papermind 1.3.1

Scientific knowledge base — papers, packages, codebases → queryable markdown

PaperMind

Scientific knowledge base: papers, packages, and codebases → queryable markdown.

PaperMind ingests heterogeneous scientific sources — PDFs, PyPI packages, and source trees — into a portable, plain-text knowledge base. A CLI manages ingestion, search, and discovery. An MCP server exposes the KB as tools to any AI assistant that speaks the Model Context Protocol.

Install

Minimum (no PDF or browser support):

pip install papermind

With PDF ingestion (GLM-OCR — requires GPU):

pip install "papermind[ocr]"

Note: GLM-OCR requires a recent transformers build with GLM-OCR support. If pip install "papermind[ocr]" gives a model loading error, install the dev branch: pip install "transformers @ git+https://github.com/huggingface/transformers.git"

With semantic search (qmd):

npm install -g @tobilu/qmd

With browser-based package docs:

pip install "papermind[browser]"
playwright install chromium

Requirements: Python 3.11+, GPU recommended for PDF ingestion

Quick Start

# 1. Create a knowledge base
papermind --kb ~/kb init

# 2. Fetch papers (search + download + OCR + ingest in one step)
papermind --kb ~/kb fetch "SWAT+ calibration machine learning" -n 10 -t swat_ml

# 3. Ingest a local PDF
papermind --kb ~/kb ingest paper path/to/paper.pdf --topic hydrology

# 4. Ingest a Python package's API docs
papermind --kb ~/kb ingest package numpy

# 5. Ingest a codebase (Python, Fortran, C, Rust)
papermind --kb ~/kb ingest codebase ~/src/myproject --name myproject

# 6. Search
papermind --kb ~/kb search "evapotranspiration calibration"
papermind --kb ~/kb search "SWAT" --topic swat_ml

# 7. Check what's in the KB
papermind --kb ~/kb catalog show

CLI Reference

All commands take --kb <path> as a global option. Pass --offline to disable all network access.

Command	Description
init	Initialize a new knowledge base directory
fetch <query>	Search + download + OCR + ingest papers in one step
ingest paper <path>	Add a paper (PDF) via GLM-OCR
ingest package <name>	Extract a PyPI package's API and docs
ingest codebase <path>	Walk a source tree (Python, Fortran, C)
search <query>	Search the KB (semantic via qmd, or grep fallback)
catalog show	List all KB entries (--json for machine-readable)
catalog stats	Summary statistics by type and topic
remove <id>	Remove an entry from the KB
discover <query>	Find papers via OpenAlex / Semantic Scholar / Exa
download <url|doi>	Download a paper PDF
export-bibtex	Export paper citations as BibTeX
doctor	Check installed dependencies and tool availability
reindex	Rebuild catalog.json and catalog.md from filesystem
serve	Start the MCP server (stdio transport)
version	Print version

Examples

# Fetch 10 papers on a topic, auto-download and ingest
papermind --kb ~/kb fetch "differentiable hydrology neural ODE" -n 10 -t diff_hydro

# Preview what fetch would do (no download/ingest)
papermind --kb ~/kb fetch "SWAT calibration" -n 5 --dry-run

# Ingest multiple papers from a directory
papermind --kb ~/kb ingest paper papers/ --topic swat

# Export citations for reference managers
papermind --kb ~/kb export-bibtex > references.bib

# Machine-readable catalog
papermind --kb ~/kb catalog show --json

# Search with topic filter
papermind --kb ~/kb search "calibration" --topic swat_ml

# Run fully offline (no network calls at all)
papermind --kb ~/kb --offline search "groundwater recharge"

# Check tool health
papermind --kb ~/kb doctor

Paper Discovery

PaperMind searches three academic APIs in parallel:

• OpenAlex — free, no API key, direct PDF URLs for open-access papers
• Semantic Scholar — structured metadata, citation counts (optional API key for higher rate limits)
• Exa — broad web search (requires API key)
• Unpaywall — DOI→PDF resolver fallback (free, no key)

PDF OCR

PaperMind uses GLM-OCR (MIT, 0.9B params, #1 OmniDocBench) for PDF→markdown conversion. Features:

• Runs locally on GPU (RTX 3060+ recommended, ~2GB VRAM)
• Outputs structured markdown with LaTeX equations
• Auto-detects section headings (numbered sections, ALL-CAPS)
• Extracts embedded figures as PNG files alongside the markdown
• Source PDF copied next to markdown for easy comparison

Install with pip install "papermind[ocr]". Model downloaded from HuggingFace on first use (~2GB, cached).

MCP Server

PaperMind exposes your KB to AI assistants via the Model Context Protocol.

Claude Code (.claude/mcp.json):

{
  "mcpServers": {
    "papermind": {
      "command": "papermind",
      "args": ["--kb", "/path/to/kb", "serve"]
    }
  }
}

Available MCP tools:

Tool	Description
query	Search the KB; optional scope, topic, limit
get	Read a single document by relative path
multi_get	Read multiple documents in one call
catalog_stats	KB statistics (counts by type and topic)
list_topics	All topics in the KB
discover_papers	Search academic APIs

Search

Two search backends:

• qmd — hybrid search (BM25 + vector embeddings + LLM reranking). Install: npm install -g @tobilu/qmd, then qmd collection add ~/kb --name my-kb
• Built-in fallback — grep-based term matching (zero dependencies)

Configuration

Each KB has a .papermind/config.toml. All keys are optional.

[search]
qmd_path = "qmd"
fallback_search = true

[apis]
semantic_scholar_key = ""
exa_key = ""

[ingestion]
ocr_model = "zai-org/GLM-OCR"
ocr_dpi = 150
default_paper_topic = "uncategorized"

[firecrawl]
api_key = ""

[privacy]
offline_only = false

Environment variables override config file values:

Variable	Purpose
PAPERMIND_EXA_KEY	Exa search API key
PAPERMIND_SEMANTIC_SCHOLAR_KEY	Semantic Scholar API key
PAPERMIND_FIRECRAWL_KEY	Firecrawl API key
HF_TOKEN	HuggingFace token (faster model downloads)

Contributing

git clone https://github.com/dmbrmv/papermind
cd papermind
pip install -e ".[dev]"
uv run pytest tests/ -v
uv run ruff check src/

The test suite is fully offline — no network calls, no external tools required.

License

MIT — see LICENSE.

Third-party dependency licenses: LICENSE_THIRD_PARTY.md.