SQLBot is an intelligent number system based on large models and RAG

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It supports rapid embedding into third-party business systems and can be integrated and called by AI application development platforms such as n8n, MaxKB, Dify, and Coze, allowing various applications to quickly have intelligent numbering capabilities.
Provide workspace-based resource isolation mechanism to achieve fine-grained data permission control.

1. Project introduction

  • Name: SQLBot
  • Purpose: To allow users to use natural language (“ask”) databases, generate SQL query statements, and return query results. This is also commonly known as text-to-SQL.
  • Core technology: A mechanism that combines large models (LLMs, such as ChatGPT or other generative models) and RAG. RAG allows for the retrieval of relevant context/data before using this content as a basis for generation to improve accuracy and contextual relevance.

2. Main functional characteristics

From the README and project description, SQLBot has these key features:

  1. Out-of-the-box
    You only need to configure the large model and data source, and you can get started, without writing a lot of SQL from scratch or constructing all the components yourself.
  2. Easy to integrate
    It supports embedding in third-party systems and can also be integrated by some AI application platforms (e.g., n8n, MaxKB, Dify, Coze, etc.). If other applications want to have the ability to “ask for numbers”, they can use SQLBot directly.
  3. Safe and controllable
    • There is a workspace isolation mechanism that allows different users/teams to isolate resources.
    • Supports fine-grained data permission control. That is, not all Q&A/SQL queries have access to all tables/all data, but you can set permissions to limit what data can be retrieved/queried.

3. Working principle/architecture

Here’s the basic architecture (as seen in the project):

  • Users enter natural language questions through the front-end interface, such as “What are the products with the highest sales in the past month?”
  • The system first goes through the RAG model: retrieves content related to this issue, which may include the schema (table structure, column descriptions) of the data source, historical queries, data dictionaries, etc.
  • Based on the retrieved context + the user’s question, the large model generates a SQL query statement (Text-to-SQL).
  • This SQL is then executed on the connected data source, resulting in results that are ultimately returned to the user.
  • The system may also have some auxiliary functions, such as logging of user queries, permission checks, security verification, etc.

The directory structure in the project shows backend,frontend , installer, docker configuration, etc., indicating that it is a complete deployable system.

4. Installation and use

The general process is as follows:

  • Have a Linux server (docker supported).
  • One-click deployment via docker or docker-compose. The project provides a Dockerfile, docker-compose.yaml, startup scripts, etc.
  • Configure the data source (PostgreSQL or other database) and configure the large model (perhaps an open source LLM or commercial interface).
  • After deployment, access the server with a browser (the default port is 8000/8001), and the user logs in with the account password.

5. Advantages and challenges

Pros:

  • Low barrier to use: Users don’t need to know SQL to query databases. Friendly to non-technical personnel.
  • Efficient: Automation in generating SQL reduces the time spent manually writing and debugging queries.
  • Flexibility: Combined with RAG retrieval context, it can perform better in complex schemas and business environments.
  • Permissions and Security Considerations: There are workspaces and permission controls, suitable for enterprises/organizations.

Challenges / Limitations

  • Accuracy issues with generating SQL: Large models may misunderstand natural language and generate incorrect or non-optimal SQL. This can lead to performance issues or incorrect queries.
  • Context Acquisition Issues: The quality of the content retrieved during the RAG phase is critical. If the schema is incomplete, the data dictionary is poor, and the retrieval mechanism is weak, the SQL generation effect will be poor.
  • Permission security risks: While there are permission controls, it is challenging to achieve truly fine-grained security (e.g., private/sensitive data, users not being able to access certain rows/columns).
  • Cost and resources: large model computing power, storage, and maintenance overhead are not small; RAG retrieval also maintains indexing, stores contextual material, and more.

6. Application scenarios

Some typical use cases may include:

  • Internal BI (Business Intelligence): Business personnel query sales/user/operational data in databases through natural language.
  • Customer service support: For example, when a customer asks about the inventory status of a product, the system automatically queries the database and returns the answer.
  • Dashboard & Reporting System: Automatically generate SQL reports/charts.
  • Data-driven decision support system: Non-technical executives have direct access to data.
  • Embed it into other AI tools/application platforms to add “ask data” capabilities to these applications.

Github：https://github.com/dataease/SQLBot

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