Retrieval-Augmented Web Application Navigation from Offline UI Metadata
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Solution Overview
Problem
Existing chatbots for navigating web applications rely on computationally expensive and error-prone API queries to infer user interfaces, lacking an efficient and adaptable method for generating metadata for user navigation.
Innovation Solution
Utilize a multimodal LLM to process HTML documents and screenshots for offline metadata generation, creating a database that augments a second LLM for chatbot functionality, enabling retrieval-augmented generation to respond to user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If API queries are used to infer user interfaces for web application navigation, then the chatbot can provide navigation assistance, but the computational cost increases and errors occur
Solution Approach 1:
The system performs preliminary actions by crawling webpages and generating UI element metadata databases offline before actual user navigation queries. This pre-computation stores interface information in advance, eliminating the need for expensive real-time API queries during user interactions, thus reducing computational cost while maintaining navigation accuracy
Solution Approach 2:
The system creates copies of UI interface information by generating metadata databases that replicate the structure and content of web application interfaces. These metadata copies serve as lightweight alternatives to querying the actual live application APIs, providing the same navigation information at fraction of the computational cost
2Ease of operation
If API queries are used to infer user interfaces, then navigation assistance is provided, but the process becomes error-prone
Solution Approach 1:
The system creates accurate copies of UI interface information through metadata databases generated from crawled webpages. These static metadata copies provide reliable, consistent interface information without the variability and potential errors associated with dynamic API queries, thereby improving navigation reliability while maintaining ease of operation
Solution Approach 2:
By performing preliminary webpage crawling and metadata extraction before user interactions, the system establishes a reliable foundation of interface information. This pre-computed metadata serves as a stable reference that eliminates errors from real-time API inference, ensuring consistent and accurate navigation assistance
3Productivity
If offline metadata generation using multimodal LLM is implemented, then user navigation becomes faster and more adaptable, but the initial data processing complexity increases
Solution Approach 1:
The system performs complex data processing in advance by using multimodal LLM to crawl webpages, analyze UI elements, and generate metadata databases offline. This preliminary processing transforms complex interface information into structured, queryable formats before users arrive, enabling fast and adaptable navigation responses without requiring complex processing during actual user interactions
Data Source
AI summary
An offline collection system comprises a pipeline for storing metadata of user interface (UI) elements at web pages of a web application. The pipeline comprises crawling uniform resource locators (URLs) of web pages of the web application for content and rendering screenshots of the web pages. The pipeline then prompts a multimodal large language model (LLM) to generate database entries for the web pages comprising UI element metadata derived from the crawled content and rendered screenshots. A response system receives user queries to navigate the web application and augments prompts to an LLM to respond to the user queries with metadata of UI elements relevant to the user queries stored by the offline collection system.