fix(docs): sandbox documentation and example corrected

docs/v3/agent.mdx

@@ -243,3 +243,46 @@ smart_df = pai.SmartDataframe(df, config={
 ```
 
 The agent will use your custom head instead of the default first 5 rows of the dataframe when analyzing and responding to queries.
+
+### Using Sandbox
+
+To use the sandbox environment, you first need to install the required package and have Docker running on your system:
+
+```bash
+pip install pandasai-docker
+```
+
+<Note title="Sandbox Requirements">
+  Make sure you have Docker running on your system before using the sandbox
+  environment.
+</Note>
+
+Here's how to enable the sandbox for your PandaAI agent:
+
+```python
+from pandasai import Agent
+from pandasai_docker import DockerSandbox
+
+# Initialize and start the sandbox
+sandbox = DockerSandbox()
+sandbox.start()
+
+# Create an agent with the sandbox enabled
+agent = Agent("data.csv", sandbox=sandbox)
+
+# The code will now run in an isolated Docker container
+response = agent.chat("What is the total sales for each country?")
+
+# Don't forget to stop the sandbox when done
+sandbox.stop()
+```
+
+You can also customize the sandbox environment:
+
+```python
+# Custom sandbox configuration
+sandbox = DockerSandbox(
+    "custom-sandbox-name",
+    "/path/to/custom/Dockerfile"
+)
+```

docs/v3/large-language-models.mdx

@@ -4,7 +4,8 @@ description: "Set up Large Language Model in PandaAI"
 ---
 
 <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>
 
 PandaAI supports multiple LLMs.
@@ -79,7 +80,7 @@ llm = AzureOpenAI(api_base="https://<your-endpoint>.openai.azure.com/",
 pai.config.set({"llm": llm})
 ```
 
-## LiteLLM
+## How to set up any LLM?
 
 LiteLLM provides a unified interface to interact with 100+ LLM models from various providers including OpenAI, Azure, Anthropic, Google, AWS, Hugging Face, and many more. This makes it easy to switch between different LLM providers without changing your code.
 
@@ -115,6 +116,7 @@ pai.config.set({"llm": llm})
 ```
 
 LiteLLM supports a wide range of models from various providers, including but not limited to:
+
 - OpenAI (gpt-3.5-turbo, gpt-4, etc.)
 - Anthropic (claude-2, claude-instant-1, etc.)
 - Google (gemini-pro, palm2, etc.)

docs/v3/privacy-security.mdx

@@ -4,7 +4,8 @@ description: "Understanding security implications and sandbox options in PandaAI
 ---
 
 <Note title="Beta Notice">
-PandaAI 3.0 is currently in beta. This documentation reflects the latest features and functionality, which may evolve before the final release.
+  PandaAI 3.0 is currently in beta. This documentation reflects the latest
+  features and functionality, which may evolve before the final release.
 </Note>
 
 ## Code Execution and Sandbox Environment
@@ -27,53 +28,35 @@ To use the sandbox environment, you first need to install the required package a
 ```bash
 pip install pandasai-docker
 ```
+
 <Note title="Sandbox Requirements">
-Make sure you have Docker running on your system before using the sandbox environment.
+  Make sure you have Docker running on your system before using the sandbox
+  environment.
 </Note>
 
-Here's how to enable the sandbox for your PandaAI agent:
+Here's how to enable the sandbox for your PandaAI chat:
 
-```python
-from pandasai import Agent
+```
+import pandasai as pai
 from pandasai_docker import DockerSandbox
 
-# Initialize and start the sandbox
+pai.api_key.set("YOUR_API_KEY")
+
+# initialize the sandbox
 sandbox = DockerSandbox()
 sandbox.start()
 
-# Create an agent with the sandbox enabled
-agent = Agent("data.csv", sandbox=sandbox)
+# read a csv as df
+df = pai.read_csv("./data/heart.csv")
 
-# The code will now run in an isolated Docker container
-response = agent.chat("What is the total sales for each country?")
+# pass the df and the sandbox
+result = pai.chat("plot total heart patients by gender", df, sandbox=sandbox)
 
-# Don't forget to stop the sandbox when done
-sandbox.stop()
-```
-
-You can also customize the sandbox environment:
+# display the chart
+result.show()
 
-```python
-# Custom sandbox configuration
-sandbox = DockerSandbox(
-    "custom-sandbox-name",
-    "/path/to/custom/Dockerfile"
-)
-```
-
-For additional security in production environments, you can combine the sandbox with the Advanced Security Agent:
-
-```python
-from pandasai.ee.agents.advanced_security_agent import AdvancedSecurityAgent
-
-# Create a security agent
-security = AdvancedSecurityAgent()
-
-# Use both sandbox and security agent
-agent = Agent("data.csv", sandbox=sandbox, security=security)
-
-# Queries will be checked for security risks and run in isolation
-response = agent.chat("Calculate total sales")
+# stop the sandbox (docker container)
+sandbox.stop()
 ```
 
 ### When to Use the Sandbox

examples/docker_sandbox.ipynb

@@ -29,6 +29,43 @@
     "%pip install pandasai-docker"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Execute the code in the sandbox\n",
+    "\n",
+    "Please keep in mind the sandbox works offline. \n",
+    "Once you have installed the package, you can start the sandbox with the following code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandasai as pai\n",
+    "from pandasai_docker import DockerSandbox\n",
+    "\n",
+    "pai.api_key.set(\"YOUR_API_KEY\")\n",
+    "\n",
+    "# initialize the sandbox\n",
+    "sandbox = DockerSandbox()\n",
+    "sandbox.start()\n",
+    "\n",
+    "# read a csv as df\n",
+    "df = pai.read_csv(\"./data/heart.csv\")\n",
+    "\n",
+    "# pass the csv and the sandbox to the agent\n",
+    "result = pai.chat(\"plot total heart patients by gender\", df, sandbox=sandbox)\n",
+    "\n",
+    "result.show()\n",
+    "\n",
+    "# stop the sandbox (docker container)\n",
+    "sandbox.stop()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -50,7 +87,7 @@
     "from pandasai import Agent\n",
     "from pandasai_docker import DockerSandbox\n",
     "\n",
-    "pai.set_api_key(\"YOUR_API_KEY\")\n",
+    "pai.api_key.set(\"YOUR_API_KEY\")\n",
     "\n",
     "# initialize the sandbox\n",
     "sandbox = DockerSandbox()\n",

pandasai/exceptions.py

@@ -208,7 +208,7 @@ class PandaAIApiKeyError(Exception):
     """
 
     def __init__(self, message=None):
-        default_message = "PandaAI API key not found. Please set your API key using PandaAI.set_api_key() or by setting the PANDASAI_API_KEY environment variable."
+        default_message = "PandaAI API key not found. Please set your API key using PandaAI.api_key.set() or by setting the PANDASAI_API_KEY environment variable."
         super().__init__(message or default_message)