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Mindsql

MindSQL is a Python retrieval-augmented generation library that lets developers build natural-language database query systems using language models, vector stores and database connections.

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Independent OpenViST project profile, based on linked sources.

Mindsql — OpenViST project profile in RAG & Knowledge Systems.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use MindSQL when building a Python application that needs a natural-language interface to database information and you want to combine schema or example indexing with an LLM and vector store.

Who it is for

  • Python developers adding natural-language question answering to database-backed applications.
  • Teams building text-to-SQL assistants that use indexed schema definitions or question–SQL examples.
  • Developers who want to request visualized database results in the documented workflow.

Documented uses

  • Connect an application to a supported database and configure a language model and vector store.
  • Index database DDL statements and question–SQL examples for contextual retrieval.
  • Submit a natural-language question to a database and optionally display a returned chart.
Before you choose

Requirements & limits

The repository is public and lists the GPL-3.0 license. The README specifies Python 3.10 or higher.

  • MindSQL is a Python library that developers incorporate into applications, not a ready-made hosted query interface.
  • The documented database options include PostgreSQL, MySQL and SQLite; Snowflake and BigQuery require extending the IDatabase interface.
  • The README lists GPT-4, Llama 2 and Google Gemini, alongside ChromaDB and Faiss.
  • The example requires a model API key and a database connection URL.
  • The repository is licensed under GPL-3.0.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up a SQLite, Gemini and ChromaDB query workflow

Connect the documented components, index database context and call the natural-language query method.

Before you start

  • Python 3.10 or higher
  • MindSQL installed with pip
  • A Google Gemini API key
  • A SQLite database connection URL

Steps

  1. Install MindSQL with pip install mindsql.

  2. Create a MindSQLCore instance configured with GoogleGenAi, ChromaDB and Sqlite.

  3. Create a database connection using the database URL.

  4. Index the database DDL statements and, if using examples, bulk-index question–SQL pairs from a JSON file.

  5. Call ask_db with a question and the connection; the documented example requests a chart and displays the returned chart.

Expected outcome

A configured MindSQL workflow that can submit a natural-language question to the database and display the returned chart.

Guide limitations

  • The documented sample uses SQLite, Google Gemini and ChromaDB; it does not establish that other configurations work identically.
  • An API key and database connection URL must be supplied by the developer.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Retrieval-augmented applications

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
MindsqlConnect an application to a supported database and configure a language model and vector store.Cloud, Self-hostedThe repository is public and lists the GPL-3.0 license. The README specifies Python 3.10 or higher.
LangsearchCrawl website or local data for a knowledge base.CLI, Self-hosted, Server
Hybrid Search RagCombine document retrieval and language-model generation in a configurable workflow.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
Mindinventory/MindSQL · Official homepage title · retained in its source language
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