PGVector

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.ai

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: "{{ kv('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.ai.embeddings.PGVector
      host: localhost
      port: 5432
      user: "{{ kv('POSTGRES_USER') }}"
      password: "{{ kv('POSTGRES_PASSWORD') }}"
      database: postgres
      table: embeddings
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-24.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