update chatapp readme (containers#85)

chatbot-langchain/README.md

@@ -1,18 +1,88 @@
-# Streamlit + Langchain ChatBot Demo
+# Chat Application
+
+  This demo provides a simple recipe to help developers start building out their own custom LLM enabled chat applications. It consists of two main components; the Model Service and the AI Application.
+
+  There are a few options today for local Model Serving, but this recipe will use [`llama-cpp-python`](https://github.com/abetlen/llama-cpp-python) and their OpenAI compatible Model Service. There is a Containerfile provided that can be used to build this Model Service within the repo, [`playground/Containerfile`](/playground/Containerfile).
+
+  Our AI Application will connect to our Model Service via it's OpenAI compatible API. In this example we rely on [Langchain's](https://python.langchain.com/docs/get_started/introduction) python package to simplify communication with our Model Service and we use [Streamlit](https://streamlit.io/) for our UI layer. Below please see an example of the chatbot application.               
+
+
+![](/assets/chatbot_ui.png) 
+
+
+# Build the Application
+
+In order to build this application we will need a model, a Model Service and an AI Application.  
+
+* [Download a model](#download-a-model)
+* [Build the Model Service](#build-the-model-service)
+* [Deploy the Model Service](#deploy-the-model-service)
+* [Build the AI Application](#build-the-ai-application)
+* [Deploy the AI Application](#deploy-the-ai-application)
+* [Interact with the AI Application](#interact-with-the-ai-application)
+
+### Download a model
+
+If you are just getting started, we recommend using [Mistral-7B-Instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1). This is a well performant mid-sized model with an apache-2.0 license. In order to use it with our Model Service we need it converted and quantized into the [GGUF format](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md). There are a number of ways to get a GGUF version of Mistral-7B, but the simplest is to download a pre-converted one from [huggingface.co](https://huggingface.co) here: https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF. There are a number of options for quantization level, but we recommend `Q4_K_M`. 
+
+The recommended model can be downloaded using the code snippet below:
 
-### Build image
 ```bash
-cd chatbot-langchain
-podman build -t stchat . -f builds/Containerfile   
+cd models
+wget https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF/resolve/main/mistral-7b-instruct-v0.1.Q4_K_M.gguf
+cd ../
+```
+
+_A full list of supported open models is forthcoming._  
+
+
+### Build the Model Service
+
+The complete instructions for building and deploying the Model Service can be found in the [the playground model-service document](../playground/README.md).
+
+The Model Service can be built from the root directory with the following code snippet:
+
+```bash
+podman build -t llamacppserver playground/
 ```
-### Run image locally
 
-Make sure the playground model service is up and running before starting this container image. 
-To start the model service, refer to [the playground document](../playground/README.md)
 
+### Deploy the Model Service
+
+The complete instructions for building and deploying the Model Service can be found in the [the playground model-service document](../playground/README.md).
+
+The local Model Service relies on a volume mount to the localhost to access the model files. You can start your local Model Service using the following podman command:  
+```
+podman run --rm -it \
+        -p 8001:8001 \
+        -v Local/path/to/locallm/models:/locallm/models \
+        -e MODEL_PATH=models/<model-filename> \
+        -e HOST=0.0.0.0 \
+        -e PORT=8001 \
+        llamacppserver
+```
+
+### Build the AI Application
 
+Now that the Model Service is running we want to build and deploy our AI Application. Use the provided Containerfile to build the AI Application image from the `chatbot-langchain/` directory.
 ```bash
-podman run --rm -it -p 8501:8501 -e MODEL_SERVICE_ENDPOINT=http://10.88.0.1:8001/v1 stchat   
+cd chatbot-langchain
+podman build -t chatbot . -f builds/Containerfile   
 ```
+### Deploy the AI Application
+
+Make sure the Model Service is up and running before starting this container image. When starting the AI Application container image we need to direct it to the correct `MODEL_SERVICE_ENDPOINT`. This could be any appropriately hosted Model Service (running locally or in the cloud) using an OpenAI compatible API. In our case the Model Service is running inside the podman machine so we need to provide it with the appropriate address `10.88.0.1`. The following podman command can be used to run your AI Application:  
+
+```bash
+podman run --rm -it -p 8501:8501 -e MODEL_SERVICE_ENDPOINT=http://10.88.0.1:8001/v1 chatbot   
+```
+
+### Interact with the AI Application
+
+Everything should now be up an running with the chat application available at [`http://localhost:8501`](http://localhost:8501). By using this recipe and getting this starting point established, users should now have an easier time customizing and building their own LLM enabled chatbot applications.   
+
+
+
+
+
 
-Interact with the application from your local browser at `localhost:8501`