SQLDatabaseToolkit#

class langchain_community.agent_toolkits.sql.toolkit.SQLDatabaseToolkit[source]#

Bases: BaseToolkit

SQLDatabaseToolkit for interacting with SQL databases.

Setup:

Install langchain-community.

pip install -U langchain-community

Key init args:

db: SQLDatabase: The SQL database.
llm: BaseLanguageModel: The language model (for use with QuerySQLCheckerTool)

Instantiate:

from langchain_community.agent_toolkits.sql.toolkit import SQLDatabaseToolkit
from langchain_community.utilities.sql_database import SQLDatabase
from langchain_openai import ChatOpenAI

db = SQLDatabase.from_uri("sqlite:///Chinook.db")
llm = ChatOpenAI(temperature=0)

toolkit = SQLDatabaseToolkit(db=db, llm=llm)

Tools:

toolkit.get_tools()

Use within an agent:

from langchain import hub
from langgraph.prebuilt import create_react_agent

# Pull prompt (or define your own)
prompt_template = hub.pull("langchain-ai/sql-agent-system-prompt")
system_message = prompt_template.format(dialect="SQLite", top_k=5)

# Create agent
agent_executor = create_react_agent(
    llm, toolkit.get_tools(), state_modifier=system_message
)

# Query agent
example_query = "Which country's customers spent the most?"

events = agent_executor.stream(
    {"messages": [("user", example_query)]},
    stream_mode="values",
)
for event in events:
    event["messages"][-1].pretty_print()

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

param db: SQLDatabase [Required]#

param llm: BaseLanguageModel [Required]#

get_context() → dict[source]#

Return db context that you may want in agent prompt.

Return type:: dict

get_tools() → List[BaseTool][source]#

Get the tools in the toolkit.

Return type:: List[BaseTool]

property dialect: str#: Return string representation of SQL dialect to use.

Examples using SQLDatabaseToolkit