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Supersonic
Overview :
SuperSonic is a next-generation business intelligence (BI) platform that integrates both Chat BI driven by large language models (LLMs) and Headless BI driven by a semantic layer. It ensures that Chat BI can access the same curated and governed semantic data models as traditional BI. Additionally, both paradigms benefit from the integration: Chat BI's Text2SQL is enhanced by retrieving context from the semantic model; Headless BI's query interface is extended through a natural language API. SuperSonic offers a Chat BI interface that allows users to query data using natural language and visualize the results with appropriate charts. To achieve this experience, all you need is to build a logical semantic model (defining metrics/dimensions/tags and their meanings and relationships) through the Headless BI interface. Furthermore, SuperSonic is designed to be extensible and composable, allowing the addition and configuration of custom implementations using Java SPI.
Target Users :
SuperSonic is designed for data analysts and business users who need a platform to query and analyze data through natural language. It is suitable for them because it simplifies the data query and analysis process, allowing non-technical users to easily access and analyze data, while providing technical users with powerful tools for building semantic data models.
Use Cases
Data analysts quickly retrieve key business metrics using SuperSonic's natural language querying.
Business users leverage SuperSonic's visualization tools to intuitively display data analysis results.
Technical teams build and optimize semantic data models using SuperSonic's Headless BI interface.
Features
Built-in Chat BI interface, supporting natural language data querying
Built-in Headless BI interface, allowing analysts to build semantic data models
Built-in rule-based semantic parser for improved efficiency in specific scenarios
Supports input auto-completion, multi-round conversations, and post-query recommendations
Supports three-level data access control: dataset level, column level, and row level
Knowledge base regularly extracts architecture information from the semantic model to build dictionaries and indexes, promoting architecture mapping
Pattern mapper identifies user query references to architecture elements and matches query text with the knowledge base
Semantic parser understands user queries and generates semantic query statements, combining rule-based and model-based parsers
Semantic corrector checks the validity of semantic query statements and performs correction and optimization when necessary
Semantic translator converts semantic query statements into executable SQL statements
How to Use
Download the latest pre-built binary files.
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Run the script 'assembly/bin/supersonic-daemon.sh start' to launch a standalone Java service.
Access http://localhost:9080 in your browser to begin exploring.
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