SchemaRAG Database Schema RAG Plugin

Author: joto
Version: 0.1.7
Type: tool
Repository: https://github.com/JOTO-AI/SchemaRAG-dify-plugin

Overview

SchemaRAG is a database schema RAG plugin designed specifically for the Dify platform. It can automatically analyze database structures, build knowledge bases, and implement natural language to SQL queries. This plugin provides a complete database schema analysis and intelligent query solution, ready to use out of the box.

Example workflow download

✨ Core Features

Multi-Database Support: MySQL, PostgreSQL, MSSQL, Oracle, DM (达梦), automatic syntax adaptation
Schema Auto-Analysis: One-click data dictionary generation, structure visualization
Knowledge Base Upload: Automatic upload to Dify, supports incremental updates
Natural Language to SQL: Ready to use out of the box, supports complex queries
AI Data Analysis: Analyze query data, supports custom rules
Data Visualization: Provides visualization tools, LLM recommends charts and fields
Security Mechanism: SELECT-only access, supports field whitelist, minimum privilege principle
Flexible Support: Compatible with mainstream large language models

📋 Configuration Parameters

Parameter Name	Type	Required	Description	Example
Dataset API Key	secret	Yes	Dify knowledge base API key	dataset-xxx
Database Type	select	Yes	Database type MySQL/PostgreSQL/MSSQL/Oracle/DM	MySQL
Database Host	string	Yes	Database host/IP	127.0.0.1
Database Port	number	Yes	Database port	3306/5432
Database User	string	Yes	Database username	root
Database Password	secret	Yes	Database password	******
Database Name	string	Yes	Database name	mydb
Dify Base URL	string	No	Dify API base URL

Supported Database Types

Database Type	Default Port	Driver
MySQL	3306	pymysql
PostgreSQL	5432	psycopg2-binary
Microsoft SQL Server	1433	pymssql
Oracle	1521	oracledb
DM Database (达梦)	5236	dm+pymysql

🚀 Quick Start

Method 1: Command Line

Method 2: Dify Plugin Integration

Fill in the above parameters in the Dify platform plugin configuration interface
After configuration is complete and accurate, click save to automatically build the configured database schema knowledge base in Dify
Add tools in the workflow and configure the knowledge base ID that was just created (the knowledge base ID is in the URL of the knowledge base page)
Provide SQL execution tool, input the generated SQL for direct execution, supports markdown and json output

Method 3: Code Invocation

🛠️ Tool Components

1. text2sql Tool

Natural Language to SQL Query Tool - Convert natural language questions to SQL queries using database schema knowledge base

Core Features

Intelligent Query Conversion: Automatically convert natural language questions to accurate SQL query statements
Multi-Database Support: Supports MySQL, PostgreSQL, MSSQL, Oracle, and DM SQL dialects
Knowledge Base Retrieval: Intelligent retrieval and matching based on database schema knowledge base
Ready to Use: Can be used directly after configuring the knowledge base, no additional setup required
Customize propt rules: Add custom to prompt words and configure custom rules

Parameter Configuration

Parameter	Type	Required	Description
dataset_id	string	Yes	Dify knowledge base ID containing database schema
llm	model-selector	Yes	Large language model for SQL generation
content	string	Yes	Natural language question to convert to SQL
dialect	select	Yes	SQL dialect (MySQL/PostgreSQL/MSSQL/Oracle/DM)
top_k	number	No	Number of results to retrieve from knowledge base (default 5)

2. sql_executer Tool

SQL Query Execution Tool - Safely execute SQL queries and return formatted results

Core Features

Safe Execution: Only supports SELECT queries to ensure data security
Output Control: Provides interface to control maximum query rows to prevent excessive data queries
Multi-Format Output: Supports JSON and Markdown output formats
Direct Connection: Direct database connection for query execution, real-time results
Error Handling: Comprehensive error handling mechanism with detailed error information

Parameter Configuration

Parameter	Type	Required	Description
sql	string	Yes	SQL query statement to execute
output_format	select	Yes	Output format (JSON/Markdown)
max_line	int	No	Maximum number of query rows (default 1000)

3. sql_executer_cust Tool

Custom SQL Query Execution Tool - Custom database connection and safely execute SQL queries to return formatted results

Core Features

Custom Database Connection: Supports multiple databases without plugin configuration
Safe Execution: Only supports SELECT queries to ensure data security
Output Control: Provides interface to control maximum query rows to prevent excessive data queries
Multi-Format Output: Supports JSON and Markdown output formats
Direct Connection: Direct database connection for query execution, real-time results
Error Handling: Comprehensive error handling mechanism with detailed error information

Parameter Configuration

Parameter	Type	Required	Description
database_url	string	Yes	Database connection URL
sql	string	Yes	SQL query statement to execute
output_format	select	Yes	Output format (JSON/Markdown)
max_line	int	No	Maximum number of query rows (default 1000)

Database connection URL examples:

mysql: mysql://user:password@host:port/dbname
postgresql: postgresql://user:password@host:port/dbname
DM: dameng://user:password@host:port/dbname
mssql: mssql://user:password@host:port/dbname
oracle: oracle://user:password@host:port/dbname

4. text2data Tool (recommend)

Natural Language to Data Query Tool - Integrates text2sql and sql_executer functionality for one-stop conversion from questions to data

Core Features

End-to-End Query: Convert natural language questions directly to query results without intermediate steps
Multi-Database Support: Supports MySQL, PostgreSQL, MSSQL, Oracle, and DM databases
Smart Output: Supports JSON, Markdown, and Summary output formats
SQL Auto-Repair: Experimental feature that automatically analyzes and fixes SQL errors when execution fails (requires enablement)
Safe Execution: Built-in SQL security policies to prevent dangerous operations
Optimized Experience: Uses tags to fold intermediate processes, with clear result display

Parameter Configuration

Parameter	Type	Required	Description
dataset_id	string	Yes	Dify knowledge base ID containing database schema, supports multiple IDs separated by commas
llm	model-selector	Yes	Large language model for SQL generation and analysis
content	string	Yes	Natural language question to convert to SQL
dialect	select	Yes	SQL dialect (MySQL/PostgreSQL/MSSQL/Oracle/DM)
output_format	select	Yes	Output format (JSON/Markdown/Summary)
top_k	number	No	Number of results to retrieve from knowledge base (default 5)
max_rows	number	No	Maximum number of rows to return (default 500, prevents excessive data)
example_dataset_id	string	No	Example knowledge base ID, can provide SQL examples to improve generation quality
enable_refiner	boolean	No	Enable SQL auto-repair feature (experimental, default false)
max_refine_iterations	number	No	Maximum SQL repair attempts (1-5, default 3)

SQL Auto-Repair Feature (Experimental)

When is enabled, if the generated SQL execution fails, the system will:

Auto-Analyze Errors: Capture database error messages and specific causes
Intelligent Repair: Use LLM to analyze errors and generate repaired SQL
Iterative Optimization: Support up to N repair attempts (configurable)
Transparent Process: Display repair process within tags

Repair Scenario Examples:

✅ Column name spelling errors (e.g., → )
✅ Table name does not exist or is incorrect
✅ JOIN condition errors
✅ Data type mismatches
✅ Syntax errors (dialect-specific syntax)

Usage Recommendations:

🧪 Experimental feature,Enabling it will increase the consumption of tokens additionally.
📝 Better results in complex Schema scenarios
⚡ Adds 2-10 seconds to response time
💰 Each repair consumes approximately 2000-3000 tokens

5. data_summary Tool

Data Summary Analysis Tool - Intelligent data content analysis and summarization using large language models

Analysis Capabilities

Custom Rules: Supports user-defined analysis rules and guidelines
Smart Data Format Recognition: Automatically identifies JSON and other data formats for optimized processing
Performance Optimized: Cached common configurations to reduce response time

Configuration Options

Parameter	Type	Required	Description
data_content	string	Yes	Data content to be analyzed
llm	model-selector	Yes	Large language model for analysis
query	string	Yes	Analysis query or focus area
custom_rules	string	No	Custom analysis rules
user_prompt	string	No	Custom prompt

6. llm_chart_generator Tool

LLM Intelligent Chart Generation Module - Based on large language models to recommend chart types and fields, using antv to render charts, providing highly maintainable end-to-end chart solutions

Features

Intelligent Analysis: Automatically analyzes user questions and data, intelligently selects the most suitable chart type
Multi-Chart Support: Supports mainstream charts such as bar charts, line charts, pie charts, scatter plots, histograms
High Maintainability: Modular design with clear interfaces, easy to extend and maintain
Unified Standards: Chart configuration uses standardized JSON format for easy integration and parsing
Fallback Solutions: Automatically falls back to table display when chart generation fails
Configuration Validation: Comprehensive configuration validation and error handling mechanisms to ensure stability

Configuration Options

Parameter	Type	Required	Description
user_question	string	Yes	User question describing the chart type and requirements (e.g., sales trends, market share)
data	string	Yes	Data for visualization, supports JSON, CSV, or structured data
llm	model-selector	Yes	Large language model for analysis and chart generation
sql_query	string	Yes	SQL query statement used to recommend charts and fields

❓ FAQ

Q: Which databases are supported?
A: Currently supports MySQL, PostgreSQL, MSSQL, Oracle, and DM (达梦).

Q: Is the data secure?
A: The plugin only reads database structure information to build Dify knowledge base. Sensitive information is not uploaded.

Q: How to configure the database?
A: Configure database and knowledge base related information in the Dify plugin page. After configuration, it will automatically build the schema knowledge base in Dify.

Q: How to use the text2sql tool?
A: After configuring the database and generating the schema knowledge base, you need to obtain the dataset_id from the generated knowledge base URL and fill it into the tool to specify the indexed knowledge base, and configure other information to use it.

Q: What data formats does the data_summary tool support?
A: Supports multiple data formats including text and JSON. The tool automatically recognizes and optimizes processing. Supports data content up to 50,000 characters.

Q: How to use custom rules?
A: You can specify specific analysis requirements, focus points, or constraints in the custom_rules parameter, supporting up to 2,000 characters.

📸 Example Screenshots

📞 Contact

Developer: Dylan Jiang
Email: [email protected]

📄 License

Apache-2.0 license
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