The koryki.ai platform enables human-readable and supervised interaction with relational databases. It reduces complexity while preserving full control over what is queried and executed.
At its core is KQL (Koryki Query Language), a concise and human-readable language designed for ease of learning, interpretation, and validation. A well-defined grammar is key to making queries reliable and verifiable — for both humans and large language models.
From:
Find customers who have placed
more than 10 orders in January 2023,
return companyname and count, sort by count.
To:
FIND customers c, orders o
FILTER count(o) > 10 AND
o.order_date BETWEEN
DATE '2023-01-01' AND DATE '2023-01-31'
FETCH c.company_name, count(o) DESC
The purpose of koryki is:
- Shift control to human-centric queries
- Simplify data analysis
- Enhance workflows with AI while keeping full control
Read more, see sample query, or have a look at a guide to Koryki Query Language.
A demo application is available at: demo.koryki.ai.
- core: the koryki core library
- duckdb: DuckDB dialect
- northwind:
Northwind sample databasefor testing purpose - oracle: Oracle dialect
- postgresql: PostgreSQL dialect
-
Package
ai.koryki.antlr– Grammar and parsing layer -
Package
ai.koryki.iql– Intermediate representation, IQL language, query rewriting rules and validation. -
Package
ai.koryki.kql– KQL language, transpiler and engine to retrieve results from databases -
Package
ai.koryki.jdbc– JDBC database access -
Package
ai.koryki.scaffold– Database schema description and semantic layer
koryki is in early stage and open source under
Apache 2.0 License
Any kind of feedback is welcome: info@koryki.ai