LLMAgentR (version 0.2.2)

build_sql_agent: 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.

Value

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

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).

Examples

if (FALSE) {
# 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)
}