- Receive natural language questions from a user (e.g., “Show me last month’s sales by region”).
- Convert that natural language question into an accurate, executable database query (such as a complex SQL statement).
- Execute the query against the database.
- Process the results and provide accurate, data-driven insights in clear, natural language back to the user in real time.
The Role of the Semantic Model
Connecting an entire, complex, and often massive corporate database directly to a single agent is inefficient, resource-intensive, and provides excessive, irrelevant context. Lyzr solves this challenge by requiring the creation of a Semantic Model.- Definition: A Semantic Model is a crucial conceptual layer that holds only a relevant subset of your database schema. It includes only the specific tables, columns, and relationships required for that particular agent’s task.
- Purpose: By holding only the relevant information, the Semantic Model ensures:
- Optimal Performance: The agent has a focused scope, which drastically improves the speed and accuracy of query generation and execution.
- Accurate Context: It gives the agent the precise, necessary context to understand your data relationships and terminology, allowing it to generate highly accurate and context-appropriate queries.
- Data Governance: It acts as a controlled boundary, ensuring the agent can only access the data authorized for its function, enhancing security.
🔌 Supported Database Connectors
Lyzr offers robust, built-in connectivity to a comprehensive range of popular and enterprise-grade data stores, covering major cloud and on-premise solutions. The image below displays the variety of database connectors available within the Lyzr Agent Studio:
| Type | Data Connector | Details | Status |
|---|---|---|---|
| Relational (SQL) | PostgreSQL | A powerful open-source object-relational database system. | Fully Supported |
| MySQL | The world’s most popular open-source relational database. | Fully Supported | |
| Amazon Redshift | Amazon’s fully managed, petabyte-scale cloud data warehouse service. | Fully Supported | |
| Google BigQuery | Google Cloud’s serverless, highly scalable, and cost-effective cloud data warehouse. | Fully Supported | |
| Azure SQL | Microsoft’s intelligent, scalable, cloud database service. | Fully Supported | |
| Microsoft SQL Server | The widely-used enterprise-level relational database management system. | Fully Supported | |
| Snowflake | The cloud-based data platform known for its unique architecture and scalability. | Upcoming | |
| NoSQL | MongoDB | A leading general purpose, document-based distributed database. | Fully Supported |
| Cloud/Data Lake | Databricks | Integration with the unified data platform for data engineering, ML, and analytics. | Upcoming |
| Files | File Upload (CSV, Excel, JSON) | Direct connectivity for analyzing data stored in common flat file formats. | Fully Supported |