To create natural language interactions with Elasticsearch data, use Kestra with the OpenAI plugin. This combination transforms structured data into an intuitive, conversational interface.

Key advantages:

• Custom data processing and AI prompts for your specific use case
• Easy configuration and deployment with Kestra’s YAML-based workflow

Here’s a code example demonstrating this workflow:

id: movie_recommendation_system
namespace: entertainment.movies

inputs:
  - id: user_preference
    type: STRING
    defaults: I like action movies with a bit of comedy

tasks:
  - id: search_movies
    type: io.kestra.plugin.elasticsearch.Search
    connection:
      hosts: 
        - http://localhost:9200/
    indexes:
      - movies_database
    request:
      size: 5
      query: 
        bool:
          must:
            multi_match:
              query: "{{ inputs.user_preference }}"
              fields: ["title", "description", "genre"]
              type: best_fields

  - id: format_movie_results
    type: io.kestra.plugin.core.debug.Return
    format: >
      {% for movie in outputs.search_movies.rows %}
        Title: {{ movie.title }}
        Genre: {{ movie.genre }}
        Description: {{ movie.description }}
      {% endfor %}

  - id: generate_recommendations
    type: io.kestra.plugin.openai.ChatCompletion
    apiKey: sk-proj-your-OpenAI-API-KEY
    model: gpt-4
    maxTokens: 500
    prompt: |
      You're a movie recommendation assistant. 
      Based on the USER PREFERENCE and the MOVIE RESULTS, suggest 3 movies from the list.
      Explain why each movie matches the user's preference.
      USER PREFERENCE: {{ inputs.user_preference }}
      MOVIE RESULTS: {{ outputs.format_movie_results.value }}

  - id: display_recommendations
    type: io.kestra.plugin.core.log.Log
    message: "{{ outputs.generate_recommendations.choices | jq('.[].message.content') | first }}"

This example shows:

1. search_movies task uses Elasticsearch Search to query the index based on user preference.
2. format_movie_results task formats the search results.
3. generate_recommendations task uses OpenAI ChatCompletion to analyze results and user preference, creating personalized movie recommendations with explanations.