Editorial

Author: madeline-underwood

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

@@ -6,18 +6,18 @@ weight: 4
 layout: learningpathall
 ---
 
-### Llama 3.1 model and llama.cpp
+### Llama 3.1 Model and Llama.cpp
 
 In this section, you will build and run the `llama.cpp` server program using an OpenAI-compatible API on your AWS Arm-based server instance.
 
 The [Llama-3.1-8B model](https://huggingface.co/cognitivecomputations/dolphin-2.9.4-llama3.1-8b-gguf) from Meta belongs to the Llama 3.1 model family and is free to use for research and commercial purposes. Before you use the model, visit the Llama [website](https://llama.meta.com/llama-downloads/) and fill in the form to request access.
 
-[llama.cpp](https://github.com/ggerganov/llama.cpp) is an open-source C/C++ project that enables efficient LLM inference on a variety of hardware - both locally, and in the cloud. You can conveniently host a Llama 3.1 model using `llama.cpp`.
+[Llama.cpp](https://github.com/ggerganov/llama.cpp) is an open-source C/C++ project that enables efficient LLM inference on a variety of hardware - both locally, and in the cloud. You can conveniently host a Llama 3.1 model using `llama.cpp`.
 
 
-### Download and build llama.cpp
+### Download and build Llama.cpp
 
-Run the following commands to install make, cmake, gcc, g++, and other essential tools required for building llama.cpp from source:
+Run the following commands to install make, cmake, gcc, g++, and other essential tools required for building Llama.cpp from source:
 
 ```bash
 sudo apt install make cmake -y
@@ -27,7 +27,7 @@ sudo apt install build-essential -y
 
 You are now ready to start building `llama.cpp`. 
 
-Clone the source repository for llama.cpp:
+Clone the source repository for Llama.cpp:
 
 ```bash
 git clone https://github.com/ggerganov/llama.cpp
@@ -64,7 +64,7 @@ You can now download the model using the huggingface cli:
 ```bash
 huggingface-cli download cognitivecomputations/dolphin-2.9.4-llama3.1-8b-gguf dolphin-2.9.4-llama3.1-8b-Q4_0.gguf --local-dir . --local-dir-use-symlinks False
 ```
-The GGUF model format, introduced by the llama.cpp team, uses compression and quantization to reduce weight precision to 4-bit integers, significantly decreasing computational and memory demands and making Arm CPUs effective for LLM inference.
+The GGUF model format, introduced by the Llama.cpp team, uses compression and quantization to reduce weight precision to 4-bit integers, significantly decreasing computational and memory demands and making Arm CPUs effective for LLM inference.
 
 
 ### Re-quantize the model weights
@@ -91,10 +91,10 @@ Start the server from the command line, and it listens on port 8080:
 The output from this command should look like:
 
 ```output
-'main: server is listening on 127.0.0.1:8080 - starting the main loop
+main: server is listening on 127.0.0.1:8080 - starting the main loop
 ```
 
-You can also adjust the parameters of the launched LLM to adapt it to your server hardware to obtain ideal performance. For more parameter information, see the `llama-server --help` command.
+You can also adjust the parameters of the launched LLM to adapt it to your server hardware to achieve an ideal performance. For more parameter information, see the `llama-server --help` command.
 
 You have started the LLM service on your AWS Graviton instance with an Arm-based CPU. In the next section, you will directly interact with the service using the OpenAI SDK.
 

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

@@ -7,29 +7,29 @@ layout: learningpathall
 ---
 ## Create a dedicated cluster
 
-In this section, you will learn how to set up a cluster on Zilliz Cloud.
+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. 
 
-After you register, [create a cluster](https://docs.zilliz.com/docs/create-cluster) on Zilliz Cloud. 
+After you register, [create a cluster](https://docs.zilliz.com/docs/create-cluster). 
 
-In this Learning Path, you will create a dedicated cluster deployed in AWS using Arm-based machines to store and retrieve the vector data as shown:
+Now create a **Dedicated** cluster deployed in AWS using Arm-based machines to store and retrieve the vector data as shown:
 
 ![cluster](create_cluster.png)
 
-When you select the **Create Cluster** Button, you should see the cluster running in your Default Project.
+When you select the **Create Cluster** Button, you should see the cluster running in your **Default Project**.
 
 ![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 Milvus installation, please refer to the [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) 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).
 {{% /notice  %}}
 
 ## Create the Collection
 
-With the dedicated cluster running in Zilliz Cloud, you are now ready to create a collection in your cluster.
+With the Dedicated cluster running in Zilliz Cloud, you are now ready to create a collection in your cluster.
 
-Within your activated python virtual environment `venv`, start by creating a file named `zilliz-llm-rag.py`, and copy the contents below into it:
+Within your activated Python virtual environment `venv`, start by creating a file named `zilliz-llm-rag.py`, and copy the contents below into it:
 
 ```python
 from pymilvus import MilvusClient
@@ -59,7 +59,7 @@ 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.
+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 now prepare the data to use in this collection.
@@ -116,10 +116,10 @@ for i, (line, embedding) in enumerate(
 
 milvus_client.insert(collection_name=collection_name, data=data)
 ```
-Run the python script, to check that you have successfully created the embeddings on the data you loaded into the RAG collection:
+Run the Python script, to check that you have successfully created the embeddings on the data you loaded into the RAG collection:
 
 ```bash
-python3 python3 zilliz-llm-rag.py
+python3 zilliz-llm-rag.py
 ```
 
 The output should look like:

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

@@ -14,7 +14,7 @@ RAG applications often use vector databases to efficiently store and retrieve hi
 
 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. 
 
-Specifically, you will use Zilliz Cloud deployed on AWS with Arm-based servers. For the LLM, you will use the Llama-3.1-8B model running on an AWS Arm-based server using `llama.cpp`. 
+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`. 
 
 
 ## Install dependencies