In-memory Embedding Store that then store its serialization form as a Kestra K/V pair

pluginsplugin-langchain4jembeddings

This plugin is currently in beta. While it is considered safe for use, please be aware that its API could change in ways that are not compatible with earlier versions in future releases, or it might become unsupported.

yaml
type: "io.kestra.plugin.langchain4j.embeddings.kestrakvstore"

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 from there into memory, this won't scale with a large number of documents.

yaml
id: document-ingestion
namespace: company.team

tasks:
  - id: ingest
    type: io.kestra.plugin.langchain4j.rag.IngestDocument
    provider:
      type: io.kestra.plugin.langchain4j.provider.GoogleGemini
      modelName: gemini-embedding-exp-03-07
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.langchain4j.embeddings.KestraKVStore
    drop: true
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-22.md

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

The name of the K/V entry to use