AI KestraKVStore

AI KestraKVStore
Certified
Prototype embeddings in Kestra KV

Stores embeddings in-memory and persists them to Kestra namespace KV on completion. Suitable for small demos; not scalable. drop=true discards any previously saved KV snapshot.

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

Examples

Ingest documents into a KV embedding store.\nWARNING: the KestraKVStore embeddings are for quick prototyping only; since they are stored in a KV store and loaded into memory, this won't scale with large numbers of documents.

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.KestraKVStore
    drop: true
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-24.md

Properties

kvName string

Default{{ flow.id }}-embedding-store

KV pair name

Key of the Kestra KV pair used to persist the in-memory embedding store between task runs. Defaults to {{ flow.id }}-embedding-store.

Huggingface

Tasks that call Hugging Face Inference APIs.

Gemini

Tasks that call Google Gemini models for text, chat, structured outputs, multimodal prompts, and video generation.

AI KestraKVStoreCertifiedPrototype embeddings in Kestra KV

AI KestraKVStore

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AI KestraKVStore
Certified
Prototype embeddings in Kestra KV