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Conduct a Vector Similarity Search

This topic describes how to search entities with Milvus.

A vector similarity search in Milvus calculates the distance between query vector(s) and vectors in the collection with specified similarity metrics, and returns the most similar results. By specifying a boolean expression that filters the scalar field or the primary key field, you can perform a hybrid search or even a search with Time Travel.

The following example shows how to perform a vector similarity search on a 2000-row dataset of book ID (primary key), word count (scalar field), and book introduction (vector field), simulating the situation that you search for certain books based on their vectorized introductions. Milvus will return the most similar results according to the query vector and search parameters you have defined.

Preparations

The following example code demonstrates the steps prior to a search.

If you work with your own dataset in an existing Milvus instance, you can move forward to the next step.

  1. Connect to the Milvus server. See Manage Connection for more instruction.
from pymilvus import connections
connections.connect("default", host='localhost', port='19530')
const { MilvusClient } =require("@zilliz/milvus2-sdk-node");
const milvusClient = new MilvusClient("localhost:19530");
connect -h localhost -p 19530 -a default
  1. Create a collection. See Create a Collection for more instruction.
schema = CollectionSchema([
    		FieldSchema("book_id", DataType.INT64, is_primary=True),
			FieldSchema("word_count", DataType.INT64),
    		FieldSchema("book_intro", dtype=DataType.FLOAT_VECTOR, dim=2)
		])
collection = Collection("book", schema, using='default', shards_num=2)
const params = {
  collection_name: "book",
  fields: [
    {
      name: "book_intro",
      description: "",
      data_type: 101,  // DataType.FloatVector
      type_params: {
        dim: "2",
      },
    },
	{
      name: "book_id",
      data_type: 5,   //DataType.Int64
      is_primary_key: true,
      description: "",
    },
    {
      name: "word_count",
      data_type: 5,    //DataType.Int64
      description: "",
    },
  ],
};
await milvusClient.collectionManager.createCollection(params);
create collection -c book -f book_intro:FLOAT_VECTOR:2 -f book_id:INT64 book_id -f word_count:INT64 word_count -p book_id
  1. Insert data into the collection (Milvus CLI example uses a pre-built, remote CSV file containing similar data). See Insert Data for more instruction.
import random
data = [
    		[i for i in range(2000)],
			[i for i in range(10000, 12000)],
    		[[random.random() for _ in range(2)] for _ in range(2000)],
		]
collection.insert(data)
const data = Array.from({ length: 2000 }, (v,k) => ({
  "book_intro": Array.from({ length: 2 }, () => Math.random()),
  "book_id": k,
  "word_count": k+10000,
}));
await milvusClient.dataManager.insert({
  collection_name: "book",
  fields_data: entities,
});
import -c book 'https://raw.githubusercontent.com/milvus-io/milvus_cli/main/examples/user_guide/search.csv'
  1. Create an index for the vector field. See Build Index for more instruction.
index_params = {
        "metric_type":"L2",
        "index_type":"IVF_FLAT",
        "params":{"nlist":1024}
    }
collection.create_index("book_intro", index_params=index_params)
const index_params = {
  metric_type: "L2",
  index_type: "IVF_FLAT",
  params: JSON.stringify({ nlist: 1024 }),
};
await milvusClient.indexManager.createIndex({
  collection_name: "book",
  field_name: "book_intro",
  extra_params: index_params,
});
create index

Collection name (book): book

The name of the field to create an index for (book_intro): book_intro

Index type (FLAT, IVF_FLAT, IVF_SQ8, IVF_PQ, RNSG, HNSW, ANNOY): IVF_FLAT

Index metric type (L2, IP, HAMMING, TANIMOTO): L2

Index params nlist: 1024

Timeout []:

Load collection

All CRUD operations within Milvus are executed in memory. Load the collection to memory before conducting a vector similarity search.

from pymilvus import Collection
collection = Collection("book")      # Get an existing collection.
collection.load()
await milvusClient.collectionManager.loadCollection({
  collection_name: "book",
});
load -c book
In current release, volume of the data to load must be under 70% of the total memory resources of all query nodes to reserve memory resources for execution engine.

Prepare search parameters

Prepare the parameters that suit your search scenario. The following example defines that the search will calculate the distance with Euclidean distance, and retrieve vectors from ten closest clusters built by the IVF_FLAT index.

search_params = {"metric_type": "L2", "params": {"nprobe": 10}}
const searchParams = {
  anns_field: "book_intro",
  topk: "10",
  metric_type: "L2",
  params: JSON.stringify({ nprobe: 10 }),
};
search

Collection name (book): book

The vectors of search data(the length of data is number of query (nq), the dim of every vector in data must be equal to vector field’s of collection. You can also import a csv file without headers): [[0.1, 0.2]]

The vector field used to search of collection (book_intro): book_intro

Metric type: L2

Search parameter nprobe's value: 10

The max number of returned record, also known as topk: 10

The boolean expression used to filter attribute []: 

The names of partitions to search (split by "," if multiple) ['_default'] []: 

timeout []:

Guarantee Timestamp(It instructs Milvus to see all operations performed before a provided timestamp. If no such timestamp is provided, then Milvus will search all operations performed to date) [0]: 

Travel Timestamp(Specify a timestamp in a search to get results based on a data view) [0]:
Parameter Description
metric_type Metrics used to measure similarity of vectors. See Simlarity Metrics for more information.
params Search parameter(s) specific to the index. See Index Selection for more information.
Parameter Description
anns_field Name of the field to search on.
topk Number of the most similar results to return.
metric_type Metrics used to measure similarity of vectors. See Simlarity Metrics for more information.
params Search parameter(s) specific to the index. See Index Selection for more information.
Option Full name Description
--help n/a Displays help for using the command.

Search vectors with Milvus. To search in a specific partition, specify the list of partition names.

results = collection.search(data=[[0.1, 0.2]], anns_field="book_intro", param=search_params, limit=10, expr=None)
const results = await milvusClient.dataManager.search({
  collection_name: "book",
  expr: "",
  vectors: [[0.1, 0.2]],
  search_params: searchParams,
  vector_type: 101,    // DataType.FloatVector
});
Parameter Description
data Vectors to search with.
anns_field Name of the field to search on.
params Search parameter(s) specific to the index. See Index Selection for more information.
limit Number of the most similar results to return.
expr Boolean expression used to filter attribute. See Boolean Expression Rules for more information.
partition_names (optional) List of names of the partition to search in.
output_fields (optional) Name of the field to return. Vector field is not supported in current release.
timeout (optional) A duration of time in seconds to allow for RPC. Clients wait until server responds or error occurs when it is set to None.
round_decimal (optional) Number of decimal places of returned distance.
Parameter Description
collection_name Name of the collection to search in.
search_params Parameters (as an object) used for search.
vectors Vectors to search with.
vector_type Pre-check of binary or float vectors. 100 for binary vectors and 101 for float vectors.
partition_names (optional) List of names of the partition to search in.
expr (optional) Boolean expression used to filter attribute. See Boolean Expression Rules for more information.
output_fields (optional) Name of the field to return. Vector field is not supported in current release.

Check the primary key values of the most similar vectors and their distances.

results[0].ids
results[0].distances
console.log(results.results)
# Milvus CLI automatically returns the primary key values of the most similar vectors and their distances.

Release the collection loaded in Milvus to reduce memory consumption when the search is completed.

collection.release()
await milvusClient.collectionManager.releaseCollection({  collection_name: "book",});
release -c book

Limits

Feature Maximum limit
Length of a collection name 255 characters
Number of partitions in a collection 4,096
Number of fields in a collection 256
Number of shards in a collection 256
Dimensions of a vector 32,768
Top K 16,384
Target input vectors 16,384

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