Natural Language Search Across Multiple Data Sources
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Solution Overview
Problem
Existing natural language processing systems face challenges in accurately and efficiently searching disparate data sources due to the complex nature of human language and the need for nuanced understanding, leading to suboptimal response accuracy and resource utilization.
Innovation Solution
A computing system employing a conversational AI agent trained to communicate with users and search multiple data sources using specific configurations, generating queries tailored to each source's authentication and labeling methods, and consolidating results for improved response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large language model is used to search multiple data sources, then response accuracy is improved, but resource consumption increases
Solution Approach 1:
The system segments the search process by generating multiple specialized queries tailored to different data sources (e.g., structured queries for databases, keyword searches for document repositories, metadata filters for file systems) rather than using a single monolithic search approach. This allows the LLM to leverage the specific strengths of each data source while reducing overall computational resource consumption through targeted searching.
Solution Approach 2:
The system applies local quality by customizing query generation for each specific data source based on its configuration and characteristics. The LLM adjusts query syntax, search parameters, and retrieval strategies according to the particular data source being queried (e.g., using SQL-like syntax for database sources, natural language queries for document sources), thereby optimizing both accuracy and resource efficiency for each local search operation.
2Productivity
If queries are generated for each data source configuration, then search effectiveness is improved, but system complexity increases
Solution Approach 1:
The system implements universality by using a single LLM-based query generation module that handles multiple data source types through a unified configuration-driven approach. The LLM receives data source configuration information and automatically adapts query generation to match each source's specific requirements, eliminating the need for separate specialized query generators for different data sources while maintaining high search effectiveness.
Solution Approach 2:
The system employs feedback mechanisms where the LLM iteratively refines queries based on the data source configuration and preliminary search results. If initial queries return insufficient or irrelevant results, the LLM analyzes the response and generates follow-up queries with adjusted parameters, creating a feedback loop that improves search effectiveness while managing complexity through automated adaptive query generation.
Data Source
AI summary
An application executing on a processor may receive a natural language request. A large language model (LLM) may determine, for each of a plurality of data sources, a respective data source configuration. The LLM may generate, for each data source, a respective query based on the natural language request and the data source configuration. The application may, based on the configuration, process the queries against the plurality of data sources. The LLM may receive, based on the processing, a plurality of results from the plurality of data sources and generate a natural language response to the natural language request. The natural language response may include an indication of a first result of the plurality of results. The application may output the natural language response for display.