fix: remove deprecated method from documentation

docs/v3/agent.mdx

@@ -34,38 +34,21 @@ agent.chat('And which one has the most deals?')
 # Output: United States has the most deals
 ```
 
-### Clarification questions
+### Follow-up Questions
 
-An agent will also be able to ask clarification questions if it does not have enough information to answer the query. For example:
+An agent can handle follow-up questions that continue the existing conversation without starting a new chat. This maintains the conversation context. For example:
 
 ```python
-agent.clarification_questions('What is the GDP of the United States?')
-```
-
-This will return up to 3 clarification questions that the agent can ask the user to get more information to answer the query.
-
-### Explanation
-
-An agent will also be able to explain the answer given to the user. For example:
+# Start a new conversation
+response = agent.chat('What is the total sales?')
+print("First response:", response)
 
-```python
-response = agent.chat('What is the GDP of the United States?')
-explanation = agent.explain()
-
-print("The answer is", response)
-print("The explanation is", explanation)
+# Continue the conversation without clearing memory
+follow_up_response = agent.follow_up('What about last year?')
+print("Follow-up response:", follow_up_response)
 ```
 
-### Rephrase Question
-
-Rephrase question to get accurate and comprehensive response from the model. For example:
-
-```python
-rephrased_query = agent.rephrase_query('What is the GDP of the United States?')
-
-print("The rephrased query is", rephrased_query)
-
-```
+The `follow_up` method works just like `chat` but doesn't clear the conversation memory, allowing the agent to understand context from previous messages.
 
 ## Using the Agent in a Sandbox Environment
 
@@ -124,10 +107,12 @@ sandbox = DockerSandbox(
 ## Training the Agent with local Vector stores
 
 <Note>
-  Training agents with local vector stores requires a PandasAI Enterprise license. See [Enterprise Features](/v3/enterprise-features) for more details or [contact us](https://pandas-ai.com/) for production use.
+  Training agents with local vector stores requires a PandasAI Enterprise
+  license. See [Enterprise Features](/v3/enterprise-features) for more details
+  or [contact us](https://pandas-ai.com/) for production use.
 </Note>
 
-It is possible also to use PandasAI with a few-shot learning agent, thanks to the "train with local vector store" enterprise feature (requiring an enterprise license). 
+It is possible also to use PandasAI with a few-shot learning agent, thanks to the "train with local vector store" enterprise feature (requiring an enterprise license).
 
 If you want to train the agent 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. See [Enterprise Features](/v3/enterprise-features) for licensing information.
@@ -174,4 +159,4 @@ agent.train(queries=[query], codes=[response])
 response = agent.chat("What is the total sales for the last fiscal year?")
 print(response)
 # The model will use the information provided in the training to generate a response
-```
+```

docs/v3/migration-backwards-compatibility.mdx

@@ -4,7 +4,10 @@ description: "Using v2 classes in PandasAI v3"
 ---
 
 <Note>
-  PandasAI v3 maintains backward compatibility for `SmartDataframe`, `SmartDatalake`, and `Agent`. However, we recommend migrating to the new `pai.DataFrame()` and `pai.chat()` methods for better performance and features.
+  PandasAI v3 maintains backward compatibility for `SmartDataframe`,
+  `SmartDatalake`, and `Agent`. However, we recommend migrating to the new
+  `pai.DataFrame()` and `pai.chat()` methods for better performance and
+  features.
 </Note>
 
 ## SmartDataframe
@@ -54,6 +57,7 @@ response = df.chat("What are the top countries by sales?")
 ```
 
 **Benefits of pai.DataFrame():**
+
 - Better integration with semantic layer
 - Improved context management
 - Enhanced performance
@@ -114,6 +118,7 @@ response = pai.chat("Who gets paid the most?", employees, salaries)
 ```
 
 **Benefits of pai.chat():**
+
 - No need to instantiate `SmartDatalake`
 - Cleaner, more intuitive API
 - Better performance
@@ -122,7 +127,7 @@ response = pai.chat("Who gets paid the most?", employees, salaries)
 
 ## Agent
 
-The `Agent` class works the same way in v3 as it did in v2. The only requirement is to configure the LLM globally.
+The `Agent` class works mostly the same way in v3 as it did in v2, but some methods have been removed. The main requirement is to configure the LLM globally.
 
 ```python
 from pandasai import Agent
@@ -143,4 +148,24 @@ response = agent.chat("Analyze the data and provide insights")
 
 **Key Change:** Configure LLM globally with `pai.config.set()` instead of passing it per-agent.
 
-For detailed information about Agent usage, see the [Agent documentation](/v3/agent). For information about using Skills with Agent, see the [Skills documentation](/v3/skills).
+### New Agent Methods in v3
+
+PandasAI v3 introduces new Agent methods that enhance conversational capabilities:
+
+- **`follow_up(query)`**: Continue conversations without clearing memory (maintains context)
+
+```python
+agent = Agent([df1, df2])
+
+# Start conversation
+response = agent.chat('What is the total revenue?')
+
+# Follow up without losing context
+follow_up = agent.follow_up('What about last quarter?')
+```
+
+**Note:** The `clarification_questions()`, `explain()` and `rephrase_query()` methods have been removed in v3.
+
+These methods provide enhanced conversational capabilities not available in v2.
+
+For detailed information about Agent usage, see the [Agent documentation](/v3/agent). For information about using Skills with Agent, see the [Skills documentation](/v3/skills).

docs/v3/migration-guide.mdx

@@ -197,6 +197,41 @@ agent = Agent([df])
 
 **More details:** See [Skills](/v3/skills) for detailed usage and examples.
 
+### Agent
+
+Agent class works mostly the same, but some methods have been removed in v3.
+
+**Removed methods:** `clarification_questions()`, `rephrase_query()`, `explain()`
+
+**v2:**
+
+```python
+from pandasai import Agent
+
+agent = Agent(df)
+clarifications = agent.clarification_questions('What is the GDP?')
+rephrased = agent.rephrase_query('What is the GDP?')
+explanation = agent.explain()
+```
+
+**v3:**
+
+```python
+from pandasai import Agent
+
+agent = Agent(df)
+# ❌ These methods are removed in v3
+# Use chat() and follow_up() instead
+response = agent.chat('What is the GDP?')
+follow_up = agent.follow_up('What about last year?')  # New: maintains context
+```
+
+**Key Changes:**
+
+- `clarification_questions()`, `rephrase_query()`, and `explain()` have been removed
+- New `follow_up()` method maintains conversation context
+- Global LLM configuration required
+
 ### Training
 
 <Note title="Enterprise Feature">

docs/v3/migration-troubleshooting.mdx

@@ -4,7 +4,8 @@ description: "Common issues and solutions when migrating from v2 to v3"
 ---
 
 <Note>
-  This guide covers common issues encountered during migration. For breaking changes and migration steps, see the [Migration Guide](/v3/migration-guide).
+  This guide covers common issues encountered during migration. For breaking
+  changes and migration steps, see the [Migration Guide](/v3/migration-guide).
 </Note>
 
 ## Common Issues and Solutions
@@ -55,6 +56,23 @@ pai.config.set(config)
 df = pai.DataFrame(data)
 ```
 
+### Issue: Agent Methods Not Found
+
+**Problem**: `AttributeError: 'Agent' object has no attribute 'clarification_questions'` (or `rephrase_query`, `explain`)
+
+**Solution**: These methods have been removed in v3. Use alternatives:
+
+```python
+# v2 - These methods are removed
+agent.clarification_questions('What is the GDP?')
+agent.rephrase_query('What is the GDP?')
+agent.explain()
+
+# v3 - Use these instead
+response = agent.chat('What is the GDP?')
+follow_up = agent.follow_up('What about last year?')  # Maintains context
+```
+
 ## Get Support
 
 ### Community Support