https://github.com/akshadk7/langflow-adhoc-agent
https://github.com/akshadk7/langflow-adhoc-agent
Last synced: 3 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/akshadk7/langflow-adhoc-agent
- Owner: AkshadK7
- Created: 2025-02-10T12:59:25.000Z (8 months ago)
- Default Branch: master
- Last Pushed: 2025-02-10T13:08:54.000Z (8 months ago)
- Last Synced: 2025-02-10T14:26:30.631Z (8 months ago)
- Language: Jupyter Notebook
- Size: 915 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Langflow-AdHoc-Agent
An interactive application leveraging Langflow for dynamic SQL query generation and execution on user-provided datasets.
## Overview
The **Langflow-AdHoc-Agent** allows users to generate and execute SQL queries dynamically on their datasets through a simple and interactive interface. The project utilizes **Langflow**, **Streamlit**, and **SQLite** to streamline ad-hoc querying of structured data.
With **Retrieval-Augmented Generation (RAG)**, the application enhances query formulation and execution, making it accessible to both technical and non-technical users.
---
## Features
- **Ad-Hoc SQL Querying**
Automatically generate and execute SQL queries on structured data using Langflow.
- **Interactive User Interface**
A simple and intuitive Streamlit UI to upload, process, and query datasets.
- **Automated Schema Generation**
Convert user-provided CSV files into an SQLite database with structured tables.
- **Natural Language Processing**
Allows users to ask questions in natural language, which is then converted into SQL queries.
- **Example Queries and Schema**
Sample queries and dataset schema for reference and testing.
---
## Requirements
- **Python**: 3.11.4
- **pip**: 24.0
- **Libraries**:
- `sqlite3`
- `pandas`
- `streamlit`
- `langflow`
- `python-dotenv`
---
## Setup Instructions
### **1. Clone the Repository**
```bash
git clone https://github.com/AkshadK7/Langflow-AdHoc-Agent.git
cd Langflow-AdHoc-Agent
```
### **2. Create Environment Variables**
Create a `.env` file in the project root and add your **Google Gemini API credentials**.
### **3. Prepare Langflow Configuration**
Create a `T2SQL_Flow.json` file and copy the JSON flow from **Langflow** into it.
### **4. Insert Your Dataset**
Place your CSV file in the `/data` directory.
### **5. Set Up Virtual Environment**
```bash
conda create --name env_name
conda activate env_name
```
### **6. Install Dependencies**
```bash
pip install -r requirements.txt
```
### **7. Install Langflow (if not installed)**
```bash
pip install langflow
```
*If the installation is slow, use:*
```bash
pip install uv
uv pip install langflow
```
### **8. Convert CSV to Database**
```bash
python create_db.py
```
### **9. Run the Application**
```bash
streamlit run app.py
```
---
## Example Usage
### **Sample Dataset (`user_data.csv`)**
| user_id | first_name | last_name | email | city | country | age | annual_income | job_title | department |
|---------|------------|-----------|-----------------|---------------|---------|-----|--------------|------------------|-----------|
| 1 | John | Doe | john.doe@example.com | New York | USA | 34 | 85000 | Software Engineer | IT |
| 6 | Sarah | Lewis | sarah.lewis@example.com | Berlin | Germany | 27 | 72000 | UI/UX Designer | Design |
| 17 | Mason | Phillips | mason.phillips@example.com | Shanghai | China | 32 | 83000 | AI Engineer | AI |
| 25 | Alexander | Carter | alexander.carter@example.com | Helsinki | Finland | 38 | 84000 | Product Designer | Design |
### **Ad-Hoc Query Example**
#### **Question:**
*"Can I get the list of people working in the Design Department?"*
#### **Generated SQL Query**
```sql
SELECT first_name, last_name, email, job_title
FROM user_data
WHERE department = 'Design';
```
#### **Result**
| first_name | last_name | email | job_title |
|------------|----------|----------------------------|-------------------|
| Sarah | Lewis | sarah.lewis@example.com | UI/UX Designer |
| Alexander | Carter | alexander.carter@example.com | Product Designer |
---
## Future Enhancements
- **Integration with More Databases**: Support for PostgreSQL and MySQL.
- **Advanced NLP Models**: Improve SQL generation accuracy using GPT-4 or Transformer-based models.
- **Multi-Language Support**: Process queries in multiple languages.
- **Data Visualization**: Generate charts and graphs for better insights.
---
## License
This project is licensed under the MIT License. See the [LICENSE](https://github.com/AkshadK7/Langflow-AdHoc-Agent/blob/master/LICENSE) file for details.
---
## Acknowledgements
Special thanks to the **Langflow** team and the **open-source community** for their contributions.
---
🚀 **Enhance your SQL querying experience with Langflow-AdHoc-Agent!** 🚀