OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
RAG & Knowledge Systems

PaperQA2

A Python library that performs retrieval-augmented generation over scientific documents and returns cited answers.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

PaperQA2 — 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 answers need to be grounded in a scientific document collection and accompanied by citations, especially when a Python-based library or command-line workflow is suitable.

Who it is for

  • Researchers working with scientific literature
  • Scientific teams building document search and synthesis tools
  • Developers creating citation-backed question-answering applications

Documented uses

  • Ask questions across collections of PDFs and text files
  • Retrieve and synthesize relevant scientific passages
  • Produce answers with in-text citations
  • Search a local repository of research documents
  • Support literature-review and internal knowledge-base workflows
Before you choose

Requirements & limits

The repository is public and identified as open source under the Apache-2.0 license. It is distributed as a Python package and documents cloud, self-hosted and server deployment contexts.

  • The package supports PDFs, text files, Microsoft Office documents and source code files.
  • Default operation uses OpenAI embeddings and models with a NumPy vector database.
  • Alternative models and embeddings, including locally hosted options, are documented.
  • Agentic and manual document-querying workflows are both documented.
  • Metadata-aware embeddings, LLM-based reranking and contextual summarization are part of the documented implementation.

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 PaperQA2 and ask a question about PDFs

Set up the package, prepare a folder of research papers, and query that collection from the command line.

Before you start

  • Python package installation with pip
  • A folder named my_papers containing research-paper PDFs
  • A question to submit to the PaperQA2 command-line interface

Steps

  1. Install the package with pip install paper-qa.

  2. Create a folder named my_papers and place research-paper PDFs in it.

  3. Change into the document folder.

  4. Run pqa ask followed by a question about the collection.

Expected outcome

PaperQA2 processes the document collection and returns an answer with in-text citations.

Guide limitations

  • The documented default uses OpenAI embeddings and models with a NumPy vector database.
  • The guide does not establish the requirements for alternative models, external databases or deployment as a service.
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
PaperQA2Ask questions across collections of PDFs and text filesCloud, Self-hosted, ServerThe repository is public and identified as open source under the Apache-2.0 license. It is distributed as a Python package and documents cloud, self-hosted and server deployment contexts.
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
1 October 2026
Original website title
Future-House/paper-qa · Official homepage title · retained in its source language
How we review sourcesSuggest a correction