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# SuperSonic (超音数)
**SupeSonic is a new-generation data analytics platform that integrates ChatBI and HeadlessBI**. SuperSonic provides a chat interface that empowers users to query data using natural language and visualize the results with suitable charts. To enable such experience, the only thing necessary is to build logical semantic models (definition of entities/metrics/dimensions/tags, along with their meaning, context and relationships) on top of physical data models, and **no data modification or copying** is required. Meanwhile, SuperSonic is designed to be **highly extensible**, allowing custom functionalities to be added and configured with Java SPI.
**SuperSonic is the next-generation LLM-powered data analytics platform that integrates ChatBI and HeadlessBI**. SuperSonic provides a chat interface that empowers users to query data using natural language and visualize the results with suitable charts. To enable such experience, the only thing necessary is to build logical semantic models (definition of entities/metrics/dimensions/tags, along with their meaning, context and relationships) on top of physical data models, and **no data modification or copying** is required. Meanwhile, SuperSonic is designed to be **highly extensible**, allowing custom functionalities to be added and configured with Java SPI.
<img src="./docs/images/supersonic_demo.gif" height="100%" width="100%" align="center"/>
## Motivation
The emergence of Large Language Model (LLM) like ChatGPT is reshaping the way information is retrieved. In the field of data analytics, both academia and industry are primarily focused on leveraging LLM to convert natural language into SQL (so called Text2SQL or NL2SQL). While some works exhibit promising results, their **reliability** is inadequate for real-world applications.
The emergence of Large Language Model (LLM) like ChatGPT is reshaping the way information is retrieved. In the field of data analytics, both academia and industry are primarily focused on leveraging LLM to convert natural language into SQL (so called Text2SQL or NL2SQL). While some approaches exhibit promising results, their **reliability** and **efficiency** are insufficient for real-world applications.
From our perspective, the key to filling the real-world gap lies in three aspects:
1. Introduce a semantic layer (so called HeadlessBI) encapsulating underlying data context(joins, formulas, etc) to reduce **complexity**.
2. Augment the LLM with schema mappers(as a kind of preprocessor) and semantic correctors(as a kind of postprocessor) to mitigate **hallucination**.
3. Utilize heuristic rules when necessary to improve **efficiency**(in terms of latency and cost).
1. Integrate ChatBI with HeadlessBI encapsulating underlying data context (joins, keys, formulas, etc) to **reduce complexity**.
2. Augment the LLM with schema mappers(as a kind of preprocessor) and semantic correctors(as a kind of postprocessor) to **mitigate hallucination**.
3. Utilize rule-based schema parsers when necessary to **improve efficiency**(in terms of latency and cost).
With these ideas in mind, we develop SuperSonic as a practical reference implementation and use it to power our real-world products. Additionally, to facilitate further development of ChatBI, we decide to open source SuperSonic as an extensible framework.