build_sql_agent: Build a SQL Agent Graph

In LLMAgentR: Language Model Agents in R for AI Workflows and Research

Build a SQL Agent Graph

Description

This function constructs a full SQL database agent using a graph-based workflow. It supports step recommendation, SQL code generation, error handling, optional human review, and automatic explanation of the final code.

Arguments

model	A function that accepts prompts and returns LLM responses.
connection	A DBI connection object to the target SQL database.
n_samples	Number of candidate SQL plans to consider (used in prompt).
human_validation	Whether to include a human review node.
bypass_recommended_steps	If TRUE, skip the step recommendation node.
bypass_explain_code	If TRUE, skip the final explanation step.
verbose	Logical indicating whether to print progress messages (default: TRUE).

Value

A compiled SQL agent function that runs via a state machine (graph execution).

Examples

## Not run: 
# 1) Connect to the database
conn <- DBI::dbConnect(RSQLite::SQLite(), "tests/testthat/test-data/northwind.db")

# 2) Create the SQL agent
sql_agent <- build_sql_agent(
  model                    = my_llm_wrapper,
  connection               = conn,
  human_validation         = FALSE,
  bypass_recommended_steps = FALSE,
  bypass_explain_code      = FALSE,
  verbose                  = FALSE
)

# 3) Define the initial state
initial_state <- list(
  user_instructions = "Identify the Regions (or Territories) with the highest
  CustomerCount and TotalSales.
  Return a table with columns: Region, CustomerCount, and TotalSales.
Hint: (UnitPrice × Quantity).",
  max_retries       = 3,
  retry_count       = 0
)

# 4) Run the agent
final_state <- sql_agent(initial_state)

## End(Not run)

LLMAgentR documentation built on June 8, 2025, 10:02 a.m.