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manuelaschrittwieser/SQL-Assistant-Prod

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App README

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
—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:
—"Brain" (Internal Monologue): The generated SQL query.
—"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

1.Install Dependencies:

bash

    pip install -r requirements.txt

1.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.