AI Chatbot Search Indexing for Accurate Data Retrieval
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Solution Overview
Problem
Existing AI/ML chatbots are limited by their inability to be customized for specific uses or contexts, often relying on default behaviors and requiring large amounts of training data and computing power, and they struggle with accuracy and consistency in interpreting user prompts.
Innovation Solution
A computer system enhances AI/ML chatbots with search functionality using a search index to identify relevant data objects and metadata, allowing AI/ML models to accurately interpret user prompts by referencing correct data objects, and combines AI/ML models with non-AI/ML data processing systems for reliable data retrieval and visualization generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If AI/ML models are trained to be highly capable and generalized, then they can respond to various user prompts, but they cannot be customized for particular uses or contexts and require large amounts of training data and computing power
Solution Approach 1:
The system separates the AI/ML model from the data processing system. The AI/ML model handles natural language interpretation while the data processing system (with search index) handles data retrieval and contextual information. This segmentation allows customization without retraining the entire model.
Solution Approach 2:
A search index acts as an intermediary between the user prompt and the data set. It pre-processes and indexes data objects, attributes, and metrics, allowing the AI/ML model to query contextual information without requiring extensive training on the actual data.
2Measurement precision
If AI/ML models are used to interpret user prompts directly, then they can generate responses, but they struggle with accuracy and consistency in interpreting terms and obtaining information from data sets
Solution Approach 1:
The search index serves as an intermediary that bridges the AI/ML model and the data set. It translates user prompt terms into relevant data objects, attributes, and metrics from the indexed data, improving interpretation accuracy without complicating the AI model itself.
Solution Approach 2:
The system performs preliminary indexing of data sets before AI/ML processing. Data objects, attributes, and metrics are pre-processed and organized in the search index, enabling accurate and consistent retrieval during inference without requiring the AI model to learn these structures.
3Reliability
If extensive training data is provided to AI/ML models, then they become highly capable, but the need for large amounts of training data and computing power increases
Solution Approach 1:
The system divides functionality between the AI/ML model (for natural language understanding) and the data processing system with search index (for data retrieval). This segmentation allows the AI model to be smaller and require less training data while maintaining high capability through collaboration with the specialized data processing component.
Solution Approach 2:
The data processing system with the search index serves itself by autonomously retrieving and processing relevant data from the data set based on user prompts. This self-service capability reduces the burden on the AI/ML model, allowing it to be more efficient with less computing power and training data.
Data Source
AI summary
Methods, systems, and apparatus, including computer-readable media, for enhancing artificial intelligence chatbots with search functionality. In some implementations, a system stores a search index for data sets, where the search index describes data objects of the data sets and values for the data objects in the data sets. The system receives a user prompt to a chatbot and searches for data objects and values that are relevant to the user prompt, including using the search index to search for data objects and values of one or more data sets that the chatbot is configured to access. The system uses one or more results obtained using the search index to generate a chatbot response to the user prompt, including providing the one or more results to an artificial intelligence and/or machine learning (AI/ML) model. The system provides the chatbot response as a response to the user prompt.