doc: API doc example for langchain database tool kit (#5498)

python/packages/autogen-ext/src/autogen_ext/tools/langchain/_langchain_adapter.py

@@ -19,14 +19,18 @@ class LangChainToolAdapter(BaseTool[BaseModel, Any]):
 
         This class requires the :code:`langchain` extra for the :code:`autogen-ext` package.
 
+        .. code-block:: bash
+
+            pip install -U "autogen-ext[langchain]"
+
 
     Args:
         langchain_tool (LangChainTool): A LangChain tool to wrap
 
     Examples:
 
-    Use the `PythonAstREPLTool` from the `langchain_experimental` package to
-    create a tool that allows you to interact with a Pandas DataFrame.
+        Use the `PythonAstREPLTool` from the `langchain_experimental` package to
+        create a tool that allows you to interact with a Pandas DataFrame.
 
         .. code-block:: python
 
@@ -60,6 +64,82 @@ class LangChainToolAdapter(BaseTool[BaseModel, Any]):
 
             asyncio.run(main())
 
+        This example demonstrates how to use the `SQLDatabaseToolkit` from the `langchain_community`
+        package to interact with an SQLite database.
+        It uses the :class:`~autogen_agentchat.team.RoundRobinGroupChat` to iterate the single agent over multiple steps.
+        If you want to one step at a time, you can just call `run_stream` method of the
+        :class:`~autogen_agentchat.agents.AssistantAgent` class directly.
+
+        .. code-block:: python
+
+            import asyncio
+            import sqlite3
+
+            import requests
+            from autogen_agentchat.agents import AssistantAgent
+            from autogen_agentchat.conditions import TextMentionTermination
+            from autogen_agentchat.teams import RoundRobinGroupChat
+            from autogen_agentchat.ui import Console
+            from autogen_ext.models.openai import OpenAIChatCompletionClient
+            from autogen_ext.tools.langchain import LangChainToolAdapter
+            from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
+            from langchain_community.utilities.sql_database import SQLDatabase
+            from langchain_openai import ChatOpenAI
+            from sqlalchemy import Engine, create_engine
+            from sqlalchemy.pool import StaticPool
+
+
+            def get_engine_for_chinook_db() -> Engine:
+                url = "https://raw.githubusercontent.com/lerocha/chinook-database/master/ChinookDatabase/DataSources/Chinook_Sqlite.sql"
+                response = requests.get(url)
+                sql_script = response.text
+                connection = sqlite3.connect(":memory:", check_same_thread=False)
+                connection.executescript(sql_script)
+                return create_engine(
+                    "sqlite://",
+                    creator=lambda: connection,
+                    poolclass=StaticPool,
+                    connect_args={"check_same_thread": False},
+                )
+
+
+            async def main() -> None:
+                # Create the engine and database wrapper.
+                engine = get_engine_for_chinook_db()
+                db = SQLDatabase(engine)
+
+                # Create the toolkit.
+                llm = ChatOpenAI(temperature=0)
+                toolkit = SQLDatabaseToolkit(db=db, llm=llm)
+
+                # Create the LangChain tool adapter for every tool in the toolkit.
+                tools = [LangChainToolAdapter(tool) for tool in toolkit.get_tools()]
+
+                # Create the chat completion client.
+                model_client = OpenAIChatCompletionClient(model="gpt-4o")
+
+                # Create the assistant agent.
+                agent = AssistantAgent(
+                    "assistant",
+                    model_client=model_client,
+                    tools=tools,  # type: ignore
+                    model_client_stream=True,
+                    system_message="Respond with 'TERMINATE' if the task is completed.",
+                )
+
+                # Create termination condition.
+                termination = TextMentionTermination("TERMINATE")
+
+                # Create a round-robin group chat to iterate the single agent over multiple steps.
+                chat = RoundRobinGroupChat([agent], termination_condition=termination)
+
+                # Run the chat.
+                await Console(chat.run_stream(task="Show some tables in the database"))
+
+
+            if __name__ == "__main__":
+                asyncio.run(main())
+
     """
 
     def __init__(self, langchain_tool: LangChainTool):