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# SuperSonic (超音数)
**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) with semantic layer, 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 BI platform that integrates **Chat BI** (powered by LLM) and **Headless BI** (powered by semantic layer). Both paradigms benefit from the integration:
- Chat BI's Text2SQL capability gets enhanced with semantic data models.
- Headless BI's query interface gets augmented with natural language support.
<img src="./docs/images/supersonic_ideas.png" height="75%" width="75%" align="center"/>
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 metric/dimension/entity/tag, along with their meaning and relationships) with semantic layer, 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"/>
@@ -13,17 +20,16 @@
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. Integrate ChatBI with HeadlessBI encapsulating underlying data context (joins, keys, formulas, etc) to **reduce complexity**.
<img src="./docs/images/supersonic_ideas.png" height="65%" width="65%" align="center"/>
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).
1. Incorporate data semantics (such as business terms, column values, etc.) into the prompt, enabling LLM to better understand the semantics and **reduce hallucination**.
2. Offload the generation of advanced SQL syntax (such as join, formula, etc.) from LLM to the semantic layer to **reduce complexity**.
3. Utilize rule-based semantic 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.
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 we decide to open source SuperSonic as an extensible framework.
## Out-of-the-box Features
- Built-in ChatBI interface for *business users* to enter natural language queries
- Built-in HeadlessBI interface for *analytics engineers* to build semantic models
- Built-in Chat BI interface for *business users* to enter natural language queries
- Built-in Headless BI interface for *analytics engineers* to build semantic models
- Built-in GUI for *system administrators* to manage chat agents and third-party plugins
- Support input auto-completion as well as query recommendation
- Support multi-turn conversation and history context management