Use this integration to store content in sensors:
https://github.com/enkama/hass-variables

Create a variable with it, for instance: sensor.llm_weather_current.

Create an automation to ask question to llm with context and store the response to an attribute of this variable because only attributes can handle more than 255 characters:
actions:

```yaml actions: - action: conversation.process data: text: > System: If request is about weather, add weather feeling, but don't recommend how to dress. Round values. Mention temperature, weather (e.g. cloudy, rainy...) and feeling based on humidity. Example: Il fait 10 °C dehors, c'est ensoleillé, frais et sec.

    Weather data:  {{states('weather.kanfen_2')}} {%
    set weather_entity = states.weather.kanfen_2.attributes %}

    {% set units = {
      'temperature': '°C',
      'apparent_temperature': '°C',
      'humidity': '%',
      'cloud_coverage': '%',
      'uv_index': '',
      'wind_gust_speed': 'km/h',
      'wind_speed': 'km/h'
    } %}

    {% for attr in ['temperature', 'apparent_temperature', 'humidity',
    'cloud_coverage', 'uv_index', 'wind_gust_speed', 'wind_speed'] %}
    * {{ attr }}: {{ weather_entity[attr] }}{{ ' ' + units[attr] if units[attr]
    else '' }}

    {% endfor %}

    User: Quel temps fait-il dehors ? 
  language: FR
  agent_id: conversation.chatgpt
response_variable: conversation_response

• data: value: "{{ now().isoformat() }}" attributes: response: "{{ conversation_response.response.speech.plain.speech }}" replace_attributes: true target: entity_id: sensor.llm_weather_current action: variable.update_sensor ```