Spider Example

This example demonstrates how to train a text-to-SQL agent on the Spider dataset using Agent-Lightning with reinforcement learning. It's compatible with Agent-lightning v0.2 or later.

Requirements

This example depends on LangChain v0.x and several SQL-related libraries. Install the required dependencies with:

bash

pip install "langgraph<1.0" "langchain[openai]<1.0" "langchain-community" "langchain-text-splitters<1.0" "sqlparse" "nltk"

Additionally, follow the installation guide to install Agent-Lightning and VERL-related dependencies.

Dataset

Detailed dataset preparation instructions are available in the How to Train a SQL Agent guide.

Included Files

File/Directory	Description
`train_sql_agent.py`	Training script for SQL agents with support for multiple model configurations (Qwen, LLaMA, fast mode for CI)
`sql_agent.py`	SQL agent implementation using LangGraph and LangChain, with debugging capabilities
`data/`	Directory containing the Spider dataset files
`spider_eval/`	Evaluation utilities for assessing SQL agent performance

Running Examples

Training

Train a SQL agent using the Qwen2.5-Coder-1.5B-Instruct model with the following command. This requires a single node with at least one 40GB GPU:

bash

python train_sql_agent.py qwen

If you want to use an NPU for training, please refer to the Launch Training with NPUS section in How to Train a SQL Agent.

Debugging

To test and debug the SQL agent interactively:

bash

python sql_agent.py

This command requires an OpenAI-compatible API service. Configure your service endpoint and credentials using the OPENAI_API_BASE and OPENAI_API_KEY environment variables.