Add security warning to experimental `SQLDatabaseChain` class. (#9867)

The most reliable way to not have a chain run an undesirable SQL command
is to not give it database permissions to run that command. That way the
database itself performs the rule enforcement, so it's much easier to
configure and use properly than anything we could add in ourselves.

libs/experimental/langchain_experimental/sql/base.py

@@ -29,6 +29,15 @@ class SQLDatabaseChain(Chain):
             from langchain import OpenAI, SQLDatabase
             db = SQLDatabase(...)
             db_chain = SQLDatabaseChain.from_llm(OpenAI(), db)
+
+    *Security note*: Make sure that the database connection uses credentials
+        that are narrowly-scoped to only include the permissions this chain needs.
+        Failure to do so may result in data corruption or loss, since this chain may
+        attempt commands like `DROP TABLE` or `INSERT` if appropriately prompted.
+        The best way to guard against such negative outcomes is to (as appropriate)
+        limit the permissions granted to the credentials used with this chain.
+        This issue shows an example negative outcome if these steps are not taken:
+        https://github.com/langchain-ai/langchain/issues/5923
     """
 
     llm_chain: LLMChain
@@ -49,7 +58,7 @@ class SQLDatabaseChain(Chain):
     return_direct: bool = False
     """Whether or not to return the result of querying the SQL table directly."""
     use_query_checker: bool = False
-    """Whether or not the query checker tool should be used to attempt 
+    """Whether or not the query checker tool should be used to attempt
     to fix the initial SQL from the LLM."""
     query_checker_prompt: Optional[BasePromptTemplate] = None
     """The prompt template that should be used by the query checker"""
@@ -197,6 +206,17 @@ class SQLDatabaseChain(Chain):
         prompt: Optional[BasePromptTemplate] = None,
         **kwargs: Any,
     ) -> SQLDatabaseChain:
+        """Create a SQLDatabaseChain from an LLM and a database connection.
+
+        *Security note*: Make sure that the database connection uses credentials
+            that are narrowly-scoped to only include the permissions this chain needs.
+            Failure to do so may result in data corruption or loss, since this chain may
+            attempt commands like `DROP TABLE` or `INSERT` if appropriately prompted.
+            The best way to guard against such negative outcomes is to (as appropriate)
+            limit the permissions granted to the credentials used with this chain.
+            This issue shows an example negative outcome if these steps are not taken:
+            https://github.com/langchain-ai/langchain/issues/5923
+        """
         prompt = prompt or SQL_PROMPTS.get(db.dialect, PROMPT)
         llm_chain = LLMChain(llm=llm, prompt=prompt)
         return cls(llm_chain=llm_chain, database=db, **kwargs)