AI Qdrant

AI Qdrant
Certified
Store embeddings in Qdrant

Uses the Qdrant gRPC client with API key, host, and port. Targets the specified collection; drop=true removes its contents. Distance metric follows the Qdrant collection defaults.

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

Examples

Ingest documents into a Qdrant 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.Qdrant
      apiKey: "{{ secret('QDRANT_API_KEY') }}"
      host: localhost
      port: 6334
      collectionName: embeddings
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-24.md

Properties

apiKey *string

The API key

collectionName *string

The collection name

host *string

The database server host

port *integerstring

The database server port

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Interact with Pinecone, a managed vector database, from your Kestra workflows. Create serverless indexes, upsert vectors, query by similarity, fetch or delete vectors by ID, and inspect index statistics.

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AI QdrantCertifiedStore embeddings in Qdrant

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