EmbeddingStoreRetriever

id: "embeddingstoreretriever"
type: "io.kestra.plugin.ai.retriever.EmbeddingStoreRetriever"
id: agent_with_rag
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
      googleApiKey: "{{ kv('GEMINI_API_KEY') }}"
    embeddings:
      type: io.kestra.plugin.ai.embeddings.KestraKVStore
    drop: true
    fromDocuments:
      - content: Paris is the capital of France with a population of over 2.1 million people
      - content: The Eiffel Tower is the most famous landmark in Paris at 330 meters tall

  - id: agent
    type: io.kestra.plugin.ai.agent.AIAgent
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-2.0-flash
      googleApiKey: "{{ kv('GEMINI_API_KEY') }}"
    contentRetrievers:
      - type: io.kestra.plugin.ai.retriever.EmbeddingStoreRetriever
        embeddings:
          type: io.kestra.plugin.ai.embeddings.KestraKVStore
        embeddingProvider:
          type: io.kestra.plugin.ai.provider.GoogleGemini
          modelName: gemini-embedding-exp-03-07
          googleApiKey: "{{ kv('GEMINI_API_KEY') }}"
        maxResults: 3
        minScore: 0.0
    prompt: What is the capital of France and how many people live there?

id: "embeddingstoreretriever"
type: "io.kestra.plugin.ai.retriever.EmbeddingStoreRetriever"
id: multi_store_rag
namespace: company.ai

tasks:
  - id: agent
    type: io.kestra.plugin.ai.agent.AIAgent
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-2.0-flash
      googleApiKey: "{{ kv('GEMINI_API_KEY') }}"
    contentRetrievers:
      - type: io.kestra.plugin.ai.retriever.EmbeddingStoreRetriever
        embeddings:
          type: io.kestra.plugin.ai.embeddings.Pinecone
          pineconeApiKey: "{{ kv('PINECONE_API_KEY') }}"
          index: technical-docs
        embeddingProvider:
          type: io.kestra.plugin.ai.provider.OpenAI
          googleApiKey: "{{ kv('OPENAI_API_KEY') }}"
          modelName: text-embedding-3-small
      - type: io.kestra.plugin.ai.retriever.EmbeddingStoreRetriever
        embeddings:
          type: io.kestra.plugin.ai.embeddings.Qdrant
          host: localhost
          port: 6333
          collectionName: business-docs
        embeddingProvider:
          type: io.kestra.plugin.ai.provider.GoogleGemini
          modelName: gemini-embedding-exp-03-07
          googleApiKey: "{{ kv('GEMINI_API_KEY') }}"
      - type: io.kestra.plugin.ai.retriever.TavilyWebSearch
        tavilyApiKey: "{{ kv('TAVILY_API_KEY') }}"
    prompt: What are the latest trends in data orchestration?