id: 1_chat_without_rag
namespace: zoomcamp

description: |
  This flow demonstrates what happens when you query an LLM WITHOUT RAG.
  The model can only rely on its training data, which may be outdated or incomplete.
  
  After running this, check out 2_chat_with_rag.yaml to see how RAG fixes these issues!  

tasks:
  - id: chat_without_rag
    type: io.kestra.plugin.ai.completion.ChatCompletion
    description: Query about Kestra 1.1 features WITHOUT RAG
    provider:
      type: io.kestra.plugin.ai.provider.GoogleGemini
      modelName: gemini-2.5-flash
      apiKey: "{{ secret('GEMINI_API_KEY') }}"
    messages:
      - type: USER
        content: |
          Which features were released in Kestra 1.1? 
          Please list at least 5 major features with brief descriptions.          

  - id: log_results
    type: io.kestra.plugin.core.log.Log
    message: |
      ❌ Response WITHOUT RAG (no retrieved context):
      {{ outputs.chat_without_rag.textOutput }}
      
      🤔 Did you notice that this response seems to be:
      - Incorrect?
      - Vague/generic?
      - Listing features that haven't been added in exactly this version but rather a long time ago?
      
      👉 This is why context matters! Run `2_chat_with_rag.yaml` to see the accurate, context-grounded response.