Chatbot Data Routing for Structured and Unstructured Queries
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing chat interfaces struggle to accurately interpret and respond to natural language questions that require either tabular or non-tabular data sources, often failing to provide contextually relevant answers due to limitations in understanding user intent and data type classification.
Innovation Solution
A chatbot system that employs a semantic parsing model to determine user intent and generates either LLM prompts or structured queries, leveraging pre-trained and fine-tuned Large Language Models (LLMs) to retrieve and generate answers from non-tabular and tabular data sources, respectively, while maintaining a conversation state to enhance context awareness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a chatbot system uses a single unified approach to handle both tabular and non-tabular data sources, then the system architecture is simpler, but the accuracy of interpreting user intent and generating contextually relevant answers deteriorates
Solution Approach 1:
The system segments the question processing into two distinct pathways: one for tabular data sources and one for non-tabular data sources. This segmentation allows each pathway to be optimized for its specific data type, improving the accuracy of user intent interpretation and answer generation while maintaining manageable system complexity through modular design.
2Reliability
If the chatbot system retrieves data from both tabular and non-tabular data sources for every question, then the comprehensiveness of answers is improved, but the resource consumption increases
Solution Approach 1:
The system dynamically determines which data source type (tabular or non-tabular) to query based on the semantic analysis of each user question. This dynamic routing ensures that data is retrieved from the most appropriate source for the specific question being asked, maintaining answer comprehensiveness while minimizing unnecessary resource consumption by avoiding queries to both data sources for every question.
3Measurement precision
If the chatbot system uses advanced semantic parsing models and multiple LLMs to accurately determine user intent, then the quality of contextually relevant answers is improved, but the device complexity increases
Solution Approach 1:
The system employs a multi-functional semantic parsing model that can handle both tabular and non-tabular data source routing, as well as a unified LLM that serves multiple purposes including intent determination and answer generation. This multi-functionality reduces the need for separate specialized components for each function, thereby improving answer relevance quality while managing model architecture complexity through versatile, multi-purpose models.
Data Source
AI summary
Techniques for operating a chatbot system for enterprise-level conversational agents are disclosed. These techniques are performed by an application or cloud service executing on one or more computing devices. An enterprise system can deploy conversational agents onto user devices to run as chat interfaces for logging analytics question-answering. One example application or cloud service may be a multi-model chat mechanism configured to support these chat interfaces with backend functionality. In response to an incoming question, the chat mechanism first consolidates the question with any conversation history and then, classifies the user's question as either a question regarding unstructured document data, a question regarding structured log data, or a hybrid question. Based on the classification, the chat mechanism can generate a proper large language model (LLM) response.