AI Redis

AI Redis

Certified

Store embeddings in Redis

Backs an embedding index with Redis (jedis). Uses indexName (defaults to embedding-index); drop=true clears stored vectors. Ensure Redis deployment supports vector search modules for production use.

yaml
type: io.kestra.plugin.ai.embeddings.Redis

Ingest documents into a Redis embedding store

yaml
id: document_ingestion
namespace: company.ai

tasks:
  - id: ingest
    type: io.kestra.plugin.ai.rag.IngestDocument
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-embedding-001
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.ai.embeddings.Redis
      host: localhost
      port: 6379
      indexName: embeddings
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-24.md
Properties

The database server host

The database server port

Defaultembedding-index

The index name

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