Future-House / paper-qa

# PaperQA2
PaperQA2 is a package for doing high-accuracy retrieval augmented generation (RAG) on PDFs, text files, Microsoft Office documents, and source code files,
with a focus on the scientific literature.
See our recent 2024 paper
to see examples of PaperQA2's superhuman performance in scientific tasks like
question answering, summarization, and contradiction detection.
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**Table of Contents**
- Quickstart
 - Example Output
- What is PaperQA2
 - PaperQA2 vs PaperQA
 - PaperQA2 Goes CalVer in December 2025
 - What's New in Version 5 (aka PaperQA2)?
 - What's New in December 2025?
 - PaperQA2 Algorithm
- Installation
- CLI Usage
 - Bundled Settings
 - Rate Limits
- Library Usage
 - Agentic Adding/Querying Documents
 - Manual (No Agent) Adding/Querying Documents
 - Async
 - Choosing Model
 - Locally Hosted
 - Embedding Model
 - Specifying the Embedding Model
 - Local Embedding Models (Sentence Transformers)
 - Adjusting number of sources
 - Using Code or HTML
 - Multimodal Support
 - Using External DB/Vector DB and Caching
 - Creating Index
 - Manifest Files
 - Reusing Index
 - Using Clients Directly
- Settings Cheatsheet
- Where do I get papers?
- Callbacks
 - Caching Embeddings
- Customizing Prompts
 - Pre and Post Prompts
- FAQ
 - How come I get different results than your papers?
 - How is this different from LlamaIndex or LangChain?
 - Can I save or load?
- Reproduction
- Citation
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## Quickstart
In this example we take a folder of research paper PDFs,
magically get their metadata - including citation counts with a retraction check,
then parse and cache PDFs into a full-text search index,
and finally answer the user question with an LLM agent.
 [code] 
### Example Output
Question: Has anyone designed neural networks that compute with proteins or DNA?
> The claim that neural networks have been designed to compute with DNA is supported by multiple sources.
> The work by Qian, Winfree, and Bruck demonstrates the use of DNA strand displacement cascades
> to construct neural network components, such as artificial neurons and associative memories,
> using a DNA-based system (Qian2011Neural pages 1-2, Qian2011Neural pages 15-16, Qian2011Neural pages 54-56).
> This research includes the implementation of a 3-bit XOR gate and a four-neuron Hopfield associative memory,
> showcasing the potential of DNA for neural network computation.
> Additionally, the application of deep learning techniques to genomics,
> which involves computing with DNA sequences, is well-documented.
> Studies have applied convolutional neural networks (CNNs) to predict genomic features such as
> transcription factor binding and DNA accessibility (Eraslan2019Deep pages 4-5, Eraslan2019Deep pages 5-6).
> These models leverage DNA sequences as input data,
> effectively using neural networks to compute with DNA.
> While the provided excerpts do not explicitly mention protein-based neural network computation,
> they do highlight the use of neural networks in tasks related to prot