp)
await client.insert({
  collection_name: 'time_partitioned',
  data: [
    {
      vector: Array.from({ length: 128 }, () => Math.random()),
      timestamp: Date.now(),
    },
  ],
});

Multi-Database Setup

Database Management Example

// Create database
await client.createDatabase({
  db_name: 'production_db',
});
 
// Create client for specific database
const prodClient = new MilvusClient({
  address: 'localhost:19530',
  database: 'production_db',
});
 
// Create collection in production database
await prodClient.createCollection({
  collection_name: 'prod_collection',
  fields: [/* ... */],
});
 
// List databases
const databases = await client.listDatabases();
console.log('Databases:', databases.db_names);

Authentication Examples

Basic Authentication

const client = new MilvusClient({
  address: 'localhost:19530',
  username: 'root',
  password: 'Milvus',
});
 
await client.connectPromise;

Token Authentication (Zilliz Cloud)

const client = new MilvusClient({
  address: 'your-endpoint.zillizcloud.com:443',
  token: 'your-api-key',
  ssl: true,
});
 
await client.connectPromise;

Next.js Integration

API Route Example

// app/api/search/route.ts
import { MilvusClient } from '@zilliz/milvus2-sdk-node';
 
const client = new MilvusClient({
  address: process.env.MILVUS_ADDRESS,
  token: process.env.MILVUS_TOKEN,
});
 
export async function POST(request) {
  const { vector } = await request.json();
 
  const results = await client.search({
    collection_name: 'my_collection',
    data: [vector],
    limit: 10,
  });
 
  return Response.json({ results });
}

LangChain Integration

LangChain.js Example

import { MilvusClient } from '@zilliz/milvus2-sdk-node';
import { Milvus } from '@langchain/community/vectorstores/milvus';
 
const client = new MilvusClient({
  address: 'localhost:19530',
});
 
const vectorStore = await Milvus.fromDocuments(
  documents,
  embeddings,
  {
    client,
    collectionName: 'langchain_collection',
  }
);
 
const results = await vectorStore.similaritySearch('query', 5);

Cloud Deployment

Zilliz Cloud Example

const client = new MilvusClient({
  address: 'your-endpoint.zillizcloud.com:443',
  token: 'your-api-key',
  ssl: true,
  timeout: 60000,
  maxRetries: 5,
});
 
await client.connectPromise;
 
// Use client as normal
await client.createCollection({ /* ... */ });

Error Handling Pattern

Comprehensive Error Handling

async function safeOperation(operation) {
  try {
    return await operation();
  } catch (error) {
    if (error.error_code === 'CollectionNotExists') {
      console.error('Collection does not exist');
    } else if (error.error_code === 'IllegalArgument') {
      console.error('Invalid argument:', error.reason);
    } else {
      console.error('Unexpected error:', error);
    }
    throw error;
  }
}
 
// Usage
await safeOperation(() =>
  client.createCollection({
    collection_name: 'my_collection',
    fields: schema,
  })
);

Next Steps

Last updated on
DeploymentClient Configuration