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paper-qa【文档与办公】Use PaperQA/PaperQA2 for scientific literature RAG, document parsing, metadata hydration, CLI indexing, Settings configuration, and external paper/trial source workflows.
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Use this repo skill when a task involves PaperQA/PaperQA2: high-accuracy retrieval-augmented question answering over scientific papers and local documents, the pqa CLI, document parsing/chunking, metadata providers, named Settings, model/embedding configuration, and literature-source helpers.
PaperQA requires Python 3.11+. For normal use install the public package:
python -m pip install "paper-qa>=5"
Use optional extras only for the workflows you need:
python -m pip install "paper-qa[local]" # sentence-transformers local embeddings
python -m pip install "paper-qa[qdrant]" # Qdrant vector store
python -m pip install "paper-qa[office]" # docx/xlsx/pptx parsing
python -m pip install "paper-qa[pymupdf]" # PyMuPDF PDF reader
python -m pip install "paper-qa[docling]" # Docling PDF reader
python -m pip install "paper-qa[nemotron]" # NVIDIA nemotron-parse reader API
python -m pip install "paper-qa[zotero]" # Zotero helper
python -m pip install "paper-qa[openreview]" # OpenReview helper
Minimal import check:
python - <<'PY'
import paperqa
from paperqa import Docs, Settings
print(paperqa.__version__)
print(Settings().answer.evidence_k)
PY
For a broader no-network readiness check, run scripts/check_paperqa_env.py.
| Task intent | Read |
|---|---|
Use Python APIs such as Docs, ask, agent_query, Docs.aadd_texts, Docs.aget_evidence, Docs.aquery, callbacks, contexts, answers, or sessions | agentic-rag |
| Parse and chunk PDFs, text, Markdown/code, HTML, Office docs, images, multimodal media, or choose a PDF reader extra | docs-and-parsing |
Operate the pqa CLI, build/search/reuse indexes, validate manifest CSVs, manage PQA_HOME, or inspect CLI flags | cli-and-indexing |
Configure Settings, named configs, LiteLLM providers, local model servers, embeddings, prompts, vector stores, or optional dependency choices | settings-and-configuration |
| Use metadata clients, DOI/title hydration, Crossref/Semantic Scholar/OpenAlex/Unpaywall, ClinicalTrials.gov, OpenReview, or Zotero helpers | metadata-and-sources |
pqa ask or paperqa.ask after configuring provider credentials and index paths.Docs() object, add Doc/Text objects or local files, then call await docs.aget_evidence(...) or await docs.aquery(...).Text objects with Settings(parsing={"defer_embedding": True}) and the bundled smoke scripts; do not treat these as answer-quality validation.llm, summary_llm, agent.agent_llm, embedding, and any parser enrichment LLM.PQA_HOME or agent.index.index_directory, decide whether the paper directory should remain relative, and validate manifest CSVs before long indexing runs.pqa ask, Docs.aquery, ask, or agent_query.repo-skills-router import.