Custom classifier

This guide explains how to create a custom classifier for the Multi-Agent Orchestrator by extending the abstract Classifier class. Custom classifiers allow you to implement your own logic for intent classification and agent selection.

Overview

To create a custom classifier, you need to:

1. Extend the abstract Classifier class
2. Implement the required process_request method
3. Optionally override other methods for additional customization

Step-by-Step Guide

1. Extend the Classifier Class

Create a new class that extends the abstract Classifier class:

TypeScript

import { Classifier } from './path-to-classifier';
import { ClassifierResult, ConversationMessage } from './path-to-types';

export class MyCustomClassifier extends Classifier {
  // Implementation will go here
}

Python

from multi_agent_orchestrator.classifiers import Classifier
from multi_agent_orchestrator.types import ClassifierResult, ConversationMessage
from typing import List

class MyCustomClassifier(Classifier):
    # Implementation will go here
    pass

2. Implement the process_request Method

The process_request method is the core of your custom classifier. It should analyze the input and return a ClassifierResult:

TypeScript

export class MyCustomClassifier extends Classifier {
  async processRequest(
    inputText: string,
    chatHistory: ConversationMessage[]
  ): Promise<ClassifierResult> {
    // Your custom classification logic goes here

    return {
      selectedAgent: firstAgent,
      confidence: 1.0
    };
  }
}

Python

class MyCustomClassifier(Classifier):
    async def process_request(
        self,
        input_text: str,
        chat_history: List[ConversationMessage]
    ) -> ClassifierResult:
        # Your custom classification logic goes here

        first_agent = next(iter(self.agents.values()))
        return ClassifierResult(
            selected_agent=first_agent,
            confidence=1.0
        )

Using Your Custom Classifier

To use your custom classifier with the Multi-Agent Orchestrator:

TypeScript

import { MultiAgentOrchestrator } from './path-to-multi-agent-orchestrator';
import { MyCustomClassifier } from './path-to-my-custom-classifier';

const customClassifier = new MyCustomClassifier();
const orchestrator = new MultiAgentOrchestrator({ classifier: customClassifier });

Python

from multi_agent_orchestrator.orchestrator import MultiAgentOrchestrator
from path_to_my_custom_classifier import MyCustomClassifier

custom_classifier = MyCustomClassifier()
orchestrator = MultiAgentOrchestrator(classifier=custom_classifier)

Best Practices

1. Robust Analysis: Implement thorough analysis of the input text and chat history to make informed classification decisions.
2. Error Handling: Include proper error handling in your process_request method to gracefully handle unexpected inputs or processing errors.
3. Extensibility: Design your custom classifier to be easily extensible for future improvements or adaptations.
4. Performance: Consider the performance implications of your classification logic, especially for high-volume applications.

Example: Keyword-Based Classifier

Here’s an example of a simple keyword-based classifier:

TypeScript

import { Classifier } from './path-to-classifier';
import { ClassifierResult, ConversationMessage, Agent } from './path-to-types';

export class KeywordClassifier extends Classifier {
  private keywordMap: { [keyword: string]: string };

  constructor(keywordMap: { [keyword: string]: string }) {
    super();
    this.keywordMap = keywordMap;
  }

  async processRequest(
    inputText: string,
    chatHistory: ConversationMessage[]
  ): Promise<ClassifierResult> {
    const lowercaseInput = inputText.toLowerCase();

    for (const [keyword, agentId] of Object.entries(this.keywordMap)) {
      if (lowercaseInput.includes(keyword)) {
        const selectedAgent = this.getAgentById(agentId);
        return {
          selectedAgent,
          confidence: 0.8 // Simple fixed confidence
        };
      }
    }

    // Default to the first agent if no keyword matches
    const defaultAgent = Object.values(this.agents)[0];
    return {
      selectedAgent: defaultAgent,
      confidence: 0.5
    };
  }
}

// Usage
const keywordMap = {
  'technical': 'tech-support-agent',
  'billing': 'billing-agent',
  'sales': 'sales-agent'
};
const keywordClassifier = new KeywordClassifier(keywordMap);
const orchestrator = new MultiAgentOrchestrator({ classifier: keywordClassifier });

Python

from multi_agent_orchestrator.classifiers import Classifier
from multi_agent_orchestrator.types import ClassifierResult, ConversationMessage
from multi_agent_orchestrator.orchestrator import MultiAgentOrchestrator
from typing import List, Dict

class KeywordClassifier(Classifier):
    def __init__(self, keyword_map: Dict[str, str]):
        super().__init__()
        self.keyword_map = keyword_map

    async def process_request(
        self,
        input_text: str,
        chat_history: List[ConversationMessage]
    ) -> ClassifierResult:
        lowercase_input = input_text.lower()

        for keyword, agent_id in self.keyword_map.items():
            if keyword in lowercase_input:
                selected_agent = self.get_agent_by_id(agent_id)
                return ClassifierResult(
                    selected_agent=selected_agent,
                    confidence=0.8  # Simple fixed confidence
                )

        # Default to the first agent if no keyword matches
        default_agent = next(iter(self.agents.values()))
        return ClassifierResult(
            selected_agent=default_agent,
            confidence=0.5
        )

# Usage
keyword_map = {
    'technical': 'tech-support-agent',
    'billing': 'billing-agent',
    'sales': 'sales-agent'
}
keyword_classifier = KeywordClassifier(keyword_map)
orchestrator = MultiAgentOrchestrator(classifier=keyword_classifier)

This example demonstrates a basic keyword-based classification strategy. You can expand on this concept to create more sophisticated custom classifiers based on your specific needs.

Conclusion

Creating a custom classifier allows you to implement specialized logic for intent classification and agent selection in the Multi-Agent Orchestrator. By extending the Classifier class and implementing the process_request method, you can tailor the classification process to your specific use case and requirements.

Remember to thoroughly test your custom classifier to ensure it performs well across a wide range of inputs and scenarios.