Typo fix

Author: wildmanonline

docs/guides/kubernetes/ai-chatbot-and-rag-pipeline-for-inference-on-lke/index.md

@@ -37,7 +37,7 @@ Follow this tutorial to deploy a RAG pipeline on Akamai’s LKE service using ou
     - **Kubeflow Pipeline:** Used to deploy pipelines, reusable machine learning workflows built using the Kubeflow Pipelines SDK. In this tutorial, a pipeline is used to run LlamaIndex to process the dataset and store embeddings.
 - **Meta’s Llama 3 LLM:** The [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) model is used as the LLM. You should review and agree to the licensing agreement before deploying.
 - **Milvus:** Milvus is an open-source vector database and is used for generative AI workloads. This tutorial uses Milvus to store embeddings generated by LlamaIndex and make them available to queries sent to the Llama 3 LLM.
-- **Open WebUI:** This is an self-hosted AI chatbot application that’s compatible with LLMs like Llama 3 and includes a built-in inference engine for RAG solutions. Users interact with this interface to query the LLM. This can be configured to send queries straight to Llama 3 or to first load data from Milvus and send that context along with the query.
+- **Open WebUI:** This is a self-hosted AI chatbot application that’s compatible with LLMs like Llama 3 and includes a built-in inference engine for RAG solutions. Users interact with this interface to query the LLM. This can be configured to send queries straight to Llama 3 or to first load data from Milvus and send that context along with the query.
 
 ## Prerequisites