From fddfd272b5ef2e9a1d508e6a97cdc6e831eec14f Mon Sep 17 00:00:00 2001 From: Christy Bergman Date: Thu, 6 Jun 2024 22:47:35 -0700 Subject: [PATCH] Christy (#1355) * Add connect notebook for Milvus Lite Signed-off-by: Christy Bergman * Add connect notebook for Milvus Lite Signed-off-by: Christy Bergman --------- Signed-off-by: Christy Bergman --- bootcamp/RAG/rtdocs/.html | 353 ----- .../architecture_overview.html | 6 +- bootcamp/RAG/{rtdocs => rtdocs_new}/aws.html | 2 +- .../configure-docker.html | 10 +- .../RAG/{rtdocs => rtdocs_new}/deploy_s3.html | 3 +- .../{rtdocs => rtdocs_new}/embeddings.html | 20 +- .../get-and-scalar-query.html | 1193 ++++++++++++++-- .../RAG/{rtdocs => rtdocs_new}/glossary.html | 129 +- .../RAG/{rtdocs => rtdocs_new}/gpu_index.html | 2 +- .../index-vector-fields.html | 219 ++- .../RAG/{rtdocs => rtdocs_new}/index.html | 10 +- .../insert-update-delete.html | 365 ++++- .../install_standalone-docker.html | 2 +- .../manage-collections.html | 831 ++++++++++- .../RAG/{rtdocs => rtdocs_new}/metric.html | 6 +- .../milvus-cdc-overview.html | 2 +- .../monitor_overview.html | 2 +- .../multi-vector-search.html | 340 ++--- .../{rtdocs => rtdocs_new}/quickstart.html | 32 +- bootcamp/RAG/{rtdocs => rtdocs_new}/rbac.html | 147 +- .../RAG/{rtdocs => rtdocs_new}/scaleout.html | 2 +- .../single-vector-search.html | 1241 ++++++++++++++++- .../system_configuration.html | 2 +- bootcamp/milvus_connect.ipynb | 79 +- 24 files changed, 4148 insertions(+), 850 deletions(-) delete mode 100644 bootcamp/RAG/rtdocs/.html rename bootcamp/RAG/{rtdocs => rtdocs_new}/architecture_overview.html (98%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/aws.html (98%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/configure-docker.html (98%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/deploy_s3.html (98%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/embeddings.html (98%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/get-and-scalar-query.html (95%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/glossary.html (90%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/gpu_index.html (99%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/index-vector-fields.html (98%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/index.html (99%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/insert-update-delete.html (96%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/install_standalone-docker.html (99%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/manage-collections.html (96%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/metric.html (99%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/milvus-cdc-overview.html (98%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/monitor_overview.html (98%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/multi-vector-search.html (95%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/quickstart.html (97%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/rbac.html (95%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/scaleout.html (98%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/single-vector-search.html (94%) rename bootcamp/RAG/{rtdocs => rtdocs_new}/system_configuration.html (98%) diff --git a/bootcamp/RAG/rtdocs/.html b/bootcamp/RAG/rtdocs/.html deleted file mode 100644 index 8257f5b0a..000000000 --- a/bootcamp/RAG/rtdocs/.html +++ /dev/null @@ -1,353 +0,0 @@ -Milvus documentation
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- Welcome to Milvus Docs! -

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- Here you will learn about what Milvus is, and how to install, use, and deploy Milvus to build an application according to your business need. -

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Learn how to install Milvus using either Docker Compose or on Kubernetes.

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Quick Start

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Learn how to quickly run Milvus with sample code.

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Bootcamp

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- Learn how to build vector similarity search applications with Milvus. -

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Mar 2024 - Milvus 2.4.0 release

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\ No newline at end of file diff --git a/bootcamp/RAG/rtdocs/architecture_overview.html b/bootcamp/RAG/rtdocs_new/architecture_overview.html similarity index 98% rename from bootcamp/RAG/rtdocs/architecture_overview.html rename to bootcamp/RAG/rtdocs_new/architecture_overview.html index 1e1b09f28..ed6f7684f 100644 --- a/bootcamp/RAG/rtdocs/architecture_overview.html +++ b/bootcamp/RAG/rtdocs_new/architecture_overview.html @@ -263,9 +263,9 @@ } } }) -
milvus-logo
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Milvus Architecture Overview

-

Built on top of popular vector search libraries including Faiss, Annoy, HNSW, and more, Milvus was designed for similarity search on dense vector datasets containing millions, billions, or even trillions of vectors. Before proceeding, familiarize yourself with the basic principles of embedding retrieval.

-

Milvus also supports data sharding, data persistence, streaming data ingestion, hybrid search between vector and scalar data, and many other advanced functions. The platform offers performance on demand and can be optimized to suit any embedding retrieval scenario. We recommend deploying Milvus using Kubernetes for optimal availability and elasticity.

+
milvus-logo
Stars0

Milvus Architecture Overview

+

Built on top of popular vector search libraries including Faiss, HNSW, DiskANN, SCANN and more, Milvus was designed for similarity search on dense vector datasets containing millions, billions, or even trillions of vectors. Before proceeding, familiarize yourself with the basic principles of embedding retrieval.

+

Milvus also supports data sharding, streaming data ingestion, dynamic schema, search combine vector and scalar data, multi-vetor and hybrid search, sparse vector and many other advanced functions. The platform offers performance on demand and can be optimized to suit any embedding retrieval scenario. We recommend deploying Milvus using Kubernetes for optimal availability and elasticity.

Milvus adopts a shared-storage architecture featuring storage and computing disaggregation and horizontal scalability for its computing nodes. Following the principle of data plane and control plane disaggregation, Milvus comprises four layers: access layer, coordinator service, worker node, and storage. These layers are mutually independent when it comes to scaling or disaster recovery.

milvus-logo
Stars0

Deploy a Milvus Cluster on EC2

+
milvus-logo
Stars0

Deploy a Milvus Cluster on EC2

This topic describes how to deploy a Milvus cluster on Amazon EC2 with Terraform and Ansible.

Provision a Milvus cluster

This section describes how to use Terraform to provision a Milvus cluster.

diff --git a/bootcamp/RAG/rtdocs/configure-docker.html b/bootcamp/RAG/rtdocs_new/configure-docker.html similarity index 98% rename from bootcamp/RAG/rtdocs/configure-docker.html rename to bootcamp/RAG/rtdocs_new/configure-docker.html index 382dc3aa5..1753d1934 100644 --- a/bootcamp/RAG/rtdocs/configure-docker.html +++ b/bootcamp/RAG/rtdocs_new/configure-docker.html @@ -263,14 +263,14 @@ } } }) -
milvus-logo
Stars0

Configure Milvus with Docker Compose

+
milvus-logo
Stars0

Configure Milvus with Docker Compose

This topic describes how to configure Milvus components and its third-party dependencies with Docker Compose.

In current release, all parameters take effect only after Milvus restarts.

Download a configuration file

-

Download milvus.yaml directly or with the following command.

-
$ wget https://raw.githubusercontent.com/milvus-io/milvus/v2.4.0-rc.1/configs/milvus.yaml
+
Download milvus.yaml directly or with the following command.

+
$ wget https://raw.githubusercontent.com/milvus-io/milvus/v2.4.1/configs/milvus.yaml
 

 
Modify the configuration file

 
Configure your Milvus instance to suit your application scenarios by adjusting corresponding parameters in milvus.yaml.

@@ -409,10 +409,10 @@ 
Modify the con

Download an installation file

-

Download the installation file for Milvus standalone, and save it as docker-compose.yml.

+

Download the installation file for Milvus standalone, and save it as docker-compose.yml.

You can also simply run the following command.

# For Milvus standalone
-$ wget https://github.com/milvus-io/milvus/releases/download/v2.4.0-rc.1/milvus-standalone-docker-compose.yml -O docker-compose.yml
+$ wget https://github.com/milvus-io/milvus/releases/download/v2.4.1/milvus-standalone-docker-compose.yml -O docker-compose.yml

Modify the installation file

In docker-compose.yml, add a volumes section under each milvus-standalone.

diff --git a/bootcamp/RAG/rtdocs/deploy_s3.html b/bootcamp/RAG/rtdocs_new/deploy_s3.html similarity index 98% rename from bootcamp/RAG/rtdocs/deploy_s3.html rename to bootcamp/RAG/rtdocs_new/deploy_s3.html index 9f173f6a4..7f3d240d3 100644 --- a/bootcamp/RAG/rtdocs/deploy_s3.html +++ b/bootcamp/RAG/rtdocs_new/deploy_s3.html @@ -263,7 +263,7 @@ } } }) -
milvus-logo
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Configure Object Storage with Docker Compose or Helm

+
milvus-logo
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Configure Object Storage with Docker Compose or Helm

Milvus uses MinIO for object storage by default, but it also supports using Amazon Simple Storage Service (S3) as persistent object storage for log and index files. This topic describes how to configure S3 for Milvus. You can skip this topic if you are satisfied with MinIO.

You can configure S3 with Docker Compose or on K8s.

Configure S3 with Docker Compose

@@ -362,7 +362,6 @@

Using the YAML file" useSSL: <true/false> bucketName: "<your_bucket_name>" - useSSL: <true/false>
  1. After configuring the preceding sections and saving the values.yaml file, run the following command to install Milvus that uses the S3 configurations.
    2. diff --git a/bootcamp/RAG/rtdocs/embeddings.html b/bootcamp/RAG/rtdocs_new/embeddings.html similarity index 98% rename from bootcamp/RAG/rtdocs/embeddings.html rename to bootcamp/RAG/rtdocs_new/embeddings.html index 358bb8f07..d08f0b2b9 100644 --- a/bootcamp/RAG/rtdocs/embeddings.html +++ b/bootcamp/RAG/rtdocs_new/embeddings.html @@ -263,7 +263,7 @@ } } }) -
    milvus-logo
    Stars0

    Overview

    +
    milvus-logo
    Stars0

    Overview

    Embedding is a machine learning concept for mapping data into a high-dimensional space, where data of similar semantic are placed close together. Typically being a Deep Neural Network from BERT or other Transformer families, the embedding model can effectively represent the semantics of text, images, and other data types with a series of numbers known as vectors. A key feature of these models is that the mathematical distance between vectors in the high-dimensional space can indicate the similarity of the semantics of original text or images. This property unlocks many information retrieval applications, such as web search engines like Google and Bing, product search and recommendations on e-commerce sites, and the recently popular Retrieval Augmented Generation (RAG) paradigm in generative AI.

    There are two main categories of embeddings, each producing a different type of vector:

      @@ -274,7 +274,7 @@

      Sparse embedding: In contrast, the output vectors of sparse embeddings has most dimensions being zero, namely "sparse" vectors. These vectors often have much higher dimensions (tens of thousands or more) which is determined by the size of the token vocabulary. Sparse vectors can be generated by Deep Neural Networks or statistical analysis of text corpora. Due to their interpretability and observed better out-of-domain generalization capabilities, sparse embeddings are increasingly adopted by developers as a complement to dense embeddings.

    -

    Milvus offers built-in embedding functions that work with popular embedding providers. Before creating a collection in Milvus, you can use these functions to generate embeddings for your datasets, streamlining the process of preparing data and vector searches.

    +

    Milvus is a vector database designed for vector data management, storage, and retrieval. By integrating mainstream embedding and reranking models, you can easily transform original text into searchable vectors or rerank the results using powerful models to achieve more accurate results for RAG. This integration simplifies text transformation and eliminates the need for additional embedding or reranking components, thereby streamlining RAG development and validation.

    To create embeddings in action, refer to Using PyMilvus's Model To Generate Text Embeddings.

    @@ -286,35 +286,41 @@ - + - + - + - + - + + + + + +
    openaiopenai Dense API
    sentence-transformersentence-transformer Dense Open-sourced
    bm25bm25 Sparse Open-sourced
    SpladeSplade Sparse Open-sourced
    bge-m3bge-m3 Hybrid Open-sourced
    voyageaiDenseAPI

    Example 1: Use default embedding function to generate dense vectors

    To use embedding functions with Milvus, first install the PyMilvus client library with the model subpackage that wraps all the utilities for embedding generation.

    pip install pymilvus[model]
+# or pip install "pymilvus[model]" for zsh.

    The model subpackage supports various embedding models, from OpenAI, Sentence Transformers, BGE M3, BM25, to SPLADE pretrained models. For simpilicity, this example uses the DefaultEmbeddingFunction which is all-MiniLM-L6-v2 sentence transformer model, the model is about 70MB and it will be downloaded during first use:

    from pymilvus import model
diff --git a/bootcamp/RAG/rtdocs/get-and-scalar-query.html b/bootcamp/RAG/rtdocs_new/get-and-scalar-query.html
similarity index 95%
rename from bootcamp/RAG/rtdocs/get-and-scalar-query.html
rename to bootcamp/RAG/rtdocs_new/get-and-scalar-query.html
index d6c881b9a..9216978b0 100644
--- a/bootcamp/RAG/rtdocs/get-and-scalar-query.html
+++ b/bootcamp/RAG/rtdocs_new/get-and-scalar-query.html
@@ -263,7 +263,7 @@
         }
       }
     })
-  
    milvus-logo
    Stars0
    Get & Scalar Query
    
+  
    milvus-logo
    Stars0
    Get & Scalar Query
    
 
    This guide demonstrates how to get entities by ID and conduct scalar filtering. A scalar filtering retrieves entities that match the specified filtering conditions.
    
 
    Overview
    
 
    A scalar query filters entities in a collection based on a defined condition using boolean expressions. The query result is a set of entities that match the defined condition. Unlike a vector search, which identifies the closest vector to a given vector in a collection, queries filter entities based on specific criteria.
    
@@ -274,6 +274,11 @@ 
    OverviewPreparations
    
 
    The following steps repurpose the code to connect to Milvus, quickly set up a collection, and insert over 1,000 randomly generated entities into the collection.
    
 
    Step 1: Create a collection
    
+
    
+    Python 
+    Java
+    Node.js
+
    
 
    from pymilvus import MilvusClient
 
 # 1. Set up a Milvus client
@@ -287,18 +292,57 @@ 
    Step 1: Create a
     dimension=5,
 )
 
    
+
    String CLUSTER_ENDPOINT = "http://localhost:19530";
+
+// 1. Connect to Milvus server
+ConnectConfig connectConfig = ConnectConfig.builder()
+    .uri(CLUSTER_ENDPOINT)
+    .build();
+
+MilvusClientV2 client = new MilvusClientV2(connectConfig);  
+
+// 2. Create a collection in quick setup mode
+CreateCollectionReq quickSetupReq = CreateCollectionReq.builder()
+    .collectionName("quick_setup")
+    .dimension(5)
+    .metricType("IP")
+    .build();
+
+client.createCollection(quickSetupReq);
+
    
+
    const { MilvusClient, DataType, sleep } = require("@zilliz/milvus2-sdk-node")
+
+const address = "http://localhost:19530"
+
+// 1. Set up a Milvus Client
+client = new MilvusClient({address}); 
+
+// 2. Create a collection in quick setup mode
+await client.createCollection({
+    collection_name: "quick_setup",
+    dimension: 5,
+}); 
+
    
 
    Step 2: Insert randomly generated entities
    
+
    
+    Python 
+    Java
+    Node.js
+
    
 
    # 3. Insert randomly generated vectors 
 colors = ["green", "blue", "yellow", "red", "black", "white", "purple", "pink", "orange", "brown", "grey"]
-data = [ {
-        "id": i, 
-        "vector": [ random.uniform(-1, 1) for _ in range(5) ], 
-        "color": random.choice(colors), 
-        "tag": random.randint(1000, 9999) 
-    } for i in range(1000) ]
+data = []
 
-for i in data:
-    i["color_tag"] = "{}_{}".format(i["color"], i["tag"])
+for i in range(1000):
+    current_color = random.choice(colors)
+    current_tag = random.randint(1000, 9999)
+    data.append({
+        "id": i,
+        "vector": [ random.uniform(-1, 1) for _ in range(5) ],
+        "color": current_color,
+        "tag": current_tag,
+        "color_tag": f"{current_color}_{str(current_tag)}"
+    })
 
 print(data[0])
 
@@ -307,18 +351,17 @@ 
    St
 # {
 #     "id": 0,
 #     "vector": [
-#         0.5913205104316952,
-#         -0.5474675922381218,
-#         0.9433357315736743,
-#         0.22479148416151284,
-#         0.28294612647978834
+#         0.7371107800002366,
+#         -0.7290389773227746,
+#         0.38367002049157417,
+#         0.36996000494220627,
+#         -0.3641898951462792
 #     ],
-#     "color": "grey",
-#     "tag": 5024,
-#     "color_tag": "grey_5024"
+#     "color": "yellow",
+#     "tag": 6781,
+#     "color_tag": "yellow_6781"
 # }
 
-# 4. Insert entities to the collection
 res = client.insert(
     collection_name="quick_setup",
     data=data
@@ -329,24 +372,126 @@ 
    St
 # Output
 #
 # {
-#     "insert_count": 1000
+#     "insert_count": 1000,
+#     "ids": [
+#         0,
+#         1,
+#         2,
+#         3,
+#         4,
+#         5,
+#         6,
+#         7,
+#         8,
+#         9,
+#         "(990 more items hidden)"
+#     ]
 # }
 
    
+
    // 3. Insert randomly generated vectors into the collection
+List<String> colors = Arrays.asList("green", "blue", "yellow", "red", "black", "white", "purple", "pink", "orange", "brown", "grey");
+List<JSONObject> data = new ArrayList<>();
+
+for (int i=0; i<1000; i++) {
+    Random rand = new Random();
+    String current_color = colors.get(rand.nextInt(colors.size()-1));
+    int current_tag = rand.nextInt(8999) + 1000;
+    JSONObject row = new JSONObject();
+    row.put("id", Long.valueOf(i));
+    row.put("vector", Arrays.asList(rand.nextFloat(), rand.nextFloat(), rand.nextFloat(), rand.nextFloat(), rand.nextFloat()));
+    row.put("color", current_color);
+    row.put("tag", current_tag);
+    row.put("color_tag", current_color + '_' + String.valueOf(rand.nextInt(8999) + 1000));
+    data.add(row);
+}
+
+InsertReq insertReq = InsertReq.builder()
+    .collectionName("quick_setup")
+    .data(data)
+    .build();
+
+InsertResp insertResp = client.insert(insertReq);
+
+System.out.println(JSONObject.toJSON(insertResp));
+
+// Output:
+// {"insertCnt": 1000}
+
    
+
    // 3. Insert randomly generated vectors
+const colors = ["green", "blue", "yellow", "red", "black", "white", "purple", "pink", "orange", "brown", "grey"]
+var data = []
+
+for (let i = 0; i < 1000; i++) {
+    current_color = colors[Math.floor(Math.random() * colors.length)]
+    current_tag = Math.floor(Math.random() * 8999 + 1000)
+    data.push({
+        "id": i,
+        "vector": [Math.random(), Math.random(), Math.random(), Math.random(), Math.random()],
+        "color": current_color,
+        "tag": current_tag,
+        "color_tag": `${current_color}_${current_tag}`
+    })
+}
+
+console.log(data[0])
+
+// Output
+// 
+// {
+//   id: 0,
+//   vector: [
+//     0.16022394821966035,
+//     0.6514875214491056,
+//     0.18294484964044666,
+//     0.30227694168725394,
+//     0.47553087493572255
+//   ],
+//   color: 'blue',
+//   tag: 8907,
+//   color_tag: 'blue_8907'
+// }
+// 
+
+res = await client.insert({
+    collection_name: "quick_setup",
+    data: data
+})
+
+console.log(res.insert_cnt)
+
+// Output
+// 
+// 1000
+// 
+
    
 
    Step 3: Create partitions and insert more entities
    
-
    # 5. Create two partitions
-client.create_partition(collection_name="quick_setup", partition_name="partitionA")
-client.create_partition(collection_name="quick_setup", partition_name="partitionB")
+
    
+    Python 
+    Java
+    Node.js
+
    
+
    # 4. Create partitions and insert more entities
+client.create_partition(
+    collection_name="quick_setup",
+    partition_name="partitionA"
+)
 
-# 6. Insert 500 entities in partition A
-data = [ {
-        "id": i + 1000, 
-        "vector": [ random.uniform(-1, 1) for _ in range(5) ], 
-        "color": random.choice(colors), 
-        "tag": random.randint(1000, 9999) 
-    } for i in range(500) ]
+client.create_partition(
+    collection_name="quick_setup",
+    partition_name="partitionB"
+)
 
-for i in data:
-    i["color_tag"] = "{}_{}".format(i["color"], i["tag"])
+data = []
+
+for i in range(1000, 1500):
+    current_color = random.choice(colors)
+    data.append({
+        "id": i,
+        "vector": [ random.uniform(-1, 1) for _ in range(5) ],
+        "color": current_color,
+        "tag": current_tag,
+        "color_tag": f"{current_color}_{str(current_tag)}"
+    })
 
 res = client.insert(
     collection_name="quick_setup",
@@ -359,19 +504,33 @@ 
    
+
    // 4. Create partitions and insert some more data
+CreatePartitionReq createPartitionReq = CreatePartitionReq.builder()
+    .collectionName("quick_setup")
+    .partitionName("partitionA")
+    .build();
+
+client.createPartition(createPartitionReq);
+
+createPartitionReq = CreatePartitionReq.builder()
+    .collectionName("quick_setup")
+    .partitionName("partitionB")
+    .build();
+
+client.createPartition(createPartitionReq);
+
+data.clear();
+
+for (int i=1000; i<1500; i++) {
+    Random rand = new Random();
+    String current_color = colors.get(rand.nextInt(colors.size()-1));
+    int current_tag = rand.nextInt(8999) + 1000;
+    JSONObject row = new JSONObject();
+    row.put("id", Long.valueOf(i));
+    row.put("vector", Arrays.asList(rand.nextFloat(), rand.nextFloat(), rand.nextFloat(), rand.nextFloat(), rand.nextFloat()));
+    row.put("color", current_color);
+    row.put("tag", current_tag);
+    data.add(row);
+}
+
+insertReq = InsertReq.builder()
+    .collectionName("quick_setup")
+    .data(data)
+    .partitionName("partitionA")
+    .build();
+
+insertResp = client.insert(insertReq);
+
+System.out.println(JSONObject.toJSON(insertResp));
+
+// Output:
+// {"insertCnt": 500}
+
+data.clear();
+
+for (int i=1500; i<2000; i++) {
+    Random rand = new Random();
+    String current_color = colors.get(rand.nextInt(colors.size()-1));
+    int current_tag = rand.nextInt(8999) + 1000;
+    JSONObject row = new JSONObject();
+    row.put("id", Long.valueOf(i));
+    row.put("vector", Arrays.asList(rand.nextFloat(), rand.nextFloat(), rand.nextFloat(), rand.nextFloat(), rand.nextFloat()));
+    row.put("color", current_color);
+    row.put("tag", current_tag);
+    data.add(row);
+}
+
+insertReq = InsertReq.builder()
+    .collectionName("quick_setup")
+    .data(data)
+    .partitionName("partitionB")
+    .build();
+
+insertResp = client.insert(insertReq);
+
+System.out.println(JSONObject.toJSON(insertResp));
+
+// Output:
+// {"insertCnt": 500}
+
    
+
    // 4. Create partitions and insert more entities
+await client.createPartition({
+    collection_name: "quick_setup",
+    partition_name: "partitionA"
+})
+
+await client.createPartition({
+    collection_name: "quick_setup",
+    partition_name: "partitionB"
+})
+
+data = []
+
+for (let i = 1000; i < 1500; i++) {
+    current_color = colors[Math.floor(Math.random() * colors.length)]
+    current_tag = Math.floor(Math.random() * 8999 + 1000)
+    data.push({
+        "id": i,
+        "vector": [Math.random(), Math.random(), Math.random(), Math.random(), Math.random()],
+        "color": current_color,
+        "tag": current_tag,
+        "color_tag": `${current_color}_${current_tag}`
+    })
+}
+
+res = await client.insert({
+    collection_name: "quick_setup",
+    data: data,
+    partition_name: "partitionA"
+})
+
+console.log(res.insert_cnt)
+
+// Output
+// 
+// 500
+// 
+
+await sleep(5000)
+
+data = []
+
+for (let i = 1500; i < 2000; i++) {
+    current_color = colors[Math.floor(Math.random() * colors.length)]
+    current_tag = Math.floor(Math.random() * 8999 + 1000)
+    data.push({
+        "id": i,
+        "vector": [Math.random(), Math.random(), Math.random(), Math.random(), Math.random()],
+        "color": current_color,
+        "tag": current_tag,
+        "color_tag": `${current_color}_${current_tag}`
+    })
+}
+
+res = await client.insert({
+    collection_name: "quick_setup",
+    data: data,
+    partition_name: "partitionB"
+})
+
+console.log(res.insert_cnt)
+
+// Output
+// 
+// 500
+// 
+
    
 
    Get Entities by ID
    
 
    If you know the IDs of the entities of your interests, you can use the get() method.
    
+
    
+    Python 
+    Java
+    Node.js
+
    
 
    # 4. Get entities by ID
 res = client.get(
     collection_name="quick_setup",
@@ -438,13 +751,119 @@ 
    Get Entities by ID
    
+
    // 5. Get entities by ID
+GetReq getReq = GetReq.builder()
+    .collectionName("quick_setup")
+    .ids(Arrays.asList(0L, 1L, 2L))
+    .build();
+
+GetResp entities = client.get(getReq);
+
+System.out.println(JSONObject.toJSON(entities));
+
+// Output:
+// {"getResults": [
+//     {"entity": {
+//         "color": "white",
+//         "color_tag": "white_4597",
+//         "vector": [
+//             0.09665024,
+//             0.1163497,
+//             0.0701347,
+//             0.32577968,
+//             0.40943468
+//         ],
+//         "tag": 8946,
+//         "id": 0
+//     }},
+//     {"entity": {
+//         "color": "green",
+//         "color_tag": "green_3039",
+//         "vector": [
+//             0.90689456,
+//             0.4377399,
+//             0.75387514,
+//             0.36454988,
+//             0.8702918
+//         ],
+//         "tag": 2341,
+//         "id": 1
+//     }},
+//     {"entity": {
+//         "color": "white",
+//         "color_tag": "white_8708",
+//         "vector": [
+//             0.9757728,
+//             0.13974023,
+//             0.8023141,
+//             0.61947155,
+//             0.8290197
+//         ],
+//         "tag": 9913,
+//         "id": 2
+//     }}
+// ]}
+
    
+
    // 5. Get entities by id
+res = await client.get({
+    collection_name: "quick_setup",
+    ids: [0, 1, 2],
+    output_fields: ["vector", "color_tag"]
+})
+
+console.log(res.data)
+
+// Output
+// 
+// [
+//   {
+//     vector: [
+//       0.16022394597530365,
+//       0.6514875292778015,
+//       0.18294484913349152,
+//       0.30227693915367126,
+//       0.47553086280822754
+//     ],
+//     '$meta': { color: 'blue', tag: 8907, color_tag: 'blue_8907' },
+//     id: '0'
+//   },
+//   {
+//     vector: [
+//       0.2459285855293274,
+//       0.4974019527435303,
+//       0.2154673933982849,
+//       0.03719571232795715,
+//       0.8348019123077393
+//     ],
+//     '$meta': { color: 'grey', tag: 3710, color_tag: 'grey_3710' },
+//     id: '1'
+//   },
+//   {
+//     vector: [
+//       0.9404329061508179,
+//       0.49662265181541443,
+//       0.8088793158531189,
+//       0.9337621331214905,
+//       0.8269071578979492
+//     ],
+//     '$meta': { color: 'blue', tag: 2993, color_tag: 'blue_2993' },
+//     id: '2'
+//   }
+// ]
+// 
+
    
 
    Get entities from partitions
    
 
    You can also get entities from specific partitions.
    
+
    
+    Python 
+    Java
+    Node.js
+
    
 
    # 5. Get entities from partitions
 res = client.get(
     collection_name="quick_setup",
-    ids=[0, 1, 2],
-    partition_names=["_default"]
+    ids=[1000, 1001, 1002],
+    partition_names=["partitionA"]
 )
 
 print(res)
@@ -453,49 +872,159 @@ 
    Get entities fr
 #
 # [
 #     {
-#         "color_tag": "green_2006",
 #         "color": "green",
-#         "id": 0,
+#         "tag": 1995,
+#         "color_tag": "green_1995",
+#         "id": 1000,
 #         "vector": [
-#             0.68824464,
-#             0.6552274,
-#             0.33593303,
-#             -0.7099536,
-#             -0.07070546
+#             0.7807706,
+#             0.8083741,
+#             0.17276904,
+#             -0.8580777,
+#             0.024156934
 #         ]
 #     },
 #     {
-#         "color_tag": "white_9298",
-#         "color": "white",
-#         "id": 1,
+#         "color": "red",
+#         "tag": 1995,
+#         "color_tag": "red_1995",
+#         "id": 1001,
 #         "vector": [
-#             -0.98531723,
-#             0.33456197,
-#             0.2844234,
-#             0.42886782,
-#             0.32753858
+#             0.065074645,
+#             -0.44882354,
+#             -0.29479212,
+#             -0.19798489,
+#             -0.77542555
 #         ]
 #     },
 #     {
-#         "color_tag": "grey_5312",
-#         "color": "grey",
-#         "id": 2,
+#         "color": "green",
+#         "tag": 1995,
+#         "color_tag": "green_1995",
+#         "id": 1002,
 #         "vector": [
-#             -0.9886812,
-#             -0.44129863,
-#             -0.29859528,
-#             0.06059075,
-#             -0.43817034
+#             0.027934508,
+#             -0.44199976,
+#             -0.40262738,
+#             -0.041511405,
+#             0.024782438
 #         ]
 #     }
 # ]
 
    
+
    // 5. Get entities by ID in a partition
+getReq = GetReq.builder()
+    .collectionName("quick_setup")
+    .ids(Arrays.asList(1001L, 1002L, 1003L))
+    .partitionName("partitionA")
+    .build();
+
+entities = client.get(getReq);
+
+System.out.println(JSONObject.toJSON(entities));
+
+// Output:
+// {"getResults": [
+//     {"entity": {
+//         "color": "yellow",
+//         "vector": [
+//             0.4300114,
+//             0.599917,
+//             0.799163,
+//             0.75395125,
+//             0.89947814
+//         ],
+//         "id": 1001,
+//         "tag": 5803
+//     }},
+//     {"entity": {
+//         "color": "blue",
+//         "vector": [
+//             0.009218454,
+//             0.64637834,
+//             0.19815737,
+//             0.30519038,
+//             0.8218663
+//         ],
+//         "id": 1002,
+//         "tag": 7212
+//     }},
+//     {"entity": {
+//         "color": "black",
+//         "vector": [
+//             0.76521933,
+//             0.7818409,
+//             0.16976339,
+//             0.8719652,
+//             0.1434964
+//         ],
+//         "id": 1003,
+//         "tag": 1710
+//     }}
+// ]}
+
    
+
    // 5.1 Get entities by id in a partition
+res = await client.get({
+    collection_name: "quick_setup",
+    ids: [1000, 1001, 1002],
+    partition_names: ["partitionA"],
+    output_fields: ["vector", "color_tag"]
+})
+
+console.log(res.data)
+
+// Output
+// 
+// [
+//   {
+//     id: '1000',
+//     vector: [
+//       0.014254206791520119,
+//       0.5817716121673584,
+//       0.19793470203876495,
+//       0.8064294457435608,
+//       0.7745839357376099
+//     ],
+//     '$meta': { color: 'white', tag: 5996, color_tag: 'white_5996' }
+//   },
+//   {
+//     id: '1001',
+//     vector: [
+//       0.6073881983757019,
+//       0.05214758217334747,
+//       0.730999231338501,
+//       0.20900958776474,
+//       0.03665429726243019
+//     ],
+//     '$meta': { color: 'grey', tag: 2834, color_tag: 'grey_2834' }
+//   },
+//   {
+//     id: '1002',
+//     vector: [
+//       0.48877206444740295,
+//       0.34028753638267517,
+//       0.6527213454246521,
+//       0.9763909578323364,
+//       0.8031482100486755
+//     ],
+//     '$meta': { color: 'pink', tag: 9107, color_tag: 'pink_9107' }
+//   }
+// ]
+// 
+
    
 
    Use Basic Operators
    
 
    In this section, you will find examples of how to use basic operators in scalar filtering. You can apply these filters to vector searches and data deletions too.
    
 
@@ -580,58 +1477,170 @@ 
    Count entitiesres = client.query(
+  
    
+     Python 
+     Java
+     Node.js
+  
    
+
    # 7. Use advanced operators
+
+# Count the total number of entities in a collection
+res = client.query(
     collection_name="quick_setup",
     # highlight-start
     output_fields=["count(*)"]
     # highlight-end
 )
 
+print(res)
+
 # Output
 #
-# 
+# [
+#     {
+#         "count(*)": 2000
+#     }
+# ]
+
    
+
    // 7. Use advanced operators
+// Count the total number of entities in the collection
+queryReq = QueryReq.builder()
+    .collectionName("quick_setup")
+    .filter("")
+    .outputFields(Arrays.asList("count(*)"))
+    .build();
+
+queryResp = client.query(queryReq);
+
+System.out.println(JSONObject.toJSON(queryResp));
+
+// Output:
+// {"queryResults": [{"entity": {"count(*)": 2000}}]}
+
    
+
    // 7. Use advanced operators
+// Count the total number of entities in a collection
+res = await client.query({
+    collection_name: "quick_setup",
+    output_fields: ["count(*)"]
+})
+
+console.log(res.data)   
+
+// Output
+// 
+// [ { 'count(*)': '2000' } ]
+// 
 
    
 
 
  2. 
 
    Counts the total number of entities in specific partitions.
    
-
    res = client.query(
+  
    
+     Python 
+     Java
+     Node.js
+  
    
+
    # Count the number of entities in a partition
+res = client.query(
     collection_name="quick_setup",
     # highlight-start
     output_fields=["count(*)"],
-    partition_name="partitionA"
+    partition_names=["partitionA"]
     # highlight-end
 )
 
+print(res)
+
 # Output
 #
-# 
+# [
+#     {
+#         "count(*)": 500
+#     }
+# ]
+
    
+
    // Count the number of entities in a partition
+queryReq = QueryReq.builder()
+    .collectionName("quick_setup")
+    .partitionNames(Arrays.asList("partitionA"))
+    .filter("")
+    .outputFields(Arrays.asList("count(*)"))
+    .build();
 
-res = client.query(
-    collection_name="quick_setup",
-    # highlight-start
-    output_fields=["count(*)"],
-    partition_name="partitionB"
-    # highlight-end
-)
+queryResp = client.query(queryReq);
 
-# Output
-#
-# 
+System.out.println(JSONObject.toJSON(queryResp));
+
+// Output:
+// {"queryResults": [{"entity": {"count(*)": 500}}]}
+
    
+
    // Count the number of entities in a partition
+res = await client.query({
+    collection_name: "quick_setup",
+    output_fields: ["count(*)"],
+    partition_names: ["partitionA"]
+})
+
+console.log(res.data)     
+
+// Output
+// 
+// [ { 'count(*)': '500' } ]
+// 
 
    
 
    3. 
 
  3. 
 
    Counts the number of entities that match a filtering condition
    
-
    res = client.query(
+  
    
+     Python 
+     Java
+     Node.js
+  
    
+
    # Count the number of entities that match a specific filter
+res = client.query(
     collection_name="quick_setup",
     # highlight-start
-    filter='(publication == "Towards Data Science") and ((claps > 1500 and responses > 15) or (10 < reading_time < 15))',
+    filter='(color == "red") and (1000 < tag < 1500)',
     output_fields=["count(*)"],
     # highlight-end
 )
 
+print(res)
+
 # Output
 #
-# 
+# [
+#     {
+#         "count(*)": 3
+#     }
+# ]
+
    
+
    // Count the number of entities that match a specific filter
+queryReq = QueryReq.builder()
+    .collectionName("quick_setup")
+    .filter("(color == \"red\") and (1000 < tag < 1500)")
+    .outputFields(Arrays.asList("count(*)"))
+    .build();
+
+queryResp = client.query(queryReq);
+
+System.out.println(JSONObject.toJSON(queryResp));
+
+// Output:
+// {"queryResults": [{"entity": {"count(*)": 7}}]}
+
    
+
    // Count the number of entities that match a specific filter
+res = await client.query({
+    collection_name: "quick_setup",
+    filter: '(color == "red") and (1000 < tag < 1500)',
+    output_fields: ["count(*)"]
+})
+
+console.log(res.data)   
+
+// Output
+// 
+// [ { 'count(*)': '10' } ]
+// 
 
    
 
    4. 
 
diff --git a/bootcamp/RAG/rtdocs/glossary.html b/bootcamp/RAG/rtdocs_new/glossary.html
similarity index 90%
rename from bootcamp/RAG/rtdocs/glossary.html
rename to bootcamp/RAG/rtdocs_new/glossary.html
index ecce7efc7..043c4e6ba 100644
--- a/bootcamp/RAG/rtdocs/glossary.html
+++ b/bootcamp/RAG/rtdocs_new/glossary.html
@@ -213,7 +213,7 @@
 table,
 tr,
 td,
-th{font-style:normal;font-weight:normal;font-size:16px;line-height:24px;color:#000000}a{text-decoration:none;color:var(--color-text-primary)}@media only screen and (max-width: 744px){.col-1{width:calc((100vw - 100px) / 4)}}@media only screen and (max-width: 1024px)  and (min-width: 744px){.col-1{width:calc((100vw - 180px) / 10)}}@media only screen and (max-width: 1439px)  and (min-width: 1024px), only screen and (max-width: 1920px)  and (min-width: 1439px){.col-1{width:calc((100vw - 260px) / 14)}}@media only screen and (min-width: 1920px){.col-1{width:calc((100vw - 340px) / 18)}}@media only screen and (max-width: 744px){.col-2{width:calc((100vw - 100px) / 2 + 20px)}}@media only screen and (max-width: 1024px)  and (min-width: 744px){.col-2{width:calc((100vw - 180px) / 5 + 20px)}}@media only screen and (max-width: 1439px)  and (min-width: 1024px), only screen and (max-width: 1920px)  and (min-width: 1439px){.col-2{width:calc((100vw - 260px) / 7 + 20px)}}@media only screen and (min-width: 1920px){.col-2{width:calc((100vw - 340px) / 9 + 20px)}}@media only screen and (max-width: 744px){.col-3{width:calc((100vw - 100px) / 4 * 3 + 40px)}}@media only screen and (max-width: 1024px)  and (min-width: 744px){.col-3{width:calc((100vw - 180px) / 10 * 3 + 40px)}}@media only screen and (max-width: 1439px)  and (min-width: 1024px), only screen and (max-width: 1920px)  and (min-width: 1439px){.col-3{width:calc((100vw - 260px) / 14 * 3 + 40px)}}@media only screen and (min-width: 1920px){.col-3{width:calc((100vw - 340px) / 6 + 40px)}}@media only screen and (max-width: 744px){.col-4{width:calc(100vw - 40px)}}@media only screen and (max-width: 1024px)  and (min-width: 744px){.col-4{width:calc((100vw - 180px) / 10 * 4 + 60px)}}@media only screen and (max-width: 1439px)  and (min-width: 1024px), only screen and (max-width: 1920px)  and (min-width: 1439px){.col-4{width:calc((100vw - 260px) / 14 * 4 + 60px)}}@media only screen and (min-width: 1920px){.col-4{width:calc((100vw - 340px) / 18 * 4 + 60px)}}@media only screen and (max-width: 1024px)  and (min-width: 744px){.col-5{width:calc((100vw - 180px) / 10 * 5 + 80px)}}@media only screen and (max-width: 1439px)  and (min-width: 1024px), only screen and (max-width: 1920px)  and (min-width: 1439px){.col-5{width:calc((100vw - 260px) / 14 * 5 + 80px)}}@media only screen and (min-width: 1920px){.col-5{width:calc((100vw - 340px) / 18 * 5 + 80px)}}@media only screen and (max-width: 1024px)  and (min-width: 744px){.col-6{width:calc((100vw - 180px) / 10 * 6 + 100px)}}@media only screen and (max-width: 1439px)  and (min-width: 1024px), only screen and (max-width: 1920px)  and (min-width: 1439px){.col-6{width:calc((100vw - 260px) / 14 * 6 + 100px)}}@media only screen and (min-width: 1920px){.col-6{width:calc((100vw - 340px) / 18 * 6 + 100px)}}@media only screen and (max-width: 1024px)  and (min-width: 744px){.col-7{width:calc((100vw - 180px) / 10 * 7 + 120px)}}@media only screen and (max-width: 1439px)  and (min-width: 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8ubBRKN5gJj2qSBnL2ZNwekYFoAlIHYWZE4NP9xjgFKRCASFy/RVH9gqYKrJfGwocPvIZ9KU0s9cLuIDq0lQ8WtZesJfYDC4H839dQ7Xr9+xtm994MBm710LzfmTklJzO37hzoGgewAaJUDxgPARLqwNB1YjoIdPoLa6ODChmA7fAV7GGY7rLGsIxDEYGN/fISu/7q0cWUPBhuDocX+zB+HTj1pEKUprux/4pq56NTi3iXcDVtRqby9vWus3in8dYjPKY3sz+0FgcrVg1nFL4bm9FpKLj/3u5CEp9XKg4lXeLo3yTYVtCPmEbFmFzx56TElN0pp7iHkb64Kv/XlAWMh9IuTZt0/gVds7k4Ifg0ZG8h1nEqWT/RVpD4YKRSenjh58NUxce9hw1Rpzf4Gz4OZtiFv/ezzu3o/80KU2mVr0p+KM510Rc+Bp64ZFGS5HN5o/UVFW6fy8bq9Zdl5VvkJu0NEVQ/Ndq+i9re94Clk6LwedKS8+60ndcsP2jZ88aUpy1q9Vfpwm+zko5o8XO9ADvlRXXXcWb+04qXhessqmQ6OwWYl4xVT3ZOo1/ekt5QhivlzmSGYtdo7AmxYMpkeUNAbaJuLdNXXz/9eWgHjWr1yaETTUXAjIf0WT4Va77u9SX8aelNeUttEUwx/9iad1+TTCw8uJpCbmxNMHrRfuCBiltnkckOH30OoU7DVTrn77FMnPCfs2it5yWfdoU/iW8/ptl/PUgLKnJmzORdX2pvYcMf59qWkv+YuJTveUUr7E+OPNdHWgz+zNwVVSWuk47JkUzvPRMJ4Y4NEJUnCAb0S+PmHMPXDQpjKDw9T3y9cH+D7+e5fQveZ3Lv0uFlq+tqCdcX2lShYiTRUewkBl/HpdUXlsu4gmD2CRDO1sOEs1hynAQwL/RYUgM/ZJ8kwifZFZl5ImNfFFl3Tu+jv5J2eJ1jBdw1G4tEOlw4j1vqdrYoAsdWJxT1fFHahsEa03gn/bL0dq1ovktIsXfF0d7FZrarm2OOrwVtoM+5ZNfgXWT5PTiyuub2nSPQgNr89O5ffM5UgU2CKI8OR6a3Z6bFNl/fmZt4MLpQQUgxE+fW/nKr6Ry0a6z7XXRn82OxKvb3qBarqGES9zkGMMrLD8qVfi9acUGJirNVr9OnWOGOramTYqcGBALRVqtK5UnA8rsBccHm3x9O+/N4a5nJ9Ev9S/dr2C3Wzl7nLcQWgYBTq17OE3lvVccEK4Mz4Ck+3bz9ougHHwqZ19VI+j8O7me3Yr/r9j18erpNzMRYFgEGS9awwMHnrztjQVUHrAxgUe7aRcbPR7/UdXBOf2X4jSQYxYsDCALCaXTTrSZiR6MNAD6giZBsGNHcVUDVg7GnM6LkdIApvoUiX19ixadM7aRV21csvUpKeuR1us/jla5yOTknA9s9RY+rYWui6g0C6Z5hssVm08ou1Jig4mGutjDgwILNwbcKc6xJd0Oo7OckDyfVvnj3hZBG8veefkP3qCCwYe/8UGFNv3n/u098t8lfOPBPtNA72Nt7L4t+3PA2Wql63bb3vvmTN3lVY6a1UbQnv/pzb2dfrCkKlWo/vrNtEUeS+5v1Ysxfu9XQv3Dm971XphPjAv0TeGZ1ym+QN4UjsDzTj+Wrrd6OG7CuXUmwH6RMy/yXf9z1jjg3sfiSU7ftVUh4VdaYyalaaPotFNYyCGVE73dzf3zyBd7ENJfxtWqZ9eGJRsNVgQ+hPv/769ey1/g2yMCVoPNe7mkb/+jsl3SfYhR1unfVpa7a736T40ra7q2CLxsZTZvHo4cqClS+uVf2PMJcwPSaz6olTgq5vfWeaxc+tAxrc3dI9EwUtbPo57J9jQ7wuu1Mbl7Im+yXbX22CawvzpmmCmtucIuGDvcXdZfy00f8Ujio0C2nUdGxk0P4c9ThxJr67Pf6F8OoAdVvLoxvuZWP/heh3ItZtxfNW9gwj4qtMx2THy+knwN+hpBE6kEgAK9gSuloCdiemeXoeLKoE/cS7bstlh9ve2fd9Y16Ni7W6vUHlM2IfMf/K6/I69Yqp3NY4u4dvwyr+RssHHhc5fXigB1SiB844YBogOuiBBCBwxlPSPtIXaoUSvaR0oZae9NFJRbEh1wukeqnArB6Kq+bJaT1Zp2ruFxzfE5T3jd8B9Bl6urKMl1ZXWll1TUA4YMSX1I5+ypa3/ZlToTxSUdd60I66jyhM3HWBV332ETGnsx3JbBgFVawrW0EFEugVrgQFDUigBx2QQC+TObe4Zb11Ztm4fap6JoayKkz4nHPhu2U3Gir//aOreYWPKn1Fa3HMpbjCY4OvUs44V2G0urIEVDGFj6j8Vgv149tVKhtqfZVWbEIoxSgYeI4DNoGtrgQmF8TFvP1jt3ifXGkkKa074P5GA9HBc7k9eWTTytTbXQlC2Ikw7UTK6TWVoHQZBywY3EqCmACo1g57VNkYF/Oq7zrDhQmlh40JsKz6Gx8c0QCl7/+Ye7J2F7nFcorDIy0JragSlOKP0xkaJTpg05jRXibRAZKUQ01c97TXe9ddO7+xP42E4Ip5D26VVK8NIGdZvqbHmy7ZkrZ7c61lRop1oHNQpVk7PxwiK3n+Jlj7EguxPb/+Cj2duG2FlV6vL4o+17Sb1/DXFtjKZqMYOnYL6uKsWM+GzW0XPzlZNv1JNChS3jk3Vl+rLzhyral6eE+Fcf3H70d+hj3DbEUw2zCZOUqWhEgjO2DqffaDsMHfOwTkIZkV3Nnk+KG2Wm14zhOHp42xE94PGuMcnj7pJErPfm6ajUxsuBAXDSfg1qWEz2XeFTzjzfaSO5gcA69WLPihT8eAs/J8FfphoMXKazLV6sLR6G6esEil5YsZx3nhcS+FV8gx4bPA8yOOE8Fbb5d+/8PpuhW7npasahNwFYmVVao3GZUHcHJbkQ4xrZ+AkAoTmFP383cHZ/p6MlF1g1isbstc1js4PZS8+9FPE+5TXbnvcm4ednrRLcF25uSyVUdyLhcs0rQeq3ndUA7y4St5P0VH5z2QD3f3k3ecBX8SYFrsENHBun5umIuZnnBg9nZFEMl0VScaGyY+fyEH4XVXyC1/MHb5JBKcWB4flsdFziXZDWVsliuX+oP9VMWI/bodzEPW6j4DeN1jDikUVRRxsX7RmV9uErLET2XbbPP9Mqzfsv4fVOaf1/HNxslYJWBySovvJq54DSPkf79tW13ETt+8sIq7a2dlaOF3lb1tjyubKH0XZ1YdgtWkRo8ONKsj+3UHwbkDVpPhukWmFxA624GtAU2nBjyO3w9re/1Q+f2rh23Y1/sKMzJiCmE5WW0RssesPLOw/FnbJLll/5Ft/v4p7rko+LzT7p50+jip+zi5/uwk5LGFWCQTsIpyeO29WuntX1LuL5s3UzMzHaNTHzM2Ltwp5T3V63Z1Dbneszlx6O797KqmV9Jiy7HGoSPLX8i8dDY6sBCDfNS5oksvv9D1h/vxCU2n+uWIStIw3K+Sj01puMVUs5tdJOeTo+yMmH3+nODE1O25/GuRjp1HDvUJ+v/f89xb0BUb3ZE39Mvoc7UJSfrhw8cPSiq/WJxVTIVPq4A4bQTidO/NrsLTN7p625N0d5WXj19fZfO/hKPykzoeOzEw8T3vtNx4tOZfCNwx3DPbY17FfOy5fmrjW/LHXp5Jgfewr2DUvdFP6O7jx2UG0UfcZL21TaES9wa/K+6/RfKDNJuSPESz1Vv8s5OiHrfOFjaP3qfeb75TKBnq6mXPsJWSf78clcvnxWdoT6jaXf78SbZwdrZtPVBC01KfgFRVG59C6bvMvTe6Tgta7tyn3m8ZhYDdbn2ybHyLps+4GKSjRNhjZ+pAXqk5+fvI/jh6Apic0ZDxw20ut5nnpizfLm/9Z+fVurjfOucZ829uXxiXV1zZGXm5gVtJmPWetSZEOc9EsTco1IMrdXNckpN94vs2zKi3J2m8bbyBL0rGQpmAISqZj+y1PhygeLf6kl2cqlwcNqDQt1IMTmRPiwd96zmwjMXUT+izdhlpRmCzmLNzswS1kuUeiRLq5m/wM2bx63XDOcNAwYv+H0f7lD7iyQuTJ0ntO1LooeMCsFgYQSLtOlgXPfbcc7YL2SxddSZYCfyXE/oJWBcw/9ayk4AfC28FnioCG8bSmNNvvM+y+0evH7rS9e96krbcf7O+WmK579H1Y+2uVGJPVj9qCBv3/qkxafVjbTe5/iyxm5C59+LZWF7FxdYg5Iz3sUCDQmajpWmBRAJ5/6LUxuvFR5qv05NGNAq14lZxZsKl5Avysdear8JuTp6oe2ZaMsTWNJ5/mci/1hWo+gOp7Ptz6D++Z7sPFxR+X3pHjbvbFmp3PEHViDFIud+q9pue2vJb1vB54SSrDD+VLQtJqXNTF67ZYG4FU68ZekZ9ezWvcKC6vh5PVs3PuN7d4je/8aURZ9YWRJTtiH8eL6p5zS9/A9sCBECDxf70x47bUfZG/P6K9idgNa9Ly7+MSEfLP78uqan5mV/2ppuJMeubqwt55y73S3vPdW0VYhivvqtX2l9/mVd4obp+zu5eUv+aL1l6kSVln0elI2Vffi51vMUP0riG60WzshOiHIN8h5mCEfcmP6G77+Yey7OptyZS6362Gbbd8+4JwTPtmQ+6hRm6y9+85tdINHS3gWaoL6lmk3HDD5jgeRPk+QUDNhoVV/MzTn71wdf3hn35V9zP+tW6hzb11jV4jtA5XJafr9C9yW/EXcCElRrqW0t+bEgrfgwFt/EPZubm9KNb8+CdJu+q4APmJe5cYQAhyvpD7e9JV3Xen4YMHvanfo+/Wv+75YdT75Ou/Rz4z5jea93wRqZ9C0uqKPqeOY5BagBr8jA2bAjIcs/0q3JP8isQ/j6qeiAfzD7nzvdLcAdvklbGl0mqRC0SfuMWv1BOkG+EB3JNltdYV1Ulq8uTbfUNC+L4hC3MLom2SERVstOTbfENDfLfHroYj2szpAQW6vdyOKp3Lx8FyZXonnjEXgyMcq98LB90QmrWP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26v6e8fx+EnW0QymuszY1oSfC/vb6VebU2Mcedae2XwU1t4x0f4dNuBbwhGAOs1VbeFYc1IltApUCIUSwsMEnTlj9xXFBJMR4ikVxUFh5DyCbdpF04z33+ENLXdtvj4vviVe0JDjtvNqiqaoiE3IbE5F/7RmnPguB7g9UcyIMy/4bAPm33IJ6AhPOM4YOHgpoB+9Gwyh3M2WT9aAAdRdnrtxCPyZH+BuwsW4W7hzXPGVeUOOQspcd2iJo5y00aqjrMNpDC3sOY50+oq+9zFuPiDf+kO/AG+NzAJeJgUqKqW+BnQPy578uS4TEDfTwkvifzDFLcyH6pb5WFqvgTQpYHVDdgjk4AHIP/2f5yDRFmGMphVEQh9US+9Ik2DBNnqR2FBJRDmdTvGR9vH47kt3ZcIdMj+FwAPo4DttWEe5QDinrJY8e8ay8elNfZCrDn5i4T+868htzSmdTSmtdZDIID4n+ufqtmHftvweYtRtfzH9U/54mbUF3i9GVhxjpztXnYpiDtSAjXcmcuhpZ4lV8OzTteWZA1fDVBiNQIeegAPkHgPjncJPchsXOJd5WtcepQp1DXY1Zu8dTetb7luNTLh7ilqJ+DhGOABilnFIhNpV1OR9QYGGqIb1tepBg1Us46MMtoSkWKW3lKi7vHT9N/t41WETz1d3POhDYOURxXtB3mQ4X+jLYHQIZnns7ZOhsaEaXsj92ssSdvmrSb3v6Fjt5o8fXOs0zxZ4qGJJ/2ouJmTqYys6slu4BuGEem2/7UtpoyI4Jrm2qJD2s1Yx+TG/orEo4aiZE+eYLQbs4KG5gy/BtV6rLUlZXBrs2wvFxXaXK3Ny0xvT/G4DIZpxCXQuXw89t3bSYbB6lzBpaC2Pp3kkrhj/OcxbvxojJCnWWdgeaFPAGt/BmW1vML7al1WBLXYP3Lf3k5BVz5xDJmvHBPXla+d1DB6PQb2tPUub6j3HxQ+On1MVOHQZOw/pED3f6T9POrqaNFRtZ6epdRA//wGh8ByLOFw1SfPs00Jl4obmwRFx88fFAxlZnjv4Vom1dZk+hQ4uBaFAe5qJ+Bew9JpGYtDgMbCuNhJDdF1jRPEzG+UNgpI3jkDCzYvhRhjigwl29MzGlFpCZZmcoERFjSFwHTXWgX5B1N+zosPn7rNTrltyLdz/r86WaCoWULeNyvsZydV4nKhPj2WVZ3msloiFe0zU1scIsKuT3VdBL1WdbOjm57lGr5UIcS5Na62qY7KR+lAc9LGz2V9sq5cc0qaW4Vq8HWu2x//gnSbZNZ236Yx1rnCpXHybzP2xRm1pPEr2KYjeI9mt/TishhwAKcaWv4muL0B9HskN5xTtuA0xQwvqOASk5XWLG4v5OSMfutN2uixt7fJlJelOnmKs2U+kv7wL5YbXWecaZgLS3k1mxidCOrknJwRwEKHsJhJ1acjI30f6sqQk4AVGwGeCw1N3aUB5e7u9RDK7Lb/Ze9xQ5gOOmOlVueu37Q9N1mqS9ecKLOeu3GdNDdZemT+9rDrteLw4PrW5/DpNvxsf9YYWBTmch0E2NAN/koQRrP11mpnWDmUSLiuvbuOfIKwzzJ1V+pM96FfHTtq1aTs70ZNwoXIRLkbpxDsF8UXnWYU/G8Rg8zcLfgz0tQt1+gCoVRZDUlvvnMF4kdbIDD7th151qSaXqnrFGAX28M8tPFx85tjTN+lQ33H31oUbyRfVhX5Znnz9Vrzqge7Jbmkp2tSs1/1qv6pEchB/AKn2wYiHe90kx1tudqGHmQK4Zc/yu3F/+8bTBtBljmQEgFI7YCFbsCC+hElAVVHVxlz1Q2GlX2RpOfa++vwCQatgSfuy4in1cj6PLaw/6GHyqD8s2Zf4XMLJ7dG84xVL827uEjtt85L9gxMycs1nAemfJ7Z1YL8JGZOptl54IKZA5i5EM5Ltx+U/qeuNWc/h16ZQL7SqpbmfKCLWaY6vmeW7ZTp1rLwq5p1v8fe0THVxNh+r6/4E2vZs5WSy2o/RmLslmD8LhGuHXN3WNhGSKFRkQGySap/Hy14BqXy8PeAjcXsk22nC8MBAztE4FguVqRx4nLSTBe5xATTqbzEhJTyRNKUc3+Kj74PhO8AJQiAcrfLwJboeFZrzxGcDul/OaLUra/ffwHfEHRfZBqm0/YYRtfXeN3LRuVqYPeCZnq1jp/bF125lhGq+l9bAvlzYl5EC7A1tgfs9Br67uMNt6XZsO0etsrL5/nlUgoVdW8/x5oeH/STbFoPJitsLNAO6Z1Bktpi3/PUvr2zq89t1CPFLqsl6UFWJB9HwGYBiS79nwTj6ln/xw/PUZlX/xOP+i0efXWO+vDJ2QDmld8Eribw0euAj44WNysWOF0b/RwaoSH60emaYn7NYv7TNdGPoAEao5+fri0WAL4hOLB5rnUTwgceYCu5bw8iNMmTWrPmU0reXA/HTuPTCmsJwyzj51/ya6V15kZ30jWNUHfaAN/cBgpDUOfTwcDpfeGsZ39JY2QvzBAtdl7k+e7Mzs+JTbRFIjxwO7cm6MlwXdqx3BrkNvC0RYpNzM39nM31fbFjTpzJXmB4xiaZXW7g6H1h4EAYc4LnuV83z3k/6JeJqa+Pia6vi6E31UU7OiYl2kOOFF+yQLkb1nujgPDW5JY+D88+TCrfc1RMfR0zur62hLXR5B+QHYgNnUnYLHy/CfLYWyuTN0o3++jede6j9S0v9/S4gABOnLsHhx3gn8E2f+IGHulxwbQMNniBe0YMLTidBZEvKsvp5YrTK71D2HigxFBYQywymcJSZ0EEr8mikA+nb983L+/zuljCoC6ijvNnu6fLrawQTNb9UXLPXoyJzZpDqDGG3Oa2UnXgFo3bjU+XR7EC0d+Siiyw/kkB4MMWdOW6swRFJ6UMp715QJN58KDgX9kOj4HW/sZpxeqtY31LyRvdZX3nw6v/XcjiYjpU9/XgH6x03mng/Ei96YSK/UvFyTGuVMnJkTWgTL4VC+5f5RH3lHQbO+0KV4m27Bu2cl+ak3+m82+1VD9FvaaPrtsRNieFzpw6PtmynE3/OMU57PvkWEeZ9uODBw06L7a0993TzgaHk9+XJTrb5JfmhnSsxh788FwfHrLyi735K0tgZ/Api3MihZU98znjTPBNhUL1AF4PxT6iox/bWNcowD4gpT8//pYJXzM99Guwrm9w7V7vchaLvL/Kh8Xe50Pez+o4tB/4Y8Z/vfoFGmwAvM0g4G2cv173NvYmrVz3HvU2UdemhdKA6XYKyG4X4G9V8OcFdo4rTZbJRXTjigXMKke8GFUaY8w71gindswvvF51LHQrzFg0JsM9TKJsQC+hlA80sQBYGAIsREyA3vTatHGzYIMFIgkd8ZG31cX3Qsj3ZgqGomOSrmy6EP9N7vxlO4K/TPmJZaJTcwDsU5OEM1LcSJHQtmweZ0RVGnesG9cuNi0t5QsDJUUP9hk8gM4hUGlrBpWQomif8yX1khoWv3si6znVXfOqYb1za6T4pf421/h9iXrW6GuUkK/FluxVgeOTUpEtAl5Opp64s3piSWfHUrKlOuSu+xRxrHp+cZo+M9euBfQU3LnII//1WIrj1e1tx9kh902d1VZrpVOKPumVMaQBhk6i2cmKlGxuRbzldLoOnTRQyQSc63DlzZWT3o/8kvHnJd9XXB0OOnpVKmIEG7rXuNjJMfG4mhONrdDKfkL8/xWwNNZwCogy4kHb6TpFJaosMloP+78Zpqx2upCWP8wpNvdJI2u16PLC3ryMbJbeSDylLLJ/KDJRySQoT6NrBAagMOrYMfo9LUgYyBCix8mUyYfzGvyumN+ibhmM1vz/35rBbzIWkv5rM9rW/WvXLI+P6cASuZ7c3Hm8pKBlsK0iyJZqZWFPgQAEyUmavEFvV95/iiNW+O8Ptv9YuFY4XpwGeDgDeICEY+aeztQEJUt5037pO8l8ZPqCehcz8tyB7GkfjA+kxa7/pv+++Zvx2+GzHuBhFPCg9NdSPXBxA8BDr/a/TqMbwr/6a9/X9Ne018D8Um5gVvCp3I1VAFgYBCxY9srPb+2qYEgs2pBmTOBqkG7780EVbx3iYBeZZkzJysrRXIFUvTiNtayKnOL8blRlHWqtT1qpef6t1VpXxCM92OwNsbr4+gyxbugn7P9MiWMGhcUywcsrgR0SFB83t/LBeq0r/LHao+4I0tq7ecWmfuGlWhS38Eh+Xna52lq8WoLaxeySguSaTFR67YnRXaV6qMLVKVkyUhxpeVnl6sIVFiZXV6C66+DSX9LE/eMfST2KrjV9P6dU27eTsxIWx6SAZwI7NDA+zgO8XAa0gv0gpg89JPSCmZrU/U+UAetx5UniGfD/+RoxBLXb1WQ+qLV3uwD2W5e1PFEHJX6pX5Hz0ytlXPQfNzvNcdzTldHwT0iHViR2xxTjyFeXxwgPdOtlX9BrELdXPSi5KBsduVciNsfVT4Y5oJdTJGBSMerFqNIaYwK115I7yXX1u+Dzfb3vBw4/uPNt3dv7293Bn3hc38/vd6cob4s0H9Q0NT2pLdbcfl+q9riqselBVZEaODwh5fTnbAQ997w8FQtv4M1UjOfq85eeq1Mx29tTsZ6XXwRlt3DJl6EcKshrLRnZmS0c8qXyCvLlFi6Y9JGGqEMfbC70BN7XvtcTYLv6fshnKKMw0WShQDtGZ6WgJIKi0asBqVZDaSUJ5rPZanS9heLiwgGfgY+2F3po62obPTSbfz4M/ZOs2akJjsuvpQdkAtR2QEbtFfgxAG8zBHib4LFpDeD66Izw7Omx8b75OproxeHL7vRuqRnuW+sQXZ4W3rpHih/aFit6tzapN6B3c6ryreg7+tR1O3h2LOlgbrXKH6IOTkfiOf/A0GY3oQIQgFoDAYCV8ggGweMH4N4NvhBo5rpXAH5Khp0BwTrBHiVRdVuJ3NeJdLqdWg4PlOg7SIDUyWVaKKxX8qCIsp2IKuE0qIEGaCmd+046vSn8BF3YA0bKBJSZY8qs2wIqu2miNJAvS6ljS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) 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    Glossary
    
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    This topic explains some of the core concepts in the Milvus vector database.
    
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    Bitset
    
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    In Milvus, bitsets are arrays of bit numbers 0 and 1 that can be used to represent certain data compactly and efficiently as opposed to in ints, floats, or chars. A bit number is 0 by default and is only set to 1 if it meets certain requirements.
    
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    Terminology
    
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    AutoID
    
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    AutoID is an attribute of the primary field that determines whether to enable AutoIncrement for the primary field. The value of AutoID is defined based on a timestamp. For more information, refer to create_schema.
    
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    AutoIndex
    
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    Milvus automatically decides the most appropriate index type and params for a specific field based on empirical data. This is ideal for situations when you do not need to control the specific index params. For more information, refer to add_index.
    
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    Attu
    
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    Attu is an all-in-one administration tool for Milvus that significantly reduces the complexity and cost of managing the system.
    
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    Birdwatcher
    
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    Birdwatcher is a debugging tool for Milvus that connects to etcd, allowing you to monitor the status of the Milvus server and make adjustments in real-time. It also supports etcd file backups, aiding developers in troubleshooting.
    
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    Bulk Writer
    
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    Bulk Writer is a data processing tool provided by Milvus SDKs (e.g. PyMilvus, Java SDK) , designed to convert raw datasets into a format compatible with Milvus for efficient importing.
    
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    Bulk Insert
    
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    Bulk Insert is an API that enhances writing performance by allowing multiple files to be imported in a single request, optimizing operations with large datasets.
    
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    Cardinal
    
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    Cardinal, developed by Zilliz Cloud, is a cutter-edge vector search algorithm that delivers unparalleled search quality and performance. With its innovative design and extensive optimizations, Cardinal outperforms Knowhere by several times to an order of magnitude while adaptively handling diverse production scenarios, such as varying K sizes, high filtering, different data distributions, and so on.
    
 
    Channel
    
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    There are two different channels in Milvus. They are PChannel and VChannel. Each PChannel corresponds to a topic for log storage. While each VChannel corresponds a shard in a collection.
    
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    Milvus utilizes two types of channels, PChannel and VChannel. Each PChannel corresponds to a topic for log storage, while each VChannel corresponds to a shard in a collection.
    
 
    Collection
    
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    A collection in Milvus is equivalent to a table in a relational database management system (RDBMS). In Milvus, collections are used to store and manage entities.
    
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    In Milvus, a collection is equivalent to a table in a relational database management system (RDBMS). Collections are major logical objects used to store and manage entities. For more information, refer to Manage Collections.
    
 
    Dependency
    
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    A dependency is a program that another program relies on to work. Milvus' dependencies include etcd (stores meta data), MinIO or S3 (object storage), and Pulsar (manages snapshot logs).
    
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    A dependency is a program that another program relies on to work. Milvus' dependencies include etcd (stores meta data), MinIO or S3 (object storage), and Pulsar (manages snapshot logs). For more information, refer to Manage Dependencies.
    
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    Dynamic schema
    
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    Dynamic schema allows you to insert entities with new fields into a collection without modifying the existing schema. This means that you can insert data without knowing the full schema of a collection and can include fields that are not yet defined. You can enable this schema-free capability by enableing the dynamic field when creating a collection. For more information, refer to Enable Dynamic Field.
    
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    Embeddings
    
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    Milvus offers built-in embedding functions that work with popular embedding providers. Before creating a collection in Milvus, you can use these functions to generate embeddings for your datasets, streamlining the process of preparing data and vector searches. To create embeddings in action, refer to Using PyMilvus's Model To Generate Text Embeddings.
    
 
    Entity
    
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    An entity consists of a group of fields that represent real world objects. Each entity in Milvus is represented by a unique primary key.
    
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-You can customize primary keys. If you do not configure manually, Milvus automatically assigns primary keys to entities. If you choose to configure your own customized primary keys, note that Milvus does not support primary key de-duplication for now. Therefore, there can be duplicate primary keys in the same collection.
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    An entity consists of a group of fields that represent real-world objects. Each entity in Milvus is represented by a unique primary key.
    
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    You can customize primary keys. If you do not configure manually, Milvus automatically assigns the primary key to entities. If you choose to customize the primary key, note that Milvus does not support primary key de-duplication for now. Therefore, there can be duplicate primary keys in the same collection. For more information, refer to Insert Entities.
    
 
    Field
    
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    Fields are the units that make up entities. Fields can be structured data (e.g., numbers, strings) or vectors.
    
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-Starting from Milvus 2.0, scalar field filtering is available!
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    A field in a Milvus collection is equivalent to a column of table in a RDBMS. Fields can be either scalar fields for structured data (e.g., numbers, strings), or vector fields for embedding vectors.
    
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    Filter
    
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    Milvus supports scalar filtering by searching with predicates, allowing you to define filter conditions within queries and searches to refine results.
    
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    Filtered search applies scalar filters to vector searches, allowing you to refine the search results based on specific criteria. For more information, refer to Filtered search.
    
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    Hybrid Search is an API for multi-vector search since Milvus 2.4.0. You can search multiple vector fields and fusion them. For a vector search combined with scalar field filtering, it is referred to as "filtered search". For more information, refer to Multi-Vector Search.
    
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    Index
    
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    A vector index is a reorganized data structure derived from raw data that can greatly accelerate the process of vector similarity search. Milvus supports a wide range of index types for both vector fields and scalar fields. For more information, refer to Vector index types.
    
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    Kafka-Milvus Connector
    
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    Kafka-Milvus Connector refers to a Kafka sink connector for Milvus. It allows you to stream vector data from Kafka to Milvus.
    
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    Knowhere
    
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    Knowhere is the core vector execution engine of Milvus which incorporates several vector similarity search libraries including Faiss, Hnswlib, and Annoy. Knowhere is also designed to support heterogeneous computing. It controls on which hardware (CPU or GPU) to execute index building and search requests. This is how Knowhere gets its name - knowing where to execute the operations.
    
 
    Log broker
    
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    The log broker is a publish-subscribe system that supports playback. It is responsible for streaming data persistence, execution of reliable asynchronous queries, event notification, and return of query results. It also ensures integrity of the incremental data when the worker nodes recover from system breakdown.
    
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    Log sequence
    
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    The log sequence records all operations that change collection states in Milvus.
    
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    The log broker is a publish-subscribe system that supports playback. It is responsible for streaming data persistence, execution of reliable asynchronous queries, event notification, and return of query results. It also ensures integrity of the incremental data when the worker nodes recover from system breakdown.
    
 
    Log snapshot
    
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    A log snapshot is a binary log, a smaller unit in segment that records and handles the updates and changes made to data in the Milvus vector database. Data from a segment is persisted in multiple binlogs. There are three types of binlogs in Milvus: InsertBinlog, DeleteBinlog, and DDLBinlog.
    
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    A log snapshot is a binary log, a smaller unit in segment that records and handles the updates and changes made to data in Milvus. Data from a segment is persisted in multiple binlogs. There are three types of binlogs in Milvus: InsertBinlog, DeleteBinlog, and DDLBinlog. For more information, refer to Meta storage.
    
 
    Log subscriber
    
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    Log subscribers subscribe to the log sequence to update the local data and provides services in the form of read-only copies.
    
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    Log subscribers subscribe to the log sequence to update the local data and provide services in the form of read-only copies.
    
 
    Message storage
    
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    Message storage is the log storage engine of Milvus.
    
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    Message storage is the log storage engine of Milvus. Milvus supports Kafka or Pulsa as message storage. For more information, refer to Configure Message Storage.
    
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    Metric type
    
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    Similarity metric types are used to measure similarities between vectors. Currently, Milvus supports Euclidean distance (L2), Inner product (IP), Cosine similarity (COSINE), and binary metric types. You can choose the most appropriate metric type based on your scenario. For more information, refer to Similarity Metrics.
    
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    Mmap
    
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    Memory-mapped files enable efficient data handling by mapping file contents directly into memory. This is especially useful when memory is limited and loading all data is not possible. This technique can boost data capacity and maintain performance to a point. However, if the data greatly exceeds memory capacity, search and query speeds could significantly decrease. For more information, refer to MMap-enabled Data Storage.
    
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    Milvus Backup
    
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    Milvus Backup is a tool for creating copies of data, which can be used to restore the original after a data loss event.
    
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    Milvus CDC
    
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    Milvus CDC (Change data capture) is a user-friendly tool that can capture and synchronize incremental data in Milvus instances. It ensures the reliability of business data by seamlessly transferring it between source and target instances, allowing for easy incremental backup and disaster recovery.
    
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    Milvus CLI
    
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    Milvus Command-Line Interface (CLI) is a command-line tool that supports database connection, data operations, and import and export of data. Based on Milvus Python SDK, it allows the execution of commands through a terminal using interactive command-line prompts.
    
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    Milvus Migration
    
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    Milvus Migration is an open-source tool designed to facilitate the easy migration of data from various data sources into Milvus 2.x.
    
 
    Milvus cluster
    
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    In a cluster deployment of Milvus, services are provided by a group of nodes to achieve high availability and easy scalability.
    
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    In cluster deployment of Milvus, services are provided by a group of nodes to achieve high availability and easy scalability.
    
 
    Milvus standalone
    
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    In a standalone deployment of Milvus, all operations including data insertion, index building, and vector similarity search are completed in one single process.
    
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    Normalization
    
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    Normalization refers to the process of converting an embedding (vector) so that its norm equals one. If inner product (IP) is used to calculate embeddings similarities, all embeddings must be normalized. After normalization, inner product equals cosine similarity.
    
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    In standalone deployment of Milvus, all operations including data insertion, index building, and vector similarity search are completed in one single process.
    
+
    Multi-Vector
    
+
    Milvus supports multiple vector fields in one collection since 2.4.0. For more information, refer to Multi-Vector Search.
    
 
    Partition
    
-
    A partition is a division of a collection. Milvus supports dividing collection data into multiple parts on physical storage. This process is called partitioning, and each partition can contain multiple segments.
    
+
    A partition is a division of a collection. Milvus supports dividing collection data into multiple parts on physical storage. This process is called partitioning, and each partition can contain multiple segments. For more information, refer to Manage Partitions.
    
+
    Partition key
    
+
    The partition key attribute of a field enables the segregation of entities into distinct partitions based on their partition key values. This grouping ensures that entities sharing the same key value are stored together, which can speed up search operations by allowing the system to bypass irrelevant partitions during queries filtered by the partition key field. For more information, refer to Use Partition Key.
    
 
    PChannel
    
-
    PChannel stands for physical channel. Each PChannel corresponds to a topic for log storage. A group of 256 PChannels by default will be assigned to store logs that record data insertion, deletion, and update when the Milvus cluster is started.
    
+
    PChannel stands for physical channel. Each PChannel corresponds to a topic for log storage. By default, a group of 16 PChannels will be assigned to store logs that record data insertion, deletion, and update when the Milvus cluster is started. For more information, refer to Message Channel-related Configurations.
    
+
    PyMilvus
    
+
    PyMilvus is a Python SDK of Milvus. Its source code is open-sourced and hosted on GitHub. You have the flexibility to choose MilvusClient (new version Python SDK) or the original ORM module to talk with Milvus.
    
+
    Query
    
+
    Query is an API that conducts scalar filtering with a specified boolean expression as filter. For more information, refer to Get & Scalar Query.
    
+
+
    Range search allows you to find vectors that lie within a specified distance from your search vector. For more information, refer to Range search.
    
 
    Schema
    
-
    Schema is the meta information that defines data type and data property. Each collection has its own collection schema that defines all the fields of a collection, automatic ID (primary key) allocation enablement, and collection description. Also included in collection schemas are field schemas that defines the name, data type, and other properties of a field.
    
+
    Schema is the meta information that defines the data type and data property. Each collection has its own collection schema that defines all the fields of a collection, automatic ID (primary key) allocation enablement, and collection description. Field schemas are also included in collection schemas, which defines the name, data type, and other properties of a field. For more information, refer to Manage Schema.
    
+
+
    Search is an API that performs an operation to conduct a vector similarity search, requiring vector data for its execution. For more information, refer to Single-Vector Search.
    
 
    Segment
    
-
    A segment is a data file automatically created by Milvus for holding inserted data. A collection can have multiple segments and a segment can have multiple entities. During vector similarity search, Milvus scans each segment and returns the search results. A segment can be either growing or sealed. A growing segment keeps receiving the newly inserted data till it is sealed. A sealed segment no longer receives any new data, and will be flushed to the object storage, leaving new data to be inserted into a freshly created growing segment. A growing segment will be sealed either because the number of entities it holds reaches the pre-defined threshold, or because the span of "growing" status exceeds the specified limit.
    
-
    Sharding
    
-
    Sharding refers to distributing write operations to different nodes to make the most of the parallel computing potential of a Milvus cluster for writing data. By default, a single collection contains two shards. Milvus adopts a sharding method based on primary key hashing. Milvus' development roadmap includes supporting more flexible sharding methods such as random and custom sharding.
    
-
    
-Partitioning works to reduce read load by specifying a partition name, while sharding spreads write load among multiple servers.
-
    
+
    A segment is an automatically created data file that stores inserted data. A collection may contain multiple segments, and each segment can hold numerous entities. During a vector similarity search, Milvus examines each segment to compile search results.
    
+
    There are two types of segments: growing and sealed. A growing segment continues to collect new data until it hits a specific threshold or time limit, after which it becomes sealed. Once sealed, a segment no longer accepts new data and is transferred to object storage. Meanwhile, incoming data is routed to a new growing segment. The transition from a growing to a sealed segment is triggered either by reaching the predefined entity limit or by exceeding the maximum allowed duration in the growing state. For more information, refer to Design Details.
    
+
    Spark-Milvus Connector
    
+
    Spark-Milvus Connector provides seamless integration between Apache Spark and Milvus, combining the data processing and machine learning (ML) features of Apache Spark with the vector data storage and search capabilities of Milvus.
    
+
    Shard
    
+
    Milvus enhances data write performance by distributing write operations across multiple nodes using shards, which are organized based on the hashing of primary keys. This leverages the cluster's parallel computing capabilities.
    
+
    Partitioning works to reduce read load by specifying a partition name, while sharding spreads write load among multiple servers.
    
+
    Sparse vector
    
+
    Sparse vectors represent words or phrases using vector embeddings where most elements are zero, with only one non-zero element indicating the presence of a specific word. Sparse vector models, such as SPLADEv2, outperform dense models in out-of-domain knowledge search, keyword-awareness, and interpretability. For more information, refer to Sparse Vectors.
    
 
    Unstructured data
    
-
    Unstructured data, including images, video, audio, and natural language, is information that doesn't follow a predefined model or manner of organization. This data type accounts for around 80% of the world's data, and can be converted into vectors using various artificial intelligence (AI) and machine learning (ML) models.
    
+
    Unstructured data, including images, video, audio, and natural language, is information that does not follow a predefined model or manner of organization. This data type accounts for around 80% of the world's data, and can be converted into vectors using various artificial intelligence (AI) and ML models.
    
 
    VChannel
    
-
    VChannel stands for logical channel. Each VChannel represents a shard in a collection. Each collection will be assigned a group of VChannels for recording data insertion, deletion, and update. VChannels are logically separated but physically share resources.
    
-
    Embedding Vector
    
-
    An embedding vector is a feature abstraction of unstructured data, such as emails, IoT sensor data, Instagram photos, protein structures, and much more. Mathematically speaking, an embedding vector is an array of floating-point numbers or binaries. Modern embedding techniques are used to convert unstructured data to embedding vectors.
    
-
    Vector index
    
-
    A vector index is a reorganized data structure derived from raw data that can greatly accelerate the process of vector similarity search. Milvus supports several vector index types.
    
-
-
    Vector similarity search is the process of comparing a vector to a database to find vectors that are most similar to the target search vector. Approximate nearest neighbor (ANN) search algorithms are used to calculate similarity between vectors.
    On this page
    • Glossary
    ResourcesDocsBlogManaged service
    TutorialsBootcampDemoVideo
    ToolsAttuMilvus CLISizing ToolMilvus backup tool
    CommunityGet involvedDiscordGitHub
    Milvus. 2024 All rights reserved.
    
\ No newline at end of file
+
    VChannel stands for logical channel. Each VChannel represents a shard in a collection. Each collection will be assigned a group of VChannels for recording data insertion, deletion, and update. VChannels are logically separated but physically share resources.
    
+
    Vector
    
+
    An embedding vector is a feature abstraction of unstructured data, such as emails, IoT sensor data, Instagram photos, protein structures, and more. Mathematically speaking, an embedding vector is an array of floating-point numbers or binaries. Modern embedding techniques are used to convert unstructured data to embedding vectors. Milvus support both dense and sparse vector since 2.4.0.
    
+
    Zilliz Cloud
    
+
    Fully-managed Milvus on Zilliz Cloud, with more enterprise features and highly optimized performance.
    On this page
    ResourcesDocsBlogManaged service
    TutorialsBootcampDemoVideo
    ToolsAttuMilvus CLISizing ToolMilvus backup tool
    CommunityGet involvedDiscordGitHub
    Milvus. 2024 All rights reserved.
    
\ No newline at end of file
diff --git a/bootcamp/RAG/rtdocs/gpu_index.html b/bootcamp/RAG/rtdocs_new/gpu_index.html
similarity index 99%
rename from bootcamp/RAG/rtdocs/gpu_index.html
rename to bootcamp/RAG/rtdocs_new/gpu_index.html
index 790afec1a..2d5db2aa0 100644
--- a/bootcamp/RAG/rtdocs/gpu_index.html
+++ b/bootcamp/RAG/rtdocs_new/gpu_index.html
@@ -263,7 +263,7 @@
         }
       }
     })
-  
    milvus-logo
    Stars0
    GPU Index
    
+  
    milvus-logo
    Stars0
    GPU Index
    
 
    Milvus supports various GPU index types to accelerate search performance and efficiency, especially in high-throughput, low-latency, and high-recall scenarios. This topic provides an overview of the GPU index types supported by Milvus, their suitable use cases, and performance characteristics. For information on building indexes with GPU, refer to Index with GPU.
    
 
    GPU acceleration can greatly improve the search performance and efficiency of Milvus, especially for high-throughput, low-latency and high-recall scenarios, and is also very friendly to large nq batch search secnario.
    
 
    milvus-logo
    Stars0
    Index Vector Fields
    
+  
    milvus-logo
    Stars0
    Index Vector Fields
    
 
    This guide walks you through the basic operations on creating and managing indexes on vector fields in a collection.
    
 
    Overview
    
 
    Leveraging the metadata stored in an index file, Milvus organizes your data in a specialized structure, facilitating rapid retrieval of requested information during searches or queries.
    
@@ -283,6 +283,11 @@ 
    Preparations
+    Python 
+    Java
+    Node.js
+
    
 
    from pymilvus import MilvusClient, DataType
 
 # 1. Set up a Milvus client
@@ -307,8 +312,75 @@ 
    Preparationsimport io.milvus.v2.client.ConnectConfig;
+import io.milvus.v2.client.MilvusClientV2;
+import io.milvus.v2.common.DataType;
+import io.milvus.v2.service.collection.request.CreateCollectionReq;
+
+String CLUSTER_ENDPOINT = "http://localhost:19530";
+
+// 1. Connect to Milvus server
+ConnectConfig connectConfig = ConnectConfig.builder()
+    .uri(CLUSTER_ENDPOINT)
+    .build();
+
+MilvusClientV2 client = new MilvusClientV2(connectConfig);
+
+// 2. Create a collection
+
+// 2.1 Create schema
+CreateCollectionReq.CollectionSchema schema = client.createSchema();
+
+// 2.2 Add fields to schema
+schema.addField(AddFieldReq.builder().fieldName("id").dataType(DataType.Int64).isPrimaryKey(true).autoID(false).build());
+schema.addField(AddFieldReq.builder().fieldName("vector").dataType(DataType.FloatVector).dimension(5).build());
+
+// 3 Create a collection without schema and index parameters
+CreateCollectionReq customizedSetupReq = CreateCollectionReq.builder()
+.collectionName("customized_setup")
+.collectionSchema(schema)
+.build();
+
+client.createCollection(customizedSetupReq);
+
    
+
    // 1. Set up a Milvus Client
+client = new MilvusClient({address, token});
+
+// 2. Define fields for the collection
+const fields = [
+    {
+        name: "id",
+        data_type: DataType.Int64,
+        is_primary_key: true,
+        auto_id: false
+    },
+    {
+        name: "vector",
+        data_type: DataType.FloatVector,
+        dim: 5
+    },
+]
+
+// 3. Create a collection
+res = await client.createCollection({
+    collection_name: "customized_setup",
+    fields: fields,
+})
+
+console.log(res.error_code)  
+
+// Output
+// 
+// Success
+// 
+
    
 
    Index a Collection
    
 
    To create an index for a collection or index a collection, you need to set up the index parameters and call create_index().
    
+
    
+    Python 
+    Java
+    Node.js
+
    
 
    # 4.1. Set up the index parameters
 index_params = MilvusClient.prepare_index_params()
 
@@ -316,7 +388,7 @@ 
    Index a CollectionIndex a Collection
    
+
    import io.milvus.v2.common.IndexParam;
+import io.milvus.v2.service.index.request.CreateIndexReq;
+
+// 4 Prepare index parameters
+
+// 4.2 Add an index for the vector field "vector"
+IndexParam indexParamForVectorField = IndexParam.builder()
+    .fieldName("vector")
+    .indexName("vector_index")
+    .indexType(IndexParam.IndexType.AUTOINDEX)
+    .metricType(IndexParam.MetricType.COSINE)
+    .build();
+
+List<IndexParam> indexParams = new ArrayList<>();
+indexParams.add(indexParamForVectorField);
+
+// 4.3 Crate an index file
+CreateIndexReq createIndexReq = CreateIndexReq.builder()
+    .collectionName("customized_setup")
+    .indexParams(indexParams)
+    .build();
+
+client.createIndex(createIndexReq);
+
    
+
    // 4. Set up index for the collection
+// 4.1. Set up the index parameters
+res = await client.createIndex({
+    collection_name: "customized_setup",
+    field_name: "vector",
+    index_type: "AUTOINDEX",
+    metric_type: "COSINE",   
+    index_name: "vector_index"
+})
+
+console.log(res.error_code)
+
+// Output
+// 
+// Success
+// 
+
    
 
    
 
    notes
    
 
    Currently, you can create only one index file for each field in a collection.
    
 
    
 
    Check Index Details
    
 
    Once you have created an index, you can check its details.
    
+
    
+    Python 
+    Java
+    Node.js
+
    
 
    # 5. Describe index
 res = client.list_indexes(
     collection_name="customized_setup"
@@ -361,12 +479,109 @@ 
    Check Index Details
    
+
    import io.milvus.v2.service.index.request.DescribeIndexReq;
+import io.milvus.v2.service.index.response.DescribeIndexResp;
+
+// 5. Describe index
+// 5.1 List the index names
+ListIndexesReq listIndexesReq = ListIndexesReq.builder()
+    .collectionName("customized_setup")
+    .build();
+
+List<String> indexNames = client.listIndexes(listIndexesReq);
+
+System.out.println(indexNames);
+
+// Output:
+// [
+//     "vector_index"
+// ]
+
+// 5.2 Describe an index
+DescribeIndexReq describeIndexReq = DescribeIndexReq.builder()
+    .collectionName("customized_setup")
+    .indexName("vector_index")
+    .build();
+
+DescribeIndexResp describeIndexResp = client.describeIndex(describeIndexReq);
+
+System.out.println(JSONObject.toJSON(describeIndexResp));
+
+// Output:
+// {
+//     "metricType": "COSINE",
+//     "indexType": "AUTOINDEX",
+//     "fieldName": "vector",
+//     "indexName": "vector_index"
+// }
+
    
+
    // 5. Describe the index
+res = await client.describeIndex({
+    collection_name: "customized_setup",
+    index_name: "vector_index"
+})
+
+console.log(JSON.stringify(res.index_descriptions, null, 2))
+
+// Output
+// 
+// [
+//   {
+//     "params": [
+//       {
+//         "key": "index_type",
+//         "value": "AUTOINDEX"
+//       },
+//       {
+//         "key": "metric_type",
+//         "value": "COSINE"
+//       }
+//     ],
+//     "index_name": "vector_index",
+//     "indexID": "449007919953063141",
+//     "field_name": "vector",
+//     "indexed_rows": "0",
+//     "total_rows": "0",
+//     "state": "Finished",
+//     "index_state_fail_reason": "",
+//     "pending_index_rows": "0"
+//   }
+// ]
+// 
+
    
 
    You can check the index file created on a specific field, and collect the statistics on the number of rows indexed using this index file.
    
 
    Drop an Index
    
 
    You can simply drop an index if it is no longer needed.
    
+
    
+    Python 
+    Java
+    Node.js
+
    
 
    # 6. Drop index
 client.drop_index(
     collection_name="customized_setup",
     index_name="vector_index"
 )
+
    
+
    // 6. Drop index
+
+DropIndexReq dropIndexReq = DropIndexReq.builder()
+    .collectionName("customized_setup")
+    .indexName("vector_index")
+    .build();
+
+client.dropIndex(dropIndexReq);
+
    
+
    // 6. Drop the index
+res = await client.dropIndex({
+    collection_name: "customized_setup",
+    index_name: "vector_index"
+})
+
+console.log(res.error_code)
+
+// Output
+// 
+// Success
+// 
 
    On this page
    • Index Vector Fields
    ResourcesDocsBlogManaged service
    TutorialsBootcampDemoVideo
    ToolsAttuMilvus CLISizing ToolMilvus backup tool
    CommunityGet involvedDiscordGitHub
    Milvus. 2024 All rights reserved.
    
\ No newline at end of file
diff --git a/bootcamp/RAG/rtdocs/index.html b/bootcamp/RAG/rtdocs_new/index.html
similarity index 99%
rename from bootcamp/RAG/rtdocs/index.html
rename to bootcamp/RAG/rtdocs_new/index.html
index a4e26d89b..a9e531f45 100644
--- a/bootcamp/RAG/rtdocs/index.html
+++ b/bootcamp/RAG/rtdocs_new/index.html
@@ -263,7 +263,7 @@
         }
       }
     })
-  
    milvus-logo
    Stars0
    In-memory Index
    
+  
    milvus-logo
    Stars0
    In-memory Index
    
 
    This topic lists various types of in-memory indexes Milvus supports, scenarios each of them best suits, and parameters users can configure to achieve better search performance. For on-disk indexes, see On-disk Index.
    
 
    Indexing is the process of efficiently organizing data, and it plays a major role in making similarity search useful by dramatically accelerating time-consuming queries on large datasets.
    
 
    To improve query performance, you can specify an index type for each vector field.
    
@@ -813,12 +813,12 @@ 
    HNSWHNSW
    milvus-logo
    Stars0
    Insert, Upsert & Delete
    
+  
    milvus-logo
    Stars0
    Insert, Upsert & Delete
    
 
    This guide walks you through the data manipulation operations within a collection, including insertion, upsertion, and deletion.
    
 
    Before you start
    
 
    On this page
    • Insert, Upsert & Delete
    ResourcesDocsBlogManaged service
    TutorialsBootcampDemoVideo
    ToolsAttuMilvus CLISizing ToolMilvus backup tool
    CommunityGet involvedDiscordGitHub
    Milvus. 2024 All rights reserved.
    
\ No newline at end of file
diff --git a/bootcamp/RAG/rtdocs/install_standalone-docker.html b/bootcamp/RAG/rtdocs_new/install_standalone-docker.html
similarity index 99%
rename from bootcamp/RAG/rtdocs/install_standalone-docker.html
rename to bootcamp/RAG/rtdocs_new/install_standalone-docker.html
index be354c84b..569c2d094 100644
--- a/bootcamp/RAG/rtdocs/install_standalone-docker.html
+++ b/bootcamp/RAG/rtdocs_new/install_standalone-docker.html
@@ -263,7 +263,7 @@
         }
       }
     })
-  
    milvus-logo
    Stars0
    DockerMilvus OperatorHelmDEB/RPMDocker Compose
    
+  
    milvus-logo
    Stars0
    DockerMilvus OperatorHelmDEB/RPMDocker Compose
    
 
    Install Milvus Standalone with Docker
    
 
    This topic describes how to install Milvus standalone using Docker.
    
 
    Prerequisites
    
diff --git a/bootcamp/RAG/rtdocs/manage-collections.html b/bootcamp/RAG/rtdocs_new/manage-collections.html
similarity index 96%
rename from bootcamp/RAG/rtdocs/manage-collections.html
rename to bootcamp/RAG/rtdocs_new/manage-collections.html
index d595be094..c57f6b683 100644
--- a/bootcamp/RAG/rtdocs/manage-collections.html
+++ b/bootcamp/RAG/rtdocs_new/manage-collections.html
@@ -263,7 +263,7 @@
         }
       }
     })
-  
    milvus-logo
    Stars0
    Manage Collections
    
+  
    milvus-logo
    Stars0
    Manage Collections
    
 
    This guide walks you through creating and managing collections using the SDK of your choice.
    
 
    Before you start
    
 
    
 
    from pymilvus import MilvusClient, DataType
 
 # 1. Set up a Milvus client
@@ -332,6 +337,70 @@ 
    Quick setup"
 # }
 
    
+
    import io.milvus.v2.client.ConnectConfig;
+import io.milvus.v2.client.MilvusClientV2;
+import io.milvus.v2.service.collection.request.GetLoadStateReq;
+import io.milvus.v2.service.collection.request.CreateCollectionReq;
+
+String CLUSTER_ENDPOINT = "http://localhost:19530";
+
+// 1. Connect to Milvus server
+ConnectConfig connectConfig = ConnectConfig.builder()
+    .uri(CLUSTER_ENDPOINT)
+    .build();
+
+MilvusClientV2 client = new MilvusClientV2(connectConfig);
+
+// 2. Create a collection in quick setup mode
+CreateCollectionReq quickSetupReq = CreateCollectionReq.builder()
+    .collectionName("quick_setup")
+    .dimension(5)
+    .build();
+
+client.createCollection(quickSetupReq);
+
+// Thread.sleep(5000);
+
+GetLoadStateReq quickSetupLoadStateReq = GetLoadStateReq.builder()
+    .collectionName("quick_setup")
+    .build();
+
+Boolean res = client.getLoadState(quickSetupLoadStateReq);
+
+System.out.println(res);
+
+// Output:
+// true
+
    
+
    address = "http://localhost:19530"
+
+// 1. Set up a Milvus Client
+client = new MilvusClient({address});
+
+// 2. Create a collection in quick setup mode
+let res = await client.createCollection({
+    collection_name: "quick_setup",
+    dimension: 5,
+});  
+
+console.log(res.error_code)
+
+// Output
+// 
+// Success
+// 
+
+res = await client.getLoadState({
+    collection_name: "quick_setup"
+})
+
+console.log(res.state)
+
+// Output
+// 
+// LoadStateLoaded
+// 
+
    
 
    The collection generated in the above code contains only two fields: id (as the primary key) and vector (as the vector field), with auto_id and enable_dynamic_field settings enabled by default.
    
 
    On this page
    • Manage Collections
    ResourcesDocsBlogManaged service
    TutorialsBootcampDemoVideo
    ToolsAttuMilvus CLISizing ToolMilvus backup tool
    CommunityGet involvedDiscordGitHub
    Milvus. 2024 All rights reserved.
    
\ No newline at end of file
diff --git a/bootcamp/RAG/rtdocs/metric.html b/bootcamp/RAG/rtdocs_new/metric.html
similarity index 99%
rename from bootcamp/RAG/rtdocs/metric.html
rename to bootcamp/RAG/rtdocs_new/metric.html
index 5ba0b94bd..9ad88d3f3 100644
--- a/bootcamp/RAG/rtdocs/metric.html
+++ b/bootcamp/RAG/rtdocs_new/metric.html
@@ -263,7 +263,7 @@
         }
       }
     })
-  
    milvus-logo
    Stars0
    Similarity Metrics
    
+  
    milvus-logo
    Stars0
    Similarity Metrics
    
 
    In Milvus, similarity metrics are used to measure similarities among vectors. Choosing a good distance metric helps improve the classification and clustering performance significantly.
    
 
    The following table shows how these widely used similarity metrics fit with various input data forms and Milvus indexes.
    
 
    
@@ -273,7 +273,7 @@
 
-    
+    
@@ -362,7 +362,7 @@ 
    Inner product (IP)
-
    If you use IP to calculate embeddings similarities, you must normalize your embeddings. After normalization, the inner product equals cosine similarity.
    
+
    If you apply the IP distance metric to normalized embeddings, the result will be equivalent to calculating the cosine similarity between the embeddings.
    
 
 
    Suppose X' is normalized from embedding X:
    
 
    
diff --git a/bootcamp/RAG/rtdocs/milvus-cdc-overview.html b/bootcamp/RAG/rtdocs_new/milvus-cdc-overview.html
similarity index 98%
rename from bootcamp/RAG/rtdocs/milvus-cdc-overview.html
rename to bootcamp/RAG/rtdocs_new/milvus-cdc-overview.html
index abd588366..fa8b93299 100644
--- a/bootcamp/RAG/rtdocs/milvus-cdc-overview.html
+++ b/bootcamp/RAG/rtdocs_new/milvus-cdc-overview.html
@@ -263,7 +263,7 @@
         }
       }
     })
-  
    milvus-logo
    Stars0
    Overview
    
+  
    milvus-logo
    Stars0
    Overview
    
 
    Milvus-CDC is a user-friendly tool that can capture and synchronize incremental data in Milvus instances. It ensures the reliability of business data by seamlessly transferring it between source and target instances, allowing for easy incremental backup and disaster recovery.
    
 
    Key capabilities
    
 
      
diff --git a/bootcamp/RAG/rtdocs/monitor_overview.html b/bootcamp/RAG/rtdocs_new/monitor_overview.html
similarity index 98%
rename from bootcamp/RAG/rtdocs/monitor_overview.html
rename to bootcamp/RAG/rtdocs_new/monitor_overview.html
index f1f80da8f..31ccbdb2d 100644
--- a/bootcamp/RAG/rtdocs/monitor_overview.html
+++ b/bootcamp/RAG/rtdocs_new/monitor_overview.html
@@ -263,7 +263,7 @@
         }
       }
     })
-  
      milvus-logo
      Stars0
      Milvus monitoring framework overview
      
+  
      milvus-logo
      Stars0
      Milvus monitoring framework overview
      
 
      This topic explains how Milvus uses Prometheus to monitor metrics and Grafana to visualize metrics and create alerts.
      
 
      Prometheus in Milvus
      
 
      Prometheus is an open-source monitoring and alerting toolkit for Kubernetes implementations. It collects and stores metrics as time-series data. This means that metrics are stored with timestamps when recorded, alongside with optional key-value pairs called labels.
      
diff --git a/bootcamp/RAG/rtdocs/multi-vector-search.html b/bootcamp/RAG/rtdocs_new/multi-vector-search.html
similarity index 95%
rename from bootcamp/RAG/rtdocs/multi-vector-search.html
rename to bootcamp/RAG/rtdocs_new/multi-vector-search.html
index aa04ec2b6..0bf58e384 100644
--- a/bootcamp/RAG/rtdocs/multi-vector-search.html
+++ b/bootcamp/RAG/rtdocs_new/multi-vector-search.html
@@ -263,93 +263,41 @@
         }
       }
     })
-  
      milvus-logo
      Stars0
      Multi-Vector Search
      
-
      Since Milvus 2.4, we introduced multi-vector support and a hybrid search framework, which means users can bring in several vector fields (max to 10) into one collection. Different vector fields can represent different aspects, different embedding models or even different modalities of data characterizing the same entity, which greatly expands the richness of information.
      
-
      Multi-vector search allows conducting a search that includes multiple vector fields within a single collection. This feature enables executing search requests over various vector fields and integrating the results using reranking strategies, including Reciprocal Rank Fusion (RRF) and Weighted Scoring.
      
-
      It is mainly used in comprehensive search scenarios, such as finding the most similar person in the vector library based on multiple elements such as someone's picture, voice, fingerprint, etc.
      
-
      This guide provides a step-by-step explanation of how to execute multi-vector search in Milvus and understand the reranking of results.
      
-
      API overview
      
-
      The hybrid_search API is central to performing a multi-vector search. The following 2 key params represent two essential phases: multi-way recalls and a hybrid rerank.
      
+  
      milvus-logo
      Stars0
      Multi-Vector Search
      
+
      Since Milvus 2.4, we introduced multi-vector support and a hybrid search framework, which means users can bring in several vector fields (up to 10) into a single collection. Different vector fields can represent different aspects, different embedding models or even different modalities of data characterizing the same entity, which greatly expands the richness of information. This feature is particularly useful in comprehensive search scenarios, such as identifying the most similar person in a vector library based on various attributes like pictures, voice, fingerprints, etc.
      
+
      A multi-vector search enables executing search requests over various vector fields and combines the results using reranking strategies, such as Reciprocal Rank Fusion (RRF) and Weighted Scoring. To learn more about reranking strategies, refer to Reranking.
      
+
      In this tutorial, you will learn how to:
      
 
        
 
      • 
-
        reqs: This is a list of ANN search requests. Each search request is an ANNSearchRequest object tied to a unique vector field and its search parameters.
        
+
        Create multiple AnnSearchRequest instances for similarity searches on different vector fields;
        
 
        • 
 
      • 
-
        rerank: Specifies the reranking strategy. Options include WeightedRanker and RRFRanker.
        
-
          
-
        • 
-
          WeightedRanker: The Average Weighted Scoring reranking strategy, which prioritizes vectors based on relevance, averaging their significance.
          
+
          Configure a reranking strategy to combine and rerank search results from multiple AnnSearchRequest instances;
          
 
          • 
 
        • 
-
          RRFRanker: The RRF reranking strategy, which merges results from multiple searches, favoring items that consistently appear.
          
+
          Use the hybrid_search() method to perform a multi-vector search.
          
 
          • 
 
        
-
        More rerank models are coming soon to enhance our reranking capabilities, such as Cohere ranking, BGE ranking, etc. Stay tuned!
        
-
        • 
-
      
-
      Example of a hybrid_search call:
      
-
      # Create a Collection instance
-collection = Collection(name='{your_collection_name}') # Replace with the actual name of your collection
+
      
+
      The code snippets on this page use the PyMilvus ORM module to interact with Milvus. Code snippets with the new MilvusClient SDK will be available soon.
      
+
      
+
      Preparations
      
+
      Before starting a multi-vector search, ensure you have a collection with multiple vector fields.
      
+
      Below is an example of creating a collection named test_collection with two vector fields, filmVector and posterVector, and inserting random entities into it.
      
+
      from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
+import random
 
-# Perform hybrid search with placeholder configs
-res = collection.hybrid_search(
-    reqs=[
-        AnnSearchRequest(
-            data=[['{your_text_query_vector}']],  # Replace with your text vector data
-            anns_field='{text_vector_field_name}',  # Textual data vector field
-            param={"metric_type": "IP", "params": {"nprobe": 10}}, # Search parameters
-            limit=2
-        ),
-        AnnSearchRequest(
-            data=[['{your_image_query_vector}']],  # Replace with your image vector data
-            anns_field='{image_vector_field_name}',  # Image data vector field
-            param={"metric_type": "IP", "params": {"nprobe": 10}}, # Search parameters
-            limit=2
-        )
-    ],
-    # Use WeightedRanker to combine results with specified weights
-    rerank=WeightedRanker(0.8, 0.2), # Assign weights of 0.8 to text search and 0.2 to image search
-    # Alternatively, use RRFRanker for reciprocal rank fusion reranking
-    # rerank=RRFRanker(),
-    limit=2
+# Connect to Milvus
+connections.connect(
+    host="10.102.7.3", # Replace with your Milvus server IP
+    port="19530"
 )
-
      
-
      Expected output:
      
-
      [
-  "['id: 2, distance: 0.4452269673347473, entity: {}', 'id: 1, distance: 0.0, entity: {}']"
-]
-
      
-
      Considerations:
      
-
        
-
      • 
-
        A collection must have multiple vector fields, each with its own index.
        
-
        • 
-
      • 
-
        Partially indexed or loaded vector fields in a collection will result in an error.
        
-
        • 
-
      • 
-
        Currently, each AnnSearchRequest in a hybrid search can carry one query vector.
        
-
        • 
-
      • 
-
        Typically, each collection has a default allowance of up to 4 vector fields. However, you have the option to adjust the proxy.maxVectorFieldNum configuration to expand the maximum number of vector fields in a collection, with a maximum limit of 10 vector fields per collection. See Proxy-related Configurations for more.
        
-
        • 
-
      
-
      Practical examples
      
-
      Consider a collection named test_collection with two vector fields: filmVector and posterVector.
      
-
        
-
      • 
-
        filmVector: Represents textual content of a film.
        
-
        • 
-
      • 
-
        posterVector: Converts visual features of a film's poster into vector format.
        
-
        • 
-
      
-
      Here is an example of creating and indexing the collection:
      
-
      # Create schema
+
+# Create schema
 fields = [
     FieldSchema(name="film_id", dtype=DataType.INT64, is_primary=True),
-    FieldSchema(name="filmVector", dtype=DataType.FLOAT_VECTOR, dim=5),
-    FieldSchema(name="posterVector", dtype=DataType.FLOAT_VECTOR, dim=5)]
+    FieldSchema(name="filmVector", dtype=DataType.FLOAT_VECTOR, dim=5), # Vector field for film vectors
+    FieldSchema(name="posterVector", dtype=DataType.FLOAT_VECTOR, dim=5)] # Vector field for poster vectors
 
 schema = CollectionSchema(fields=fields,enable_dynamic_field=False)
 
@@ -358,7 +306,7 @@ 
      Practical examplesPractical examples
      
-
      To insert data into the collection, use the insert method.
      
-
      Here is how to insert 5 rows of data:
      
-
      # Insert data
-data = [
-    [1, 2, 3, 4, 5],
-    [
-        [0.8896863042430693, 0.370613100114602, 0.23779315077113428, 0.38227915951132996, 0.5997064603128835],
-        [0.5078114059712959, 0.3432028079630215, 0.8089418399592051, 0.474462050627378, 0.5856421849875101],
-        [0.2990413172901394, 0.9028391994278029, 0.34082510211853334, 0.4107540298194492, 0.47539164233358744],
-        [0.5832605600308075, 0.8511790894069673, 0.7112488464298848, 0.553514109969526, 0.15985473038541032],
-        [0.21188658802419225, 0.572143948100824, 0.4585998365439241, 0.565993613724163, 0.5862558542959135]
-    ],
-    [
-        [0.02550758562349764, 0.006085637357292062, 0.5325251250159071, 0.7676432650114147, 0.5521074424751443],
-        [0.19516017744052183, 0.22918923173953565, 0.9548363036811129, 0.5643725931032165, 0.5964664905051439],
-        [0.06260894301791908, 0.814777822276412, 0.8672567702540677, 0.1374189887611933, 0.9268283838873627],
-        [0.5364943790237713, 0.9962551093178361, 0.31902289153816554, 0.9924305856358849, 0.6287783946443399],
-        [0.7644141951092023, 0.8478868932552704, 0.5442341774477372, 0.8379655462947587, 0.5167658776852181]
-    ]
-]
+for _ in range(1000):
+    # generate random values for each field in the schema
+    film_id = random.randint(1, 1000)
+    film_vector = [ random.random() for _ in range(5) ]
+    poster_vector = [ random.random() for _ in range(5) ]
 
-collection.insert(data)
+    # creat a dictionary for each entity
+    entity = {
+        "film_id": film_id,
+        "filmVector": film_vector,
+        "posterVector": poster_vector
+    }
 
-# Output:
-# (insert count: 5, delete count: 0, upsert count: 0, timestamp: 447370828842532866, success count: 5, err count: 0)
+    # add the entity to the list
+    entities.append(entity)
+    
+collection.insert(entities)
 
      
-
      Because Milvus executes searches over multiple vector fields, you need to create separate ANNSearchRequest objects for each vector field:
      
-
      # Create ANN search request for filmVector
+
      Step 1: Create Multiple AnnSearchRequest Instances
      
+
      A multi-vector search uses the hybrid_search() API to perform multiple ANN search requests in a single call. Each AnnSearchRequest represents a single search request on a specific vector field.
      
+
      The following example creates two AnnSearchRequest instances to perform individual similarity searches on two vector fields.
      
+
      from pymilvus import AnnSearchRequest
+
+# Create ANN search request 1 for filmVector
 query_filmVector = [[0.8896863042430693, 0.370613100114602, 0.23779315077113428, 0.38227915951132996, 0.5997064603128835]]
 
 search_param_1 = {
     "data": query_filmVector, # Query vector
     "anns_field": "filmVector", # Vector field name
     "param": {
-        "metric_type": "IP",
+        "metric_type": "L2", # This parameter value must be identical to the one used in the collection schema
         "params": {"nprobe": 10}
     },
-    "limit": 2
+    "limit": 2 # Number of search results to return in this AnnSearchRequest
 }
 request_1 = AnnSearchRequest(**search_param_1)
 
-# Create ANN search request for posterVector
+# Create ANN search request 2 for posterVector
 query_posterVector = [[0.02550758562349764, 0.006085637357292062, 0.5325251250159071, 0.7676432650114147, 0.5521074424751443]]
 search_param_2 = {
     "data": query_posterVector, # Query vector
     "anns_field": "posterVector", # Vector field name
     "param": {
-        "metric_type": "IP",
+        "metric_type": "L2", # This parameter value must be identical to the one used in the collection schema
         "params": {"nprobe": 10}
     },
-    "limit": 2
+    "limit": 2 # Number of search results to return in this AnnSearchRequest
 }
 request_2 = AnnSearchRequest(**search_param_2)
-
      
-
      Once request_1 and request_2 are created, implement hybrid search with reranking strategies: Weighted Scoring and RRF.
      
-
      Use weighted scoring
      
-
      To use this strategy, set rerank to WeightedRanker and assign weights to each ANNSearchRequest.
      
-
      # hybrid search with WeightedRanker
-
-weighted_result = collection.hybrid_search(
-    reqs=[request_1, request_2],
-    # Combine the results with weight 0.8 for request_1 and 0.2 for request_2
-    rerank=WeightedRanker(0.8, 0.2),
-    limit=2
-)
 
-# Print the results
-print(weighted_result)
+# Store these two requests as a list in `reqs`
+reqs = [request_1, request_2]
+
      
+
      Parameters:
      
+
        
+
      • 
+
        AnnSearchRequest (object)
        
+
        A class representing an ANN search request. Each hybrid search can contain 1 to 1,024 ANNSearchRequest objects at a time.
        
+
        • 
+
      • 
+
        data (list)
        
+
        The query vector to search in a single AnnSearchRequest. Currently, this parameter accepts a list containing only a single query vector, for example, [[0.5791814851218929, 0.5792985702614121, 0.8480776460143558, 0.16098005945243, 0.2842979317256803]]. In the future, this parameter will be expanded to accept multiple query vectors.
        
+
        • 
+
      • 
+
        anns_field (string)
        
+
        The name of the vector field to use in a single AnnSearchRequest.
        
+
        • 
+
      • 
+
        param (dict)
        
+
        A dictionary of search parameters for a single AnnSearchRequest. These search parameters are identical to those for a single-vector search. For more information, refer to Search parameters.
        
+
        • 
+
      • 
+
        limit (int)
        
+
        The maximum number of search results to include in a single ANNSearchRequest.
        
+
        This parameter only affects the number of search results to return within an individual ANNSearchRequest, and it does not decide the final results to return for a hybrid_search call. In a hybrid search, the final results are determined by combining and reranking the results from multiple ANNSearchRequest instances.
        
+
        • 
+
      
+
      Step 2: Configure a Reranking Strategy
      
+
      After creating AnnSearchRequest instances, configure a reranking strategy to combine and rerank the results. Currently, there are two options: WeightedRanker and RRFRanker. For more information about reranking strategies, refer to Reranking.
      
+
        
+
      • 
+
        Use weighted scoring
        
+
        The WeightedRanker is used to assign importance to the results from each vector field search with specified weights. If you prioritize some vector fields over others, WeightedRanker(value1, value2, ..., valueN) can reflect this in the combined search results.
        
+
        from pymilvus import WeightedRanker
+# Use WeightedRanker to combine results with specified weights
+# Assign weights of 0.8 to text search and 0.2 to image search
+rerank = WeightedRanker(0.8, 0.2)  
+
        
+
        When using WeightedRanker, note that:
        
+
          
+
        • Each weight value ranges from 0 (least important) to 1 (most important), influencing the final aggregated score.
          • 
+
        • The total number of weight values provided in WeightedRanker should equal the number of AnnSearchRequest instances you have created.
          • 
+
        
+
        • 
+
      • 
+
        Use Reciprocal Rank Fusion (RFF)
        
+
        # Alternatively, use RRFRanker for reciprocal rank fusion reranking
+from pymilvus import RRFRanker
 
-# Output:
-# ["['id: 2, distance: 0.4452269673347473, entity: {}', 'id: 1, distance: 0.0, entity: {}']"]
+rerank = RRFRanker()
 
        
-
        Use RRF
        
-
        For RRF reranking, set rerank to RRFRanker:
        
-
        # hybrid search with RRFRanker
+
        • 
+
      
+
+
      With the AnnSearchRequest instances and reranking strategy set, use the hybrid_search() method to perform the multi-vector search.
      
+
      # Before conducting multi-vector search, load the collection into memory.
+collection.load()
 
-rrf_result = collection.hybrid_search(
-    reqs=[request_1, request_2],
-    rerank=RRFRanker(),
-    limit=2
+res = collection.hybrid_search(
+    reqs, # List of AnnSearchRequests created in step 1
+    rerank, # Reranking strategy specified in step 2
+    limit=2 # Number of final search results to return
 )
 
-# Print the results
-print(rrf_result)
-
-# Output:
-# ["['id: 1, distance: 0.032786883413791656, entity: {}', 'id: 2, distance: 0.032258063554763794, entity: {}']"]
+print(res)
+
      
+
      Parameters:
      
+
        
+
      • 
+
        reqs (list)
        
+
        A list of search requests, where each request is an ANNSearchRequest object. Each request can correspond to a different vector field and a different set of search parameters.
        
+
        • 
+
      • 
+
        rerank (object)
        
+
        The reranking strategy to use for hybrid search. Possible values: WeightedRanker(value1, value2, ..., valueN) and RRFRanker().
        
+
        For more information about reranking strategies, refer to Reranking.
        
+
        • 
+
      • 
+
        limit (int)
        
+
        The maximum number of final results to return in the hybrid search.
        
+
        • 
+
      
+
      The output is similar to the following:
      
+
      ["['id: 844, distance: 0.006047376897186041, entity: {}', 'id: 876, distance: 0.006422005593776703, entity: {}']"]
 
      
-
      Hybrid search parameters
      
-
      The following table outlines the parameters used in a hybrid search.
      
-
    
 
   
    Similarity MetricsMetric Types
     Index Types
   
    
 
    
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
    ParameterDescription
    reqsA list of search requests, where each request is an ANNSearchRequest object. Each request corresponds to a different vector field and a different set of search parameters.
    reqs.ANNSearchRequestA class representing an ANN search request.
    reqs.ANNSearchRequest.dataThe query vector to search in the request. This parameter accepts a list containing one element (or query vector).
    reqs.ANNSearchRequest.anns_fieldThe vector field to use in the request.
    reqs.ANNSearchRequest.paramA dictionary of search parameters for the request. For details, refer to Search parameters.
    reqs.ANNSearchRequest.limitThe maximum number of results to return in the request. When performing a hybrid search with multiple ANN search requests, the top results defined by limit from each request will be combined and re-ranked before returning the final search results.
    reqs.ANNSearchRequest.expr(Optional) The expression to filter the results.
    rerankThe reranking strategy to use for hybrid search. Valid values: WeightedRanker and RRFRanker.
    limitThe maximum number of results to return in the hybrid search.
    
+
    Limits
    
+
      
+
    • 
+
      Typically, each collection has a default allowance of up to 4 vector fields. However, you have the option to adjust the proxy.maxVectorFieldNum configuration to expand the maximum number of vector fields in a collection, with a maximum limit of 10 vector fields per collection. See Proxy-related Configurations for more.
      
+
      • 
+
    • 
+
      Partially indexed or loaded vector fields in a collection will result in an error.
      
+
      • 
+
    • 
+
      Currently, each AnnSearchRequest in a hybrid search can carry one query vector only.
      
+
      • 
+
    
 
    FAQ
    
-
      
+
        
 
      • 
-
        In which scenarios is multi-vector search recommended?
        
+
        In which scenario is multi-vector search recommended?
        
 
        Multi-vector search is ideal for complex situations demanding high accuracy, especially when an entity can be represented by multiple, diverse vectors. This applies to cases where the same data, such as a sentence, is processed through different embedding models or when multimodal information (like images, fingerprints, and voiceprints of an individual) is converted into various vector formats. By assigning weights to these vectors, their combined influence can significantly enrich recall and improve the effectiveness of search results.
        
 
        • 
 
      • 
@@ -526,4 +482,8 @@ 
        FAQ
        On this page
        • Multi-Vector Search
    ResourcesDocsBlogManaged service
    TutorialsBootcampDemoVideo
    ToolsAttuMilvus CLISizing ToolMilvus backup tool
    CommunityGet involvedDiscordGitHub
    Milvus. 2024 All rights reserved.
    
\ No newline at end of file
+
  4. 
+
    Can I use the same vector field in multiple AnnSearchRequest objects to perform hybrid searches?
    
+
    Technically, it is possible to use the same vector field in multiple AnnSearchRequest objects for hybrid searches. It is not necessary to have multiple vector fields for a hybrid search.
    
+
    5. 
+
    On this page
    • Multi-Vector Search
    ResourcesDocsBlogManaged service
    TutorialsBootcampDemoVideo
    ToolsAttuMilvus CLISizing ToolMilvus backup tool
    CommunityGet involvedDiscordGitHub
    Milvus. 2024 All rights reserved.
    
\ No newline at end of file
diff --git a/bootcamp/RAG/rtdocs/quickstart.html b/bootcamp/RAG/rtdocs_new/quickstart.html
similarity index 97%
rename from bootcamp/RAG/rtdocs/quickstart.html
rename to bootcamp/RAG/rtdocs_new/quickstart.html
index a284042f7..6aa902352 100644
--- a/bootcamp/RAG/rtdocs/quickstart.html
+++ b/bootcamp/RAG/rtdocs_new/quickstart.html
@@ -263,7 +263,7 @@
         }
       }
     })
-  
    milvus-logo
    Stars0
    Quickstart
    
+  
    milvus-logo
    Stars0
    Quickstart
    
 
    This guide explains how to connect to your Milvus cluster and performs CRUD operations in minutes
    
 
    Before you start
    
 
+
    Hello Milvus
    
+
    To confirm that your Milvus instance is operational and Python SDK is set up correctly, start by downloading the hello_milvus.py script. You can do this using the following command:
    
+
    wget https://raw.githubusercontent.com/milvus-io/milvus-docs/v2.4.x/assets/hello_milvus.py
+
    
+
    Next, update the uri parameter in the script with the address of your Milvus instance. Once updated, run the script using the command below:
    
+
    python hello_milvus.py
+
    
+
    If the script executes without returning any error messages, your Milvus instance is functioning correctly and the Python SDK is properly installed.
    
 
    Connect to Milvus
    
 
    Once you have obtained the cluster credentials or an API key, you can use it to connect to your Milvus now.
    
 
    from pymilvus import MilvusClient, DataType
@@ -284,7 +292,10 @@ 
    Connect to Milvus
-
    If you have enabled authentication on your Milvus instance, you should add token as a parameter when initiating MilvusClient and set the value to a colon-separated username and password. To authenticate using the default username and password, set token to root:Milvus.
    
+    
 
    
 
    Create a Collection
    
 
    In Milvus, you need to store your vector embeddings in collections. All vector embeddings stored in a collection share the same dimensionality and distance metric for measuring similarity. You can create a collection in either of the following manners.
    
@@ -358,14 +369,16 @@ 
    Customized setupSchema.
    
 
 
-
    For a detailed explanation of the schema, refer to Schema.
    
 
 
  5. 
 
    Index parameters
    
@@ -376,9 +389,9 @@ 
    Customized setupIndex.
    
 
    6. 
 
-
    For additional insights into index types, refer to Index.
    
 
 
 
    
@@ -710,7 +723,7 @@ 
    Get EntitiesGet EntitiesGet EntitiesGet EntitiesRecaps
    On this page
    • Quickstart
    ResourcesDocsBlogManaged service
    TutorialsBootcampDemoVideo
    ToolsAttuMilvus CLISizing ToolMilvus backup tool
    CommunityGet involvedDiscordGitHub
    Milvus. 2024 All rights reserved.
    
\ No newline at end of file
+
    On this page
    • Quickstart
    ResourcesDocsBlogManaged service
    TutorialsBootcampDemoVideo
    ToolsAttuMilvus CLISizing ToolMilvus backup tool
    CommunityGet involvedDiscordGitHub
    Milvus. 2024 All rights reserved.
    
\ No newline at end of file
diff --git a/bootcamp/RAG/rtdocs/rbac.html b/bootcamp/RAG/rtdocs_new/rbac.html
similarity index 95%
rename from bootcamp/RAG/rtdocs/rbac.html
rename to bootcamp/RAG/rtdocs_new/rbac.html
index 185893d61..00d62fb76 100644
--- a/bootcamp/RAG/rtdocs/rbac.html
+++ b/bootcamp/RAG/rtdocs_new/rbac.html
@@ -263,103 +263,146 @@
         }
       }
     })
-  
    milvus-logo
    Stars0
    Enable RBAC
    
+  
    milvus-logo
    Stars0
    Enable RBAC
    
 
    By enabling RBAC, you can control access to specific Milvus resources (Eg. a collection or a partition) or permissions based on user role and privileges. Currently, this feature is only available in Python and Java.
    
 
    This topic describes how to enable RBAC and manage users and roles.
    
-
    1. Create a user
    
-
    from pymilvus import utility
+
    
+
    The code snippets on this page use new MilvusClient (Python) to interact with Milvus. New MilvusClient SDKs for other languages will be released in future updates.
    
+
    
+
    1. Initiate a Milvus client to establish a connection
    
+
    After you enable user authentication, connect to your Milvus instance using token that consists of a username and a password. By default, Milvus uses the root user with the password Milvus.
    
+
    from pymilvus import MilvusClient
 
-utility.create_user(user, password, using="default")
+client = MilvusClient(
+    uri='http://localhost:19530', # replace with your own Milvus server address
+    token='root:Milvus' # replace with your own Milvus server token
+)
+
    
+
    2. Create a user
    
+
    Create a user named user_1 with the password P@ssw0rd:
    
+
    client.create_user(
+    user_name='user_1',
+    password='P@ssw0rd'
+)
 
    
 
    After creating a user, you can:
    
 
      
 
    • Update a user password. You need to provide both the original and the new password.
      • 
 
    
-
    utility.update_password(user, old_password, new_password, using="default")
+
    client.update_password(
+    user_name='user_1',
+    old_password='P@ssw0rd',
+    new_password='P@ssw0rd123'
+)
 
    
 
      
 
    • List all users.
      • 
 
    
-
    utility.list_usernames(using="default")
+
    client.list_users()
+
+# output:
+# ['root', 'user_1']
 
    
 
      
 
    • Check the role of a particular user.
      • 
 
    
-
    utility.list_user(username, include_role_info, using="default")
-
    
-
      
-
    • Check the roles of all users.
      • 
-
    
-
    utility.list_users(include_role_info, using="default")
+
    client.describe_user(user_name='user_1')
+
+# output:
+# {'user_name': 'user_1', 'roles': ()}
 
    
-
    2. Create a role
    
+
    3. Create a role
    
 
    The following example creates a role named roleA.
    
-
    from pymilvus import Role, utility
-
-role_name = "roleA"
-role = Role(role_name, using=_CONNECTION)
-role.create()
+
    client.create_role(
+    role_name="roleA",
+)
 
    
 
    After creating a role, you can:
    
 
      
-
    • Check if a role exists.
      • 
-
    
-
    role.is_exist()
-
    
-
      
 
    • List all roles.
      • 
 
    
-
    utility.list_roles(include_user_info, using="default")
+
    client.list_roles()
+
+# output:
+# ['admin', 'public', 'roleA']
 
    
-
    3. Grant a privilege to a role
    
+
    4. Grant a privilege to a role
    
 
    The following example demonstrates how to grant the permission of searching all collections to the role named roleA. See Users and Roles for other types of privileges you can grant.
    
-
    Before granting permission to the role to manipulate collections in other databases, use db.using_database() or directly connect to the desired database to change the default database to the desired one. For details, refer to Manage Databases.
    
-
    role.grant("Collection", "*", "Search")
+
    Before managing role privileges, make sure you have enabled user authentication. Otherwise, an error may occur. For information on how to enable user authentication, refer to Authenticate User Access.
    
+
    # grant privilege to a role
+
+client.grant_privilege(
+    role_name='roleA',
+    object_type='User',
+    object_name='SelectUser',
+    privilege='SelectUser'
+)
 
    
 
    After granting a privilege to a role, you can:
    
 
      
-
    • List certain privileges to an object granted to a role.
      • 
-
    
-
    role.list_grant("Collection","CollectionA")
-
    
-
      
-
    • List all privileges granted to a role.
      • 
+
    • View the privileges granted to a role.
      • 
 
    
-
    role.list_grants()
+
    client.describe_role(
+    role_name='roleA'
+)
+
+# output:
+# {'role': 'roleA',
+#  'privileges': [{'object_type': 'User',
+#    'object_name': 'SelectUser',
+#    'db_name': 'default',
+#    'role_name': 'roleA',
+#    'privilege': 'SelectUser',
+#    'grantor_name': 'root'}]}
 
    
-
    4. Bind a role to a user
    
-
    Bind the role to a user so that this user can inherit all the privileges of the role.
    
-
    role.add_user(username)
+
    5. Grant a role to a user
    
+
    Grant the role to a user so that this user can inherit all the privileges of the role.
    
+
    # grant a role to a user
+
+client.grant_role(
+    user_name='user_1',
+    role_name='roleA'
+)
 
    
-
    After binding a role to a user, you can:
    
-
      
-
    • List all users bind to a role
      • 
-
    
-
    role.get_users()
+
    After granting the role, verity that it has been granted:
    
+
    client.describe_user(
+    user_name='user_1'
+)
+
+# output:
+# {'user_name': 'user_1', 'roles': ('roleA',)}
 
    
-
    5. Deny access or privileges
    
+
    6. Revoke privileges
    
 
    
 
    Exercise caution when performing the following operations because these operations are irreversible.
    
 
    
 
      
-
    • Remove a privilege from a role.
      • 
+
    • Remove a privilege from a role. If you revoke a privilege that has not been granted to the role, an error will occur.
      • 
 
    
-
    role.revoke("Collection","*","Search")
+
    client.revoke_privilege(
+    role_name='roleA',
+    object_type='User',
+    object_name='SelectUser',
+    privilege='SelectUser'
+)
 
    
 
      
-
    • Remove a user from a role
      • 
+
    • Remove a user from a role. If you revoke a role that has not been granted to the user, an error will occur.
      • 
 
    
-
    role.remove_user(username)
+
    client.revoke_role(
+    user_name='user_1',
+    role_name='roleA'
+)
 
    
 
      
-
    • Delete a role
      • 
+
    • Drop a role.
      • 
 
    
-
    role.drop("roleA"):
+
    client.drop_role(role_name='roleA')
 
    
 
      
-
    • Delete a user
      • 
+
    • Drop a user.
      • 
 
    
-
    utility.delete_user(user, using="default")
+
    client.drop_user(user_name='user_1')
 
    
 
    What's next
    
 
    On this page
    • Enable RBAC
    ResourcesDocsBlogManaged service
    TutorialsBootcampDemoVideo
    ToolsAttuMilvus CLISizing ToolMilvus backup tool
    CommunityGet involvedDiscordGitHub
    Milvus. 2024 All rights reserved.
    
\ No newline at end of file
+
    On this page
    • Enable RBAC
    ResourcesDocsBlogManaged service
    TutorialsBootcampDemoVideo
    ToolsAttuMilvus CLISizing ToolMilvus backup tool
    CommunityGet involvedDiscordGitHub
    Milvus. 2024 All rights reserved.
    
\ No newline at end of file
diff --git a/bootcamp/RAG/rtdocs/scaleout.html b/bootcamp/RAG/rtdocs_new/scaleout.html
similarity index 98%
rename from bootcamp/RAG/rtdocs/scaleout.html
rename to bootcamp/RAG/rtdocs_new/scaleout.html
index 2e66fb5b7..1960aa3bd 100644
--- a/bootcamp/RAG/rtdocs/scaleout.html
+++ b/bootcamp/RAG/rtdocs_new/scaleout.html
@@ -263,7 +263,7 @@
         }
       }
     })
-  
    milvus-logo
    Stars0
    Scale a Milvus Cluster
    
+  
    milvus-logo
    Stars0
    Scale a Milvus Cluster
    
 
    Milvus supports horizontal scaling of its components. This means you can either increase or decrease  the number of worker nodes of each type according to your own need.
    
 
    This topic describes how to scale out and scale in a Milvus cluster. We assume that you have already installed a Milvus cluster before scaling. Also, we recommend familiarizing yourself with the Milvus architecture before you begin.
    
 
    This tutorial takes scaling out three query nodes as an example. To scale out other types of nodes, replace queryNode with the corresponding node type in the command line.
    
diff --git a/bootcamp/RAG/rtdocs/single-vector-search.html b/bootcamp/RAG/rtdocs_new/single-vector-search.html
similarity index 94%
rename from bootcamp/RAG/rtdocs/single-vector-search.html
rename to bootcamp/RAG/rtdocs_new/single-vector-search.html
index 1b34d9c3a..28aa1dbfe 100644
--- a/bootcamp/RAG/rtdocs/single-vector-search.html
+++ b/bootcamp/RAG/rtdocs_new/single-vector-search.html
@@ -263,7 +263,7 @@
         }
       }
     })
-  
    milvus-logo
    Stars0
    Single-Vector Search
    
+  
    milvus-logo
    Stars0
    Single-Vector Search
    
 
    Once you have inserted your data, the next step is to perform similarity searches on your collection in Milvus.
    
 
    Milvus allows you to conduct two types of searches, depending on the number of vector fields in your collection:
    
 
    
 
    # Conduct a range search
 search_params = {
     "metric_type": "IP",
@@ -686,7 +1766,43 @@ 
    
+
    // 9. Range search
+query_vector = [0.3580376395471989, -0.6023495712049978, 0.18414012509913835, -0.26286205330961354, 0.9029438446296592]
+
+res = await client.search({
+    collection_name: "quick_setup",
+    data: [query_vector],
+    limit: 5,
+    params: {
+        radius: 0.1,
+        range: 1.0
+    },
+    output_fields: ["color_tag"]
+})
+
+console.log(res.results)
+
    
 
    The output is similar to the following:
    
+
    
+    Python 
+    Java
+    Node.js
+
    
 
    [
     [
         {
@@ -713,6 +1829,39 @@ 
    
+
    [
+  { score: 2.3387961387634277, id: '718', color_tag: 'black_7154' },
+  { score: 2.3352415561676025, id: '1745', color_tag: 'blue_8741' },
+  { score: 2.290485382080078, id: '1408', color_tag: 'red_2324' },
+  { score: 2.285870313644409, id: '854', color_tag: 'black_5990' },
+  { score: 2.2593345642089844, id: '1309', color_tag: 'red_8458' }
+]
+
    
 
    You will observe that all the entities returned have a distance that falls within the range of 0.8 to 1.0 from the query vector.
    
 
    The parameter settings for radius and range_filter vary with the metric type in use.
    
 
@@ -812,7 +1961,7 @@ 
    Search parameters
    
diff --git a/bootcamp/RAG/rtdocs/system_configuration.html b/bootcamp/RAG/rtdocs_new/system_configuration.html
similarity index 98%
rename from bootcamp/RAG/rtdocs/system_configuration.html
rename to bootcamp/RAG/rtdocs_new/system_configuration.html
index 590f268f6..9bc56e391 100644
--- a/bootcamp/RAG/rtdocs/system_configuration.html
+++ b/bootcamp/RAG/rtdocs_new/system_configuration.html
@@ -263,7 +263,7 @@
         }
       }
     })
-  
    milvus-logo
    Stars0
    Milvus System Configurations Checklist
    
+  
    milvus-logo
    Stars0
    Milvus System Configurations Checklist
    
 
    This topic introduces the general sections of the system configurations in Milvus.
    
 
    Milvus maintains a considerable number of parameters that configure the system. Each configuration has a default value, which can be used directly. You can modify these parameters flexibly so that Milvus can better serve your application. See Configure Milvus for more information.
    
 
    
diff --git a/bootcamp/milvus_connect.ipynb b/bootcamp/milvus_connect.ipynb
index 9cbb331c8..4808174fb 100644
--- a/bootcamp/milvus_connect.ipynb
+++ b/bootcamp/milvus_connect.ipynb
@@ -357,7 +357,7 @@
    ],
    "source": [
     "COLLECTION_NAME = \"movies\"\n",
-    "EMBEDDING_LENGTH = 256\n",
+    "EMBEDDING_DIM = 256\n",
     "\n",
     "# Check if collection already exists, if so drop it.\n",
     "has = utility.has_collection(COLLECTION_NAME)\n",
@@ -368,7 +368,7 @@
     "# Create a collection with flexible schema and AUTOINDEX.\n",
     "mc.create_collection(\n",
     "        COLLECTION_NAME, \n",
-    "        EMBEDDING_LENGTH, \n",
+    "        EMBEDDING_DIM, \n",
     "        consistency_level=\"Eventually\", \n",
     "        auto_id=True,  \n",
     "        overwrite=True,\n",
@@ -380,10 +380,26 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mWARN\u001b[0m[0000] /Users/christy/Documents/bootcamp_scratch/bootcamp/docker-compose.yml: `version` is obsolete \n",
+      "Successfully disconnected from the server.\n"
+     ]
+    }
+   ],
    "source": [
     "# Stop local milvus.\n",
-    "!docker compose down"
+    "!docker compose down\n",
+    "\n",
+    "# Disconnect from the server.\n",
+    "try:\n",
+    "  connections.disconnect(alias=\"default\")\n",
+    "  print(\"Successfully disconnected from the server.\")\n",
+    "except:\n",
+    "  pass"
    ]
   },
   {
@@ -464,14 +480,15 @@
      "output_type": "stream",
      "text": [
       "EMBEDDING_DIM: 1024\n",
-      "Created Milvus collection from 22 docs in 7.84 seconds\n"
+      "Created Milvus collection from 22 docs in 7.64 seconds\n"
      ]
     }
    ],
    "source": [
     "from langchain_milvus import Milvus\n",
     "from langchain_huggingface import HuggingFaceEmbeddings\n",
-    "import time\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "import time, pprint\n",
     "\n",
     "# Define the embedding model.\n",
     "model_name = \"BAAI/bge-large-en-v1.5\"\n",
@@ -485,15 +502,19 @@
     "EMBEDDING_DIM = embed_model.dict()['client'].get_sentence_embedding_dimension()\n",
     "print(f\"EMBEDDING_DIM: {EMBEDDING_DIM}\")\n",
     "\n",
+    "# # Chunking\n",
+    "# text_splitter = RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=51)\n",
+    "\n",
     "# Create a Milvus collection from the documents and embeddings.\n",
     "start_time = time.time()\n",
+    "# docs = text_splitter.split_documents(docs)\n",
     "vectorstore = Milvus.from_documents(\n",
     "    documents=docs,\n",
     "    embedding=embed_model,\n",
     "    connection_args={\n",
     "        \"uri\": \"./milvus_demo.db\",\n",
     "    },\n",
-    "    # Override LangChain default values.\n",
+    "    # Override LangChain default values for Milvus.\n",
     "    consistency_level=\"Eventually\",\n",
     "    drop_old=True,\n",
     "    index_params = {\n",
@@ -524,8 +545,6 @@
     }
    ],
    "source": [
-    "import pprint\n",
-    "\n",
     "# Describe the collection.\n",
     "print(f\"collection_name: {vectorstore.collection_name}\")\n",
     "print(f\"schema: {vectorstore.fields}\")\n",
@@ -613,7 +632,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/vn/4v5_m9mx69x3h7jcl1chb7nr0000gn/T/ipykernel_11915/2544016635.py:13: DeprecationWarning: Call to deprecated class method from_defaults. (ServiceContext is deprecated, please use `llama_index.settings.Settings` instead.) -- Deprecated since version 0.10.0.\n",
+      "/var/folders/vn/4v5_m9mx69x3h7jcl1chb7nr0000gn/T/ipykernel_28726/1337788918.py:12: DeprecationWarning: Call to deprecated class method from_defaults. (ServiceContext is deprecated, please use `llama_index.settings.Settings` instead.) -- Deprecated since version 0.10.0.\n",
       "  service_context = ServiceContext.from_defaults(\n",
       "/opt/miniconda3/envs/py311-unum/lib/python3.11/site-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
       "  warnings.warn(\n"
@@ -635,12 +654,11 @@
       " 'text_instruction': None}\n",
       "\n",
       "Start chunking, embedding, inserting...\n",
-      "Created LlamaIndex collection from 1 docs in 109.35 seconds\n"
+      "Created LlamaIndex collection from 1 docs in 98.19 seconds\n"
      ]
     }
    ],
    "source": [
-    "from pymilvus import MilvusClient\n",
     "from llama_index.core import (\n",
     "    Settings,\n",
     "    ServiceContext,\n",
@@ -649,11 +667,11 @@
     ")\n",
     "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n",
     "from llama_index.vector_stores.milvus import MilvusVectorStore\n",
+    "import time, pprint\n",
     "\n",
     "# Define the embedding model.\n",
-    "milvus_client = MilvusClient()\n",
     "service_context = ServiceContext.from_defaults(\n",
-    "    # LlamaIndex local location is same as default HF cache location.\n",
+    "    # LlamaIndex local: translates to the same location as default HF cache.\n",
     "    embed_model=\"local:BAAI/bge-large-en-v1.5\",\n",
     ")\n",
     "# Display what LlamaIndex exposes.\n",
@@ -665,13 +683,20 @@
     "EMBEDDING_DIM = 1024\n",
     "\n",
     "# Create a Milvus collection from the documents and embeddings.\n",
-    "vector_store = MilvusVectorStore(\n",
-    "    client=milvus_client, \n",
+    "vectorstore = MilvusVectorStore(\n",
+    "    uri=\"./milvus_llamaindex.db\",\n",
     "    dim=EMBEDDING_DIM,\n",
-    "    overwrite=True\n",
+    "    # Override LlamaIndex default values for Milvus.\n",
+    "    consistency_level=\"Eventually\",\n",
+    "    drop_old=True,\n",
+    "    index_params = {\n",
+    "        \"metric_type\": \"COSINE\",\n",
+    "        \"index_type\": \"AUTOINDEX\",\n",
+    "        \"params\": {},}\n",
     ")\n",
     "storage_context = StorageContext.from_defaults(\n",
-    "    vector_store=vector_store)\n",
+    "    vector_store=vectorstore\n",
+    ")\n",
     "\n",
     "print(f\"Start chunking, embedding, inserting...\")\n",
     "start_time = time.time()\n",
@@ -683,7 +708,7 @@
     ")\n",
     "end_time = time.time()\n",
     "print(f\"Created LlamaIndex collection from {len(docs[:1])} docs in {end_time - start_time:.2f} seconds\")\n",
-    "# Created LlamaIndex Milvus collection in 109.35 seconds"
+    "# Created LlamaIndex collection from 1 docs in 106.32 seconds"
    ]
   },
   {
@@ -695,7 +720,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Milvus vector database:\n",
       "stores_text: True\n",
       "is_embedding_query: True\n",
       "stores_node: True\n",
@@ -706,8 +730,8 @@
       "embedding_field: embedding\n",
       "doc_id_field: doc_id\n",
       "similarity_metric: IP\n",
-      "consistency_level: Strong\n",
-      "overwrite: True\n",
+      "consistency_level: Eventually\n",
+      "overwrite: False\n",
       "text_key: None\n",
       "output_fields: []\n",
       "index_config: {}\n"
@@ -715,13 +739,10 @@
     }
    ],
    "source": [
-    "import pprint\n",
-    "\n",
     "# Describe the collection.\n",
-    "print(\"Milvus vector database:\")\n",
-    "temp = vector_store.to_dict()\n",
-    "first_10_keys = list(temp.keys())[:15]\n",
-    "for key in first_10_keys:\n",
+    "temp = llamaindex.storage_context.vector_store.to_dict()\n",
+    "first_15_keys = list(temp.keys())[:15]\n",
+    "for key in first_15_keys:\n",
     "    print(f\"{key}: {temp[key]}\")"
    ]
   },
@@ -737,7 +758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {