feat(Sandbox): typo and update documentation

docs/v3/agent.mdx

@@ -1,18 +1,18 @@
 ---
-title: 'Agent'
-description: 'Add few-shot learning to your PandaAI agent'
+title: "Agent"
+description: "Add few-shot learning to your PandaAI agent"
 ---
 
 <Note title="Beta Notice">
-Release v3 is currently in beta. This documentation reflects the features and functionality in progress and may change before the final release.
+  Release v3 is currently in beta. This documentation reflects the features and
+  functionality in progress and may change before the final release.
 </Note>
 
 You can train PandaAI to understand your data better and to improve its performance. Training is as easy as calling the `train` method on the `Agent`.
 
-
 ## Prerequisites
 
-Before you start training PandaAI, you need to set your PandaAI API key. 
+Before you start training PandaAI, you need to set your PandaAI API key.
 You can generate your API key by signing up at [https://app.pandabi.ai](https://app.pandabi.ai).
 
 ```python
@@ -140,6 +140,8 @@ To enhance security and protect against malicious code through prompt injection,
 
 ### Installation
 
+Before using the sandbox, you need to install Docker on your machine and ensure it is running.
+
 First, install the sandbox package:
 
 ```bash
@@ -181,7 +183,9 @@ sandbox = DockerSandbox(
 ```
 
 <Note>
-The sandbox works offline and provides an additional layer of security for code execution. It's particularly useful when working with untrusted data or when you need to ensure that code execution is isolated from your main system.
+  The sandbox works offline and provides an additional layer of security for
+  code execution. It's particularly useful when working with untrusted data or
+  when you need to ensure that code execution is isolated from your main system.
 </Note>
 
 ## Troubleshooting
@@ -203,9 +207,11 @@ vector_store = BambooVectorStor(api_key="YOUR_PANDABI_API_KEY")
 # Instantiate the agent with the custom vector store
 agent = Agent(connector, config={...} vectorstore=vector_store)
 ```
+
 ## Custom Head
 
 In some cases, you might want to provide custom data samples to the conversational agent to improve its understanding and responses. For example, you might want to:
+
 - Provide better examples that represent your data patterns
 - Avoid sharing sensitive information
 - Guide the agent with specific data scenarios

extensions/sandbox/docker/pandasai_docker/Dockerfile

@@ -1,6 +1,6 @@
 FROM python:3.9
 
-LABEL image_name="pandaai-sandbox"
+LABEL image_name="pandasai-sandbox"
 
 # Install required Python packages
 RUN pip install pandas numpy matplotlib

extensions/sandbox/docker/pandasai_docker/docker_sandbox.py

@@ -16,7 +16,7 @@
 
 
 class DockerSandbox(Sandbox):
-    def __init__(self, image_name="pandaai-sandbox", dockerfile_path=None):
+    def __init__(self, image_name="pandasai-sandbox", dockerfile_path=None):
         super().__init__()
         self._dockerfile_path: str = dockerfile_path or os.path.join(
             os.path.dirname(__file__), "Dockerfile"