cookbook/pr_362/en/agent_text_to_sql.md

# Agent for text-to-SQL with automatic error correction
_Authored by: [Aymeric Roucher](https://huggingface.co/m-ric)_

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

What's the advantage over 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! 💪

## Setup SQL tables

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

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

# create city SQL table
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)
```

```python
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},
]
for row in rows:
    stmt = insert(receipts).values(**row)
    with engine.begin() as connection:
        cursor = connection.execute(stmt)
```

Let's check that our system works with a basic query:

```python
>>> with engine.connect() as con:
...     rows = con.execute(text("""SELECT * from receipts"""))
...     for row in rows:
...         print(row)
```

(1, 'Alan Payne', 12.06, 1.2)
(2, 'Alex Mason', 23.86, 0.24)
(3, 'Woodrow Wilson', 53.43, 5.43)
(4, 'Margaret James', 21.11, 1.0)

## Build our agent

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

Our `sql_engine` tool needs the following: (read [the documentation](https://huggingface.co/docs/transformers/en/agents#create-a-new-tool) for more detail)
- A docstring with an `Args:` part. This docstring will be parsed to become the tool's `description` attribute, which will be used as the instruction manual for the LLM powering the agent, so it's important to provide it!
- Type hints for inputs and output.

```python
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 `transformers.agents`' main agent class: an agent that writes actions in code and can iterate on previous output according to the ReAct framework.

The `llm_engine` is the LLM that powers the agent system. `InferenceClientModel` allows you to call LLMs using Hugging Face's Inference API, either via Serverless or Dedicated endpoint, but you could also use any proprietary API: check out [this other cookbook](agent_change_llm) to learn how to adapt it.

```python
from smolagents import CodeAgent, InferenceClientModel

agent = CodeAgent(
    tools=[sql_engine],
    model=InferenceClientModel("meta-llama/Meta-Llama-3-8B-Instruct"),
)
```

```python
agent.run("Can you give me the name of the client who got the most expensive receipt?")
```

## Increasing difficulty: 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`!

```python
table_name = "waiters"
receipts = 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"},
]
for row in rows:
    stmt = insert(receipts).values(**row)
    with engine.begin() as connection:
        cursor = connection.execute(stmt)
```

We need to update the `SQLExecutorTool` with this table's description to let the LLM properly leverage information from this table.

```python
>>> 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)
```

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:

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

Table 'waiters':
Columns:
  - receipt_id: INTEGER
  - waiter_name: VARCHAR(16)

Since this request is a bit harder than the previous one, we'll switch the llm engine to use the more powerful [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct)!

```python
sql_engine.description = updated_description

agent = CodeAgent(
    tools=[sql_engine],
    model=InferenceClientModel("Qwen/Qwen2.5-72B-Instruct"),
)

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

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

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