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RAG & Knowledge Systems

Dstoolkit Text2Sql And Imageprocessing

A public Python repository of adaptable sample components for enriching retrieval-augmented generation applications with SQL data and complex documents, including Text-to-SQL methods, document processing, semantic chunking, and an Azure AI Search deployment utility.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Dstoolkit Text2Sql And Imageprocessing — 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 it when adapting a RAG application to answer questions from SQL tables or retrieve insights from chart- and image-rich documents, and when you need sample code for related Azure AI Search components.

Who it is for

  • Developers building or extending RAG applications with SQL data sources.
  • Teams processing complex documents with charts and images for retrieval through Azure AI Search.

Documented uses

  • Add prompt-based or vector-based Text-to-SQL question answering over SQL tables exposed to an application.
  • Use a query cache with the vector-based Text-to-SQL approach to speed repeated generation.
  • Process charts and images in complex documents with Azure Document Intelligence and Azure AI Search.
  • Prepare documents for retrieval with semantic chunking that groups similar sentences and retains figures and tables together.
  • Deploy AI Search indexes, indexers, and skillsets with the included Python utility.
Before you choose

Requirements & limits

The repository is public and specifies the MIT license. It uses Python and uv for dependency management and common utilities.

  • The Text-to-SQL samples offer prompt-based and vector-based approaches; the vector-based variant also includes query caching.
  • The document-processing example connects Azure Document Intelligence with Azure AI Search and uses GPT-4o for multimodal interpretation.
  • The repository describes its code as an implementation accelerator that should be reviewed and adjusted before production use.

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

Add a Text-to-SQL approach to a RAG application

Adapt a prompt-based or vector-based Text-to-SQL component so a RAG application can answer questions using exposed SQL tables.

Before you start

  • An existing or new RAG application
  • SQL tables exposed to the application

Steps

  1. Review the Text-to-SQL implementations in the repository’s text_2_sql component.

  2. Choose a prompt-based or vector-based approach for generating and querying SQL.

  3. Adapt the selected plugin and add it to the RAG application so it can access exposed SQL tables.

  4. Review and adjust the sample code before production use.

Expected outcome

The RAG application can use data from exposed SQL tables to answer questions.

Guide limitations

  • The repository provides sample code that should be reviewed and adjusted before production use.
  • The documented use covers exposed SQL tables; it does not specify database setup or connection instructions.
Documentation 1 ↗
Source-based practical guide

Connect document processing for charts and images to AI Search

Adapt the repository’s document-processing skill to help a RAG application retrieve insights from charts and images in complex documents.

Before you start

  • An existing or new RAG application
  • Azure AI Search
  • Azure Document Intelligence
  • A multimodal model; the repository names GPT-4o as its example

Steps

  1. Review the code in the repository’s image_processing component.

  2. Adapt the document-processing skill to link Azure Document Intelligence with AI Search.

  3. Use the multimodal model approach to interpret charts and images during vector search.

  4. Review and adjust the sample code before production use.

Expected outcome

The RAG application can draw insights from visual content in processed documents during vector search.

Guide limitations

  • The repository identifies GPT-4o as its multimodal model example.
  • The code is an implementation accelerator and should be reviewed and adjusted before production use.
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
Dstoolkit Text2Sql And ImageprocessingAdd prompt-based or vector-based Text-to-SQL question answering over SQL tables exposed to an application.CloudThe repository is public and specifies the MIT license. It uses Python and uv for dependency management and common utilities.
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
13 October 2026
Original website title
microsoft/dstoolkit-text2sql-and-imageprocessing · Official homepage title · retained in its source language
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