Valkey: Good to know

This documentation is part of the Vector search and RAG guide. View the full guide here: Semantic search, recommendations, and retrieval-augmented generation with Valkey.

👋 Welcome to the Stackhero documentation!

Stackhero offers a ready-to-use Valkey cloud solution that provides a host of benefits, including:

  • Valkey Admin web UI included.
  • Unlimited message size and transfers.
  • Effortless updates with just a click.
  • Optimal performance and robust security powered by a private, dedicated infrastructure.

Save time and simplify your life: it only takes 5 minutes to try Stackhero's Valkey cloud hosting solution!

  • Indexing existing keys runs in the background. You can track progress with FT.INFO. Searches will return partial results until backfilling is complete.
  • The search module implements a focused set of FT.* commands. For vector search, it covers all essentials: HNSW and exact KNN, hybrid filtering, and FT.AGGREGATE.

For a full reference, see the Valkey search documentation.

Continue reading this guide

  1. Read the full guide: Vector search and RAG
    Semantic search, recommendations, and retrieval-augmented generation with Valkey
  2. Before you start
  3. Creating a vector index
  4. Querying
  5. A RAG pipeline in Node.js
  6. The same in Python
  7. Sizing your service

Other articles for Valkey

  1. Getting started
    Quick guide to getting started with Valkey
  2. Prometheus
    Prometheus metrics
  3. Using with Python
    How to connect Valkey with Python
  4. Using with Ruby
    How to connect Valkey with Ruby
  5. Search and JSON
    Store and query JSON documents with the Valkey search module
  6. Automate with the CLI
    Start Valkey, retrieve its credentials, and change its configuration programmatically with the Stackhero CLI
  7. GitHub Actions & GitLab CI
    Spin up a real Valkey service from your GitHub Actions or GitLab CI pipeline, run your tests against it, and tear it down automatically