Merge pull request #1355 from zc277584121/main

add utm in zilliz document

Author: jasonrandrews

content/learning-paths/servers-and-cloud-computing/milvus-rag/_index.md

@@ -12,7 +12,7 @@ learning_objectives:
 prerequisites:
     - A basic understanding of a RAG pipeline.
     - An AWS Graviton3 C7g.2xlarge instance, or any [Arm-based instance](/learning-paths/servers-and-cloud-computing/csp) from a cloud service provider or an on-premise Arm server.
-    - A [Zilliz account](https://zilliz.com/cloud), which you can sign up for with a free trial.
+    - A [Zilliz account](https://zilliz.com/cloud?utm_source=partner&utm_medium=referral&utm_campaign=2024-10-24_web_arm-dev-hub-data-loading_arm), which you can sign up for with a free trial.
 
 author_primary: Chen Zhang
 

content/learning-paths/servers-and-cloud-computing/milvus-rag/offline_data_loading.md

@@ -9,9 +9,9 @@ layout: learningpathall
 
 In this section, you will set up a cluster on Zilliz Cloud.
 
-Begin by [registering](https://docs.zilliz.com/docs/register-with-zilliz-cloud) for a free account on Zilliz Cloud. 
+Begin by [registering](https://docs.zilliz.com/docs/register-with-zilliz-cloud?utm_source=partner&utm_medium=referral&utm_campaign=2024-10-24_web_arm-dev-hub-data-loading_arm) for a free account on Zilliz Cloud. 
 
-After you register, [create a cluster](https://docs.zilliz.com/docs/create-cluster). 
+After you register, [create a cluster](https://docs.zilliz.com/docs/create-cluster?utm_source=partner&utm_medium=referral&utm_campaign=2024-10-24_web_arm-dev-hub-data-loading_arm). 
 
 Now create a **Dedicated** cluster deployed in AWS using Arm-based machines to store and retrieve the vector data as shown:
 
@@ -22,7 +22,7 @@ When you select the **Create Cluster** Button, you should see the cluster runnin
 ![running](running_cluster.png)
 
 {{% notice Note %}}
-You can use self-hosted Milvus as an alternative to Zilliz Cloud. This option is more complicated to set up. You can also deploy [Milvus Standalone](https://milvus.io/docs/install_standalone-docker-compose.md) and [Kubernetes](https://milvus.io/docs/install_cluster-milvusoperator.md) on Arm-based machines. For more information about installing Milvus, see the [Milvus installation documentation](https://milvus.io/docs/install-overview.md).
+You can use self-hosted Milvus as an alternative to Zilliz Cloud. This option is more complicated to set up. You can also deploy [Milvus Standalone](https://milvus.io/docs/install_standalone-docker-compose.md?utm_source=partner&utm_medium=referral&utm_campaign=2024-10-24_web_arm-dev-hub-data-loading_arm) and [Kubernetes](https://milvus.io/docs/install_cluster-milvusoperator.md?utm_source=partner&utm_medium=referral&utm_campaign=2024-10-24_web_arm-dev-hub-data-loading_arm) on Arm-based machines. For more information about installing Milvus, see the [Milvus installation documentation](https://milvus.io/docs/install-overview.md?utm_source=partner&utm_medium=referral&utm_campaign=2024-10-24_web_arm-dev-hub-data-loading_arm).
 {{% /notice  %}}
 
 ## Create the Collection
@@ -39,7 +39,7 @@ milvus_client = MilvusClient(
 )
 
 ```
-Replace *<your_zilliz_public_endpoint>* and *<your zilliz_api_key>* with the `URI` and `Token` for your running cluster. Refer to [Public Endpoint and Api key](https://docs.zilliz.com/docs/on-zilliz-cloud-console#free-cluster-details) in Zilliz Cloud for further information.
+Replace *<your_zilliz_public_endpoint>* and *<your zilliz_api_key>* with the `URI` and `Token` for your running cluster. Refer to [Public Endpoint and Api key](https://docs.zilliz.com/docs/on-zilliz-cloud-console#free-cluster-details?utm_source=partner&utm_medium=referral&utm_campaign=2024-10-24_web_arm-dev-hub-data-loading_arm) in Zilliz Cloud for further information.
 
 Now, append the following code to `zilliz-llm-rag.py` and save the contents:
 
@@ -60,7 +60,7 @@ milvus_client.create_collection(
 This code checks if a collection already exists and drops it if it does. If this happens, you can create a new collection with the specified parameters.
 
 If you do not specify any field information, Milvus automatically creates a default `id` field for the primary key, and a `vector` field to store the vector data. A reserved JSON field is used to store non-schema defined fields and their values.
-You can use inner product distance as the default metric type. For more information about distance types, you can refer to [Similarity Metrics page](https://milvus.io/docs/metric.md?tab=floating).
+You can use inner product distance as the default metric type. For more information about distance types, you can refer to [Similarity Metrics page](https://milvus.io/docs/metric.md?tab=floating?utm_source=partner&utm_medium=referral&utm_campaign=2024-10-24_web_arm-dev-hub-data-loading_arm).
 
 You can now prepare the data to use in this collection.
 

content/learning-paths/servers-and-cloud-computing/milvus-rag/prerequisite.md

@@ -12,7 +12,7 @@ In this Learning Path, you will learn how to build a Retrieval-Augmented Generat
 
 RAG applications often use vector databases to efficiently store and retrieve high-dimensional vector representations of text data. Vector databases are optimized for similarity search and can handle large volumes of vector data, making them ideal for the retrieval component of RAG systems. 
 
-In this Learning Path, you will use [Zilliz Cloud](https://zilliz.com/cloud) for your vector storage, which is a fully managed Milvus vector database. Zilliz Cloud is available on major cloud computing service providers; for example, AWS, GCP, and Azure. 
+In this Learning Path, you will use [Zilliz Cloud](https://zilliz.com/cloud?utm_source=partner&utm_medium=referral&utm_campaign=2024-10-24_web_arm-dev-hub-data-loading_arm) for your vector storage, which is a fully managed Milvus vector database. Zilliz Cloud is available on major cloud computing service providers; for example, AWS, GCP, and Azure. 
 
 Here, you will use Zilliz Cloud deployed on AWS with an Arm-based server. For the LLM, you will use the Llama-3.1-8B model also running on an AWS Arm-based server, but using `llama.cpp`.