Créez une application de planification de voyages basée sur des agents avec l'IA générative

---
model: googleai/gemini-1.5-flash-latest
config:
  temperature: 1.0
  safetySettings:
    - category: HARM_CATEGORY_HATE_SPEECH
      threshold: BLOCK_LOW_AND_ABOVE
    - category: HARM_CATEGORY_DANGEROUS_CONTENT
      threshold: BLOCK_ONLY_HIGH
    - category: HARM_CATEGORY_HARASSMENT
      threshold: BLOCK_LOW_AND_ABOVE
    - category: HARM_CATEGORY_SEXUALLY_EXPLICIT
      threshold: BLOCK_LOW_AND_ABOVE
input:
  schema:
    request: string, The users request for where they want to travel to.
    place: string, The place that closely represents the users request.
    placeDescription: string, A description of that place.
    activities(array, a stringify list of activities that can be found at the specified place): string
    restaurants?(array, a stringify list of all the restaurants found at that location): string
output:
  schema:
    place: string, The place the user is traveling to.
    itineraryName: string, a catchy itinerary name that encapsulates the spirit of the trip and includes the place name
    startDate: string, the start date of the trip
    endDate: string, the end date of the trip
    tags(array, relevant tags for the trip): string
    itinerary(array):
      day: number
      date: string
      planForDay(array):
        activityRef: string, the reference value for the activity - this comes from the available activities JSON. If no value is present use a ref value of restaurant.
        activityTitle: string, a catchy title for the activity
        activityDesc: string, a six word description of the activity
        photoUri?: string, set the photo uri value for restaurants only.
        googleMapsUri?: string, if this is a restaurant include the googleMapsUri
---

Generate an itinerary for a tourist planning on traveling to the location specified based in their request.
If there is something that does not exist within the list of activities, do not include it in your answer.
Feel free to relate the activitiy to the request in a meaningful way.
In the plan for day array, put activities as a travel brouchure might do.
Come up with a catchy name for the itinerary.

Pick three activities per day, minimum of three day trip unless otherwise specified in the request.

Output schema should not include the properties type or object.

Pick a date after 2024-05-14 but before 2024-12-31.

The output date must be in the format year-month-day.

Give each activity a unique title and description.

Limit activity descriptions to 6 words.

If no restaurants are supplied, do not recommend any restaurants to eat at.

{{#if restaurants}}
Find a restaurant to eat at each day.

Include a restaurant to visit in the itinerary for each day from the available restaurants.
The restaurant should be the only activity with a photoUri.
The photoUri for the restaurant should be from the photoUri property from the restaurant array.
If there are no restaurants to pick from, do not include it in the list.

The photoUri from the restaurantFinder should be in the format of places/${placeId}/photos/${photoId}

Each restaurant should be unique to the overall itinerary.
Each restaurant must contain a photoUri in their output JSON schema.
Each restaurant must also include  an activitiyRef, activityTitle, and activityDesc in their output
{{/if}}
Output must be in JSON format.

REQUEST : {{request}}
PLACE : {{place}}
PLACE DESCRIPTION : {{placeDescription}}
AVAILABLE ACTIVITIES : {{activities}}
RESTAURANTS : {{restaurants}}

import {defineTool} from '@genkit-ai/ai/tool';
...
{
  name: 'restaurantFinder',
  description: `Used when needing to find a restaurant based on a users location.
  The location should be used to find nearby restaurants to a place. You can also
  selectively find restaurants based on the users preferences, but you should default
  to 'Local' if there are no indications of restaurant types in the users request.
  `,
  inputSchema: z.object({
    place: z.string(),
    typeOfRestaurant: z.string().optional() }),
    outputSchema: z.unknown(),
},
...
async (input) => {
  if (input.typeOfRestaurant == undefined) {
    input.typeOfRestaurant = "Local";
  }
  const geocodeEndpoint = "https://places.googleapis.com/v1/places:searchText";
  const textQuery = {textQuery: `${input.typeOfRestaurant} restaurants in ${input.place}`};

  const  response = await axios.post(
    geocodeEndpoint,
    JSON.stringify(textQuery),
    {
      headers: {
        "Content-Type": "application/json",
        "X-Goog-Api-Key": MAPS_API_KEY,
        "X-Goog-FieldMask": "places.displayName,places.formattedAddress,places.priceLevel,places.photos.name,places.editorialSummary,places.googleMapsUri"
      }
    }
  );
  console.log(response.data);
  let data = (response.data as PlaceResponse);
  for(let i = 0; i < data.places.length; i++) {
    if (data.places[i].photos) {
      data.places[i].photos = [data.places[i].photos[0]];
    }
  }
  return data as PlaceResponse;
}

...
export const textRefinement = defineFlow(
{
  name: 'textRefinement',
  inputSchema: z.string(),
  outputSchema: z.unknown(),
},
async (userRequest) => {
  const refinementPrompt = await prompt('textRefinement')
  const result = await refinementPrompt.generate({
      input: {
          request: userRequest
      },
  });
  return result.output();
});

import 'package:http:http.dart' as http;
...
Future<List<Trip>> generateTrips(String description, List<Image> images) async {
  final uri = Uri.parse('.../generateTrips');
  final request = http.MultipartRequest('POST', uri);
  request.fields['description'] = description;
  request.files.add(http.MultipartFile.fromData(
      images.name, images.bytes,
      contentType: MediaType('image', 'png'),
  ));
  var response = await request.send();
  if (response.statusCode == 200) {
      final body = await response.body.text();
      final items = jsonDecode(body) as List<dynamic>;
      return items.map(Trip.fromJson).toList();
  }
  ...
  import { imagen2, geminiProVision } from '@genkit-ai/vertexai';
  import { generate } from '@genkit-ai/ai';

  const imageResult = await generate({
    model: imagen2,
    prompt: 'Generate an image of a very specific historical time and place.',
  });
  const generatedImage = imageResult.media();

  const descriptionResult = await generate({
    model: geminiProVision,
    prompt: [
      {
        text: 'What is the historical time and place represented in this picture?',
      },
      { media: generatedImage },
    ],
  });
  console.log(descriptionResult.text());
  }