Database

Function overview

ZGI database is not a simple “data storage” module, but a set of structured data capability base for business intelligence scenarios. It supports users to quickly generate data table structures starting from business documents, tables or explanatory materials, and connect subsequent data query, statistical analysis, complex calculations, log tracking, agent question and answer and other capabilities in series. The database function can be used as an independent data management tool or as the core data source in the agent workflow. For scenarios such as contract management, project ledgers, operating data statistics, business record analysis, rule management, etc., the database module can undertake a complete link from “structured modeling” to “natural language query”.

Core Value It’s not just about “storing data in”, but it’s about allowing these data to be stably checked, accurately calculated, clearly tracked, and finally returned to business personnel through natural language.

Database capabilities are particularly suitable for the following types of business problems:

Query single or multiple pieces of structured information
Filter data according to conditions and output details
Aggregate, group, sort, and perform statistical analysis on data
Support intelligent agents to conduct natural language question and answer
Do complex calculations with business rules, clause data, and project rule tables
Carry out log traces and troubleshooting of database operation processes

Overview of core competencies

Ability Category	Contains capabilities	illustrate
Table structure design	Field definition, type setting, required mark, manual adjustment	Supports two methods of manual table creation and intelligent generation
Intelligent generation of table structure	Upload business documents to automatically generate table structures	Automatically identify fields from documents, forms, contracts and other materials
Data Import	Structured data batch writing	Supports the import of 15 pieces of data at a time, and is continuing to be optimized.
Data Query	Precise query, conditional filtering, fuzzy matching	Supports multiple query types to meet different business needs
Statistical Analysis	Aggregation, grouping, sorting, multi-table joint query	Complex analysis and multidimensional statistics
Natural Language Query	NL2SQL automatic conversion and execution	Business staff can ask questions in Chinese and get direct results.
Operation Log	SQL execution records and operation tracking	Full-link audit, supporting investigation and review
Intelligent linkage	As a workflow data source node	Database + agent realize end-to-end business closed loop

Supported data types

data type	Instructions for use	Typical scenario
`text`	Text type, storing string content	Name, address, description, remarks
`numeric`	Exact numeric type	Amount, rate, price
`integer`	integer type	Quantity, days, serial number
`timestamp`	timestamp type	Contract date, operation time
`boolean`	Boolean type	Whether to enable and complete

Table structure design

Field definition

Each field contains the following information: storage field name, field description, data type, whether required. This design takes into account both technical executability and business understandability.

Single table and multiple table design

In a simple scenario, one table can carry complete data (employee ledger, equipment list, etc.). For complex scenarios, one database and multiple tables design is recommended:

Main table — carries core structured fields (such as contract number, amount, date)
Term Sheet — Contains original terms or explanatory content
Rule table — carries project-level or business-level rules and calculation standards
Details table — carries multiple records or change details

For scenarios such as contracts, rules, and business analysis, a multi-table design is usually more stable and more conducive to subsequent queries and complex calculations.

Intelligent generation of table structure

The user uploads a reference file and fills in the requirement description, and the system automatically identifies key fields based on the file content and business intent, and infers a reasonable table structure.

Supported input sources

Local file upload (DOC, DOCX, PDF, XLSX, XLS, PPTX, JPG)
Custom text description
Select reference files from the file management module

Generate content

The system automatically generates field names, field descriptions, data type recommendations, required field recommendations, and possible business primary key/identification fields.

Applicable scenarios

Not familiar with modeling and don’t know how to create a table
Already have business materials but no structured data design yet
Hope to quickly build a demo or verification solution
Want to convert document content into a queryable structured table

Usage Suggestions Intelligent generation is suitable for “starting”, but it is still recommended that the core field naming, field semantics, and field splitting methods be finalized by the business or implementation personnel.

Data import and maintenance

After the table structure is built, the business data is written into the table through the import capability of the database module itself. Currently, it supports importing 15 pieces of data at a time, and this capability is being continuously optimized.

Applicable scenarios

Initial ledger establishment, contract sample import, project rule data entry, tabular data rapid storage, and demo data preparation.

Recommendation process Design the table first → import in batches → verify the query to avoid finding unreasonable field design after one-time import.

Data query capabilities

Query type	illustrate	Example
Precise query	Locate a single record by primary key or unique field	”Contract information of Sunshine Company”
Conditional filtering	Filter multiple records by conditions	”All contracts expiring this month”
fuzzy matching	Keyword fuzzy search	”Items containing ‘property‘“
aggregate statistics	Summarize, count, group, sort	”Total annual rent for each building”
Multiple table joint query	Cross-table related query	”Contract Master Table + Term Sheet Joint Query”
Complex calculations	Multi-field operations and rule judgment	”Liquidated damages = daily rent × number of overdue days × rate”

Natural language query (NL2SQL)

Business personnel can directly ask questions in Chinese, and the system automatically converts natural language into SQL and executes it.

Query example

User question: What is the property fee payment method for Sunshine Company?

→ System-generated SQL:
  SELECT payment_method FROM contracts WHERE company_name = 'Sunshine Company';
→ Natural language response:
  The property fee payment method for Sunshine Company is "quarterly prepayment."

conversion process

Understanding the problem

The system identifies query objects, query indicators and query types

Generate SQL

Automatically generate corresponding SQL statements based on the table structure

Execute query

Execute SQL in database and get raw results

Natural language reply

Convert query results into natural language answers that business people can understand

Complex business calculations

The database is not only suitable for “looking up data”, but can also provide structured support for complex business calculations. Together with the rules table and terms table, you can achieve:

Liquidated Penalty Calculation — Automatically calculated based on overdue days, daily rent and rate
Property fee calculation — Calculated based on multiple dimensions such as area, unit price, cycle, etc.
Deposit Return Judgment — Comprehensive judgment based on contract terms and actual performance.
Payment Cycle Alert — Automatically identify payment nodes that are about to expire

Operation logs and tracking

Dimensions	illustrate
Operating time	Operation timestamp accurate to seconds
Operation type	Create tables, queries, update, delete, etc.
Execution status	Success/failure and error reasons
Operation sheet	Involved data table name
operating user	The account that performs the operation
Execute SQL	The actual executed SQL statement for easy troubleshooting

The log supports filtering by operation type, status, and time range, and is suitable for scenarios such as query error troubleshooting, prompt word optimization review, business data modification tracking, and abnormal location of agent results.

Linked to intelligent agent workflow

As a data source: Provide query objects, core fields, and statistical data, allowing agents to directly consume structured data
Statistical Analysis Link: In scenarios such as business analysis and contract statistics, it directly assumes the role of statistics and return results.
Complex calculation support: Provides core numerical fields and rule fields to provide a data basis for rule-driven business calculations
Natural language reply: The query results directly generate natural language answers, not just returning forms, but service business questions and answers

Typical business scenarios

scene	The role of the database	core competencies
Contract Management	Stores the main contract table + terms table, supporting natural language Q&A	Multi-table query + NL2SQL
Project Ledger	Structured management of project schedule, budget, person in charge, etc.	Conditional filtering + aggregate statistics
Operational Analysis	Multi-dimensional data statistics and visualization	Group statistics + complex calculations
Property Management	Ledger management of rent, property fees, deposits, etc.	Rule calculation + early warning
HR Management	Structured storage of employee information, attendance, and performance	Precise query + filter
Equipment Asset	Equipment list, inspection records, maintenance information	Conditional query + log tracking

Getting Started

Core Features

Developers

Use Cases

Function overview

Overview of core competencies

Supported data types

Table structure design

Field definition

Single table and multiple table design

Intelligent generation of table structure

Supported input sources

Generate content

Applicable scenarios

Data import and maintenance

Applicable scenarios

Data query capabilities

Natural language query (NL2SQL)

Query example

conversion process

Understanding the problem

Generate SQL

Execute query

Natural language reply

Complex business calculations

Operation logs and tracking

Linked to intelligent agent workflow

Typical business scenarios

​Function overview

​Overview of core competencies

​Supported data types

​Table structure design

​Field definition

​Single table and multiple table design

​Intelligent generation of table structure

​Supported input sources

​Generate content

​Applicable scenarios

​Data import and maintenance

​Applicable scenarios

​Data query capabilities

​Natural language query (NL2SQL)

​Query example

​conversion process

​Understanding the problem

​Generate SQL

​Execute query

​Natural language reply

​Complex business calculations

​Operation logs and tracking

​Linked to intelligent agent workflow

​Typical business scenarios

Function overview

Overview of core competencies

Supported data types

Table structure design

Field definition

Single table and multiple table design

Intelligent generation of table structure

Supported input sources

Generate content

Applicable scenarios

Data import and maintenance

Applicable scenarios

Data query capabilities

Natural language query (NL2SQL)

Query example

conversion process

Understanding the problem

Generate SQL

Execute query

Natural language reply

Complex business calculations

Operation logs and tracking

Linked to intelligent agent workflow

Typical business scenarios