Future-House/paper-qa
High accuracy RAG for answering questions from scientific documents with citations
Implements agentic RAG with iterative query refinement and LLM-based re-ranking, automatically enriches documents with metadata (citations, journal quality) from Semantic Scholar and Crossref, and supports multiple document formats (PDFs, text, code, Office files) with full-text search via tantivy. Integrates with any LiteLLM-supported model provider and offers local embedding alternatives, enabling deployment without proprietary APIs.
8,264 stars. Used by 2 other packages. Actively maintained with 7 commits in the last 30 days. Available on PyPI.
Stars
8,264
Forks
838
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 12, 2026
Commits (30d)
7
Dependencies
16
Reverse dependents
2
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