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---
title: Autonomous SQL Agent
emoji: 💬
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 5.42.0
app_file: app.py
pinned: false
license: mit
short_description: 'An autonomous SQL agent, based on Qwen 2.5 (fine-tuned). '
hf_oauth: true
hf_oauth_scopes:
- inference-api
models:
- manuelaschrittwieser/Qwen2.5-1.5B-SQL-Assistant-Prod
- Qwen/Qwen2.5-1.5B-Instruct
tags:
- agent
- sql
- text-to-sql
- qwen
- qlora
---
# Autonomous SQL Assistant Agent
## 📋 System Overview
The **Autonomous SQL Assistant** is a demonstrative AI agent designed to bridge the gap between natural language inquiries and database execution. Unlike standard "Text-to-SQL" generators that strictly output code, this agent operates within a closed-loop environment: it **generates** syntax, **executes** it against a live database, and **retrieves** the actual data for the user.
The system is powered by **Qwen 2.5 (1.5B)**, fine-tuned via **QLoRA** on the `b-mc2/sql-create-context` dataset to ensure high fidelity in SQL syntax generation.
**[🔗 View Source Code & Documentation](https://github.com/MANU-de/Autonomous-SQL-Agent)**
---
## 🏗️ Technical Architecture
The application runs on a lightweight CPU environment and consists of three core components:
### 1. The Inference Engine
* **Model:** [manuelaschrittwieser/Qwen2.5-1.5B-SQL-Assistant-Prod](https://huggingface.co/manuelaschrittwieser/Qwen2.5-SQL-Assistant-Prod)
* **Optimization:** The model runs in full FP32 precision (CPU optimized).
* **Role:** Translates user intent (e.g., *"Who earns the most?"*) into executable SQLite syntax, utilizing the provided schema context.
### 2. The Execution Sandbox
* **Database:** A transient **SQLite** instance.
* **Schema:** `employees` (id, name, department, salary, hire_date).
* **Lifecycle:** The database is re-instantiated upon every application restart/build to ensure a clean state for testing.
### 3. The Agent Logic
The `SQLAgent` class orchestrates the workflow:
1. **Ingest:** Receives natural language prompt.
2. **Contextualize:** Injects the `CREATE TABLE` schema into the system prompt.
3. **Generate:** produces the SQL query.
4. **Act:** Connects to the SQLite cursor, executes the query, and fetches results.
5. **Sanitize:** Catches execution errors (e.g., syntax errors) and reports them for debugging.
---
## 💻 Usage Instructions
### Interface Guide
The interface is a chat-based UI. You act as the user querying the HR database.
* **Input:** Type natural language questions regarding the `employees` table.
* **Output:** The agent provides a two-part response:
1. **"Brain" (Internal Monologue):** The generated SQL query.
2. **"Result" (Data):** The raw tuples returned from the database.
### Example Queries
Try copying these prompts to test the agent's capabilities:
| Complexity | Query |
| :--- | :--- |
| **Simple** | *Show me the names of all employees in Sales.* |
| **Conditional** | *Who earns more than 60000?* |
| **Aggregation** | *Count how many employees work in the Engineering department.* |
| **Logic** | *List employees hired after 2020.* |
---
## ⚙️ Local Reproduction
To run this Space locally on your machine (requires Python 3.10+):
1. **Clone the Repository:**
```bash
git clone https://huggingface.co/spaces/manuelaschrittwieser/sql-assistant-prod
cd sql-assistant-prod
```
2. **Install Dependencies:**
```bash
pip install -r requirements.txt
```
3. **Launch Application:**
```bash
python app.py
```
---
## ⚠️ Limitations & Scope
* **Inference Latency:** As this demo runs on **CPU Basic** hardware, generating the SQL query may take 2-10 seconds depending on server load.
* **Sandbox Restrictions:** Database modifications (INSERT/DROP) are possible but will persist only until the application restarts.
* **Hallucinations:** While fine-tuned, the model may occasionally generate invalid SQL for highly complex queries not covered in the training distribution.
---
## 📜 License
This project is open-source and available under the **MIT License**. |