The terminology library is a dictionary of business terms and their meanings. When you ask SQLBot a question, it searches the terminology library using semantic similarity and injects relevant definitions into the prompt before generating SQL. This means SQLBot understands that “GMV” refers to gross merchandise value before refunds, or that “active user” in your business means a user who logged in within the last 30 days — without you having to explain it every time. Without a terminology library, SQLBot relies solely on column names and table descriptions to understand your question. Domain-specific terms that differ from standard database naming — abbreviations, internal metric names, team-specific jargon — will often produce incorrect SQL. The terminology library closes this gap.Documentation Index
Fetch the complete documentation index at: https://mintlify.com/dataease/SQLBot/llms.txt
Use this file to discover all available pages before exploring further.
Why terminology matters
Shared ambiguity
Terms like “revenue”, “users”, and “orders” mean different things in different companies. Your terminology library captures your definitions specifically.
Abbreviations and aliases
Column names are often abbreviated (
cst_cnt for customer count). Defining the full meaning helps the LLM map natural language questions to the right columns.Composite metrics
Metrics like “retention rate” or “churn” typically require specific SQL patterns across multiple tables. A clear definition helps SQLBot choose the right approach.
Synonyms
Your users may say “orders”, “transactions”, or “purchases” to mean the same thing. The terminology library lets you register all synonyms under one term.
How terms are used
When you submit a question in chat, SQLBot embeds your query and retrieves the most semantically relevant terms from the terminology library using vector similarity search. The retrieved definitions are included in the LLM prompt as grounding context. Terms are stored with apgvector embedding and matched at query time — SQLBot does not scan every definition on every request, it finds the most relevant ones.
Terms are workspace-scoped. A term you add in one workspace does not affect queries in another workspace.
Adding a term
Open the Terminology section
In the left sidebar, navigate to Terminology. The list shows all terms already defined in your workspace.
Enter the term name
In the Term name field, enter the word or phrase exactly as users are likely to say it in a question. For example:
GMV, active users, churn rate.Add synonyms (optional)
In the Synonyms field, add alternative names for the same concept, separated by commas. For example, synonyms for
GMV might include gross merchandise value, gross sales.Write a description
In the Description field, explain what this term means in your business context and, where helpful, how it maps to your database schema.A useful description for A useful description for
GMV:active users:Choose datasource scope
Decide whether this term applies to all datasources in the workspace, or only to specific ones. If your GMV calculation differs between your main database and a legacy system, create separate terms scoped to each datasource.
Examples of useful terms
Business metrics
Business metrics
Define each KPI your team tracks. Include the SQL formula where it is unambiguous:
| Term | Description |
|---|---|
ARR | Annual recurring revenue. Sum of all active subscription monthly amounts multiplied by 12. Use the subscriptions table where status = 'active'. |
NPS | Net Promoter Score. Calculated from the survey_responses table as (% Promoters) - (% Detractors), where promoters have score >= 9 and detractors have score <= 6. |
CAC | Customer acquisition cost. Total marketing spend divided by new customers acquired in the same period. Marketing spend is in the marketing_spend table; new customers are in users where created_at is within the period. |
Table and column aliases
Table and column aliases
Map the plain-language names your team uses to actual database identifiers:
| Term | Description |
|---|---|
orders | The fact_orders table in the data warehouse. Includes all completed, refunded, and cancelled transactions. |
user ID | The uid column in the users table. Used as a foreign key in orders.customer_uid and sessions.user_uid. |
store | Refers to the retail_location table, not the online_store table, unless the user specifically says “online store”. |
Status codes and enumerations
Status codes and enumerations
Explain what numeric or string codes mean:
| Term | Description |
|---|---|
completed order | An order where orders.status = 'C' or orders.status = 'completed'. |
active subscription | A subscription where subscriptions.state = 1 (1 = active, 2 = paused, 3 = cancelled). |
Bulk importing terms
If you have many terms to add, you can import them from an Excel file:Download the template
Click Download template to get an
.xlsx file with the correct column structure: Term name, Synonyms, Description, Effective datasources, All datasources (Y/N).Fill in the spreadsheet
Each row is one term. Set All datasources to
Y to apply the term workspace-wide, or N and list specific datasource names in the Effective datasources column.Enabling and disabling terms
Each term has an enabled/disabled toggle. Disabled terms are excluded from retrieval during query generation but remain in the library so you can re-enable them later. This is useful for seasonal terms or definitions that are temporarily incorrect during a schema migration.Exporting the terminology library
Click Export to download all terms in your workspace as an.xlsx file. Use this for auditing, sharing terminology across workspaces by importing into another, or backing up your definitions before a major schema change.