community[patch]: add args_schema to sql_database tools for langGraph…

… integration (langchain-ai#18595)

- **Description:** This modification adds pydantic input definition for
sql_database tools. This helps for function calling capability in
LangGraph. Since actions nodes will usually check for the args_schema
attribute on tools, This update should make these tools compatible with
it (only implemented on the InfoSQLDatabaseTool)
  - **Issue:** N/A
  - **Dependencies:** N/A
  - **Twitter handle:** juanfe8881

libs/community/langchain_community/tools/sql_database/tool.py

@@ -39,6 +39,7 @@ class QuerySQLDataBaseTool(BaseSQLDatabaseTool, BaseTool):
     If the query is not correct, an error message will be returned.
     If an error is returned, rewrite the query, check the query, and try again.
     """
+    args_schema: Type[BaseModel] = _QuerySQLDataBaseToolInput
 
     def _run(
         self,
@@ -77,11 +78,16 @@ def _run(
         )
 
 
+class _ListSQLDataBaseToolInput(BaseModel):
+    tool_input: str = Field(..., description="An empty string")
+
+
 class ListSQLDatabaseTool(BaseSQLDatabaseTool, BaseTool):
     """Tool for getting tables names."""
 
     name: str = "sql_db_list_tables"
     description: str = "Input is an empty string, output is a comma separated list of tables in the database."
+    args_schema: Type[BaseModel] = _ListSQLDataBaseToolInput
 
     def _run(
         self,
@@ -92,6 +98,10 @@ def _run(
         return ", ".join(self.db.get_usable_table_names())
 
 
+class _QuerySQLCheckerToolInput(BaseModel):
+    query: str = Field(..., description="A detailed and SQL query to be checked.")
+
+
 class QuerySQLCheckerTool(BaseSQLDatabaseTool, BaseTool):
     """Use an LLM to check if a query is correct.
     Adapted from https://www.patterns.app/blog/2023/01/18/crunchbot-sql-analyst-gpt/"""
@@ -104,6 +114,7 @@ class QuerySQLCheckerTool(BaseSQLDatabaseTool, BaseTool):
     Use this tool to double check if your query is correct before executing it.
     Always use this tool before executing a query with sql_db_query!
     """
+    args_schema: Type[BaseModel] = _QuerySQLCheckerToolInput
 
     @root_validator(pre=True)
     def initialize_llm_chain(cls, values: Dict[str, Any]) -> Dict[str, Any]: