Anthropic Classifier

The Anthropic Classifier is an alternative classifier for the Multi-Agent Orchestrator that leverages Anthropic’s AI models for intent classification. It provides powerful classification capabilities using Anthropic’s state-of-the-art language models.

The Anthropic Classifier extends the abstract Classifier class and uses the Anthropic API client to process requests and classify user intents.

Features

• Utilizes Anthropic’s AI models (e.g., Claude) for intent classification
• Configurable model selection and inference parameters
• Supports custom system prompts and variables
• Handles conversation history for context-aware classification

Basic Usage

To use the AnthropicClassifier, you need to create an instance with your Anthropic API key and pass it to the Multi-Agent Orchestrator:

TypeScript

import { AnthropicClassifier } from "multi-agent-orchestrator";
import { MultiAgentOrchestrator } from "multi-agent-orchestrator";

const anthropicClassifier = new AnthropicClassifier({
  apiKey: 'your-anthropic-api-key'
});

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

Python

from multi_agent_orchestrator.classifiers import AnthropicClassifier, AnthropicClassifierOptions
from multi_agent_orchestrator.orchestrator import MultiAgentOrchestrator

anthropic_classifier = AnthropicClassifier(AnthropicClassifierOptions(
    api_key='your-anthropic-api-key'
))

orchestrator = MultiAgentOrchestrator(classifier=anthropic_classifier)

Custom Configuration

You can customize the AnthropicClassifier by providing additional options:

TypeScript

const customAnthropicClassifier = new AnthropicClassifier({
  apiKey: 'your-anthropic-api-key',
  modelId: 'claude-3-sonnet-20240229',
  inferenceConfig: {
    maxTokens: 500,
    temperature: 0.7,
    topP: 0.9,
    stopSequences: ['']
  }
});

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

Python

from multi_agent_orchestrator.classifiers import AnthropicClassifier, AnthropicClassifierOptions
from multi_agent_orchestrator.orchestrator import MultiAgentOrchestrator

custom_anthropic_classifier = AnthropicClassifier(AnthropicClassifierOptions(
    api_key='your-anthropic-api-key',
    model_id='claude-3-sonnet-20240229',
    inference_config={
        'max_tokens': 500,
        'temperature': 0.7,
        'top_p': 0.9,
        'stop_sequences': ['']
    }
))

orchestrator = MultiAgentOrchestrator(classifier=custom_anthropic_classifier)

The AnthropicClassifier accepts the following configuration options:

• api_key (required): Your Anthropic API key.
• model_id (optional): The ID of the Anthropic model to use. Defaults to Claude 3.5 Sonnet.
• inference_config (optional): A dictionary containing inference configuration parameters:
  • max_tokens (optional): The maximum number of tokens to generate. Defaults to 1000 if not specified.
  • temperature (optional): Controls randomness in output generation.
  • top_p (optional): Controls diversity of output generation.
  • stop_sequences (optional): A list of sequences that, when generated, will stop the generation process.

Customizing the System Prompt

You can customize the system prompt used by the AnthropicClassifier:

TypeScript

orchestrator.classifier.setSystemPrompt(
  `
  Custom prompt template with placeholders:
  {{AGENT_DESCRIPTIONS}}
  {{HISTORY}}
  {{CUSTOM_PLACEHOLDER}}
  `,
  {
    CUSTOM_PLACEHOLDER: "Value for custom placeholder"
  }
);

Python

orchestrator.classifier.set_system_prompt(
    """
    Custom prompt template with placeholders:
    {{AGENT_DESCRIPTIONS}}
    {{HISTORY}}
    {{CUSTOM_PLACEHOLDER}}
    """,
    {
        "CUSTOM_PLACEHOLDER": "Value for custom placeholder"
    }
)

Processing Requests

The AnthropicClassifier processes requests using the process_request method, which is called internally by the orchestrator. This method:

1. Prepares the user’s message.
2. Constructs a request for the Anthropic API, including the system prompt and tool configurations.
3. Sends the request to the Anthropic API and processes the response.
4. Returns a ClassifierResult containing the selected agent and confidence score.

Error Handling

The AnthropicClassifier includes error handling to manage potential issues during the classification process. If an error occurs, it will log the error and raise an exception, which can be caught and handled by the orchestrator.

Best Practices

1. API Key Security: Ensure your Anthropic API key is kept secure and not exposed in your codebase.
2. Model Selection: Choose an appropriate model based on your use case and performance requirements.
3. Inference Configuration: Experiment with different inference parameters to find the best balance between response quality and speed.
4. System Prompt: Craft a clear and comprehensive system prompt to guide the model’s classification process effectively.

Limitations

• Requires an active Anthropic API key.
• Classification quality depends on the chosen model and the quality of your system prompt and agent descriptions.
• API usage is subject to Anthropic’s pricing and rate limits.

For more information on using and customizing the Multi-Agent Orchestrator, refer to the Classifier Overview and Agents documentation.