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