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51 lines
2.0 KiB
Markdown
51 lines
2.0 KiB
Markdown
# Spider Example
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[](https://github.com/microsoft/agent-lightning/actions/workflows/examples-spider.yml)
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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.
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## Requirements
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This example depends on LangChain v0.x and several SQL-related libraries. Install the required dependencies with:
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```bash
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pip install "langgraph<1.0" "langchain[openai]<1.0" "langchain-community" "langchain-text-splitters<1.0" "sqlparse" "nltk"
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```
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Additionally, follow the [installation guide](../../docs/tutorials/installation.md) to install Agent-Lightning and VERL-related dependencies.
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## Dataset
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Detailed dataset preparation instructions are available in the [How to Train a SQL Agent](../../docs/how-to/train-sql-agent.md) guide.
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## Included Files
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| File/Directory | Description |
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| `train_sql_agent.py` | Training script for SQL agents with support for multiple model configurations (Qwen, LLaMA, fast mode for CI) |
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| `sql_agent.py` | SQL agent implementation using LangGraph and LangChain, with debugging capabilities |
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| `data/` | Directory containing the Spider dataset files |
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| `spider_eval/` | Evaluation utilities for assessing SQL agent performance |
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## Running Examples
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### Training
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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:
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```bash
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python train_sql_agent.py qwen
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```
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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](../../docs/how-to/train-sql-agent.md).
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### Debugging
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To test and debug the SQL agent interactively:
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```bash
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python sql_agent.py
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```
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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.
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