update quickstart guide

docs/src/content/docs/agents/built-in/bedrock-llm-agent.md

@@ -22,8 +22,12 @@ This agent can handle a wide range of processing tasks, making it suitable for d
 
 <br>
 
+By default, the Bedrock LLM Agent uses the `anthropic.claude-3-haiku-20240307-v1:0` model.
+
+
 ### Basic Example
 
+
 To create a new `BedrockLLMAgent` with only the required parameters, use the following code:
 
 ```typescript

docs/src/content/docs/classifiers/built-in/bedrock-classifier.md

@@ -21,7 +21,7 @@ The BedrockClassifier extends the abstract `Classifier` class and uses Amazon Be
 
 ### Basic Usage
 
-By default, the Multi-Agent Orchestrator uses the BedrockClassifier. You don't need to do anything special to use it:
+By default, the Multi-Agent Orchestrator uses the Bedrock Classifier, which in turn utilizes the `anthropic.claude-3-5-sonnet-20240620-v1:0` (Claude 3.5 Sonnet) model for classification tasks.
 
 ```typescript
 import { MultiAgentOrchestrator } from "multi-agent-orchestrator";

docs/src/content/docs/general/quickstart.md

@@ -1,137 +1,147 @@
 ---
 title: Quickstart
 ---
+# Quickstart Guide for Multi-Agent Orchestrator
 
-To help you kickstart  with the Multi-Agent Orchestrator framework, we'll walk you through the step-by-step process of setting up and running your first multi-agent conversation.
+To help you kickstart with the Multi-Agent Orchestrator framework, we'll walk you through the step-by-step process of setting up and running your first multi-agent conversation.
 
-</br>
+<br>
 
 > 💁 Ensure you have Node.js and npm installed on your development environment before proceeding.
 
----
-
+## Prerequisites
 
-<br>
+1. Create a new Node.js project:
+   ```bash
+   mkdir test_multi_agent_orchestrator
+   cd test_multi_agent_orchestrator
+   npm init
+   ```
+   Follow the steps to generate a `package.json` file.
 
-### 🚀 Get Started!
+2. Authenticate with your AWS account
 
-1. Install the Multi-Agent Orchestrator framework in your nodejs project.
 
-```bash
-npm install multi-agent-orchestrator
-```
+This quickstart demonstrates the use of Amazon Bedrock for both classification and agent responses. 
 
+By default, the framework is configured as follows:
+  - Classifier: Uses the **[Bedrock Classifier](/multi-agent-orchestrator/classifiers/built-in/bedrock-classifier/)** implementation with `anthropic.claude-3-5-sonnet-20240620-v1:0`
+  - Agent: Utilizes the **[Bedrock LLM Agent](/multi-agent-orchestrator/agents/built-in/bedrock-llm-agent)** with `anthropic.claude-3-haiku-20240307-v1:0`
 
 <br>
 
-2. Create a new file named `quickstart.ts` and add the following code:
-
-
-3. Create an Orchestrator
-
-```typescript
-import { MultiAgentOrchestrator } from "multi-agent-orchestrator";
-const orchestrator = new MultiAgentOrchestrator({
-  config: {
-    logAgentConversation: true,
-    logIntentClassifierConversation: true,
-    logIntentClassifierOutput: true,
-  },
-});
-```
+> **Important**
+> 
+>   These are merely default settings and can be easily changed to suit your needs or preferences. 
 
-2. Add Agents
-
-```typescript
-
-import { BedrockLLMAgent } from "multi-agent-orchestrator";
-
-
-orchestrator.addAgent(
-  new BedrockLLMAgent({
-    name: "Tech Agent",
-    description:
-      "Specializes in technology areas including software development, hardware, AI, cybersecurity, blockchain, cloud computing, emerging tech innovations, and pricing/costs related to technology products and services.",
-  })
-);
-
-orchestrator.addAgent(
-  new BedrockLLMAgent({
-    name: "Health Agent",
-    description:
-      "Focuses on health and medical topics such as general wellness, nutrition, diseases, treatments, mental health, fitness, healthcare systems, and medical terminology or concepts.",
-  })
-);
-```
-
-3. Send a Query
+<br>
 
-```typescript
-const userId = "quickstart-user";
-const sessionId = "quickstart-session";
+You have the flexibility to:
+  - Change the classifier model or implementation
+  - Change the agent model or implementation
+  - Use any other compatible models available through Amazon Bedrock
 
-const query = "What are the latest trends in AI?";
+Ensure you have [requested access](https://docs.aws.amazon.com/bedrock/latest/userguide/model-access.html) to the models you intend to use through the AWS console.
 
-console.log(`\nUser Query: ${query}`);
-const response = await orchestrator.routeRequest(query, userId, sessionId);
-console.log("\n** RESPONSE ** \n");
-console.log(`> Agent ID: ${response.metadata.agentId}`);
-console.log(`> Agent Name: ${response.metadata.agentName}`);
-console.log(`> User Input: ${response.metadata.userInput}`);
-console.log(`> User ID: ${response.metadata.userId}`);
-console.log(`> Session ID: ${response.metadata.sessionId}`);
-console.log(
-  `> Additional Parameters:`,
-  response.metadata.additionalParams
-);
-console.log(`\n> Response: ${response.output}`);
+<br>
 
+> **To customize the model selection**:
+>   - For the classifier, refer to [our guide](/multi-agent-orchestrator/classifiers/overview) on configuring the classifier.
+>   - For the agent, refer to our guide on configuring [agents](/multi-agent-orchestrator/agents/overview).
 
-```
+## 🚀 Get Started!
 
-<br>
+1. Install the Multi-Agent Orchestrator framework in your Node.js project:
+   ```bash
+   npm install multi-agent-orchestrator
+   ```
 
----
+2. Create a new file named `quickstart.ts` and add the following code:
 
-<br>
+3. Create an Orchestrator:
+   ```typescript
+   import { MultiAgentOrchestrator } from "multi-agent-orchestrator";
+   const orchestrator = new MultiAgentOrchestrator({
+     config: {
+       LOG_AGENT_CHAT: true,
+       LOG_CLASSIFIER_CHAT: true,
+       LOG_CLASSIFIER_RAW_OUTPUT: false,
+       LOG_CLASSIFIER_OUTPUT: true,
+       LOG_EXECUTION_TIMES: true,
+     }
+   });
+   ```
+
+4. Add Agents:
+   ```typescript
+   import { BedrockLLMAgent } from "multi-agent-orchestrator";
+   orchestrator.addAgent(
+     new BedrockLLMAgent({
+       name: "Tech Agent",
+       description: "Specializes in technology areas including software development, hardware, AI, cybersecurity, blockchain, cloud computing, emerging tech innovations, and pricing/costs related to technology products and services.",
+     })
+   );
+
+   orchestrator.addAgent(
+     new BedrockLLMAgent({
+       name: "Health Agent",
+       description: "Focuses on health and medical topics such as general wellness, nutrition, diseases, treatments, mental health, fitness, healthcare systems, and medical terminology or concepts.",
+     })
+   );
+   ```
+
+5. Send a Query:
+   ```typescript
+   const userId = "quickstart-user";
+   const sessionId = "quickstart-session";
+   const query = "What are the latest trends in AI?";
+   console.log(`\nUser Query: ${query}`);
+
+
+   async function main() {
+      try {
+        const response = await orchestrator.routeRequest(query, userId, sessionId);
+        console.log("\n** RESPONSE ** \n");
+        console.log(`> Agent ID: ${response.metadata.agentId}`);
+        console.log(`> Agent Name: ${response.metadata.agentName}`);
+        console.log(`> User Input: ${response.metadata.userInput}`);
+        console.log(`> User ID: ${response.metadata.userId}`);
+        console.log(`> Session ID: ${response.metadata.sessionId}`);
+        console.log(
+          `> Additional Parameters:`,
+          response.metadata.additionalParams
+        );
+        console.log(`\n> Response: ${response.output}`);
+        // ... rest of the logging code ...
+      } catch (error) {
+        console.error("An error occurred:", error);
+        // Here you could also add more specific error handling if needed
+      }
+    }
+
+    main();
+   ```
 
 Now, let's run the quickstart script:
-
 ```bash
 npx ts-node quickstart.ts
 ```
 
-<br>
+Congratulations! 🎉
+You've successfully set up and run your first multi-agent conversation using the Multi-Agent Orchestrator System.
 
-Congratulations! 
-
-You've successfully set up and run your first multi-agent conversation using the Multi-Agent Orchestrator System. 🎉
-
-<br>
-
----
-
-<br>
-
-### 👨‍💻 Next Steps
+## 👨‍💻 Next Steps
 
 Now that you've seen the basic functionality, here are some next steps to explore:
 
-1. Try adding other agents from those built-in in the framwork ([Bedrock LLM Agent](/multi-agent-orchestrator/agents/built-in/bedrock-llm-agent), [Amazon Lex Bot](/multi-agent-orchestrator/agents/built-in/lex-bot-agent), [Amazon Bedrock Agent](/multi-agent-orchestrator/agents/built-in/amazon-bedrock-agent), [Lambda Agent](/multi-agent-orchestrator/agents/built-in/lambda-agent), [OpenAI Agent](/multi-agent-orchestrator/agents/built-in/openai-agent)).
+1. Try adding other agents from those built-in in the framework ([Bedrock LLM Agent](/multi-agent-orchestrator/agents/built-in/bedrock-llm-agent), [Amazon Lex Bot](/multi-agent-orchestrator/agents/built-in/lex-bot-agent), [Amazon Bedrock Agent](/multi-agent-orchestrator/agents/built-in/amazon-bedrock-agent), [Lambda Agent](/multi-agent-orchestrator/agents/built-in/lambda-agent), [OpenAI Agent](/multi-agent-orchestrator/agents/built-in/openai-agent)).
 2. Experiment with different storage options, such as [Amazon DynamoDB](/multi-agent-orchestrator/storage/dynamodb) for persistent storage.
 3. Explore the [Agent Overlap Analysis](/multi-agent-orchestrator/advanced-features/agent-overlap) feature to optimize your agent configurations.
 4. Integrate the system into a web application or deploy it as an [AWS Lambda function](/multi-agent-orchestrator/deployment/aws-lambda).
 5. Try adding your own [custom agents](/multi-agent-orchestrator/agents/custom-agents) by extending the `Agent` class.
 
-
 For more detailed information on these advanced features, check out our full documentation.
 
-<br>
-
----
-
-<br>
-
-### 🧹 Cleanup
+## 🧹 Cleanup
 
 As this quickstart uses in-memory storage and local resources, there's no cleanup required. Simply stop the script when you're done experimenting.