experimental[major]: Force users to opt-in into code that relies on the python repl (#22860)

This should make it obvious that a few of the agents in langchain
experimental rely on the python REPL as a tool under the hood, and will
force users to opt-in.

libs/experimental/langchain_experimental/pal_chain/base.py

@@ -18,7 +18,7 @@ from langchain_core.language_models import BaseLanguageModel
 
 from langchain_experimental.pal_chain.colored_object_prompt import COLORED_OBJECT_PROMPT
 from langchain_experimental.pal_chain.math_prompt import MATH_PROMPT
-from langchain_experimental.pydantic_v1 import Extra, Field
+from langchain_experimental.pydantic_v1 import Extra, Field, root_validator
 
 COMMAND_EXECUTION_FUNCTIONS = ["system", "exec", "execfile", "eval", "__import__"]
 COMMAND_EXECUTION_ATTRIBUTES = [
@@ -129,6 +129,36 @@ class PALChain(Chain):
     """Validations to perform on the generated code."""
     timeout: Optional[int] = 10
     """Timeout in seconds for the generated code to execute."""
+    allow_dangerous_code: bool = False
+    """This chain relies on the execution of generated code, which can be dangerous.
+    
+    This class implements an AI technique that generates and evaluates
+    Python code, which can be dangerous and requires a specially sandboxed
+    environment to be safely used. While this class implements some basic guardrails
+    by limiting available locals/globals and by parsing and inspecting
+    the generated Python AST using `PALValidation`, those guardrails will not
+    deter sophisticated attackers and are not a replacement for a proper sandbox.
+    Do not use this class on untrusted inputs, with elevated permissions,
+    or without consulting your security team about proper sandboxing!
+    
+    Failure to properly sandbox this class can lead to arbitrary code execution
+    vulnerabilities, which can lead to data breaches, data loss, or other security
+    incidents.
+    """
+
+    @root_validator(pre=False, skip_on_failure=True)
+    def post_init(cls, values: Dict) -> Dict:
+        if not values["allow_dangerous_code"]:
+            raise ValueError(
+                "This chain relies on the execution of generated code, "
+                "which can be dangerous. "
+                "Please read the security notice for this class, and only "
+                "use it if you understand the security implications. "
+                "If you want to proceed, you will need to opt-in, by setting "
+                "`allow_dangerous_code` to `True`."
+            )
+
+        return values
 
     class Config:
         """Configuration for this pydantic object."""