added readme for customerservice chatbots

end-to-end-use-cases/customerservice_chatbots/README.md

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+This repository contains various end-to-end use cases for building customer service chatbots using Meta's Llama 3. Below is an outline of the subfolders and their contents.
+
+## Ouline
+
+- **[RAG_chatbot](https://github.com/meta-llama/llama-cookbook/tree/main/end-to-end-use-cases/customerservice_chatbots/RAG_chatbot)**
+  contains resources for building a Meta Llama 3 chatbot with Retrieval Augmented Generation (RAG). It contains a [notebook](https://github.com/meta-llama/llama-cookbook/blob/main/end-to-end-use-cases/customerservice_chatbots/RAG_chatbot/RAG_Chatbot_Example.ipynb) which shows a complete example of how to build a Meta Llama 3 chatbot hosted on your browser that can answer questions based on your own data. It covers:
+    - The deployment process of Meta Llama 3 8B with the [Text-generation-inference](https://github.com/huggingface/text-generation-inference) framework as an API server.
+    - A chatbot example built with [Gradio](https://github.com/gradio-app/gradio) and wired to the server.
+    - Adding RAG capability with Meta Llama 3 specific knowledge based on our Getting Started [guide](https://ai.meta.com/llama/get-started/).
+
+- **[ai_agent_chatbot](https://github.com/meta-llama/llama-cookbook/tree/main/end-to-end-use-cases/customerservice_chatbots/ai_agent_chatbot)** contains a 
+  [Sales Bot with Llama3 - A Summarization and RAG Use Case notebook](https://github.com/meta-llama/llama-cookbook/blob/main/end-to-end-use-cases/customerservice_chatbots/ai_agent_chatbot/SalesBot.ipynb) that demonstrates building a sales chatbot using Llama3 for targeted product recommendations. The workflow involves:
+    * Generating product review summaries using Llama3
+    * Storing summaries in a vector database (Weaviate)
+    * Leveraging Retrieval Augmented Generation (RAG) for intelligent sales interactions
+
+
+- **[messenger_chatbot](https://github.com/meta-llama/llama-cookbook/tree/main/end-to-end-use-cases/customerservice_chatbots/messenger_chatbot)** section provides a step-by-step guide to building a Llama-enabled Messenger chatbot. It includes integration details with the Messenger Platform and a [demo video](https://drive.google.com/file/d/1B4ijFH4X3jEHZfkGdTPmdsgpUes_RNud/view).
+
+- **[whatsapp_chatbot](https://github.com/Monireh2/llama-recipes/tree/main/end-to-end-use-cases/customerservice_chatbots/whatsapp_chatbot)** folder contains a tutorial for creating a Llama 3 enabled WhatsApp chatbot, similar to the Messenger chatbot guide. A demo video showcasing the use of iOS WhatsApp to send a question to a test phone number and receive a response generated by Llama 3 can be found [here](https://drive.google.com/file/d/1fZDaOsvyE1yrNGETV-e0SvL14BYeAI6R/view).
+
+## Additional Information
+
+- **RAG Architecture:** The RAG method enhances LLMs by retrieving and augmenting data, allowing for more relevant and context-aware responses.
+- **Development Tools:** The repository utilizes frameworks like LangChain and LlamaIndex for building LLM applications, and Gradio for creating chatbot UIs.
+
+For more detailed information, please refer to the individual subfolder documentation and examples.