Local Multimodal Search on Edge Devices using Qdrant Edge

2026-05-23 · Source: NLP on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Internet of Things (IoT) & Connected Devices · Depth: Intermediate, medium

Summary

This article details the creation of a local multimodal product search application designed for edge devices, leveraging Qdrant Edge and CLIP models. The system indexes grocery product images and associated catalog text into a local Qdrant Edge Shard, enabling fully offline search capabilities via either text or image queries. Qdrant Edge functions as a lightweight, in-process vector search engine, ideal for environments with limited or unstable connectivity, such as mobile apps or Raspberry Pi. The tutorial outlines steps including installing Qdrant Edge and FastEmbed, configuring an Edge Shard with 512-dimension "image" and "text" vectors using Cosine distance, and indexing product metadata alongside CLIP-generated embeddings. It further demonstrates performing nearest vector queries for both image and text inputs and highlights the use of payload indexes for filtered searches on fields like "category", "in_stock", and "price". A Streamlit UI is also presented for interactive testing of the search functionality.

Key takeaway

For AI Engineers or Robotics Engineers developing applications for edge devices, you can implement robust, fully offline multimodal search capabilities. By integrating Qdrant Edge with FastEmbed and CLIP models, your applications can perform local image and text queries without network dependency. This approach ensures reliable product discovery or object identification in environments with limited connectivity, significantly enhancing device autonomy and user experience. Consider indexing relevant payload fields to enable efficient filtered searches.

Key insights

Local multimodal search on edge devices is feasible with in-process vector databases and pre-trained embedding models.

Principles

• Edge vector databases enable offline search.
• Multimodal embeddings enhance product discovery.
• In-process engines reduce deployment complexity.

Method

Install Qdrant Edge and FastEmbed, configure an Edge Shard with named "image" and "text" vectors, embed product data using CLIP, then upsert vectors and payloads. Query the shard with QueryRequest for multimodal search.

In practice

• Use Qdrant Edge for offline vector search.
• Employ CLIP for multimodal embeddings.
• Index payload fields for filtered queries.

Topics

• Qdrant Edge
• Multimodal Search
• Edge Computing
• Vector Databases
• CLIP Embeddings
• Offline Search

Code references

• lucifertrj/ShelfScan-Edge
• qdrant/fastembed

Best for: AI Engineer, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by NLP on Medium.