id: 3_rag_with_websearch
namespace: zoomcamp

description: |
  This flow demonstrates RAG (Retrieval Augmented Generation) by means of a live web search to look up Kestra release information and answer questions accurately.
  
  Compare this with 2_chat_with_rag.yaml which uses static content ingestion and an embedding store.  

tasks:
  - id: chat_with_rag_and_websearch_content_retriever
    type: io.kestra.plugin.ai.rag.ChatCompletion
    chatProvider:
      type: io.kestra.plugin.ai.provider.OpenAI
      apiKey: "{{ secret('OPENAI_API_KEY') }}"
      modelName: gpt-5-mini
    contentRetrievers:
      - type: io.kestra.plugin.ai.retriever.TavilyWebSearch
        apiKey: "{{ secret('TAVILY_API_KEY') }}"
    systemMessage: You are a helpful assistant that can answer questions about Kestra.
    prompt: What is the latest release of Kestra?