Redis Vector MCP. Manage Semantic Search & Embeddings Directly in Redis

Your agent takes over vector management right inside Redis. It treats your database like a full-featured vector store, letting you query complex data—like semantic relationships or image feature vectors—without needing to spin up external services or write extra client code.

When you connect your AI client via MCP, the server gives it native access to all vector operations. You won't need separate database connections or specialized ML libraries; everything stays contained within Redis.

Managing Your Vector Data Structure

Before running any searches, you gotta set up the index. To see what indexes are already configured in your database, run list_indexes. If you want to know the specific details of an existing index—like its exact dimensions or the underlying search algorithm type—you use get_index_info and point it at the name you care about.

When you're ready for a new data set, you initialize the structure by calling create_vector_index, specifying both the unique name and the required dimensionality for that index.

Writing and Updating Embeddings

Your agent handles the data lifecycle too. You don't have to rebuild entire records just because an embedding changes. To inject new vectors into a document key or overwrite old embeddings without changing the overall record structure, you use upsert_vector. This tool requires the target key and the new embedding array as JSON.

If a vector component is obsolete or needs to be removed entirely, you can clean up with delete_vector. You pass it a specific key, and it wipes out that entire associated vector document from Redis.

Running Similarity Searches (KNN)

The main job is searching. To run a rapid KNN similarity query, your agent calls search_vectors. You just provide an embedding array—that's the JSON array of floats representing your query vector—and it queries that against any active index you’ve established. The result is always the absolute nearest top-K neighbors found in your cluster, giving you semantic relationships instantly.

This setup keeps your entire semantic layer self-contained and fast within Redis. You'll run searches, update data, and manage schemas all through simple function calls to your agent.