Paper-Qa

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Paper-qa is an open-source AI tool that provides high-accuracy retrieval augmented generation (RAG) for scientific documents. It offers grounded responses with in-text citations, supporting various file types including PDFs and Microsoft Office documents.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is paper-qa?

Paper-qa is an open-source AI tool designed for high-accuracy retrieval augmented generation (RAG) specifically tailored for scientific documents. It supports a wide range of file types, including PDFs, text files, Microsoft Office documents, and source code. The tool provides grounded responses with in-text citations, ensuring reliability and traceability. Key features include agentic RAG workflows, document metadata-awareness in embeddings, LLM-based re-ranking and contextual summarization, and automatic fetching of paper metadata with citation and journal quality data. It also offers a usable full-text search engine for local repositories and a robust interface for customization, with default support for LiteLLM models and various embedding options.

Best used for

Ideal for professors and researchers who need to quickly and accurately answer questions from scientific documents, summarize complex papers, and verify information with citations. Especially valuable for those working with large volumes of academic literature and requiring high-accuracy, grounded responses.

Common actions

answer questions

summarize documents

analyze scientific literature

retrieve information

detect contradictions

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Capabilities

Key features

High-accuracy RAG
In-text citations
Agentic RAG workflows
Metadata-aware embeddings
LLM re-ranking
Contextual summarization
Full-text search

Target Audience

professor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of documents can PaperQA2 process?

PaperQA2 can process a variety of document types including PDFs, plain text files, Microsoft Office documents, and source code files. It is particularly optimized for scientific literature, enabling high-accuracy information retrieval and summarization from these sources.

Does PaperQA2 support different LLMs and embedding models?

Yes, PaperQA2 is highly configurable. By default, it uses OpenAI embeddings and models with a Numpy vector DB. However, it offers robust support for any LiteLLM compatible model, as well as other closed-source or open-source embedding models, allowing for flexible customization.

How does PaperQA2 ensure the accuracy of its answers?

PaperQA2 ensures accuracy through several mechanisms, including grounded responses with in-text citations, document metadata-awareness in embeddings, and LLM-based re-ranking and contextual summarization (RCS). This approach helps to provide reliable answers directly supported by the source material.

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Subcategories trending in Research & Education

Study Assistants Knowledge Management Course Creation Scientific Computing Summarization Language Learning

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