upstash-vector-db-skills

Semantic search retrieves documents by meaning rather than keyword matching. Use Upstash Vector DB to store document embeddings, then query by semantic similarity. Create a vector index, upsert documents with embeddings, and call index.query() with your search text to get ranked results.

Yes, Upstash Vector DB is designed for serverless deployments. It works natively with Next.js API routes and Vercel without managing infrastructure. Configure your REST URL and token as environment variables, then use the SDK in serverless functions.

Create a vector index in the Upstash Console, install @upstash/vector, add REST credentials to environment variables, then upsert your documents with embeddings and run semantic queries using index.query(). The process takes minutes with no infrastructure setup.

Upstash Vector DB supports namespaces for data isolation. Use separate namespaces to partition indexes by project, tenant, or user. Query specific namespaces to retrieve only relevant data for multi-tenant search and knowledge base workflows.

Upstash can automatically generate embeddings during upsert using built-in models. You can also use optional embedding providers like MixBread. Specify your embedding choice when creating the index, then upsert raw documents without pre-computing embeddings.

Semantic search understands meaning and context, retrieving documents similar in intent even if words differ. Keyword search matches exact terms. Semantic search with vector embeddings finds relevant results for queries like chat assistants and knowledge bases where intent matters more than exact wording.