James Murray introduces the Qdrant Hybrid Stack, an advanced retrieval system that combines dense and sparse retrieval methods to effectively handle media object searches.

The stack supports images, videos, and audio with CLIP-based dense vectors and BM25 sparse vectors. It achieves 94% recall@10 on multimodal datasets and supports real-time indexing from upload streams.

Perfect for content platforms, stock media libraries, and AI training data curation.

Key Features

• Hybrid Retrieval: Dense (CLIP) + sparse (BM25) with learned fusion weights.
• Optimized for Media: Supports 512-dim image, 768-dim video frame embeddings.
• High-Performance Architecture: GPU-accelerated indexing with HNSW.
• Real-Time Ingestion: Kafka → embedding → Qdrant in <500ms.
• Metadata + Facets: Filter by resolution, license, duration, tags.
• Similarity API: Find visually similar assets across modalities.

System Design & Architecture

The Qdrant Hybrid Stack uses a combination of dense and sparse retrieval techniques to index and retrieve media objects, ensuring maximum relevance and speed. A fusion layer dynamically weights results based on query type.

Technical Stack

• Vector DB: Qdrant 1.7 (self-hosted)
• Embeddings: OpenAI CLIP ViT-L/14
• Ingestion: Kafka + Python workers
• API: FastAPI + OpenAPI spec

Related Projects

Explore other related projects:

• Advanced Vector Databases
• Semantic SEO