apps/backend/src/mcp/cognitive/MultiModalProcessor.ts

/**
 * Multi-Modal Support
 * Handles images, audio, video, and cross-modal search
 * PRODUCTION VERSION - NO MOCK DATA
 */

import { getVectorStore } from '../../platform/vector/index.js';
import { getEmbeddingService } from '../../services/embeddings/EmbeddingService';

export interface MultiModalEmbedding {
    id: string;
    type: 'image' | 'audio' | 'video' | 'text';
    embedding: number[];
    metadata: Record<string, any>;
    timestamp: Date;
}

export class MultiModalProcessor {
    private clipModelLoaded: boolean = false;
    private audioModelLoaded: boolean = false;

    /**
     * Generate image embeddings using CLIP
     * Requires CLIP model to be configured
     */
    async generateImageEmbedding(imageUrl: string): Promise<number[]> {
        if (!process.env.CLIP_MODEL_PATH && !process.env.OPENAI_API_KEY) {
            throw new Error(
                'CLIP model not configured. Set CLIP_MODEL_PATH or OPENAI_API_KEY in environment variables.'
            );
        }

        // TODO: Implement actual CLIP model integration
        // Options:
        // 1. Use OpenAI CLIP API
        // 2. Use local CLIP model via transformers.js
        // 3. Use HuggingFace Inference API

        throw new Error('CLIP model integration not yet implemented. Please configure a CLIP provider.');
    }

    /**
     * Generate audio embeddings
     * Requires audio processing model
     */
    async generateAudioEmbedding(audioUrl: string): Promise<number[]> {
        if (!process.env.AUDIO_MODEL_PATH) {
            throw new Error(
                'Audio model not configured. Set AUDIO_MODEL_PATH in environment variables.'
            );
        }

        // TODO: Implement actual audio model integration
        // Options:
        // 1. Wav2Vec 2.0
        // 2. OpenAI Whisper
        // 3. HuggingFace audio models

        throw new Error('Audio model integration not yet implemented. Please configure an audio model.');
    }

    /**
     * Generate video embeddings
     * Combines visual and audio features
     */
    async generateVideoEmbedding(videoUrl: string): Promise<number[]> {
        if (!process.env.VIDEO_MODEL_PATH) {
            throw new Error(
                'Video model not configured. Set VIDEO_MODEL_PATH in environment variables.'
            );
        }

        // TODO: Implement actual video model integration
        // Options:
        // 1. Combine CLIP (visual) + Wav2Vec (audio)
        // 2. Use specialized video models
        // 3. Frame-by-frame processing

        throw new Error('Video model integration not yet implemented. Please configure a video model.');
    }

    /**
     * Cross-modal search
     * Search for images using text query, or vice versa
     */
    async crossModalSearch(
        query: string | number[],
        targetModality: 'image' | 'audio' | 'video' | 'text',
        limit: number = 10
    ): Promise<MultiModalEmbedding[]> {
        // Convert query to embedding if needed
        let queryEmbedding: number[];
        if (typeof query === 'string') {
            queryEmbedding = await this.generateTextEmbedding(query);
        } else {
            queryEmbedding = query;
        }

        // Search in vector database
        const vectorStore = await getVectorStore();
        const results = await vectorStore.search({
            vector: queryEmbedding,
            namespace: `multimodal_${targetModality}`,
            limit
        });

        return results.map(result => ({
            id: result.id,
            type: targetModality,
            embedding: [], // Embedding not returned from search, would need separate lookup
            metadata: result.metadata || {},
            timestamp: new Date(result.metadata?.timestamp || Date.now()),
        }));
    }

    /**
     * Generate text embedding for cross-modal comparison
     */
    private async generateTextEmbedding(text: string): Promise<number[]> {
        const embeddingService = getEmbeddingService();
        const embedding = await embeddingService.generateEmbedding(text);
        return embedding;
    }

    /**
     * Multi-modal RAG
     * Retrieve relevant content across all modalities
     */
    async multiModalRAG(
        query: string,
        modalities: Array<'image' | 'audio' | 'video' | 'text'> = ['text', 'image']
    ): Promise<Map<string, MultiModalEmbedding[]>> {
        const results = new Map<string, MultiModalEmbedding[]>();

        for (const modality of modalities) {
            try {
                const modalityResults = await this.crossModalSearch(query, modality, 5);
                results.set(modality, modalityResults);
            } catch (error) {
                console.error(`Failed to search ${modality}:`, error);
                results.set(modality, []);
            }
        }

        console.log(`📚 Multi-modal RAG completed for: ${query}`);
        return results;
    }

    /**
     * Check if multi-modal features are available
     */
    isConfigured(): {
        clip: boolean;
        audio: boolean;
        video: boolean;
    } {
        return {
            clip: !!(process.env.CLIP_MODEL_PATH || process.env.OPENAI_API_KEY),
            audio: !!process.env.AUDIO_MODEL_PATH,
            video: !!process.env.VIDEO_MODEL_PATH,
        };
    }
}

export const multiModalProcessor = new MultiModalProcessor();