sinaptik-ai · gventuri · Jan 31, 2025 · Jan 31, 2025 · Jan 31, 2025
diff --git a/docs/mint.json b/docs/mint.json
@@ -54,7 +54,7 @@
     "navigation": [
       {
         "group": "Overview",
-        "pages": ["v3/introduction", "v3/getting-started", "v3/cli", "v3/privacy-security"],
+        "pages": ["v3/introduction", "v3/getting-started"],
         "version": "v3"
       },
       {
@@ -74,7 +74,7 @@
       },
       {
         "group": "Advanced Usage",
-        "pages": ["v3/agent"],
+        "pages": ["v3/cli", "v3/privacy-security","v3/agent"],
         "version": "v3"
       },
       {

diff --git a/docs/v3/chat-and-output.mdx b/docs/v3/chat-and-output.mdx
@@ -108,7 +108,7 @@ You can inspect the code that was generated to produce the result:
 
 ```python
 response = df.chat("Calculate the correlation between age and salary")
-print(response.last_code_generated)
+print(response.last_code_executed)
 # Output: df['age'].corr(df['salary'])
 ```
 

diff --git a/docs/v3/data-ingestion.mdx b/docs/v3/data-ingestion.mdx
@@ -31,7 +31,6 @@ file = pai.read_csv("data.csv")
 # Use the semantic layer on CSV
 df = pai.create(
     path="company/sales-data",
-    name="sales_data",
     df = file,
     description="Sales data from our retail stores",
     columns={
@@ -50,182 +49,48 @@ response = df.chat("Which product has the highest sales?")
 
 ## How to work with SQL in PandaAI?
 
-PandaAI provides a sql extension for you to work with SQL, PostgreSQL, MySQL, SQLite databases.
+PandaAI provides a sql extension for you to work with SQL, PostgreSQL, MySQL, and CockroachDB databases.
 To make the library lightweight and easy to use, the basic installation of the library does not include this extension.
-It can be easily installed using either `poetry` or `pip`.
+It can be easily installed using pip with the specific database you want to use:
 
 ```bash
-poetry add pandasai-sql
+pip install pandasai-sql[postgres]
+pip install pandasai-sql[mysql]
+pip install pandasai-sql[cockroachdb]
 ```
 
-```bash
-pip install pandasai-sql
-```
-
-Once you have installed the extension, you can use it to connect to SQL databases.
-
-### PostgreSQL
-
-```yaml
-name: sales_data
-
-source:
-  type: postgres
-  connection:
-    host: db.example.com
-    port: 5432
-    database: analytics
-    user: ${DB_USER}
-    password: ${DB_PASSWORD}
-  table: sales_data
-
-destination:
-  type: local
-  format: parquet
-  path: company/sales-data
-
-columns:
-  - name: transaction_id
-    type: string
-    description: Unique identifier for each sale
-  - name: sale_date
-    type: datetime
-    description: Date and time of the sale
-  - name: product_id
-    type: string
-    description: Product identifier
-  - name: quantity
-    type: integer
-    description: Number of units sold
-  - name: price
-    type: float
-    description: Price per unit
-
-transformations:
-  - type: convert_timezone
-    params:
-      column: sale_date
-      from: UTC
-      to: America/New_York
-  - type: calculate
-    params:
-      column: total_amount
-      formula: quantity * price
-
-update_frequency: daily
+Once you have installed the extension, you can use the [semantic data layer](/v3/semantic-layer#for-sql-databases-using-the-create-method) and perform [data transformations](/docs/v3/transformations).
 
-order_by:
-  - sale_date DESC
-
-limit: 100000
-```
-
-### MySQL
-
-```yaml
-name: customer_data
-
-source:
-  type: mysql
-  connection:
-    host: db.example.com
-    port: 3306
-    database: analytics
-    user: ${DB_USER}
-    password: ${DB_PASSWORD}
-  table: customers
-
-destination:
-  type: local
-  format: parquet
-  path: company/customer-data
-
-columns:
-  - name: customer_id
-    type: string
-    description: Unique identifier for each customer
-  - name: name
-    type: string
-    description: Customer's full name
-  - name: email
-    type: string
-    description: Customer's email address
-  - name: join_date
-    type: datetime
-    description: Date when customer joined
-  - name: total_purchases
-    type: integer
-    description: Total number of purchases made
-
-transformations:
-  - type: anonymize
-    params:
-      column: email
-  - type: split
-    params:
-      column: name
-      into: [first_name, last_name]
-      separator: " "
-
-update_frequency: daily
-
-order_by:
-  - join_date DESC
-
-limit: 100000
-```
-
-### SQLite 
-
-```yaml
-name: inventory_data
-
-source:
-  type: sqlite
-  connection:
-    database: path/to/database.db
-  table: inventory
-
-destination:
-  type: local
-  format: parquet
-  path: company/inventory-data
-
-columns:
-  - name: product_id
-    type: string
-    description: Unique identifier for each product
-  - name: product_name
-    type: string
-    description: Name of the product
-  - name: category
-    type: string
-    description: Product category
-  - name: stock_level
-    type: integer
-    description: Current quantity in stock
-  - name: last_updated
-    type: datetime
-    description: Last inventory update timestamp
-
-transformations:
-  - type: categorize
-    params:
-      column: stock_level
-      bins: [0, 10, 50, 100, 500]
-      labels: ["Critical", "Low", "Medium", "High"]
-  - type: convert_timezone
-    params:
-      column: last_updated
-      from: UTC
-      to: America/Los_Angeles
-
-update_frequency: hourly
-
-order_by:
-  - last_updated DESC
-
-limit: 50000
+```python
+sql_table = pai.create(
+    path="example/mysql-dataset",
+    description="Heart disease dataset from MySQL database",
+    source={
+        "type": "mysql",
+        "connection": {
+            "host": "database.example.com",
+            "port": 3306,
+            "user": "${DB_USER}",
+            "password": "${DB_PASSWORD}",
+            "database": "medical_data"
+        },
+        "table": "heart_data",
+        "columns": [
+            {"name": "Age", "type": "integer", "description": "Age of the patient in years"},
+            {"name": "Sex", "type": "string", "description": "Gender of the patient (M = male, F = female)"},
+            {"name": "ChestPainType", "type": "string", "description": "Type of chest pain (ATA, NAP, ASY, TA)"},
+            {"name": "RestingBP", "type": "integer", "description": "Resting blood pressure in mm Hg"},
+            {"name": "Cholesterol", "type": "integer", "description": "Serum cholesterol in mg/dl"},
+            {"name": "FastingBS", "type": "integer", "description": "Fasting blood sugar > 120 mg/dl (1 = true, 0 = false)"},
+            {"name": "RestingECG", "type": "string", "description": "Resting electrocardiogram results (Normal, ST, LVH)"},
+            {"name": "MaxHR", "type": "integer", "description": "Maximum heart rate achieved"},
+            {"name": "ExerciseAngina", "type": "string", "description": "Exercise-induced angina (Y = yes, N = no)"},
+            {"name": "Oldpeak", "type": "float", "description": "ST depression induced by exercise relative to rest"},
+            {"name": "ST_Slope", "type": "string", "description": "Slope of the peak exercise ST segment (Up, Flat, Down)"},
+            {"name": "HeartDisease", "type": "integer", "description": "Heart disease diagnosis (1 = present, 0 = absent)"}
+        ]
+    }
+)
 ```
 
 ## How to work with Enterprise Cloud Data in PandaAI?
@@ -590,8 +455,8 @@ limit: 100000
 </tr>
 <tr>
   <td style={{ border: '1px solid #ccc', padding: '8px 16px' }}>pandasai_sql</td>
-  <td style={{ border: '1px solid #ccc', padding: '8px 16px' }}><code>poetry add pandasai-sql</code></td>
-  <td style={{ border: '1px solid #ccc', padding: '8px 16px' }}><code>pip install pandasai-sql</code></td>
+  <td style={{ border: '1px solid #ccc', padding: '8px 16px' }}><code>poetry add pandasai-sql[postgres]</code></td>
+  <td style={{ border: '1px solid #ccc', padding: '8px 16px' }}><code>pip install pandasai-sql[postgres]</code></td>
   <td style={{ border: '1px solid #ccc', padding: '8px 16px' }}>No</td>
 </tr>
 <tr>

diff --git a/docs/v3/getting-started.mdx b/docs/v3/getting-started.mdx
@@ -72,7 +72,6 @@ df = pai.read_csv("data/companies.csv")
 companies = pai.create(
   path="my-org/companies",
   df=df,
-  name="companies",
   description="Customer companies dataset"
 )
 
@@ -89,7 +88,6 @@ By default, the column will be inferred from the data. For more control, though,
 companies = pai.create(
   path="my-org/companies",
   df=df,
-  name="companies",
   description="Customer companies dataset",
   columns=[
     {
@@ -121,7 +119,11 @@ stocks = pai.load("organization/coca_cola_stock")
 companies = pai.load("organization/companies")
 
 # Query using natural language
-result = companies.chat("What's the average revenue by region?")
+response= stocks.chat("What is the volatility of the Coca Cola stock?")
+response = companies.chat("What is the average revenue by region?")
+
+# Query using multiple datasets 
+result = pai.chat("Compare the revenue between Coca Cola and Apple", stocks, companies)
 ```
 
 ## Sharing and collaboration
@@ -130,8 +132,8 @@ Share your data layers with your team:
 
 ```python
 # Push datasets to the platform
+stocks.push()
 companies.push()
-market.push()
 ```
 
 Team members can then access and query the shared datasets through:
@@ -143,6 +145,6 @@ Of course, they will only be able to see the datasets they have access to. You c
 ## Next Steps
 
 - Learn more about [Data Schema Definition](/v3/data-layer)
-- Explore [Advanced Views and Joins](/v3/views)
-- Check out our [Example Projects](/v3/examples)
-- Join our [Discord Community](https://discord.gg/kF7FqH2FwS) for support
+- Join our [Discord Community](https://discord.gg/KYKj9F2FRH) for support
+{/* - Explore [Advanced Views and Joins](/v3/views) */}
+{/*- Check out our [Example Projects](/v3/examples) */}
diff --git a/docs/v3/introduction.mdx b/docs/v3/introduction.mdx
@@ -56,8 +56,8 @@ For enhanced capabilities, connect your PandaAI implementation to our [Data Plat
 - [Quick Start Guide](/v3/getting-started)
 - [Data Layer Documentation](/v3/data-layer)
 - [Natural Language Features](/v3/overview-nl)
-- [Example Projects](/v3/examples)
+{/* - [Example Projects](/v3/examples) */}
 
 ## Get in touch
 
-If you can’t find the information you’re looking for in the documentation, or if you need help, get in touch with our Support Team at pm@sinaptik.ai, or join our [Discord](https://discord.gg/kF7FqH2FwS), where our team and community can help you.
+If you can’t find the information you’re looking for in the documentation, or if you need help, get in touch with our Support Team at pm@sinaptik.ai, or join our [Discord](https://discord.gg/KYKj9F2FRH), where our team and community can help you.