|
--- |
|
title: nlp-to-sql-chat-assistant |
|
emoji: 🚀 |
|
colorFrom: blue |
|
colorTo: green |
|
sdk: gradio |
|
sdk_version: 5.15.0 |
|
app_file: app/app.py |
|
pinned: false |
|
--- |
|
|
|
# SQL Chat Assistant |
|
|
|
## Overview |
|
|
|
This project is a Flask-based chat assistant that converts natural language queries into SQL statements using state-of-the-art NLP models. The system leverages Hugging Face transformer models, sentence embedding techniques, and fine-tuning approaches to generate accurate SQL queries for an SQLite database. |
|
|
|
The primary goal of this project is to enable users to interact with structured data using conversational language, making database queries accessible to non-technical users. |
|
|
|
## Approach |
|
|
|
### 1. Pretrained Transformer Models (Hugging Face) |
|
|
|
Initially, multiple Hugging Face models were tested to generate SQL queries from natural language inputs. However, most of them produced inconsistent results due to their general training data. |
|
|
|
### 2. Sentence Transformers + Cosine Similarity + Parameter Extraction |
|
|
|
To improve query generation, I experimented with an approach that captures the semantic meaning of user queries and maps them to predefined SQL templates using: |
|
|
|
- **Sentence embeddings**: Extracting vector representations of queries. |
|
- **Cosine similarity**: Matching user queries with predefined SQL structures. |
|
- **Regular expression templates**: Extracting SQL parameters dynamically to refine query formation. |
|
|
|
### 3. Fine-Tuning T5-Small with ONNX Quantization |
|
|
|
To enhance accuracy, I fine-tuned the t5-small model using a custom dataset based on the structure of my SQLite database. |
|
|
|
ONNX quantization was applied to reduce the model size and improve deployment efficiency while staying within hosting constraints. |
|
|
|
## Installation Guide |
|
|
|
### 1. Clone the Repository |
|
|
|
```sh |
|
git clone https://github.com/DevashishXO/SQLite-Chat-Assistant.git |
|
cd SQLite-Chat-Assistant |
|
|
|
### 2. Install Dependencies |
|
|
|
```sh |
|
pip install -r requirements.txt |
|
|
|
### 3. Set up the SQLite Database |
|
|
|
```sh |
|
python data/initialize_db.py |
|
|
|
### 4. Run the Flask App |
|
|
|
```sh |
|
$env:FLASK_APP="app.main:app" |
|
flask run |
|
|
|
|
|
|
