AI Pinecone

AI Pinecone
Certified
Store embeddings in Pinecone

Creates or connects to a serverless Pinecone index in the given cloud/region; namespace defaults to Pinecone’s default. Requires an API key; drop=true clears the index contents.

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

Examples

Ingest documents into a Pinecone 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.Pinecone
      apiKey: "{{ secret('PINECONE_API_KEY') }}"
      cloud: AWS
      region: us-east-1
      index: embeddings
    fromExternalURLs:
      - https://raw.githubusercontent.com/kestra-io/docs/refs/heads/main/content/blogs/release-0-24.md

Properties

apiKey *string

API Key

Pinecone API key used to authenticate requests. Store it as a Kestra secret rather than inline. No default: this property is required.

cloud *string

Cloud provider

Cloud provider hosting the Pinecone serverless index. No default: this property is required.

index *string

Index name

Pinecone index that stores the embeddings. It is created as a serverless index if it does not exist. No default: this property is required.

region *string

Cloud provider region

Region of the cloud provider hosting the serverless index. No default: this property is required.

namespace string

Namespace

Namespace that partitions vectors inside the index. Not set by default, in which case Pinecone's default namespace is used.

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 PineconeCertifiedStore embeddings in Pinecone

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Huggingface

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AI Pinecone
Certified
Store embeddings in Pinecone