Marqo
Tensor search engine for vector search with integrated multimodal embeddings.
Why consider Marqo?
Marqo is an open-source multimodal vector search engine. It natively handles document chunking, embedding generation using SBERT and CLIP, and vector indexing within a single service.
Learn Marqo by building
Practical setup notes, real use cases, and copy-ready examples in one focused guide.
In this guide3 sections
Overview of Marqo
Unlike conventional vector databases that require you to manage external embedding pipelines, Marqo handles the full multimodal workflow. You send raw text or image URLs to Marqo, and it handles tokenization, ML model inference, and vector indexing automatically.
Running Marqo with Docker
docker run -d \ --name marqo \ -p 8882:8882 \ --add-host loopback:127.0.0.1 \ marqoai/marqo:latestSearching with Python
import marqomq = marqo.Client(url='http://localhost:8882')mq.create_index("my-multimodal-index")mq.index("my-multimodal-index").add_documents([ {"Title": "Vintage Leather Jacket", "Description": "Classic brown motorcycle jacket"}, {"Title": "Wireless Headphones", "Description": "Noise cancelling bluetooth headphones"}])results = mq.index("my-multimodal-index").search("biker outerwear")print(results)Marqo is licensed under the Apache License Version 2.0.
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