TUTORIAL.md

Unlocking Database Intelligence with AI Agents: A smolagents Tutorial

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This guide explores how to develop an intelligent agent using the smolagents framework, specifically enabling it to interact with a SQL database.

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Beyond Simple Text-to-SQL: The Agent Advantage

Why opt for an advanced agent system instead of a straightforward text-to-SQL pipeline?

Traditional text-to-SQL solutions are often quite rigid. A direct translation from natural language to a database query can easily lead to syntactical errors, causing the database to reject the query. More insidiously, a query might execute without error but produce entirely incorrect or irrelevant results, providing no indication of its inaccuracy. This "silent failure" can be detrimental for critical applications.

👉 An agent-based system, conversely, possesses the crucial capability to critically evaluate outputs and execution logs. It can identify when a query has failed or yielded unexpected results, and then iteratively refine its strategy or reformulate the query. This inherent capacity for self-correction significantly boosts performance and reliability.

Let's dive into building such an agent! 💪

First, ensure all necessary libraries are installed by running the command below:

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

To enable interaction with Large Language Models (LLMs) via inference providers, you'll need an authentication token, such as an HF_TOKEN from Hugging Face. We'll use python-dotenv to load this from your environment variables.

from dotenv import load_dotenv
load_dotenv()

Step 1: Database Initialization

We begin by setting up our in-memory SQLite database using SQLAlchemy. This involves defining our table structures and populating them with initial data.

from sqlalchemy import (
    create_engine,
    MetaData,
    Table,
    Column,
    String,
    Integer,
    Float,
    insert,
    inspect,
    text, # Essential for executing raw SQL expressions
)

# Establish an in-memory SQLite database connection
engine = create_engine("sqlite:///:memory:")
metadata_obj = MetaData()

# Utility function for bulk data insertion
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)

# Define the 'receipts' table schema
table_name = "receipts"
receipts = Table(
    table_name,
    metadata_obj,
    Column("receipt_id", Integer, primary_key=True), # Unique identifier for each transaction
    Column("customer_name", String(255)), # Full name of the patron
    Column("price", Float), # Total cost of the receipt
    Column("tip", Float), # Gratuity amount
)
# Create the defined table within our database
metadata_obj.create_all(engine)

# Sample transaction data
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},
]
# Populate the 'receipts' table
insert_rows_into_table(rows, receipts)

Step 2: Crafting the Agent's Database Tool

For an AI agent to interact with a database, it requires specialized tools. Our sql_engine function will serve as this tool, allowing the agent to execute SQL queries.

The tool's docstring plays a critical role, as its content (the description attribute) is presented to the LLM by the agent system. This description guides the LLM on how and when to utilize the tool, including details about available tables and their column structures.

First, let's extract the schema details for our receipts 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(255)
  - price: FLOAT
  - tip: FLOAT

Now, we'll construct our sql_engine tool. Key elements include:

• The @tool decorator from smolagents to designate it as an agent capability.
• A comprehensive docstring, complete with an Args: section, to inform the LLM about the tool's purpose and expected inputs.
• Type hints for both input and output parameters, enhancing clarity and guiding the LLM's code generation.

from smolagents import tool

@tool
def sql_engine(query: str) -> str:
    """
    Enables execution of SQL queries against the database.
    Outputs the query results as a formatted string.

    Known tables and their column structures:
    Table 'receipts':
      Columns:
        - receipt_id: INTEGER (Primary Key)
        - customer_name: VARCHAR(255)
        - price: FLOAT
        - tip: FLOAT

    Args:
        query: The precise SQL query string to be executed.
               Example: "SELECT customer_name FROM receipts WHERE price > 10.0;"
    """
    output = ""
    with engine.connect() as con:
        # Utilize text() to safely execute raw SQL within SQLAlchemy
        rows = con.execute(text(query))
        for row in rows:
            output += "\n" + str(row) # Converts each row of results into a string representation
    return output

Step 3: Assembling the AI Agent

With our database and tool ready, we now instantiate the CodeAgent. This is smolagents’ flagship agent class, designed to generate and execute code, and to iteratively refine its actions based on the ReAct (Reasoning + Acting) framework.

The model parameter links our agent to a Large Language Model. InferenceClientModel facilitates access to LLMs via Hugging Face's Inference API, supporting both Serverless and Dedicated endpoints. Alternatively, you could integrate other proprietary LLM APIs.

from smolagents import CodeAgent, InferenceClientModel

agent = CodeAgent(
    tools=[sql_engine], # Provide the 'sql_engine' tool to our agent
    model=InferenceClientModel(model_id="meta-llama/Llama-3.1-8B-Instruct"), # Selecting our LLM
)

Step 4: Posing a Query to the Agent

Our agent is now configured. Let's challenge it with a natural language question. The agent will then leverage its LLM and sql_engine tool to find the answer.

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

Understanding the Agent's Iterative Solution Process:

The CodeAgent employs a self-correcting, cyclical approach:

1. Intent Comprehension: The LLM interprets the request, identifying the need to find the "most expensive receipt."
2. Tool Selection: It recognizes that the sql_engine tool is necessary for database interaction.
3. Initial Code Generation: The agent generates its first attempt at a SQL query (e.g., SELECT MAX(price) FROM receipts) to get the maximum price. It then tries to use this result in a follow-up query.
4. Execution and Feedback: The sql_engine executes the query. However, the output is a string like \n(53.43,). If the agent naively tries to embed this string directly into another SQL query (e.g., WHERE price = (53.43,)), it will encounter a syntax error.
5. Adaptive Self-Correction: Upon receiving an OperationalError (e.g., "syntax error" or "could not convert string to float"), the LLM analyzes the error. It understands that the string-formatted output needs to be correctly parsed into a numeric type before being used in subsequent SQL or Python logic. Previous attempts might fail due to unexpected characters (like newlines) or incorrect string manipulation.
6. Refined Strategy: Learning from its previous attempts, the agent eventually generates a more efficient, consolidated SQL query: SELECT MAX(price), customer_name FROM receipts ORDER BY price DESC LIMIT 1. This effectively retrieves both the highest price and the corresponding customer name in a single database call.
7. Result Parsing and Finalization: Finally, the LLM generates Python code to accurately parse the \n(53.43, 'Woodrow Wilson') string output from the sql_engine, extracting the customer name. It then provides the final_answer.

This continuous cycle of reasoning, acting via tools, observing outcomes (including errors), and self-correction is fundamental to the robustness and adaptability of agent-based systems.

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Level 2: Inter-Table Queries (Table Joins)

Let's elevate the complexity! Our goal now is to enable the agent to handle questions that require combining data from multiple tables using SQL joins.

To achieve this, we'll define a second table, waiters, which records the names of waiters associated with each receipt_id.

# Define the 'waiters' table schema
table_name = "waiters"
waiters = Table(
    table_name,
    metadata_obj,
    Column("receipt_id", Integer, primary_key=True), # Links to 'receipts' table
    Column("waiter_name", String(16), primary_key=True), # Name of the assigned waiter
)
# Create the 'waiters' table in the database
metadata_obj.create_all(engine)

# Sample data for the 'waiters' table
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"},
]
# Populate the 'waiters' table
insert_rows_into_table(rows, waiters)

With the introduction of a new table, it's crucial to update the sql_engine tool's description. This ensures the LLM is aware of the waiters table and its schema, allowing it to construct queries that span both tables.

updated_description = """This tool allows performing SQL queries on the database, returning results as a string.
It can access 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)

For more intricate requests like this, switching to a more powerful LLM can significantly enhance the agent's reasoning capabilities. Here, we'll upgrade to Qwen/Qwen2.5-Coder-32B-Instruct.

# Assign the updated description to the tool
sql_engine.description = updated_description

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

agent.run("Which waiter received the highest total amount in tips?")

The agent successfully addresses this challenge, often directly formulating the correct SQL query involving a JOIN operation, and then performing the necessary calculations in Python. The simplicity of setup versus the complexity of the task handled demonstrates the power of this agentic approach!

This tutorial covered several key concepts:

• Constructing custom tools for agents.
• Dynamically updating a tool's description to reflect changes in available data or functionalities.
• Leveraging stronger LLMs to empower an agent's reasoning for more complex tasks.

✅ You are now equipped to start building your own advanced text-to-SQL systems! ✨