milvus-io
diff --git a/‎assets/iterative-filtering.png
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diff --git a/‎assets/partition-key-isolation.png
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diff --git a/‎site/en/userGuide/search-query-get/filtered-search.md
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diff --git a/‎site/en/userGuide/search-query-get/use-partition-key.md
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@@ -11,6 +11,10 @@ An ANN search finds vector embeddings most similar to specified vector embedding
 
 ## Overview
 
+In Milvus, filtered searches are categorized into two types — **standard filtering** and **iterative filtering** — depending on the stage at which the filtering is applied.
+
+## Standard Filtering
+
 If a collection contains both vector embeddings and their metadata, you can filter metadata before ANN search to improve the relevancy of the search result. Once Milvus receives a search request carrying a filtering condition, it restricts the search scope within the entities matching the specified filtering condition.
 
 ![Filtered search](../../../../assets/filtered-search.png)
@@ -23,6 +27,18 @@ As shown in the above diagram, the search request carries `chunk like % red %` a
 
 - Returns top-K entities.
 
+## Iterative Filtering
+
+The standard filtering process effectively narrows the search scope to a small range. However, overly complex filtering expressions may result in very high search latency. In such cases, iterative filtering can serve as an alternative, helping to reduce the workload of scalar filtering.
+
+![Iterative filtering](../../../../assets/iterative-filtering.png)
+
+As illustrated in the diagram above, a search with iterative filtering performs the vector search in iterations. Each entity returned by the iterator undergoes scalar filtering, and this process continues until the specified topK results are achieved.
+
+This method significantly reduces the number of entities subjected to scalar filtering, making it especially beneficial for handling highly complex filtering expressions.
+
+However, it’s important to note that the iterator processes entities one at a time. This sequential approach can lead to longer processing times or potential performance issues, especially when a large number of entities are subjected to scalar filtering.
+
 ## Examples
 
 This section demonstrates how to conduct a filtered search. Code snippets in this section assume  you already have the following entities in your collection. Each entity has four fields, namely **id**, **vector**, **color**, and **likes**.
@@ -43,7 +59,9 @@ This section demonstrates how to conduct a filtered search. Code snippets in thi
 
 ```
 
-The search request in the following code snippet carries a filtering condition and several output fields.
+### Search with Standard Filtering
+
+The following code snippets demonstrate a search with standard filtering, and the request in the following code snippet carries a filtering condition and several output fields.
 
 <div class="multipleCode">
     <a href="#python">Python </a>
@@ -235,4 +253,177 @@ The filtering condition carried in the search request reads `color like "red%" a
 
 ```
 
-For more information on the operators that you can use in metadata filtering, refer to [Metadata Filtering](boolean.md).
+For more information on the operators that you can use in metadata filtering, refer to [Metadata Filtering](boolean.md).
+### Search with iteraive filtering
+
+To conduct a filtered search with iterative filtering, you can do as follows:
+
+<div class="multipleCode">
+    <a href="#python">Python </a>
+    <a href="#java">Java</a>
+    <a href="#javascript">Node.js</a>
+    <a href="#curl">cURL</a>
+</div>
+
+```python
+from pymilvus import MilvusClient
+
+client = MilvusClient(
+    uri="http://localhost:19530",
+    token="root:Milvus"
+)
+
+query_vector = [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592]
+
+res = client.search(
+    collection_name="my_collection",
+    data=[query_vector],
+    limit=5,
+    # highlight-start
+    filter='color like "red%" and likes > 50',
+    output_fields=["color", "likes"],
+    search_params={
+        "hints": "iterative_filter"
+    }    
+    # highlight-end
+)
+
+for hits in res:
+    print("TopK results:")
+    for hit in hits:
+        print(hit)
+
+```
+
+```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;
+import io.milvus.v2.service.vector.response.SearchResp;
+
+MilvusClientV2 client = new MilvusClientV2(ConnectConfig.builder()
+        .uri("http://localhost:19530")
+        .token("root:Milvus")
+        .build());
+
+FloatVec queryVector = new FloatVec(new float[]{0.3580376395471989f, -0.6023495712049978f, 0.18414012509913835f, -0.26286205330961354f, 0.9029438446296592f});
+SearchReq searchReq = SearchReq.builder()
+        .collectionName("filtered_search_collection")
+        .data(Collections.singletonList(queryVector))
+        .topK(5)
+        .filter("color like \"red%\" and likes > 50")
+        .outputFields(Arrays.asList("color", "likes"))
+        .searchParams(new HashMap<>("hints", "iterative_filter"))
+        .build();
+
+SearchResp searchResp = client.search(searchReq);
+
+List<List<SearchResp.SearchResult>> searchResults = searchResp.getSearchResults();
+for (List<SearchResp.SearchResult> results : searchResults) {
+    System.out.println("TopK results:");
+    for (SearchResp.SearchResult result : results) {
+        System.out.println(result);
+    }
+}
+
+// Output
+// TopK results:
+// SearchResp.SearchResult(entity={color=red_4794, likes=122}, score=0.5975797, id=4)
+// SearchResp.SearchResult(entity={color=red_9392, likes=58}, score=-0.24996188, id=6)
+
+```
+
+```go
+import (
+    "context"
+    "log"
+
+    "github.com/milvus-io/milvus/client/v2"
+    "github.com/milvus-io/milvus/client/v2/entity"
+)
+
+func ExampleClient_Search_filter() {
+        ctx, cancel := context.WithCancel(context.Background())
+        defer cancel()
+
+        milvusAddr := "127.0.0.1:19530"
+        token := "root:Milvus"
+
+        cli, err := client.New(ctx, &client.ClientConfig{
+                Address: milvusAddr,
+                APIKey:  token,
+        })
+        if err != nil {
+                log.Fatal("failed to connect to milvus server: ", err.Error())
+        }
+
+        defer cli.Close(ctx)
+
+        queryVector := []float32{0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592}
+
+        resultSets, err := cli.Search(ctx, client.NewSearchOption(
+                "filtered_search_collection", // collectionName
+                3,             // limit
+                []entity.Vector{entity.FloatVector(queryVector)},
+        ).WithFilter(`color like "red%" and likes > 50`).WithHints("iterative_filter").WithOutputFields("color", "likes"))
+        if err != nil {
+                log.Fatal("failed to perform basic ANN search collection: ", err.Error())
+        }
+
+        for _, resultSet := range resultSets {
+                log.Println("IDs: ", resultSet.IDs)
+                log.Println("Scores: ", resultSet.Scores)
+        }
+        // Output:
+        // IDs:
+        // Scores:
+}
+
+
+```
+
+```javascript
+import { MilvusClient, DataType } from "@zilliz/milvus2-sdk-node";
+
+const address = "http://localhost:19530";
+const token = "root:Milvus";
+const client = new MilvusClient({address, token});
+
+const query_vector = [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592]
+
+const res = await client.search({
+    collection_name: "filtered_search_collection",
+    data: [query_vector],
+    limit: 5,
+    // highlight-start
+    filters: 'color like "red%" and likes > 50',
+    hints: "iterative_filter",
+    output_fields: ["color", "likes"]
+    // highlight-end
+})
+
+```
+
+```curl
+export CLUSTER_ENDPOINT="http://localhost:19530"
+export TOKEN="root:Milvus"
+
+curl --request POST \
+--url "${CLUSTER_ENDPOINT}/v2/vectordb/entities/search" \
+--header "Authorization: Bearer ${TOKEN}" \
+--header "Content-Type: application/json" \
+-d '{
+    "collectionName": "quick_setup",
+    "data": [
+        [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592]
+    ],
+    "annsField": "vector",
+    "filter": "color like \"red%\" and likes > 50",
+    "searchParams": {"hints": "iterative_filter"},
+    "limit": 3,
+    "outputFields": ["color", "likes"]
+}'
+# {"code":0,"cost":0,"data":[]}
+
+```
@@ -259,3 +259,94 @@ export filter='partition_key == "x" && <other conditions>'
 export filter='partition_key in ["x", "y", "z"] && <other conditions>'
 
 ```
+
+<div class="alert note">
+
+You have to replace `partition_key` with the name of the field that is designated as the partition key.
+
+</div>
+
+## Use Partition Key Isolation
+
+In the multi-tenancy scenario, you can designate the scalar field related to tenant identities as the partition key and create a filter based on a specific value in this scalar field. To further improve search performance in similar scenarios, Milvus introduces the Partition Key Isolation feature.
+
+![Partition Key Isolation](../../../../assets/partition-key-isolation.png)
+
+As shown in the above figure, Milvus groups entities based on the Partition Key value and creates a separate index for each of these groups. Upon receiving a search request, Milvus locates the index based on the Partition Key value specified in the filtering condition and restricts the search scope within the entities included in the index, thus avoiding scanning irrelevant entities during the search and greatly enhancing the search performance.
+Once you have enabled Partition Key Isolation, you can include only a specific value in the Partition-key-based filter so that Milvus can restrict the search scope within the entities included in the index that match.
+
+<div class="alert note">
+
+Currently, the Partition-Key Isolation feature applies only to searches with the index type set to HNSW.
+
+</div>
+
+### Enable Partition Key Isolation
+
+The following code examples demonstrate how to enable Partition Key Isolation.
+
+<div class="multipleCode">
+  <a href="#python">Python </a>
+  <a href="#java">Java</a>
+  <a href="#javascript">Node.js</a>
+  <a href="#go">Go</a>
+  <a href="#curl">cURL</a>
+</div>
+
+```python
+client.create_collection(
+    collection_name="my_collection",
+    schema=schema,
+    # highlight-next-line
+    properties={"partitionkey.isolation": True}
+)
+
+```
+
+```java
+import io.milvus.v2.service.collection.request.CreateCollectionReq;
+
+Map<String, String> properties = new HashMap<>();
+properties.put("partitionkey.isolation", "true");
+
+CreateCollectionReq createCollectionReq = CreateCollectionReq.builder()
+        .collectionName("my_collection")
+        .collectionSchema(schema)
+        .numPartitions(1024)
+        .properties(properties)
+        .build();
+client.createCollection(createCollectionReq);
+
+```
+
+```javascript
+res = await client.alterCollection({
+    collection_name: "my_collection",
+    properties: {
+        "partitionkey.isolation": true
+    }
+})
+
+```
+
+```curl
+export params='{
+    "partitionKeyIsolation": true
+}'
+
+export CLUSTER_ENDPOINT="http://localhost:19530"
+export TOKEN="root:Milvus"
+
+curl --request POST \
+--url "${CLUSTER_ENDPOINT}/v2/vectordb/collections/create" \
+--header "Authorization: Bearer ${TOKEN}" \
+--header "Content-Type: application/json" \
+-d "{
+    \"collectionName\": \"myCollection\",
+    \"schema\": $schema,
+    \"params\": $params
+}"
+
+```
+
+Once you have enabled Partition Key Isolation, you can still set the Partition Key and number of partitions as described in [Set Partition Numbers](#Set-Partition-Numbers). Note that the Partition-Key-based filter should include only a specific Partition Key value.