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QueryX

★4.2
💬83
💲Free

QueryX is a powerful Text2SQL tool that translates natural language into SQL queries, making it easier for non-technical users to access and analyze data. It supports multiple languages, integrates with various databases, and offers a chat interface for seamless interaction.

💻
Platform
web
AI-driven insightsData democratizationDatabase integrationNatural Language ProcessingSQL Coder copilotSQL query generationText2SQL

What is QueryX?

QueryX is a Text2SQL tool designed to boost productivity and democratize access to dynamic data using natural language. It allows users to translate natural language conversations into SQL queries, enabling them to retrieve relevant business data. QueryX supports integrations with all main database solutions, helping teams drive business forward faster with data-driven decisions.

Core Technologies

  • Natural Language Processing
  • AI-driven insights
  • Text2SQL

Key Capabilities

  • Natural language to SQL translation
  • Support for multiple languages
  • Integration with various database solutions
  • Chat interface for easy interaction
  • API access for integration with legacy software
  • Automatic error correction
  • Confidence rating engine

Use Cases

  • Doctors using patient information for diagnosis
  • Analysts extracting valuable data from complex databases
  • Customer Experience Managers offering intuitive tools to navigate product information

Core Benefits

  • Speeds up SQL query generation
  • Democratizes access to data for non-technical users
  • Supports multiple languages
  • Offers APIs for integration with existing systems
  • Improves accuracy with built-in error correction
  • Optimizes power consumption compared to Vanilla LLM models

Key Features

  • Natural language to SQL translation
  • Support for multiple languages
  • Integration with various database solutions
  • Chat interface for easy interaction
  • API access for integration with legacy software
  • Automatic error correction
  • Confidence rating engine

How to Use

  1. 1
    Open QueryX and connect to your database.
  2. 2
    Type or dictate your question in natural language.
  3. 3
    QueryX translates the question into an SQL query.
  4. 4
    The tool executes the query and returns the results.
  5. 5
    Ask follow-up questions for deeper analysis.

Frequently Asked Questions

Q.Are my data kept confidential?

A.The QueryX server does not retain any database content. When you use QueryX to generate a SQL query, the database content is never seen by the QueryX backend. The result is never stored.

Q.How does QueryX resolve SQL query generation?

A.QueryX first uses the information you provide about the database structure (tables, columns, joins) and the descriptions you give for each field if they are not explicit from a natural language standpoint or when you want to handle synonyms or acronyms. Specific business rules can be specified.

Q.How can I generate the structure of my database?

A.When you use QueryX Chat or the APIs, you have to provide a json file describing the structure of your database. This json file can be automatically generated when you use the “DB structure extraction bash script” that you can download from QueryX Chat.

Q.Are the answers fully accurate?

A.QueryX Chat will do its best to match a question in natural language to the corresponding SQL query. Although this product surpasses the performance of its competitors, especially the generative AI engines for chat or coding, the performance depends on the nature of the question and the quality of the configuration.

Q.Which language should I use?

A.You can write or dictate your questions in your natural language. Most common languages are supported, including English, French, German, Polish, Portuguese, and many more.

Pros & Cons (Reserved)

✓ Pros

  • Speeds up SQL query generation
  • Democratizes access to data for non-technical users
  • Supports multiple languages
  • Offers APIs for integration with existing systems
  • Improves accuracy with built-in error correction
  • Optimizes power consumption compared to Vanilla LLM models

✗ Cons

  • Performance depends on the nature of the question and quality of configuration
  • Requires providing database structure in JSON format
  • May require rephrasing questions for better accuracy

Alternatives

No alternatives found.