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34367da | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 | /**
* 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();
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