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update quickstart guide
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cornelcroi authored Jul 25, 2024
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4 changes: 4 additions & 0 deletions docs/src/content/docs/agents/built-in/bedrock-llm-agent.md
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<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
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2 changes: 1 addition & 1 deletion docs/src/content/docs/classifiers/built-in/bedrock-classifier.md
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### 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";
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196 changes: 103 additions & 93 deletions docs/src/content/docs/general/quickstart.md
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---
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.

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