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import Hero from '@site/src/components/Hero';


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import Cards from '@site/src/components/Cards';


import Stories from '@site/src/components/Stories';





<Hero>

# Build with confidence and supercharge your AI applications\{#build-with-confidence-and-supercharge-your-ai-applications}

Zilliz Cloud provides a fully managed Milvus service, simplifying the deployment and scaling of vector search applications with security in mind.

## Basic Vector Search\{#basic-vector-search}

Perform approximate nearest neighbor (ANN) searches to find the most similar vectors to your query vector. [Learn more](./single-vector-search).

```json
// Dataset: 3 items with vectors and color metadata
// Search target: Find top 3 most similar items to query vector
[
    {
        "id": 0,
        "vector": [0.358, -0.602, 0.184, -0.263, 0.903],
        "color": "pink_8682"
    },
    {
        "id": 1,
        "vector": [0.199, 0.060, 0.698, 0.261, 0.839],
        "color": "red_7025"
    },
    {
        "id": 2,
        "vector": [0.437, -0.560, 0.646, 0.789, 0.208],
        "color": "orange_6781"
    }
]
```

```python
from pymilvus import MilvusClient

client = MilvusClient(
    uri="YOUR_CLUSTER_ENDPOINT",
    token="YOUR_CLUSTER_TOKEN"
)

# Query vector to search for similar items
query_vector = [0.358, -0.602, 0.184, -0.263, 0.903]

res = client.search(
    collection_name="my_collection",
    data=[query_vector],
    anns_field="vector",  # Field to search on
    limit=3,  # Return top 3 results
    search_params={"metric_type": "IP"}  # Inner Product similarity
)

for hits in res:
    for hit in hits:
        print(f"ID: {hit['id']}, Distance: {hit['distance']}")
```

```java
import io.milvus.v2.client.ConnectConfig;
import io.milvus.v2.client.MilvusClientV2;
import io.milvus.v2.service.vector.request.SearchReq;
import io.milvus.v2.service.vector.request.data.FloatVec;

MilvusClientV2 client = new MilvusClientV2(ConnectConfig.builder()
    .uri("YOUR_CLUSTER_ENDPOINT")
    .token("YOUR_CLUSTER_TOKEN")
    .build());

FloatVec queryVector = new FloatVec(new float[]{0.358f, -0.602f, 0.184f, -0.263f, 0.903f});
SearchReq searchReq = SearchReq.builder()
    .collectionName("my_collection")
    .data(Collections.singletonList(queryVector))
    .annsField("vector")
    .topK(3)
    .build();

SearchResp searchResp = client.search(searchReq);
```

```go
import (
    "context"
    "github.com/milvus-io/milvus/client/v2/entity"
    "github.com/milvus-io/milvus/client/v2/milvusclient"
)

ctx := context.Background()
client, _ := milvusclient.New(ctx, &milvusclient.ClientConfig{
    Address: "YOUR_CLUSTER_ENDPOINT",
    APIKey:  "YOUR_CLUSTER_TOKEN",
})

queryVector := []float32{0.358, -0.602, 0.184, -0.263, 0.903}
resultSets, _ := client.Search(ctx, milvusclient.NewSearchOption(
    "my_collection",
    3,
    []entity.Vector{entity.FloatVector(queryVector)},
).WithANNSField("vector"))
```

```javascript
import { MilvusClient } from "@zilliz/milvus2-sdk-node";

const client = new MilvusClient({
address: "YOUR_CLUSTER_ENDPOINT",
token: "YOUR_CLUSTER_TOKEN",
});

const query_vector = [0.358, -0.602, 0.184, -0.263, 0.903];

const res = await client.search({
collection_name: "my_collection",
data: [query_vector],
anns_field: "vector",
limit: 3,
});

console.log(res.results);
```

```bash
export CLUSTER_ENDPOINT="YOUR_CLUSTER_ENDPOINT"
export TOKEN="YOUR_CLUSTER_TOKEN"

curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/search" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "collectionName": "my_collection",
    "data": [[0.358, -0.602, 0.184, -0.263, 0.903]],
    "annsField": "vector",
    "limit": 3
}'
```

## Filtered Search\{#filtered-search}

Narrow your search by applying metadata filters before vector search to improve result relevance. [Learn more](./filtered-search).

```json
// Dataset: 3 items with vectors and color metadata
// Search target: Find items with color starting with "red"
[
    {
        "id": 0,
        "vector": [0.358, -0.602, 0.184, -0.263, 0.903],
        "color": "pink_8682"
    },
    {
        "id": 1,
        "vector": [0.199, 0.060, 0.698, 0.261, 0.839],
        "color": "red_7025"
    },
    {
        "id": 2,
        "vector": [0.437, -0.560, 0.646, 0.789, 0.208],
        "color": "orange_6781"
    }
]
```

```python
from pymilvus import MilvusClient

client = MilvusClient(
    uri="YOUR_CLUSTER_ENDPOINT",
    token="YOUR_CLUSTER_TOKEN"
)

# Search with metadata filter applied before vector search
res = client.search(
    collection_name="my_collection",
    data=[[0.358, -0.602, 0.184, -0.263, 0.903]],
    filter='color like "red%"',  # Filter: only search items with color starting with "red"
    limit=3,
    output_fields=["color"]  # Return color field in results
)
```

```java
import io.milvus.v2.service.vector.request.SearchReq;

SearchReq searchReq = SearchReq.builder()
    .collectionName("my_collection")
    .data(Collections.singletonList(new FloatVec(new float[]{0.358f, -0.602f, 0.184f, -0.263f, 0.903f})))
    .filter("color like \"red%\"")
    .topK(3)
    .outputFields(Lists.newArrayList("color"))
    .build();

SearchResp searchResp = client.search(searchReq);
```

```go
resultSets, _ := client.Search(ctx, milvusclient.NewSearchOption(
    "my_collection",
    3,
    []entity.Vector{entity.FloatVector([]float32{0.358, -0.602, 0.184, -0.263, 0.903})},
).WithFilter("color like \"red%\"").WithOutputFields("color"))
```

```javascript
const res = await client.search({
    collection_name: "my_collection",
    data: [[0.358, -0.602, 0.184, -0.263, 0.903]],
    filter: 'color like "red%"',
    limit: 3,
    output_fields: ["color"]
});
```

```bash
curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/search" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "collectionName": "my_collection",
    "data": [[0.358, -0.602, 0.184, -0.263, 0.903]],
    "filter": "color like \"red%\"",
    "limit": 3,
    "outputFields": ["color"]
}'
```

## Grouping Search\{#grouping-search}

Group search results by a field to aggregate data at a higher level and improve result diversity. [Learn more](./grouping-search).

```json
// Dataset: 3 items from different documents (docId)
// Search target: Get top result from each unique document
[
    {
        "id": 0,
        "vector": [0.358, -0.602, 0.184, -0.263, 0.903],
        "docId": 1
    },
    {
        "id": 1,
        "vector": [0.199, 0.060, 0.698, 0.261, 0.839],
        "docId": 5
    },
    {
        "id": 2,
        "vector": [0.437, -0.560, 0.646, 0.789, 0.208],
        "docId": 2
    }
]
```

```python
from pymilvus import MilvusClient

client = MilvusClient(
    uri="YOUR_CLUSTER_ENDPOINT",
    token="YOUR_CLUSTER_TOKEN"
)

# Group results by docId to get diverse results from different documents
res = client.search(
    collection_name="my_collection",
    data=[[0.145, 0.915, 0.797, 0.701, 0.561]],
    limit=3,
    group_by_field="docId",  # Group by: return top result from each unique docId
    output_fields=["docId"]
)
```

```java
SearchReq searchReq = SearchReq.builder()
    .collectionName("my_collection")
    .data(Collections.singletonList(new FloatVec(new float[]{0.145f, 0.915f, 0.797f, 0.701f, 0.561f})))
    .topK(3)
    .groupByFieldName("docId")
    .outputFields(Lists.newArrayList("docId"))
    .build();

SearchResp searchResp = client.search(searchReq);
```

```go
resultSets, _ := client.Search(ctx, milvusclient.NewSearchOption(
    "my_collection",
    3,
    []entity.Vector{entity.FloatVector([]float32{0.145, 0.915, 0.797, 0.701, 0.561})},
).WithGroupByField("docId").WithOutputFields("docId"))
```

```javascript
const res = await client.search({
    collection_name: "my_collection",
    data: [[0.145, 0.915, 0.797, 0.701, 0.561]],
    limit: 3,
    group_by_field: "docId",
    output_fields: ["docId"]
});
```

```bash
curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/search" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "collectionName": "my_collection",
    "data": [[0.145, 0.915, 0.797, 0.701, 0.561]],
    "groupByField": "docId",
    "limit": 3,
    "outputFields": ["docId"]
}'
```

## Hybrid Search\{#hybrid-search}

Combine multiple vector fields for multi-modal search across text, images, and more. [Learn more](./hybrid-search).

```json
// Dataset: 3 items with text and image embeddings
// Search target: Combine text and image similarity for multi-modal search
[
    {
        "id": 0,
        "text_dense": [0.358, -0.602, 0.184],
        "image_dense": [0.123, 0.456, 0.789]
    },
    {
        "id": 1,
        "text_dense": [0.199, 0.060, 0.698],
        "image_dense": [0.789, 0.234, 0.567]
    },
    {
        "id": 2,
        "text_dense": [0.437, -0.560, 0.646],
        "image_dense": [0.567, 0.890, 0.123]
    }
]
```

```python
from pymilvus import MilvusClient, AnnSearchRequest, RRFRanker

client = MilvusClient(
    uri="YOUR_CLUSTER_ENDPOINT",
    token="YOUR_CLUSTER_TOKEN"
)

# Search request 1: text dense vector
req1 = AnnSearchRequest(
    data=[[0.358, -0.602, ...]],
    anns_field="text_dense",  # Search on text embeddings
    param={"metric_type": "IP"},
    limit=3
)
# Search request 2: image dense vector
req2 = AnnSearchRequest(
    data=[[0.123, 0.456, ...]],
    anns_field="image_dense",  # Search on image embeddings
    param={"metric_type": "IP"},
    limit=3
)

# Combine multiple vector searches with RRF (Reciprocal Rank Fusion)
res = client.hybrid_search(
    collection_name="my_collection",
    reqs=[req1, req2],  # Multiple search requests
    ranker=RRFRanker(),  # Rerank combined results
    limit=3
)
```

```java
Map<String,Object> params1 = new HashMap<>();
params1.put("metric_type", "IP");
AnnSearchReq req1 = AnnSearchReq.builder()
    .vectorFieldName("text_dense")
    .vectors(Collections.singletonList(new FloatVec(textVector)))
    .params(params1)
    .topK(3)
    .build();

AnnSearchReq req2 = AnnSearchReq.builder()
    .vectorFieldName("image_dense")
    .vectors(Collections.singletonList(new FloatVec(imageVector)))
    .params(params1)
    .topK(3)
    .build();

HybridSearchReq hybridSearchReq = HybridSearchReq.builder()
    .collectionName("my_collection")
    .searchRequests(Arrays.asList(req1, req2))
    .ranker(new RRFRanker(60))
    .topK(3)
    .build();
```

```go
req1 := milvusclient.NewANNSearchRequest(
    "text_dense",
    entity.IP,
    "",
    []entity.Vector{entity.FloatVector(textVector)},
    3,
)
req2 := milvusclient.NewANNSearchRequest(
    "image_dense",
    entity.IP,
    "",
    []entity.Vector{entity.FloatVector(imageVector)},
    3,
)

resultSets, _ := client.HybridSearch(ctx,
    milvusclient.NewHybridSearchOption(
        "my_collection",
        3,
        []milvusclient.ANNSearchRequest{req1, req2},
    ).WithRRFRanker(milvusclient.NewRRFRanker(60)),
)
```

```javascript
const res = await client.search({
    collection_name: "my_collection",
    data: [textVector, imageVector],
    anns_field: ["text_dense", "image_dense"],
    limit: 3
});
```

```bash
curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/hybrid_search" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "collectionName": "my_collection",
    "search": [
        {"data": [textVector], "annsField": "text_dense", "limit": 3},
        {"data": [imageVector], "annsField": "image_dense", "limit": 3}
    ],
    "limit": 3
}'
```

## Full Text Search\{#full-text-search}

Search text using keyword matching with BM25 relevance scoring for precise term retrieval. [Learn more](./full-text-search).

```json
// Dataset: 3 text documents with BM25 sparse embeddings
// Search target: Find documents matching "machine learning" keywords
[
    {
        "id": 0,
        "text": "Artificial intelligence and machine learning",
        "text_sparse": {
            "indices": [12, 45, 78],
            "values": [0.8, 0.6, 0.9]
        }
    },
    {
        "id": 1,
        "text": "Neural networks for deep learning",
        "text_sparse": {
            "indices": [23, 56, 89],
            "values": [0.7, 0.5, 0.8]
        }
    },
    {
        "id": 2,
        "text": "Machine learning algorithms and applications",
        "text_sparse": {
            "indices": [12, 67, 90],
            "values": [0.9, 0.7, 0.6]
        }
    }
]
```

```python
from pymilvus import MilvusClient

client = MilvusClient(
    uri="YOUR_CLUSTER_ENDPOINT",
    token="YOUR_CLUSTER_TOKEN"
)

# Full-text search using raw text query (BM25 keyword matching)
res = client.search(
    collection_name="my_collection",
    data=["machine learning"],  # Raw text query (no manual embedding needed)
    anns_field="text_sparse",  # Search on sparse vectors generated by BM25
    limit=3
)
```

```java
SearchReq searchReq = SearchReq.builder()
    .collectionName("my_collection")
    .data(Collections.singletonList("machine learning"))
    .annsField("text_sparse")
    .topK(3)
    .build();

SearchResp searchResp = client.search(searchReq);
```

```go
resultSets, _ := client.Search(ctx, milvusclient.NewSearchOption(
    "my_collection",
    3,
    []entity.Vector{entity.NewSparseEmbedding("machine learning")},
).WithANNSField("text_sparse"))
```

```javascript
const res = await client.search({
    collection_name: "my_collection",
    data: ["machine learning"],
    anns_field: "text_sparse",
    limit: 3
});
```

```bash
curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/search" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "collectionName": "my_collection",
    "data": ["machine learning"],
    "annsField": "text_sparse",
    "limit": 3
}'
```

## Search Iterator\{#search-iterator}

Retrieve large-scale search results beyond the 16,384 limit using paginated iteration. [Learn more](./with-iterators).

```json
// Dataset: First 3 items with vectors
// Search target: Iterate through 20,000 results in batches of 50
[
    {
        "id": 0,
        "vector": [0.358, -0.602, 0.184, -0.263, 0.903]
    },
    {
        "id": 1,
        "vector": [0.199, 0.060, 0.698, 0.261, 0.839]
    },
    {
        "id": 2,
        "vector": [0.437, -0.560, 0.646, 0.789, 0.208]
    }
]
```

```python
from pymilvus import MilvusClient

client = MilvusClient(
    uri="YOUR_CLUSTER_ENDPOINT",
    token="YOUR_CLUSTER_TOKEN"
)

# Create iterator for large-scale retrieval (beyond 16,384 limit)
iterator = client.search_iterator(
    collection_name="my_collection",
    data=[[0.358, -0.602, 0.184, -0.263, 0.903]],
    anns_field="vector",
    batch_size=50,  # Return 50 results per iteration
    limit=20000  # Total results to retrieve
)

# Iterate through results in batches
while True:
    result = iterator.next()
    if not result:
        iterator.close()
        break
```

```java
SearchIterator searchIterator = client.searchIterator(
    SearchIteratorReq.builder()
        .collectionName("my_collection")
        .vectors(Collections.singletonList(queryVector))
        .vectorFieldName("vector")
        .batchSize(500L)
        .outputFields(Lists.newArrayList("id"))
        .build()
);

while (true) {
    List<QueryResultsWrapper.RowRecord> res = searchIterator.next();
    if (res.isEmpty()) {
        searchIterator.close();
        break;
    }
}
```

```go
itr, _ := client.SearchIterator(ctx, milvusclient.NewSearchIteratorOption(
    "my_collection",
    []entity.Vector{entity.FloatVector(queryVector)},
).WithBatchSize(500))

for {
    rs, err := itr.Next(ctx)
    if err != nil || len(rs) == 0 {
        itr.Close()
        break
    }
}
```

```javascript
const iterator = await client.searchIterator({
    collection_name: "my_collection",
    data: [[0.358, -0.602, 0.184, -0.263, 0.903]],
    batch_size: 50,
    limit: 20000
});

let result = await iterator.next();
while (result.length > 0) {
    result = await iterator.next();
}
```

```bash
curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/search_iterator" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "collectionName": "my_collection",
    "data": [[0.358, -0.602, 0.184, -0.263, 0.903]],
    "annsField": "vector",
    "batchSize": 50,
    "limit": 20000
}'
```

## Query\{#query}

Retrieve entities by filtering on scalar fields or primary keys without vector search. [Learn more](./get-and-scalar-query).

```json
// Dataset: 3 items with vectors and color metadata
// Search target: Query items where color starts with "red" (no vector search)
[
    {
        "id": 0,
        "vector": [0.358, -0.602, 0.184, -0.263, 0.903],
        "color": "pink_8682"
    },
    {
        "id": 1,
        "vector": [0.199, 0.060, 0.698, 0.261, 0.839],
        "color": "red_7025"
    },
    {
        "id": 2,
        "vector": [0.437, -0.560, 0.646, 0.789, 0.208],
        "color": "orange_6781"
    }
]
```

```python
from pymilvus import MilvusClient

client = MilvusClient(
    uri="YOUR_CLUSTER_ENDPOINT",
    token="YOUR_CLUSTER_TOKEN"
)

# Query entities by scalar field filter (no vector search)
res = client.query(
    collection_name="my_collection",
    filter='color like "red%"',  # Filter condition on scalar field
    output_fields=["id", "color"]  # Fields to return
)
```

```java
QueryReq queryReq = QueryReq.builder()
    .collectionName("my_collection")
    .filter("color like \"red%\"")
    .outputFields(Lists.newArrayList("id", "color"))
    .build();

QueryResp queryResp = client.query(queryReq);
```

```go
resultSets, _ := client.Query(ctx, milvusclient.NewQueryOption(
    "my_collection",
).WithFilter("color like \"red%\"").WithOutputFields("id", "color"))
```

```javascript
const res = await client.query({
    collection_name: "my_collection",
    filter: 'color like "red%"',
    output_fields: ["id", "color"]
});
```

```bash
curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/query" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
-d '{
    "collectionName": "my_collection",
    "filter": "color like \"red%\"",
    "outputFields": ["id", "color"]
}'
```

</Hero>

<Bars>

Select a project [plan](./select-zilliz-cloud-service-plans) and create clusters of different deployment options in the project.

- [Free](./create-cluster#create-a-free-cluster)

- [Serverless](./create-cluster#create-a-serverless-cluster)

- [Dedicated](./create-cluster#create-a-dedicated-cluster)

 [Not sure which deployment option to choose?](https://zilliz.com/pricing)

</Bars>

<Stories>

## Work with Your Data in Zilliz Cloud\{#work-with-your-data-in-zilliz-cloud}

## Bring Compute Resources to Your Data\{#bring-compute-resources-to-your-data}

1. Set up a storage integration.

    Integrate an AWS S3 bucket, a Google Cloud Storage bucket, or a Microsoft Azure blob storage container with Zilliz Cloud.

1. Create an [external volume](./external-volume).

    Use a path or the entire external storage as an external volume, which is a read-only reference to a bucket or path in the integrated storage, allowing Zilliz Cloud to access your data in-place without copying or moving it. 

1. [Create a database](./on-demand-database#create-database).

    Create a database in on-demand compute. The database is a project-level resource shared by all on-demand clusters in the project. 

1. Create an external collection in the database.

    Map the collection columns to your Parquet files, a Lance table, an Iceberg table, or Vortex files as of 0.56.0.

1. Create indexes and refresh the collection.

    Index all vector fields and optional scalar fields, then refresh the collection so that Zilliz Cloud creates metadata and index files for the collection. A refresh usually completes in sub-seconds.

1. Start explorations in your data.

    Then you can start vector searches and scalar filtering with on-demand compute resources in your data stored in external storage.

## Bring Your Own Vectors\{#bring-your-own-vectors}

1. Create and connect to your cluster.

    [Create a cluster](./create-cluster) with your desired compute and storage resources and then [connect](./connect-to-clusters) to it.

1. Create a collection.

    A collection is a two-dimensional table with fixed columns and variable rows. You can create an external collection to bring computation to your data and create a managed collection to enjoy a high-performance vector search service.

1. Import data.

    [Import data](./import-data) from a local file or an object storage bucket.

1. Conduct a vector similarity search.

    A [basic vector similarity search](./single-vector-search) helps you find the most similar results.

## Integrated Embedding\{#integrated-embedding}

1. Create and connect to your cluster.

    [Create a cluster](./create-cluster) with your desired compute and storage resources and then [connect](./connect-to-clusters) to it.

1. Set up a model provider integration.

    Create an AWS, GCP, or Azure storage integration to store credentials for your third-party model provider.

1. Create a collection and configure the embedding function.

    Create a managed collection with at least one vector field and one VARCHAR field, and define a text embedding function.

1. Insert raw text data.

    [Insert](./insert-entities) raw data. Zilliz Cloud automatically generates vector embeddings during ingestion.

1. Use raw text to conduct a search.

    Provide a raw query text. Zilliz Cloud embeds the query, compares it to stored vectors, and [returns](./single-vector-search) the most relevant results.

## Migrate From Other Data Infra\{#migrate-from-other-data-infra}

1. Connect to your data source.

    Zilliz Cloud supports various data sources, including Pinecone, MongoDB, Qdrant, PostgreSQL, etc. See the [migration guides](./migrate-from-pinecone).

1. Configure the migration source and target.

    Review the data source information and configure your migration target.

1. Review mappings.

    Set and review the mappings between the schema of the source and target data.

## Backup & Restore\{#backup-and-restore}

1. Create a backup for your cluster or collection.

    Backups are point-in-time copies of a cluster or collection. You can create backups [manually](./create-backup) or [set backup policy](./schedule-automatic-backups) for scheduled backups. You can also [copy backup to other regions](/docs/backup-to-other-regions) for improved disaster recovery capabilities.

1. (Optional) Export backups to object storage services.

    You can [export the backup files](./export-backup-files) you have created to AWS S3 or Azure Blob Storage.

1. Restore data.

    [Restore your data](./restore-from-backup-files) in the event of unexpected system failure or data loss.

</Stories>

<Cards>

## Go Further with Zilliz Cloud\{#go-further-with-zilliz-cloud}

- [Monitoring & Alerts](./metrics-and-alerts)

    Monitor your clusters and get alerts on time.

- Access Control

    Secure your data with fine-grained access control.

- [Private Networking](./setup-a-private-link)

    Connect your clusters to your private network.

- break

- [Billing](./payment-billing)

    Pay only for what you use, with no upfront costs.

- Integrations

    Integrate with your existing tools and workflows.

</Cards>

<Blocks>

## Start Building with Your Preferred Language\{#start-building-with-your-preferred-language}

- [Python](/reference/python)

- [Java](/reference/java)

- [Go](/reference/go)

- [Node.js](/reference/nodejs)

- [RESTful API](/reference/restful)

</Blocks>