#
Text-to-SQL

In this tutorial, we’ll see how to implement an agent that leverages SQL using `smolagents`.

> Let’s start with the golden question: why not keep it simple and use a standard text-to-SQL pipeline?

A standard text-to-sql pipeline is brittle, since the generated SQL query can be incorrect. Even worse, the query could be incorrect, but not raise an error, instead giving some incorrect/useless outputs without raising an alarm.

👉 Instead, an agent system is able to critically inspect outputs and decide if the query needs to be changed or not, thus giving it a huge performance boost.

Let’s build this agent! 💪

Run the line below to install required dependencies:

    !pip install smolagents python-dotenv sqlalchemy --upgrade -q

To call Inference Providers, you will need a valid token as your environment variable `HF_TOKEN`. We use python-dotenv to load it.

    from dotenv import load_dotenv
    load_dotenv()

Then, we setup the SQL environment:

    from sqlalchemy import (
        create_engine,
        MetaData,
        Table,
        Column,
        String,
        Integer,
        Float,
        insert,
        inspect,
        text,
    )

    engine = create_engine("sqlite:///:memory:")
    metadata_obj = MetaData()

    def insert_rows_into_table(rows, table, engine=engine):
        for row in rows:
            stmt = insert(table).values(**row)
            with engine.begin() as connection:
                connection.execute(stmt)

    table_name = "receipts"
    receipts = Table(
        table_name,
        metadata_obj,
        Column("receipt_id", Integer, primary_key=True),
        Column("customer_name", String(16), primary_key=True),
        Column("price", Float),
        Column("tip", Float),
    )
    metadata_obj.create_all(engine)

    rows = [
        {"receipt_id": 1, "customer_name": "Alan Payne", "price": 12.06, "tip": 1.20},
        {"receipt_id": 2, "customer_name": "Alex Mason", "price": 23.86, "tip": 0.24},
        {"receipt_id": 3, "customer_name": "Woodrow Wilson", "price": 53.43, "tip": 5.43},
        {"receipt_id": 4, "customer_name": "Margaret James", "price": 21.11, "tip": 1.00},
    ]
    insert_rows_into_table(rows, receipts)

###
Build our agent

Now let’s make our SQL table retrievable by a tool.

The tool’s description attribute will be embedded in the LLM’s prompt by the agent system: it gives the LLM information about how to use the tool. This is where we want to describe the SQL table.

    inspector = inspect(engine)
    columns_info = [(col["name"], col["type"]) for col in inspector.get_columns("receipts")]

    table_description = "Columns:\n" + "\n".join([f"  - {name}: {col_type}" for name, col_type in columns_info])
    print(table_description)

    Columns:
      - receipt_id: INTEGER
      - customer_name: VARCHAR(16)
      - price: FLOAT
      - tip: FLOAT

Now let’s build our tool. It needs the following: (read the tool doc for more detail)

- A docstring with an `Args:` part listing arguments.
- Type hints on both inputs and output.

    from smolagents import tool

    @tool
    def sql_engine(query: str) -> str:
        """
        Allows you to perform SQL queries on the table. Returns a string representation of the result.
        The table is named 'receipts'. Its description is as follows:
            Columns:
            - receipt_id: INTEGER
            - customer_name: VARCHAR(16)
            - price: FLOAT
            - tip: FLOAT

        Args:
            query: The query to perform. This should be correct SQL.
        """
        output = ""
        with engine.connect() as con:
            rows = con.execute(text(query))
            for row in rows:
                output += "\n" + str(row)
        return output

Now let us create an agent that leverages this tool.

We use the `CodeAgent`, which is smolagents’ main agent class: an agent that writes actions in code and can iterate on previous output according to the ReAct framework.

The model is the LLM that powers the agent system. `InferenceClientModel` allows you to call LLMs using HF’s Inference API, either via Serverless or Dedicated endpoint, but you could also use any proprietary API.

    from smolagents import CodeAgent, InferenceClientModel

    agent = CodeAgent(
        tools=[sql_engine],
        model=InferenceClientModel(model_id="meta-llama/Llama-3.1-8B-Instruct"),
    )
    agent.run("Can you give me the name of the client who got the most expensive receipt?")

###
Level 2: Table joins

Now let’s make it more challenging! We want our agent to handle joins across multiple tables.

So let’s make a second table recording the names of waiters for each receipt_id!

    table_name = "waiters"
    waiters = Table(
        table_name,
        metadata_obj,
        Column("receipt_id", Integer, primary_key=True),
        Column("waiter_name", String(16), primary_key=True),
    )
    metadata_obj.create_all(engine)

    rows = [
        {"receipt_id": 1, "waiter_name": "Corey Johnson"},
        {"receipt_id": 2, "waiter_name": "Michael Watts"},
        {"receipt_id": 3, "waiter_name": "Michael Watts"},
        {"receipt_id": 4, "waiter_name": "Margaret James"},
    ]
    insert_rows_into_table(rows, waiters)

Since we changed the table, we update the `SQLExecutorTool` with this table’s description to let the LLM properly leverage information from this table.

    updated_description = """Allows you to perform SQL queries on the table. Beware that this tool's output is a string representation of the execution output.
    It can use the following tables:"""

    inspector = inspect(engine)
    for table in ["receipts", "waiters"]:
        columns_info = [(col["name"], col["type"]) for col in inspector.get_columns(table)]

        table_description = f"Table '{table}':\n"

        table_description += "Columns:\n" + "\n".join([f"  - {name}: {col_type}" for name, col_type in columns_info])
        updated_description += "\n\n" + table_description

    print(updated_description)

Since this request is a bit harder than the previous one, we’ll switch the LLM engine to use the more powerful Qwen/Qwen2.5-Coder-32B-Instruct!

    sql_engine.description = updated_description

    agent = CodeAgent(
        tools=[sql_engine],
        model=InferenceClientModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct"),
    )

    agent.run("Which waiter got more total money from tips?")

It directly works! The setup was surprisingly simple, wasn’t it?

This example is done! We’ve touched upon these concepts:

- Building new tools.
- Updating a tool’s description.
- Switching to a stronger LLM helps agent reasoning.

✅ Now you can go build this text-to-SQL system you’ve always dreamt of! ✨

< > Update on GitHub