Hi SrikanthManthina ,
To implement the functionality of suggesting matching entries based on previous correspondence and frequently contacted people in your desktop application, you can follow these steps:
1. Collect and store contact data: Start by collecting and storing relevant contact data from your application, such as user profiles, email addresses, and previous correspondence. This data will be used to determine frequent contacts.
2. Implement the search UI: Create a search input box in your desktop application where users can enter their search queries.
3. Capture user input: Listen for user input events on the search box and capture the text entered by the user as they type.
4. Determine frequent contacts: Analyze the stored contact data to identify frequently contacted people. You can keep track of the frequency of communication or other relevant metrics to determine the level of contact.
5. Generate suggestions: As the user enters their query, match the entered text with the stored contact data. Prioritize and display suggestions based on both the user's input and the identified frequent contacts. This can include names, email addresses, or any other relevant information.
6. Update suggestions dynamically: Continuously update the suggestion list as the user continues to type or modifies their search query. Reapply the search logic and recalculate the suggestion list based on the new input.
7. Display suggestion dropdown: Show the generated suggestions in a dropdown or autocomplete menu below the search box as the user types. Ensure that the suggestions reflect both the user's input and the frequent contacts.
8. Handle suggestion selection: Allow users to select a suggestion from the dropdown by clicking or navigating through the list using arrow keys. When a suggestion is selected, populate the search box with the selected name or email address.
9. Perform the search operation: When the user submits the search query or selects a suggestion, use the entered text to perform the desired search operation in your application (e.g., retrieving relevant data, filtering results, etc.).
10. Refine contact data over time: Continuously analyze user interactions and feedback to improve the accuracy of frequent contacts. Monitor communication patterns, track successful and unsuccessful searches, and update the contact data based on user behavior.
By following these steps, you can implement the functionality of suggesting matching entries based on previous correspondence and frequent contacts in your desktop application, similar to the Teams search bar.
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