OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
Search & Research AI

Primeqa

PrimeQA is an open-source Python repository for training and researching multilingual question-answering models, combining information retrieval with reading comprehension and related NLP workflows.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Primeqa — OpenViST project profile in Search & Research AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use PrimeQA when you need a Python codebase for multilingual QA research, retrieval experiments, or model work on custom datasets, rather than a hosted general-purpose search service.

Who it is for

  • Researchers reproducing question-answering experiments from NLP papers.
  • Developers building or evaluating multilingual question-answering workflows on custom data.
  • Practitioners exploring traditional and neural passage retrieval alongside answer extraction or generation.

Documented uses

  • Train question-answering models and reproduce research experiments.
  • Download pretrained models and apply them to custom data.
  • Retrieve documents or passages with BM25, ColBERT, or DPR as part of a question-answering workflow.
  • Explore multilingual reading comprehension and question generation for text or tables.
  • Generate answers conditioned on retrieved passages using the documented GPT-based retrieval-augmented approach.
Before you choose

Requirements & limits

The repository is public and open source under the Apache-2.0 license.

  • PrimeQA is built on Transformers and uses downloadable datasets and models.
  • BM25 retrieval requires Java 11; the README documents an installation command for it.
  • GPU support is available as a pip extra, but the documented PyTorch/CUDA command is only an example configuration.
  • The README includes an optional conda-forge FAISS setup and notes that it may perform better than default pip-installed FAISS libraries in many environments.
  • The project documents pinned dependencies and says modifying them when installing from source is not officially supported.

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 PrimeQA from the repository

Install the project using one of its documented pip options.

Before you start

  • A checkout of the PrimeQA repository with access to its project root
  • Python and pip; the README specifies pinned project dependencies
  • For GPU installation, install a PyTorch build appropriate to the GPU environment

Steps

  1. Change to the project root.

  2. Choose and run one documented installation command: the command below for a minimal install, the command below for GPU support, or the command below for an editable install with all extras.

    Documented command
    pip install .
    Documented command
    pip install .[gpu]
    Documented command
    pip install -e .[all]

Expected outcome

PrimeQA is installed using the selected minimal, GPU-support, or editable-with-all-extras option.

Guide limitations

  • The README’s GPU PyTorch command is an example for torch 1.11 and CUDA 11.3, not a universal GPU setup.
  • Changing pinned dependencies when installing from source is not officially supported.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
primeqa/primeqa · Official homepage title · retained in its source language
How we review sourcesSuggest a correction