Are you tired of grappling with SQL queries and file interactions? Enter SQL_GPT, a revolutionary tool designed to streamline your database interactions through natural language, thanks to cutting-edge Large Language Models (LLMs). This guide will walk you through the process of utilizing SQL_GPT effectively, ensuring you harness its full potential.

Introduction

SQL_GPT offers powerful capabilities to generate SQL queries directly from simple text descriptions. This not only saves time but also enhances your workflow by allowing for seamless interaction with databases. Whether you’re a novice or an expert in SQL, SQL_GPT is here to make your life easier.

Feature List

• Automatic SQL Query Generation
• Error Correction Suggestions
• Database Connection Management
• Multi-Database Compatibility
• Proxy Access Support
• Multi-API KEY Rotation
• SQL Statement Optimization
• Java Persistence Layer SQL Statement Generation
• Multi-Turn Dialogue
• File System Dialogue
• Cache Operations to Accelerate File Dialogue
• Automatic Data Visualization Analysis
• Privacy Protection

Quick Start Guide

Follow these steps to get started with SQL_GPT:

1. Install Required Dependencies: Ensure Python 3.x is installed in your environment and run:

pip install requirements.txt

2. Configure OPENAI: Fill in your OPENAI-KEY and BASE_URL in config.json to facilitate interaction with Chat.
3. Configure Database Connection: Enter your database connection details (hostname, username, password) in config.json.
4. Generate SQL: Utilize the user interface to describe your query in natural language. For example:

from gpt.SQLGPT import SQL_GPT
from gpt.FILEGPT import File_GPT
sql_GPT = SQL_GPT()
file_gpt = File_GPT()
sql_GPT.generateSQL("Perform a join operation on two database tables.") 
# Output: SELECT * FROM table1 JOIN table2 ON table1.column_name = table2.column_name;

Understanding the Code with an Analogy

Imagine wearing a high-tech pair of smart glasses that instantly translate your spoken questions into instructions for a tech-savvy assistant. SQL_GPT functions similarly; you simply describe what you want in plain English, and it translates that into precise SQL commands to communicate with your database. In one command, you can instruct it to “join two tables,” and like a swift assistant, it delivers the appropriate SQL code without any complex input from you.

Troubleshooting

If you encounter issues while using SQL_GPT, here are some common solutions:

• Error in Query Generation: Ensure your natural language description is clear and follows common SQL query structures.
• Database Connection Issues: Double-check your config.json for correct connection details.
• API Key Errors: Confirm that the API keys set in config.json are valid and not expired.
• If problems persist, consider checking the relevant documentation provided in the English Docs or the Chinese Docs.

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System Architecture

SQL_GPT is a local question-answering system based on LLM that generates SQL statements based on your requirements. It can also correct and optimize queries while managing multiple database connections and performing file system interactions.

Requirements

Before diving in, ensure you have the following set up:

• Redis: Install Redis (recommended via Docker).

docker run --restart=always -p 6379:6379 --name redis-docker -d redis:7.0.12 --requirepass admin

• API Key: Acquire the necessary API key for LLM interactions.
• MySQL: Recommended installation via Docker.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Acknowledgements

This project is built upon the incredible efforts of the open-source community. A heartfelt thanks to all contributing projects like FastChat, vicuna-13b, langchain, Auto-GPT, Hugging Face, Chroma, Milvus, ChatGLM, and llama_index!