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Cdqa

Cdqa is a Python library for closed-domain question answering that retrieves relevant text from a defined corpus and uses a reading-comprehension model to answer questions. Its repository describes a pipeline based on Hugging Face Transformers, with BERT or DistilBERT reader options, and documents corpus preparation, retriever fitting, reader fine-tuning, prediction and evaluation workflows.

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Independent OpenViST project profile, based on linked sources.

Cdqa — 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 Cdqa when you want to study or prototype a Python-based system that answers questions against a defined text corpus and can prepare or fit the retrieval and reading components yourself.

Who it is for

  • Developers prototyping question answering over a fixed document collection.
  • Teams exploring retrieval and reading-comprehension workflows for domain-specific support or internal knowledge content.
  • Learners studying how a closed-domain question-answering pipeline is assembled.

Documented uses

  • Prepare article or document data in a title-and-paragraph dataframe.
  • Convert PDF or Markdown source documents into corpus data; PDF conversion requires Java OpenJDK.
  • Fit a retriever over a custom corpus using a pretrained reader.
  • Fine-tune the reader using a custom SQuAD-like annotated dataset.
  • Request the best answer or multiple answer predictions for a question.
Before you choose

Requirements & limits

The repository is public and specifies the Apache-2.0 license. Its README states that the project is no longer maintained and is retained for educational purposes.

  • Input data must be prepared as a pandas dataframe with title and paragraphs columns, or converted from supported PDF or Markdown documents.
  • PDF conversion requires Java OpenJDK.
  • The repository documents pretrained BERT and DistilBERT reader choices, retriever fitting, optional reader fine-tuning and prediction of one or multiple answers.
  • The repository states the project is no longer maintained, which is a relevant consideration for production adoption.

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

Prepare a corpus from PDF files

Convert a directory of PDFs into the dataframe format Cdqa uses for a document corpus.

Before you start

  • A directory containing PDF files
  • Java OpenJDK installed
  • Cdqa installed in the Python environment

Steps

  1. Install Cdqa with pip using the repository's documented installation command.

  2. Import pdf_converter from cdqa.utils.converters.

  3. Pass the PDF directory path to pdf_converter to create a dataframe.

Expected outcome

A dataframe representing the documents, ready for the documented corpus workflow.

Guide limitations

  • The repository identifies PDF and Markdown converters; Java OpenJDK is specifically required for the PDF converter.
  • The repository states that cdQA is no longer maintained.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
16 October 2026
Original website title
cdqa-suite/cdQA · Official homepage title · retained in its source language
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