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Neuralqa

Neuralqa is a JavaScript library and visual interface for extractive question answering over large datasets. It retrieves candidate passages through Elasticsearch and uses pretrained BERT models through Hugging Face Transformers to predict answer spans.

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Independent OpenViST project profile, based on linked sources.

Neuralqa — 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 Neuralqa when a large document collection needs a retrieval-plus-reading question-answering workflow and users should inspect the passages and answer evidence.

Who it is for

  • Developers building question-answering interfaces over large document collections.
  • Teams embedding extractive QA into a web application.
  • Researchers or practitioners exploring retrieval, BERT reading and answer-passage review.

Documented uses

  • Search an indexed document collection with a question and retrieve candidate passages.
  • Extract and inspect answer spans from retrieved or manually supplied context.
  • Review passage rankings, keyword matches and model explanations.
  • Optionally expand a query with suggested terms selected by a user.
Before you choose

Requirements & limits

The repository identifies the project as open source under the MIT license.

  • Indexed retrieval uses Elasticsearch; its local installation and launch are separate setup requirements.
  • The Reader predicts spans from retrieved passages and is subject to a stated 512-token processing limit; Neuralqa addresses longer text with chunking and optional relevant snippets.
  • The interface, retriever, reader and expander have YAML configuration options, including selection of reader models.
  • The repository describes the software as alpha and says changes are anticipated.

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

Start the local Neuralqa interface

Install Neuralqa and open its visual interface locally.

Before you start

  • Python package installation with pip3
  • A folder to use with Neuralqa

Steps

  1. Run the command below.

    Documented command
    pip3 install neuralqa

  2. Create or navigate to the folder you want to use with Neuralqa.

  3. Run neuralqa ui --port 4000 in that folder.

  4. Open http://localhost:4000/#/ in a browser.

Expected outcome

The Neuralqa visual interface is available locally on port 4000.

Guide limitations

  • The README labels the project alpha and anticipates changes.
  • This setup alone does not configure an Elasticsearch index; indexed retrieval requires a local Elasticsearch instance and configuration.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
victordibia/neuralqa · Official homepage title · retained in its source language
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