Multi-Modal Agent Development

This document provides guidance for developing agents that process multiple data modalities (text, vision, audio, sensor data, etc.) in the Meta Agent Platform.

Overview

Multi-modal agents enable workflows that combine and process diverse data types, such as images, audio, sensor telemetry, and text. The platform supports specialized runtimes and fusion strategies for these agents.

Supported Modalities

• Text: Natural language processing, code, documents.
• Vision: Image and video processing (classification, detection, OCR).
• Audio: Speech recognition, audio classification, sentiment analysis.
• Sensor Data: IoT telemetry, time-series, environmental data.
• AR/VR: 3D spatial data, immersive environments.
• Fusion: Combining results from multiple modalities.

Multi-Modal Architecture

The platform's multi-modal architecture consists of several key components:

1. Modality-Specific Processors: Specialized components for each data type
2. Fusion Engine: Combines outputs from different modalities
3. Orchestration Layer: Coordinates processing across modalities
4. Shared Memory: Efficient data exchange between components

Note: This is a placeholder for a multi-modal architecture diagram. The actual diagram should be created and added to the project.

Fusion Strategies

Early Fusion (Feature-Level)

Combines raw features from different modalities before processing:

# Early fusion example with text and image
def early_fusion_agent(text_input, image_input):
    # Extract features from each modality
    text_features = text_encoder.encode(text_input)
    image_features = vision_encoder.encode(image_input)

    # Concatenate features
    combined_features = np.concatenate([text_features, image_features], axis=1)

    # Process combined features
    result = classifier.predict(combined_features)
    return result

Late Fusion (Decision-Level)

Processes each modality separately and combines the results:

# Late fusion example with text and image
def late_fusion_agent(text_input, image_input):
    # Process each modality independently
    text_result = text_classifier.predict(text_input)
    image_result = image_classifier.predict(image_input)

    # Combine results (e.g., weighted average)
    combined_result = 0.6 * text_result + 0.4 * image_result
    return combined_result

Hybrid Fusion

Combines aspects of both early and late fusion:

# Hybrid fusion example
def hybrid_fusion_agent(text_input, image_input):
    # Extract intermediate features
    text_features = text_encoder.encode(text_input)
    image_features = vision_encoder.encode(image_input)

    # Process features separately
    text_processed = text_processor(text_features)
    image_processed = image_processor(image_features)

    # Combine processed features
    combined = fusion_module([text_processed, image_processed])

    # Final classification
    result = final_classifier(combined)
    return result

Development Patterns

• Adapter Pattern: Standardize interfaces for different modalities.
• Pipeline Pattern: Chain preprocessing, inference, and postprocessing.
• Observer Pattern: Allow components to subscribe to events from different modalities.

Modality-Specific Implementation

Vision Agent Example

from meta_agent_platform import VisionAgent, ImageProcessor
import cv2
import numpy as np

class ObjectDetectionAgent(VisionAgent):
    def __init__(self, config):
        super().__init__(config)
        # Load model based on config
        self.model = self.load_model(config.get('model_path'))
        self.confidence_threshold = config.get('confidence_threshold', 0.5)

    def process(self, image_input):
        # Preprocess image
        preprocessed = self.preprocess(image_input)

        # Run inference
        detections = self.model.predict(preprocessed)

        # Postprocess results
        results = self.postprocess(detections)
        return results

    def preprocess(self, image):
        # Resize, normalize, etc.
        resized = cv2.resize(image, (640, 640))
        normalized = resized / 255.0
        return np.expand_dims(normalized, axis=0)

    def postprocess(self, detections):
        # Filter by confidence, apply NMS, etc.
        valid_detections = [d for d in detections if d['confidence'] > self.confidence_threshold]
        return valid_detections

Audio Agent Example

from meta_agent_platform import AudioAgent
import librosa
import numpy as np

class SpeechRecognitionAgent(AudioAgent):
    def __init__(self, config):
        super().__init__(config)
        self.model = self.load_model(config.get('model_path'))
        self.sample_rate = config.get('sample_rate', 16000)

    def process(self, audio_input):
        # Preprocess audio
        features = self.extract_features(audio_input)

        # Run inference
        transcription = self.model.transcribe(features)

        return {'text': transcription}

    def extract_features(self, audio):
        # Resample if needed
        if self.sample_rate != 16000:
            audio = librosa.resample(audio, orig_sr=self.sample_rate, target_sr=16000)

        # Extract features (e.g., MFCCs)
        mfccs = librosa.feature.mfcc(y=audio, sr=16000, n_mfcc=13)
        return mfccs

Best Practices

• Clear Input/Output Schema: Define expected modalities and formats.
• Efficient Preprocessing: Optimize for speed and resource usage.
• Model Selection: Use pre-trained models where possible (e.g., CLIP, Whisper, YOLO).
• Resource Awareness: Optimize for edge deployment if needed.
• Testing: Use diverse datasets for each modality.
• Visualization: Provide tools for inspecting multi-modal outputs.
• Error Handling: Gracefully handle missing or corrupted modality data.
• Fallback Strategies: Define behavior when a modality is unavailable.

Platform Integration

Registration

Register a multi-modal agent with the platform:

# agent-metadata.yaml
name: image-text-classifier
version: 1.0.0
type: multi-modal
description: "Classifies content based on both image and text inputs"

modalities:
  - type: text
    required: true
    format: string
    max_length: 1024
  - type: image
    required: true
    formats: [jpg, png]
    max_size: 5MB

output:
  type: classification
  schema:
    type: object
    properties:
      category:
        type: string
        enum: [news, entertainment, sports, technology, other]
      confidence:
        type: number
        minimum: 0
        maximum: 1

Workflow Integration

• Workflow Builder: Multi-modal agents appear as specialized nodes.
• Monitoring: Platform provides visualization for each modality.
• Fusion: Results can be combined using built-in or custom fusion logic.

Troubleshooting

Issue	Possible Cause	Solution
Missing modality data	Input not provided	Implement fallback strategy or return clear error
Slow processing	Inefficient preprocessing	Optimize preprocessing pipeline, consider quantization
Memory issues	Large models or inputs	Use streaming processing, reduce batch size
Inconsistent results	Modality weighting issues	Adjust fusion weights, validate with diverse test cases
Format incompatibility	Unsupported input format	Add format conversion in preprocessing step

References

• Multi-Modal Development Guide
• Component Design: Multi-Modal Agent Framework
• Data Model: Modalities
• Vision Models: OpenCV, CLIP, YOLO
• Audio Models: Whisper, Librosa
• Sensor Processing: Pandas, NumPy

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Last updated: 2025-04-18