Geography and Programming in Landbot Environment

This section dissects the geography-related logic within the chatbot, focusing on a component termed a 'brick'. A brick functions as a collection of blocks representing a subroutine within the Landbot environment. The exploration delves into the sequential actions initiated once a user engages with the geographic aspect of their inquiry.

Initial Setup and Matter Type Identification

The process begins within the brick, setting a variable related to the issue at hand, which is pivotal for later stages. The primary objective is to discern the matter type, distinguishing between issues that necessitate redirecting the user to the help desk ('no match') and those manageable within the chatbot's scope. The emphasis here is on scenarios other than 'no match', which triggers an empathic response from the bot, subsequently requesting the user's zip code.

Zip Code Analysis and Data Retrieval

Upon receiving the zip code, now stored in the variable tenant zip , the bot accesses a spreadsheet (RentorVention 3.0) to fetch relevant information based on the provided zip. This step is critical for aligning the user's geographic location with specific municipal or county ordinances, enhancing the precision of assistance offered.

Ordinance Verification and Category Identification

The bot verifies the applicability of municipal or county ordinances for the provided zip code, utilizing a 'collections' switchboard within the spreadsheet. This includes executing city lookups to gather category IDs and ordinance details, which are instrumental in navigating the knowledge base for pertinent information.

Knowledge Base Category Setting and GPT Prompt Initiation

Based on the collected data, the bot sets the KB category ID to correspond with the identified ordinance categories. It informs the user of potentially relevant ordinances based on their zip code, transitioning to a GPT prompt that further customizes the interaction based on specific ordinance checks, such as the Residential Landlord and Tenant Ordinance (RLTO) for Chicago residents.

Ordinance Application Determination and Quiz

For ordinances with specific applicability criteria, the bot engages the user in a verification process. If the user is uncertain about an ordinance's relevance, the bot conducts a quiz to ascertain applicability, adjusting its subsequent prompts and actions accordingly.

Conclusion and Forward Look

This segment of the chatbot flow encapsulates the intricate process of geographic analysis, ordinance identification, and knowledge base interaction, setting the stage for detailed information retrieval via Zapier in subsequent interactions.