Does Supabase Have Vector Search? (2026 Update)

The Short Answer: Yes

Supabase supports vector search through its integration with pgvector, a PostgreSQL extension that allows for efficient vector similarity searches. This means that while Supabase itself does not natively implement vector search, it can leverage the capabilities of pgvector to enable this functionality for users, particularly those working with AI embeddings.

How to Use Vector Search in Supabase (If Yes)

  1. Navigate to the Supabase SQL editor or use the Supabase CLI to create a table with a column of type vector using the pgvector extension. This will allow you to store vector embeddings in your database.
  2. Click on the “Extensions” tab in the Supabase dashboard and ensure that the pgvector extension is enabled for your project. If it’s not enabled, you can enable it from this tab.
  3. Result: With pgvector enabled and your table set up, you can now use SQL queries to perform vector searches. For example, you can use the l2_distance or cosine_distance functions provided by pgvector to find the closest matches to a given vector, which is particularly useful in applications involving AI embeddings.

Workarounds (If No)

Since the question is about the existence of vector search and Supabase does support it via pgvector, this section is not applicable in the context of needing a workaround for a missing feature. However, for completeness and to address potential limitations or edge cases:

  1. Use an Integration: If you’re looking for more advanced vector search capabilities beyond what pgvector offers, you might consider integrating a dedicated vector database like Pinecone or Weaviate into your Supabase project.
  2. Use a 3rd Party Plugin: There might not be a specific plugin for enhancing vector search in Supabase, but you could explore PostgreSQL plugins that extend pgvector’s functionality, keeping in mind compatibility and support.
  3. The “Hack”: A manual workaround could involve implementing your own vector search algorithm outside of Supabase, using the data retrieved from Supabase, and then using the results to update or query your database. This approach would be more complex and less efficient than using a built-in or integrated solution.

If Vector Search is a critical component of your workflow (AI Embeddings) and you find the pgvector integration in Supabase limiting:

  • Alternative 1: FaunaDB (Native support) offers a more comprehensive set of features for real-time data processing and vector search, which might be appealing for complex AI applications.
  • Alternative 2: Couchbase (Better implementation) provides a scalable NoSQL database with integrated full-text search and analytics, which could be beneficial for applications requiring advanced search functionalities, including vector search.

FAQ

Q: Is Vector Search coming in the roadmap? A: As of the last public update, Supabase continues to enhance its support for PostgreSQL extensions like pgvector. While there’s no explicit mention of native vector search in the roadmap, the community and developers continue to contribute to and expand the capabilities of Supabase, including better support for AI and machine learning applications.

Q: Is this feature gated to the Enterprise plan? A: The use of pgvector for vector search in Supabase is not gated to the Enterprise plan. Supabase allows the use of PostgreSQL extensions, including pgvector, across its plans, making vector search accessible to all users, regardless of their subscription tier. However, specific limitations or enhanced support might vary between the free and paid plans.

📚 Continue Learning

Check out our guides on Supabase and Vector Search.