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# Dash Ai Search
# Ask Dash Flutter Demo

![coverage][coverage_badge]
[![style: very good analysis][very_good_analysis_badge]][very_good_analysis_link]
[![License: MIT][license_badge]][license_link]
## Overview

Generated by the [Very Good CLI][very_good_cli_link] 🤖
To set up the LLM Search backend for the demo, you'll need the following:
- A Datastore with the website you want to index
- An App to access that data
- A Cloud Function to act as an API wrapper

A Very Good Project created by Very Good CLI.
## Create a Datastore

---
1. Go to the Google Cloud Console and search for "Search And Conversation"
2. On the left, select Data Stores and then New Data Store
3. For source select Website URL and enter you site info
4. Enter a name for your Datastore to finalize

## Getting Started 🚀
Note that it might take up to 4 hours to fully index your website.

This project contains 3 flavors:
## Create an App to access your Datastore

- development
- staging
- production
1. Still in the Search And Conversation section, select Apps and New App
2. For type select Search and enter a name for your app and company
3. For data select the Datastore that you created already

To run the desired flavor either use the launch configuration in VSCode/Android Studio or use the following commands:
Once your website has been indexed you should be able to search it directly from the Console. Select your App from the list of Apps and enter a question to try it out.

```sh
# Development
$ flutter run --flavor development --target lib/main_development.dart
If the data is ready to be searched you should see a natural language answer along with a list of citations from your own website. If you get a generic negative response like "I don't know what you mean" make sure that your site has been fully indexed and it's a question which is answered somewhere on it.

# Staging
$ flutter run --flavor staging --target lib/main_staging.dart
## Build a Cloud Function API wrapper

# Production
$ flutter run --flavor production --target lib/main_production.dart
```

_\*Dash Ai Search works on iOS, Android, Web, and Windows._

---

## Running Tests 🧪

To run all unit and widget tests use the following command:

```sh
$ flutter test --coverage --test-randomize-ordering-seed random
```

To view the generated coverage report you can use [lcov](https://github.com/linux-test-project/lcov).

```sh
# Generate Coverage Report
$ genhtml coverage/lcov.info -o coverage/

# Open Coverage Report
$ open coverage/index.html
```

---

## Working with Translations 🌐

This project relies on [flutter_localizations][flutter_localizations_link] and follows the [official internationalization guide for Flutter][internationalization_link].
1. In the Cloud Console search Cloud Functions
2. Select Create Function and enter a name
3. Python 3.11 as the runtime and paste the **insert link to function code here** into the source text editor

### Adding Strings

1. To add a new localizable string, open the `app_en.arb` file at `lib/l10n/arb/app_en.arb`.

```arb
{
"@@locale": "en",
"counterAppBarTitle": "Counter",
"@counterAppBarTitle": {
"description": "Text shown in the AppBar of the Counter Page"
}
}
```

2. Then add a new key/value and description

```arb
Your Search backend is now ready to go! To validate it's working correctly, on the Function page select Testing and enter a JSON object with format:
```JSON
{
"@@locale": "en",
"counterAppBarTitle": "Counter",
"@counterAppBarTitle": {
"description": "Text shown in the AppBar of the Counter Page"
},
"helloWorld": "Hello World",
"@helloWorld": {
"description": "Hello World Text"
}
"search_term": "{your question here}"
}
```

3. Use the new string

```dart
import 'package:dash_ai_search/l10n/l10n.dart';

@override
Widget build(BuildContext context) {
final l10n = context.l10n;
return Text(l10n.helloWorld);
}
```

### Adding Supported Locales

Update the `CFBundleLocalizations` array in the `Info.plist` at `ios/Runner/Info.plist` to include the new locale.

```xml
...

<key>CFBundleLocalizations</key>
<array>
<string>en</string>
<string>es</string>
</array>

...
```

### Adding Translations

1. For each supported locale, add a new ARB file in `lib/l10n/arb`.

```
├── l10n
│ ├── arb
│ │ ├── app_en.arb
│ │ └── app_es.arb
```

2. Add the translated strings to each `.arb` file:

`app_en.arb`

```arb
{
"@@locale": "en",
"counterAppBarTitle": "Counter",
"@counterAppBarTitle": {
"description": "Text shown in the AppBar of the Counter Page"
}
}
```

`app_es.arb`

```arb
{
"@@locale": "es",
"counterAppBarTitle": "Contador",
"@counterAppBarTitle": {
"description": "Texto mostrado en la AppBar de la página del contador"
}
}
```

### Generating Translations

To use the latest translations changes, you will need to generate them:

1. Generate localizations for the current project:

```sh
flutter gen-l10n --arb-dir="lib/l10n/arb"
```

Alternatively, run `flutter run` and code generation will take place automatically.
If everything is set up correctly you'll see the response in the Output box, which includes a natural language summary, citations, and short summaries of all the pages it references. If there are any problems check the logs on the same page to troubleshoot.

[coverage_badge]: coverage_badge.svg
[flutter_localizations_link]: https://api.flutter.dev/flutter/flutter_localizations/flutter_localizations-library.html
[internationalization_link]: https://flutter.dev/docs/development/accessibility-and-localization/internationalization
[license_badge]: https://img.shields.io/badge/license-MIT-blue.svg
[license_link]: https://opensource.org/licenses/MIT
[very_good_analysis_badge]: https://img.shields.io/badge/style-very_good_analysis-B22C89.svg
[very_good_analysis_link]: https://pub.dev/packages/very_good_analysis
[very_good_cli_link]: https://github.com/VeryGoodOpenSource/very_good_cli
To control access to your API you can set up authentication using Cloud IAM, but that is beyond the scope of this tutorial.