docs: remove training mentions for SmartDatalake

docs/connectors.mdx

@@ -146,7 +146,8 @@ sql_connector = SQLConnector(
 
 ## Snowflake connector
 
-The Snowflake connector allows you to connect to Snowflake. It is very similar to the SQL connectors, but it has some differences. The usage of this connector might be subject to a license ([check it out](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE)).
+The Snowflake connector allows you to connect to Snowflake. It is very similar to the SQL connectors, but it is tailored for Snowflake.
+The usage of this connector in production is subject to a license ([check it out](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE)). If you plan to use it in production, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8).
 
 To use the Snowflake connector, you only need to import it into your Python code and pass it to a `SmartDataframe` or `SmartDatalake` object:
 
@@ -177,7 +178,8 @@ df.chat("How many records has status 'F'?")
 
 ## DataBricks connector
 
-The DataBricks connector allows you to connect to DataBricks. It is very similar to the SQL connectors, but it has some differences. The usage of this connector might be subject to a license ([check it out](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE)).
+The DataBricks connector allows you to connect to Databricks. It is very similar to the SQL connectors, but it is tailored for Databricks.
+The usage of this connector in production is subject to a license ([check it out](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE)). If you plan to use it in production, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8).
 
 To use the DataBricks connector, you only need to import it into your Python code and pass it to a `Agent`, `SmartDataframe` or `SmartDatalake` object:
 
@@ -203,7 +205,8 @@ databricks_connector = DatabricksConnector(
 
 ## GoogleBigQuery connector
 
-The GoogleBigQuery connector allows you to connect to GoogleBigQuery datasests. It is very similar to the SQL connectors, but it has some differences. The usage of this connector might be subject to a license ([check it out](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE)).
+The GoogleBigQuery connector allows you to connect to GoogleBigQuery datasests. It is very similar to the SQL connectors, but it is tailored for Google BigQuery.
+The usage of this connector in production is subject to a license ([check it out](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE)). If you plan to use it in production, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8).
 
 To use the GoogleBigQuery connector, you only need to import it into your Python code and pass it to a `Agent`, `SmartDataframe` or `SmartDatalake` object:
 

docs/judge-agent.mdx

@@ -7,7 +7,8 @@ description: "Enhance the PandasAI library with the JudgeAgent that evaluates th
 
 The `JudgeAgent` extends the capabilities of the PandasAI library by adding an extra judgement in agents pipeline that validates the code generated against the query
 
-> **Note:** Usage of the Judge Agent may be subject to a license. For more details, refer to the [license documentation](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE).
+> **Note:** The usage of the Judge Agent in production is subject to a license. For more details, refer to the [license documentation](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE).
+> If you plan to use it in production, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8).
 
 ## Instantiating the Judge Agent
 

docs/semantic-agent.mdx

@@ -7,7 +7,8 @@ description: "Enhance the PandasAI library with the Semantic Agent for more accu
 
 The `SemanticAgent` (currently in beta) extends the capabilities of the PandasAI library by adding a semantic layer to its results. Unlike the standard `Agent`, the `SemanticAgent` generates a JSON query, which can then be used to produce Python or SQL code. This approach ensures more accurate and interpretable outputs.
 
-> **Note:** Usage of the Semantic Agent may be subject to a license. For more details, refer to the [license documentation](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE).
+> **Note:** Usage of the Semantic Agent in production is subject to a license. For more details, refer to the [license documentation](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE).
+> If you plan to use it in production, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8).
 
 ## Instantiating the Semantic Agent
 

docs/train.mdx

@@ -2,7 +2,7 @@
 title: "Train PandasAI"
 ---
 
-You can train PandasAI to understand your data better and to improve its performance. Training is as easy as calling the `train` method on the `SmartDataframe`, `SmartDatalake` or `Agent`.
+You can train PandasAI to understand your data better and to improve its performance. Training is as easy as calling the `train` method on the `Agent`.
 
 There are two kinds of training:
 
@@ -40,7 +40,7 @@ Instructions training is used to teach PandasAI how you expect it to respond to
 
 For example, you might want the LLM to be aware that your company's fiscal year starts in April, or about specific ways you want to handle missing data. Or you might want to teach it about specific business rules or data analysis best practices that are specific to your organization.
 
-To train PandasAI with instructions, you can use the `train` method on the `Agent`, `SmartDataframe` or `SmartDatalake`, as it follows:
+To train PandasAI with instructions, you can use the `train` method on the `Agent`, as it follows:
 
 The training uses by default the `BambooVectorStore` to store the training data, and it's accessible with the API key.
 
@@ -66,7 +66,7 @@ Your training data is persisted, so you only need to train the model once.
 
 Q/A training is used to teach PandasAI the desired process to answer specific questions, enhancing the model's performance and determinism. One of the biggest challenges with LLMs is that they are not deterministic, meaning that the same question can produce different answers at different times. Q/A training can help to mitigate this issue.
 
-To train PandasAI with Q/A, you can use the `train` method on the `Agent`, `SmartDataframe` or `SmartDatalake`, as it follows:
+To train PandasAI with Q/A, you can use the `train` method on the `Agent`, as it follows:
 
 ```python
 from pandasai import Agent
@@ -96,10 +96,11 @@ Also in this case, your training data is persisted, so you only need to train th
 ## Training with local Vector stores
 
 If you want to train the model with a local vector store, you can use the local `ChromaDB`, `Qdrant` or `Pinecone` vector stores. Here's how to do it:
+An enterprise license is required for using the vector stores locally, ([check it out](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE)).
+If you plan to use it in production, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8).
 
 ```python
 from pandasai import Agent
-# An enterprise license might be required for using the vector stores locally
 from pandasai.ee.vectorstores import ChromaDB
 from pandasai.ee.vectorstores import Qdrant
 from pandasai.ee.vectorstores import Pinecone

examples/from_databricks.py

@@ -4,7 +4,8 @@
 
 from pandasai import Agent
 
-# A license might be required for using Snowflake with PandasAI
+# The usage of this connector in production is subject to a license ([check it out](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE)).
+# If you plan to use it in production, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8).
 from pandasai.ee.connectors import DatabricksConnector
 
 databricks_connector = DatabricksConnector(

examples/from_googlebigquery.py

@@ -4,7 +4,8 @@
 
 from pandasai import SmartDataframe
 
-# A license might be required for using BigQuery with PandasAI
+# The usage of this connector in production is subject to a license ([check it out](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE)).
+# If you plan to use it in production, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8).
 from pandasai.ee.connectors import GoogleBigQueryConnector
 
 # ENV's

examples/from_snowflake.py

@@ -4,7 +4,8 @@
 
 from pandasai import Agent
 
-# A license might be required for using Snowflake with PandasAI
+# The usage of this connector in production is subject to a license ([check it out](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE)).
+# If you plan to use it in production, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8).
 from pandasai.ee.connectors import SnowFlakeConnector
 
 snowflake_connector = SnowFlakeConnector(