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PaperQA2 is a package for doing high-accuracy retrieval augmented generation (RAG) on PDFs or text 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. In this example we take a folder of research paper PDFs, magically get their metadata - including citation counts and a retraction check, then parse and cache PDFs into a full-text search index, and finally answer the user question with an LLM agent.
Features
- A simple interface to get good answers with grounded responses containing in-text citations
- State-of-the-art implementation including document metadata-awareness in embeddings and LLM-based re-ranking and contextual summarization (RCS)
- Support for agentic RAG, where a language agent can iteratively refine queries and answers
- Documentation available
- Automatic redundant fetching of paper metadata, including citation and journal quality data from multiple providers
- A usable full-text search engine for a local repository of PDF/text files
- A robust interface for customization, with default support for all LiteLLM models
Project Samples
Categories
Scientific/EngineeringLicense
Apache License V2.0Follow PaperQA2
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