Predefined Relational Object Embedding

Abstract

As the need for robust multimodal retrieval and knowledge representation grows, embedding models must effectively align different data types into a shared vector space. Traditional approaches that attempt to train a unified model or fine-tune multiple embeddings into a single space are prohibitively expensive and computationally intensive. I propose Predefined Relational Object Embedding (PROE), a systematic methodology to encode structured relationships between objects and their embeddings, leveraging Voyage-Multimodal-3, Whisper, and Google Gemini Flash 2.0 to seamlessly integrate text, images, audio, video, and document-based data. This paper discusses the benefits of PROE, its methodology, and why it presents a more feasible alternative to expensive model alignment strategies.

Introduction

Vector embeddings power modern retrieval and AI-driven reasoning systems, allowing machines to process, search, and correlate multimodal content. However, integrating different embedding models into a unified search space poses significant challenges. Misalignment between embeddings from distinct models (e.g., text vs. image embeddings from separate networks) leads to inaccurate retrieval and degraded performance. Training a single transformer to align multiple embeddings is computationally expensive, requiring vast datasets, extensive compute resources and significant fine-tuning.

Instead of retraining models from scratch, PROE offers a predefined relational approach. In this approach, objects are embedded with structured relationships that maintain consistency across different modalities. This ensures retrieval systems can leverage multimodal embeddings without sacrificing accuracy or efficiency.

Challenges of Embedding Model Alignment

Aligning embedding models into a single vector space typically involves:

1. Training a Joint Embedding Model – This requires retraining a transformer with a multimodal dataset, which is costly and impractical.
2. Fine-tuning Pre-trained Models – Fine-tuning individual embedding models and aligning them post hoc often results in suboptimal performance and domain-specific limitations.
3. Heuristic-Based Fusion – Attempts to merge embeddings via heuristics often introduce noise and inconsistencies.

Each approach demands significant computational resources and may not generalize well across datasets. Instead, PROE establishes structured, predefined relationships that allow embeddings from different modalities to be integrated without requiring costly retraining.

Methodology: The PROE Approach

PROE leverages existing multimodal embedding capabilities while structuring relationships between different object types. This is achieved through the following pipeline:

1. Text → Directly embedded via Voyage-Multimodal-3.
2. Images (Photos, GIFs) → Processed using Google Gemini Flash 2.0 for descriptive text generation, then paired with the image for embedding.
3. Slides, PDFs → Extracted as images, then processed with Google Gemini Flash 2.0 to generate textual descriptions. Extractable text is separately embedded. Both representations are linked in metadata, ensuring context-aware retrieval.
4. Video, Audio → Transcribed via Whisper, then embedded as text. The original media file’s features (e.g., timestamps, speaker segmentation) are preserved in metadata.
5. Relational Metadata → Each embedded object stores references to its related embeddings. If a search query retrieves one component (e.g., an image), its corresponding text or multimedia embeddings are retrieved.

Benefits of PROE

1. Avoids Expensive Model Training – Using structured relationships, PROE eliminates the need to train an entirely new embedding model, drastically reducing computational costs.
2. Preserves Context Across Modalities – Instead of forcing embeddings into a single model, PROE links existing embeddings to maintain semantic integrity.
3. Optimized for Retrieval – Searches in multimodal databases benefit from relational metadata, ensuring all relevant information surfaces even if only one modality is queried.
4. Scalability and Adaptability – PROE can accommodate new embedding models without retraining, allowing enterprises to integrate new modalities seamlessly.
5. Improved Performance in Mixed-Modal Search – Unlike traditional multimodal embeddings, which suffer from modality gaps (e.g., text-based queries fail to retrieve images), PROE ensures alignment by explicitly defining relationships.

Conclusion

Aligning multiple embedding models into a shared vector space is computationally expensive and challenging to optimize. PROE provides a structured, cost-effective alternative by leveraging predefined relationships between embeddings. This approach allows seamless multimodal retrieval without requiring a unified model, which is particularly valuable in domains such as document search, knowledge management, and AI-powered discovery tools, where accuracy and efficiency are paramount.

Using Voyage-Multimodal-3, Whisper, and Google Gemini Flash 2.0, PROE ensures that textual, visual, and audio embeddings maintain contextual relationships while remaining cost-effective and scalable. Future work will explore dynamic relational embedding graphs and automatic metadata generation to enhance multimodal retrieval performance.