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DataQA

DataQA is a locally run text-labeling and document-exploration application that uses rules-based weak supervision. It supports Elasticsearch document search, classification, entity extraction and linking entity mentions to a user-provided ontology.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

DataQA — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use DataQA when you want to explore and label a text collection in a local browser-based application, particularly when domain rules can help generate weak labels or when the task involves classification, entity extraction or ontology linking.

Who it is for

  • Teams labeling or exploring collections of unstructured text.
  • Practitioners who can express domain knowledge as labeling rules.
  • Users working on document classification, named entity recognition or entity linking.

Documented uses

  • Search a document collection using Elasticsearch.
  • Classify unstructured documents with rules-based weak supervision.
  • Extract entities from user data or Wikipedia.
  • Link entity mentions to an ontology.
  • Review labeling against ground truth when a `__LABEL__` column is present.
Before you choose

Requirements & limits

The README documents local installation through pip or Docker and states that an internet connection is not required and data does not leave the local machine. The repository identifies the license as GPL-3.0.

  • Imports require UTF-8 CSV files no larger than 30 MB with a text column.
  • DataQA ignores columns other than text, except that a __LABEL__ column can be used to display ground-truth labels and performance.
  • The documented local workflow requires keeping the terminal open while the server is running.
  • Initial application startup and post-upload analysis may take several minutes.

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

Install and start DataQA locally

Set up the local application and open its browser interface.

Before you start

  • Python 3.6, 3.7, 3.8 or 3.9
  • A terminal; a new Python virtual environment is recommended
  • pip

Steps

  1. Update pip with the command below.

    Documented command
    pip install -U pip

  2. Install the application with the command below.

    Documented command
    pip install dataqa

  3. Run dataqa run in the terminal and wait for startup.

  4. Use the browser window that opens, or visit localhost:5000; keep the terminal open while using the application.

Expected outcome

A local DataQA server and browser interface at localhost:5000.

Guide limitations

  • The initial startup can take a few minutes.
  • The documented backend platforms are macOS and Ubuntu; Chrome and Firefox are the browsers listed as tested.
Documentation 1 ↗
Source-based practical guide

Prepare a text CSV for import

Format a document file to meet DataQA's documented upload requirements.

Before you start

  • A CSV file encoded in UTF-8
  • A file no larger than 30 MB

Steps

  1. Place the main document text in a column named text.

  2. Save the file as a UTF-8 CSV.

  3. Keep the file at or below 30 MB before uploading it through the application.

Expected outcome

A CSV file matching the stated input format for DataQA's upload workflow.

Guide limitations

  • Other columns are ignored.
  • Data analysis after upload can take up to five minutes.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
17 October 2026
Original website title
dataqa/nlp-labelling · Official homepage title · retained in its source language
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