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What does it do?

  • Database Interaction
  • Natural Language Processing
  • SQL Generation
  • Schema Documentation
  • Synthetic Data Generation
See more

How is it used?

  • Connect database
  • generate schema
  • deploy text-to-SQL API.
  • 1. Connect database w/ read-only access
  • 2. Generate schema documentation
See more

Who is it good for?

  • Data Analysts
  • Software Developers
  • Database Administrators
  • Startup Founders
  • Business Intelligence Professionals

Details & Features

  • Made By

    Buster
  • Released On

    2022-08-27

Buster is a platform that converts natural language queries into SQL commands, enabling users to interact with databases using everyday language. This tool bridges the gap between databases and Large Language Models (LLMs), making complex database interactions more accessible to non-technical users.

Key features:
- Text-to-SQL API Generation: Creates APIs that translate natural language queries into SQL
- Database Integration: Supports major databases and data warehouses including Postgres, MySQL, Snowflake, BigQuery, and Redshift
- Schema Documentation and Management: Generates schema documentation, defines database nuances, and manages access controls
- Synthetic Data Generation: Produces synthetic data for fine-tuning the LLM to specific database schemas
- Customizable UI Components: Offers pre-built UI components that can be customized through a no-code builder or custom CSS
- Security and Compliance: Provides read-only access, granular access controls, and is pursuing SOC 2 certification and GDPR compliance
- Performance Monitoring: Enables monitoring of model performance, detection of quality issues, and collection of user feedback

How it works:
1. Connect database or data warehouse with read-only access
2. Generate schema documentation and configure security settings
3. Generate synthetic data and fine-tune the LLM to understand the database schema
4. Deploy the model as an API or integrate into applications using pre-built UI components

Integrations:
- Compatible with Postgres, MySQL, Snowflake, BigQuery, and Redshift

Use of AI:
- Utilizes fine-tuned models, Retrieval-Augmented Generation (RAG), proprietary ranking systems, and Reinforcement Learning from Human Feedback (RLHF)

Target users:
- Developers and organizations looking to integrate natural language querying capabilities into their applications
- Suitable for startups to large enterprises, particularly those prioritizing performance, security, and handling of complex or large databases

How to access:
- Available as a web application
- Offers API and SDK for integration into existing systems

  • Supported ecosystems
    Unknown
  • What does it do?
    Database Interaction, Natural Language Processing, SQL Generation, Schema Documentation, Synthetic Data Generation, Access Control Management, Performance Monitoring
  • Who is it good for?
    Data Analysts, Software Developers, Database Administrators, Startup Founders, Business Intelligence Professionals

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