Fixed cluster connection string

Author: LakshmiPeri23

webinars/genai-chatbot/.DS_Store

@@ -1,63 +1,85 @@
 # Build a generative AI Virtual Assistant with [Amazon Bedrock](https://aws.amazon.com/bedrock/), [Langchain](https://github.com/langchain-ai/langchain) and [Amazon Elasticache](https://aws.amazon.com/elasticache/)
 
-In this 15-minute session [YouTube video](https://www.youtube.com/watch?v=yWxDmQYelvg), we will discuss how you can use [Amazon Bedrock](https://aws.amazon.com/bedrock/), [Langchain](https://github.com/langchain-ai/langchain) and [Amazon Elasticache](https://aws.amazon.com/elasticache/) services together to implement a generative AI (GenAI) chatbot. We will dive into two application patterns: they are chat history and messaging broker patterns. We will show you how ElastiCache for Redis simplifies the implementation of these application patterns by leveraging the built-in Redis data structures. 
-
-ElastiCache is a fully managed, Redis- and Memcached-compatible service delivering real-time, cost-optimized performance for modern applications. 
+In this 15-minute session [YouTube video](https://www.youtube.com/watch?v=yWxDmQYelvg), we will discuss how you can use [Amazon Bedrock](https://aws.amazon.com/bedrock/), [Langchain](https://github.com/langchain-ai/langchain) and [Amazon Elasticache](https://aws.amazon.com/elasticache/) services together to implement a generative AI (GenAI) chatbot. We will dive into two application patterns: they are chat history and messaging broker patterns. We will show you how ElastiCache simplifies the implementation of these application patterns by leveraging the built-in Redis data structures.
 
+ElastiCache is a fully managed service delivering real-time, cost-optimized performance for modern applications.
 ElastiCache scales to hundreds of millions operations per second with microsecond response time, and offers enterprise-grade security and reliability.
-
 ## Chatbot Application Deployment
 
 This guide will walk you through the steps to deploy a Python chatbot application using [Streamlit](https://github.com/streamlit/streamlit) on [Cloud9](https://aws.amazon.com/cloud9/). This is the architecture we will be implementing today.
 
+
 ![Architecture Diagram](./images/arch.png)
 
-The application is contained in the `chatbot_app.py` file, and it requires specific packages listed in `requirements.txt`.
+  
+The application is contained in the 'chatbot_app.py' file, and it requires specific packages listed in 'requirements.txt'.
 
 ## Prerequisites
 
+
 Before you proceed, make sure you have the following prerequisites in place:
 
 1. An AWS Cloud9 development environment set up.
+
 2. We will be using [Amazon Bedrock](https://aws.amazon.com/bedrock/) to access foundation models in this workshop.
-3. Enable Foundation models such as Claude, as shown below:
 
+3. Enable Foundation models such as Claude, as shown below:
 ![Bedrock Model](./images/model-access-edit.png)
 
 4. Python and pip installed in your Cloud9 environment.
-5. Internet connectivity to download packages.
 
-## Installation
 
+## Installation
 1. Clone this repository to your Cloud9 environment:
 
 ```bash
-git clone [your-repo-url]
-cd chatbot-app
+git  clone  https://github.com/aws-samples/amazon-elasticache-samples.git
+```
+
+```
+cd webinars/genai-chatbot
 ```
-   
+
 2. Install the required packages using pip:
 
 ```bash
-pip3 install -r ~/environment/workshop/setup/requirements.txt -U
+pip3  install  -r  ~/environment/workshop/setup/requirements.txt  -U
 ```
 
-3. Configure environment variables.
+3. Set the ElastiCache cluster endpoint as below. Use redis instead of rediss it encryption is not enabled.
 
+```bash
+export  ELASTICACHE_ENDPOINT_URL=rediss://ClusterURL:6379
+```
 
-4. You can run the following commands to confirm:
-(check [Amazon Bedrock endpoints and quotas](https://docs.aws.amazon.com/general/latest/gr/bedrock.html))
+## Running the Application
 
 ```bash
-echo $BWB_ENDPOINT_URL
-echo $BWB_PROFILE_NAME
-echo $BWB_REGION_NAME
+streamlit  run  'chatbot_app.py'  --server.port  8080
 ```
 
+## Testing the chat history with ElastiCache
 
-## Running the Application
+The first step is to login to the application. This would create a unique session to be stored in Amazon ElastiCache. This session data is retrieved, summarized and provided as a context to the LLM to help stay in context.
+
+![Login](./images/model-access-edit.png)
 
+Here are some sample questions you can try out to validate the LLM stays in context while loading previous conversation from Elasticache.
+
+    1. Can you help me draft a 4 sentence email to highlight some fun upcoming events for my employees?
+    
+    2. Can you add in these events into the email: 1. Internal networking event on 4/20/2024, Summer gift giveaway on 6/20/2024, 3. End of summer picnic: 8/15/2024, 4. Fall Formal on 10/10/2024, and 5. Christmas Party on 12/18/2024
+    
+    3. Can you reformat it with bullets for my events?
+    
+    4. Can you please remove everything that happens after September 2024
+
+Here is how we can check the session data stored in ElastiCache.
 ```bash
-streamlit run chatbot_app.py --server.port 8080
-```   
+redis-cli  -c  --user  $ECUserName  --askpass  -h  $ELASTICACHE_ENDPOINT_UR  --tls
+
+```
 
+```
+LRANGE "chat_history:user1" 0 -1
+```