Multimodal retrieval for text and image collections

A project template for embedding-based understanding across text, image, and metadata.

System Goal

Build a retrieval system that can search across text, images, and structured metadata using embeddings and multimodal representations.

Why This Matters

Many real knowledge systems are not text-only. Product catalogs, research archives, clinical notes, policy documents, dashboards, and educational content often require joint understanding of text, images, figures, and metadata.

Architecture

Planned components:

• Dataset curation and metadata schema.
• Text and image embedding generation.
• Vector index construction.
• Hybrid search with filters.
• Evaluation using labeled queries.
• Error analysis for false positives and missed matches.

Evaluation Plan

Track:

• Recall at k.
• Precision at k.
• Query class performance.
• Cross-modal retrieval quality.
• Robustness to noisy metadata.
• Latency and storage tradeoffs.

Notebook Plan

• notebooks/multimodal-retrieval-system/01-data-model.ipynb
• notebooks/multimodal-retrieval-system/02-embedding-pipeline.ipynb
• notebooks/multimodal-retrieval-system/03-vector-search.ipynb
• notebooks/multimodal-retrieval-system/04-error-analysis.ipynb