FiftyOne Vector Search Integrations
Search through billions of images with a single line of code and enable capabilities like reverse image search, traversing concept space, and semantic document search.
What Is Vector Search?
Traditional Vector Search
If you want to incorporate vector search into your computer vision workflows, there can be quite a bit of overhead. For example:
- How do you implement cross-modal retrieval like searching for images with text?
- How do you incorporate traditional search filters like confidence thresholds or class labels?
- What about searching over the objects (people, dogs, cars, bikes, etc.) within your images?
Vector Search + FiftyOne
FiftyOne ships with native integrations for all the major vector search engines so you can use your preferred engine to easily search all your visual data with a single line of code.
FiftyOne is the computer vision interface for vector search
Ready to start searching image data at scale?
Check out these FiftyOne + vector search engine integrations, tutorials, blogs, and videos.
MongoDB is the leading open source database for unstructured data, and now with MongoDB’s Atlas Vector Search you can build semantic search and AI-powered applications by integrating the operational database and vector search in a single, unified, and fully managed platform with a MongoDB native interface.
FiftyOne provides an API to create MongoDB vector search indexes, add/remove vectors, and run similarity queries, both programmatically in Python and via point-and-click in the FiftyOne App.
Redis is the leading open source in-memory data store, and it can now also be used as a vector database. With Redis Vector Search you can store vectors and the associated metadata within hashes or JSON documents and perform vector similarity searches to power applications like chatbots, recommendation systems, document search, image and video search, anomaly detection, and much more!
FiftyOne provides an API to create Redis vector search indexes, upload vectors, and run similarity queries, both programmatically in Python and via point-and-click in the FiftyOne App.
Qdrant is a vector database & vector similarity search engine. It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more!
FiftyOne provides an API to create Qdrant collections, upload vectors, and run similarity queries, both programmatically in Python and via point-and-click in the FiftyOne App.
Milvus is an open-source vector database built to power embedding similarity search and AI applications maintained by Zilliz. Milvus makes unstructured data search more accessible, and provides a consistent user experience regardless of the deployment environment.
FiftyOne provides an API to create Milvus collections, upload vectors, and run similarity queries, both programmatically in Python and via point-and-click in the FiftyOne App.
Pinecone‘s vector database is fully-managed, developer-friendly, and easily scalable.
FiftyOne provides an API to create Pinecone indexes, upload vectors, and run similarity queries, both programmatically in Python and via point-and-click in the FiftyOne App.
LanceDB is a serverless vector database with deep integrations with the Python ecosystem. It requires no setup and is free to use.
FiftyOne provides an API to create LanceDB tables and run similarity queries, both programmatically in Python and via point-and-click in the FiftyOne App.
Additional Resources
Schedule a Demo
Ready to schedule a vector search workshop for your team? Request a FiftyOne Teams demo to learn more about how vector search, data versioning, cloud-backed media, and enterprise-grade security can turbo charge your team’s computer vision workflows.
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