> ## Documentation Index
> Fetch the complete documentation index at: https://docs.lyzr.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Connecting Agents to Databases via Data Query

Lyzr Agents can directly connect to databases, understand schema details, and retrieve insights in **natural language** using the **Data Query** feature.

This feature acts as a bridge between raw relational data and AI-consumable knowledge, by **enriching structured tables with semantic context**. Essentially, it transforms data into a format that both AI models and humans can interpret meaningfully.

## 🚀 High-Level Workflow

The process of connecting an Agent to your data involves these steps:

1. **Connect to a Database:** Establish a connection to your desired data source.
2. **Create a Semantic Model:** Define the semantic context for your chosen tables.
3. **Create an Agent and Link the Semantic Model:** Enable the Data Query feature on an Agent and connect it to the Semantic Model.

***

### Conceptual Overview

The **Semantic Model** in Lyzr is a system designed to enable intelligent understanding, documentation, and utilization of structured tabular data (such as database tables) in AI workflows. It acts as a bridge between raw relational data and **human-readable, AI-consumable knowledge** by semantically enriching tables with detailed context.

### What is a Semantic Model?

A Semantic Model adds **meaning and context** to structured data by attaching:

* **Table-level descriptions:** High-level summaries that describe what a table represents.
* **Column-level metadata:** Detailed natural language explanations for each column’s purpose and data type.
* **Data previews:** Sample rows that provide real-world context to help AI systems and humans better understand usage.

By embedding this enriched information into a vector store, the Semantic Model enables more powerful:

* Retrieval-Augmented Generation (RAG) flows
* Search and documentation agents
* Natural language interfaces over data

> 💡 **Tip:** Avoid selecting an entire database (especially if it has 100+ tables). This can cause context overload, slower responses, and potential hallucinations. Instead, choose only the relevant tables.

### Why Use Semantic Modeling?

Traditional databases lack human-centric explanations. This creates challenges for:

* **Developers:** Slower schema understanding
* **AI models:** Less accurate data-driven responses
* **Business users:** Difficulty interacting with structured data

The Semantic Model solves this by providing clarity, structure, and semantic context:

1. Generating **semantic documentation automatically** using LLMs.
2. Structuring the output for both human consumption and AI workflows.
3. Saving the enhanced information in a vectorized format for fast and relevant retrieval.

### Core Workflow

1. **Input Source:** A database table with rows and schema is provided as input.
2. **LLM-Powered Inference:** A language model reviews table structure and sample data to generate descriptions.
3. **Semantic Description Output:**
   * What the table is about
   * What each column represents
   * How the table connects to business or analytical use cases
4. **Storage & Retrieval:**
   * These semantic blocks are embedded and stored in a **vector database**.
   * Future retrievals (like question answering or agent planning) can now pull contextually rich, accurate descriptions.

### Components of the Semantic Model

| Component             | Description                                                                           |
| :-------------------- | :------------------------------------------------------------------------------------ |
| **Table Name**        | The identifier of the dataset                                                         |
| **Table Description** | A high-level explanation of the table’s content and intent                            |
| **Columns**           | A list of column names, each paired with a natural language description and data type |
| **Preview Records**   | Optional rows from the table used for contextual grounding                            |
| RAG Config            | Configuration used to generate or retrieve this semantic documentation                |
| Task Handling         | For large datasets, semantic documentation can be generated asynchronously            |

### Benefits

* **Improved Discoverability:** Semantic metadata makes it easier to search and explore datasets.
* **Agent Integration:** Documentation agents and RAG models can use this metadata to answer user queries with high precision.
* **Auto-Documentation:** Automatically generated explanations save time for data engineers and analysts.
* **Natural Language Access:** Even non-technical users can query data through AI using the semantic layer as a knowledge base.

The Semantic Model ensures that your structured data is not just readable, but **meaningful and navigable by both humans and machines**. It turns flat schemas into rich knowledge representations that power the future of AI-driven data interfaces.

***

## Step-by-Step Guide

The Lyzr Studio’s **Semantic Model** enables intelligent querying of structured data like databases or CSVs using natural language.

### 1. Connect to Your Database (Supported Data Sources)

Lyzr supports multiple database connections. Refer to the full list here: \[Data Connectors Page URL]

* Navigate to **Data Connectors**.
* Select your desired **database type** (e.g., PostgreSQL, MySQL).
* Enter your credentials and save the connection.

<img src="https://mintlify.s3.us-west-1.amazonaws.com/lyzrinc/assets/images/studio/ks1.png" alt="" />

### 2. Choose Knowledge Base Type

When creating a new Knowledge Base:

* Go to **Knowledge Base** → Click **Create New**.
* Select **Semantic Model** from the list of KB types.

<img src="https://mintlify.s3.us-west-1.amazonaws.com/lyzrinc/assets/images/studio/kg3.png" alt="" />

This type is specifically designed for structured data sources like PostgreSQL, MySQL, and CSV files. It activates Text-to-SQL capabilities and schema-based reasoning.

### 2. Connect to Your Database (Supported Data Sources)

Lyzr supports multiple database connections. Refer to the full list here: \[Data Connectors Page URL]

* Navigate to **Data Connectors**.
* Select your desired **database type** (e.g., PostgreSQL, MySQL).
* Enter your credentials and save the connection.

<img src="https://mintlify.s3.us-west-1.amazonaws.com/lyzrinc/assets/images/studio/ks1.png" alt="" />

### 3. Generate Schema Documentation

* Fill in the required fields and select your connected database.
* At the bottom, you’ll see the **Schema Documentation Agent** section.
* If it’s your first time, click **Create New**, fill in the required details, and submit. This agent **automatically generates table and column descriptions** using LLMs, transforming raw data into AI-readable knowledge.
  * **Table Descriptions**: What each table represents in plain English.
  * **Column Metadata**: Natural language explanations for column names and data types.
  * **Sample Records**: A few representative rows to provide grounding.

### 4. Configure Tables

* Click **Create Semantic Model**.
* You’ll see a list of tables in your database.
* Select tables one by one and click **Configure**.
* Review and edit auto-generated descriptions for tables and columns.
* Click **Save** once all required tables are configured.

### 5. Connect the Semantic Model to an Agent

* Go to the **Agents page** (from the sidebar).
* Create a new agent or edit an existing one.
* Enable the **Data Query** feature.
* Select your newly created **Semantic Model** from the dropdown.

### Result: Query Structured Data via AI

Your agent is now ready to:

* **Understand user queries** in natural language.
* **Convert them into SQL queries** (e.g., “Get top 10 customers by revenue” is converted into executable SQL).
* **Return responses** in a human-readable format.
* Agents retrieve actual rows from your database or uploaded CSVs.

***

## Summary

| Feature                      | Description                                                            |
| :--------------------------- | :--------------------------------------------------------------------- |
| **Structured Data Support**  | Connect live databases (PostgreSQL, MySQL, etc.) or structured CSVs.   |
| **AI-Powered Documentation** | Auto-generate table/column descriptions using LLMs.                    |
| **Text-to-SQL Enabled**      | Agents can query data using natural language.                          |
| **Schema Agent Integration** | Deepens understanding of data context and boosts AI accuracy.          |
| **Ideal Use Case**           | For analytics, operations, sales, or any SQL-accessible business data. |

With the **Semantic Model**, Lyzr bridges the gap between raw relational databases and intuitive, natural language interfaces — letting teams unlock insights from structured data with ease.

Would you like me to elaborate on the benefits of using a Semantic Model for AI Agents?