Merge pull request #174 from Intugle/docs/sqlite-mssql-docs

added sqlite, mysql docs

Author: shiva-intugle

README.md

@@ -53,7 +53,7 @@ Intugle’s GenAI-powered open-source Python library builds a semantic data mode
 | Category | Integrations |
 | :--- | :--- |
 | **Data Warehouses** |  Snowflake, Databricks |
-| **Databases** | PostgreSQL, SQL Server |
+| **Databases** | SQLite, PostgreSQL, SQL Server, MySQL |
 | **Local** | Pandas, DuckDB (CSV, Parquet, Excel) |
 
 ## Streamlit App

docsite/docs/connectors/implementing-a-connector.md

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 5
+sidebar_position: 7
 ---
 
 # Implementing a Connector

docsite/docs/connectors/mysql.md

@@ -0,0 +1,87 @@
+---
+sidebar_position: 5
+---
+
+# MySQL
+
+`intugle` integrates with MySQL, allowing you to read data from your tables and views.
+
+## Installation
+
+To use `intugle` with MySQL, you must install the optional dependencies:
+
+```bash
+pip install "intugle[mysql]"
+```
+
+This installs the `PyMySQL` and `sqlglot` libraries.
+
+## Configuration
+
+To connect to your MySQL database, you must provide connection credentials in a `profiles.yml` file at the root of your project. The adapter looks for a top-level `mysql:` key.
+
+**Example `profiles.yml`:**
+
+```yaml
+mysql:
+  host: <your_mysql_host>
+  port: 3306 # Default MySQL port
+  user: <your_username>
+  password: <your_password>
+  database: <your_database_name>
+```
+
+## Usage
+
+### Reading Data from MySQL
+
+To include a MySQL table or view in your `SemanticModel`, define it in your input dictionary with `type: "mysql"` and use the `identifier` key to specify the object name.
+
+:::caution Important
+The dictionary key for your dataset (e.g., `"CUSTOMERS"`) must exactly match the table or view name specified in the `identifier`.
+:::
+
+```python
+from intugle import SemanticModel
+
+datasets = {
+    "CUSTOMERS": {
+        "identifier": "CUSTOMERS", # Must match the key above
+        "type": "mysql"
+    },
+    "ORDERS_VIEW": {
+        "identifier": "ORDERS_VIEW", # Can be a view
+        "type": "mysql"
+    }
+}
+
+# Initialize the semantic model
+sm = SemanticModel(datasets, domain="E-commerce")
+
+# Build the model as usual
+sm.build()
+```
+
+### Materializing Data Products
+
+When you use the `DataProduct` class with a MySQL connection, the resulting data product can be materialized as a new **table** or **view** directly within your target database.
+
+```python
+from intugle import DataProduct
+
+etl_model = {
+    "name": "top_customers",
+    "fields": [
+        {"id": "CUSTOMERS.customer_id", "name": "customer_id"},
+        {"id": "CUSTOMERS.name", "name": "customer_name"},
+    ]
+}
+
+dp = DataProduct()
+
+# Materialize as a view (default)
+dp.build(etl_model, materialize="view")
+
+# Materialize as a table
+dp.build(etl_model, materialize="table")
+```

docsite/docs/connectors/sqlite.md

@@ -0,0 +1,77 @@
+---
+sidebar_position: 6
+---
+
+# SQLite
+
+`intugle` integrates with SQLite, allowing you to read data from your local database files.
+
+## Installation
+
+To use `intugle` with SQLite, you generally only need the base package as `sqlite3` is included in Python. However, for SQL generation features, `sqlglot` is required.
+
+```bash
+pip install intugle sqlglot
+```
+
+## Configuration
+
+To connect to your SQLite database, you must provide the path to your database file in a `profiles.yml` file at the root of your project. The adapter looks for a top-level `sqlite:` key.
+
+**Example `profiles.yml`:**
+
+```yaml
+sqlite:
+  path: /absolute/path/to/your/database.db
+```
+
+## Usage
+
+### Reading Data from SQLite
+
+To include a SQLite table or view in your `SemanticModel`, define it in your input dictionary with `type: "sqlite"` and use the `identifier` key to specify the object name.
+
+```python
+from intugle import SemanticModel
+
+datasets = {
+    "CUSTOMERS": {
+        "identifier": "CUSTOMERS",
+        "type": "sqlite"
+    },
+    "ORDERS": {
+        "identifier": "ORDERS",
+        "type": "sqlite"
+    }
+}
+
+# Initialize the semantic model
+sm = SemanticModel(datasets, domain="E-commerce")
+
+# Build the model as usual
+sm.build()
+```
+
+### Materializing Data Products
+
+When you use the `DataProduct` class with a SQLite connection, the resulting data product can be materialized as a new **table** or **view** directly within your database.
+
+```python
+from intugle import DataProduct
+
+etl_model = {
+    "name": "top_customers",
+    "fields": [
+        {"id": "CUSTOMERS.customer_id", "name": "customer_id"},
+        {"id": "CUSTOMERS.name", "name": "customer_name"},
+    ]
+}
+
+dp = DataProduct()
+
+# Materialize as a view (default)
+dp.build(etl_model, materialize="view")
+
+# Materialize as a table
+dp.build(etl_model, materialize="table")
+```

docsite/docs/intro.md

@@ -25,3 +25,11 @@ Intugle’s GenAI-powered open-source Python library builds an intelligent seman
 *   **Semantic Data Model:** Transform raw, fragmented datasets into an intelligent semantic graph that captures entities, relationships, and context — the foundation for connected intelligence.
 *   **Business Glossary & Semantic Search:** Auto-generate a business glossary and enable search that understands meaning, not just keywords — making data more accessible across technical and business users.
 *   **Data Products:** Instantly generate SQL and reusable data products enriched with context, eliminating manual pipelines and accelerating data-to-insight.
+
+## Supported Integrations
+
+| Category | Integrations |
+| :--- | :--- |
+| **Data Warehouses** |  Snowflake, Databricks |
+| **Databases** | SQLite, PostgreSQL, SQL Server, MySQL |
+| **Local** | Pandas, DuckDB (CSV, Parquet, Excel) |

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "intugle"
-version = "1.2.0"
+version = "1.2.1"
 authors = [
     { name="Intugle", email="hello@intugle.ai" },
 ]