PGVector

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.

PGVector Embedding Store

yaml
type: "io.kestra.plugin.ai.embeddings.PGVector"

Examples

Ingest documents into a PGVector embedding store.

yaml
id: document-ingestion
namespace: company.team

tasks:
  - id: ingest
    type: io.kestra.plugin.ai.rag.IngestDocument
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-embedding-exp-03-07
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.ai.embeddings.PGVector
      host: localhost
      port: 5432
      user: "{{ secret('POSTGRES_USER') }}"
      password: "{{ secret('POSTGRES_PASSWORD') }}"
      database: postgres
      table: embeddings
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-22.md

Properties

database *string

The database name

host *string

The database server host

password *string

The database password

port *integerstring

The database server port

table *string

The table to store embeddings in

user *string

The database user

useIndex booleanstring

Default false

Whether to use use an IVFFlat index

An IVFFlat index divides vectors into lists, and then searches a subset of those lists closest to the query vector. It has faster build times and uses less memory than HNSW but has lower query performance (in terms of speed-recall tradeoff).

​P​G​Vector

PGVector