Indexes PDFs and Markdown files locally for semantic search and AI enrichment.

https://t.co/YQpOntM6tU https://t.co/vjmJfc0yyF

joelhooks/pdf-brain

Source: https://github.com/joelhooks/pdf-brain

pdf-brain

Local PDF & Markdown knowledge base with semantic search and AI-powered enrichment.

Works with PDFs AND Markdown files - Index your research papers, books, notes, docs, and any .md files in one unified, searchable knowledge base.

┌─────────────┐     ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│  PDF / MD   │────▶│   Ollama    │────▶│   Ollama    │────▶│   libSQL    │
│  (extract)  │     │    (LLM)    │     │ (embeddings)│     │  (vectors)  │
└─────────────┘     └─────────────┘     └─────────────┘     └─────────────┘
      │                   │                   │                   │
   pdf-parse         llama3.2:3b        mxbai-embed          HNSW index
   + markdown        enrichment          1024 dims           cosine sim

Features

• PDF + Markdown - Index .pdf and .md files with the same workflow
• Local-first - Everything runs on your machine, no API costs
• AI enrichment - LLM extracts titles, summaries, tags, and concepts
• SKOS taxonomy - Organize documents with hierarchical concepts
• Vector search - Semantic search via Ollama embeddings
• Hybrid search - Combine vector similarity with full-text search
• MCP server - Use with Claude, Cursor, and other AI assistants

Quick Start

Note: pdf-brain is agent-first and emits a single JSON envelope to stdout by default.
Use --format text for human-readable output (and TUI/progress rendering), or inspect the machine contract via pdf-brain capabilities.

# 1. Install (standalone binary, no runtime needed)
curl -fsSL https://raw.githubusercontent.com/joelhooks/pdf-brain/main/scripts/install.sh | bash

# 2. Install Ollama (macOS)
brew install ollama

# 3. Pull required models
ollama pull mxbai-embed-large   # embeddings (required)
ollama pull llama3.2:3b         # enrichment (optional but recommended)

# 4. Start Ollama
ollama serve

# 5. Initialize (creates DB + seeds starter taxonomy)
pdf-brain init

# 6. Add your first document
pdf-brain add ~/Documents/paper.pdf --enrich

Installation

Prerequisites

Ollama is required for embeddings. The LLM model is optional but recommended for enrichment.

# macOS
brew install ollama

# Linux
curl -fsSL https://ollama.com/install.sh | sh

# Windows
# Download from https://ollama.com/download

Models

# Required: Embedding model (1024 dimensions)
ollama pull mxbai-embed-large

# Recommended: Local LLM for enrichment
ollama pull llama3.2:3b

# Start Ollama server
ollama serve

Install pdf-brain

# Standalone binary (no runtime needed)
curl -fsSL https://raw.githubusercontent.com/joelhooks/pdf-brain/main/scripts/install.sh | bash

# or via npm
npm install -g pdf-brain

CLI Reference

Agent Output (Default)

pdf-brain is optimized for agentic workflows: stdout is machine-readable by default.

• --format json|ndjson|text (default: json)
• --pretty pretty-print JSON
• --quiet (alias: --no-hints) omit nextActions
• --log-level silent|error|info|debug (logs go to stderr)

Discover the full command/tool contract (including JSON Schemas) at runtime:

pdf-brain capabilities

Basic Commands

# Check Ollama status
pdf-brain check

# Show library stats
pdf-brain stats

# Initialize library (creates DB, seeds taxonomy)
pdf-brain init

Adding Documents

# Add a PDF
pdf-brain add /path/to/document.pdf

# Add a Markdown file
pdf-brain add /path/to/notes.md

# Add from URL (PDF or MD)
pdf-brain add https://example.com/paper.pdf
pdf-brain add https://raw.githubusercontent.com/user/repo/main/README.md

# Add with manual tags
pdf-brain add document.pdf --tags "ai,agents,research"

# Add with AI enrichment (extracts title, summary, concepts)
pdf-brain add document.pdf --enrich
pdf-brain add notes.md --enrich

Searching

# Semantic search (uses embeddings)
pdf-brain search "context engineering patterns"

# Full-text search only (faster, no embeddings)
pdf-brain search "context engineering" --fts

# Hybrid search (combines both)
pdf-brain search "machine learning" --hybrid

# Limit results
pdf-brain search "query" --limit 5

# Expand context around matches
pdf-brain search "query" --expand 500

Managing Documents

# List all documents
pdf-brain list

# List by tag
pdf-brain list --tag ai

# Get document details
pdf-brain read "document-title"

# Remove a document
pdf-brain remove "document-title"

# Update tags
pdf-brain tag "document-title" "new,tags,here"

Taxonomy Commands

The taxonomy system uses SKOS (Simple Knowledge Organization System) for hierarchical concept organization.

# List all concepts
pdf-brain taxonomy list

# Show concept tree
pdf-brain taxonomy tree

# Show subtree from a concept
pdf-brain taxonomy tree programming

# Search concepts
pdf-brain taxonomy search "machine learning"

# Add a new concept
pdf-brain taxonomy add ai/transformers --label "Transformers" --broader ai-ml

# Assign concept to document
pdf-brain taxonomy assign "doc-id" "programming/typescript"

# Seed taxonomy from JSON file
pdf-brain taxonomy seed --file data/taxonomy.json

Bulk Ingest

Recursively ingest directories containing PDFs and/or Markdown files:

# Ingest a directory with full LLM enrichment
pdf-brain ingest ~/Documents/papers --enrich

# Ingest your Obsidian vault or notes folder
pdf-brain ingest ~/Documents/obsidian --enrich

# Ingest multiple directories (PDFs, Markdown, mixed)
pdf-brain ingest ~/papers ~/books ~/notes --enrich

# With manual tags
pdf-brain ingest ~/books --tags "books,reference"

# Auto-tag only (faster, heuristics + light LLM)
pdf-brain ingest ~/docs --auto-tag

# Process only first N files (for testing)
pdf-brain ingest ~/papers --enrich --sample 10

# Disable TUI for simple output
pdf-brain ingest ~/papers --enrich --no-tui

Supported formats:

• .pdf - Research papers, books, documents
• .md - Notes, documentation, Obsidian vaults, READMEs

Enrichment

When you add documents with --enrich, the LLM extracts:

LLM Providers

Enrichment supports multiple providers via the config system:

# Check current config
pdf-brain config show

# Use local Ollama (default)
pdf-brain config set enrichment.provider ollama
pdf-brain config set enrichment.model llama3.2:3b

# Use AI Gateway (Anthropic, OpenAI, etc.)
pdf-brain config set enrichment.provider gateway
pdf-brain config set enrichment.model anthropic/claude-haiku-4-5
export AI_GATEWAY_API_KEY=your-key

# Provider priority: config > CLI flag > auto-detect
pdf-brain add paper.pdf --enrich              # uses config
pdf-brain add paper.pdf --enrich --provider ollama  # override

Enrichment Fallback

If LLM enrichment fails (API error, rate limit, malformed response), pdf-brain automatically falls back to heuristic-based enrichment:

• Title: Cleaned from filename
• Tags: Extracted from path, filename, and content keywords
• Category: Inferred from directory structure

The actual error is logged so you can debug provider issues.

Taxonomy

The taxonomy is a hierarchical concept system for organizing documents. It ships with a starter taxonomy covering:

• Programming - TypeScript, React, Next.js, Testing, Architecture, DevOps, AI/ML
• Education - Instructional Design, Learning Science, Course Creation, Assessment
• Business - Marketing, Copywriting, Bootstrapping, Product, Sales
• Design - UX, Visual Design, Systems Thinking, Information Architecture
• Meta - Productivity, Note-taking, Knowledge Management, Writing

Growing Your Taxonomy

When enriching documents, the LLM may propose new concepts. These are saved for review:

# See proposed concepts from enrichment
pdf-brain taxonomy proposed

# Accept a specific concept
pdf-brain taxonomy accept ai/rag --broader ai-ml

# Accept all proposed concepts
pdf-brain taxonomy accept --all

# Reject a concept
pdf-brain taxonomy reject ai/rag

# Clear all proposals
pdf-brain taxonomy clear-proposed

# Manually add a concept
pdf-brain taxonomy add ai/rag --label "RAG" --broader ai-ml

# Or edit data/taxonomy.json and re-seed
pdf-brain taxonomy seed --file data/taxonomy.json

Custom Taxonomy

Create your own taxonomy.json:

{
  "concepts": [
    { "id": "cooking", "prefLabel": "Cooking" },
    { "id": "cooking/baking", "prefLabel": "Baking" },
    { "id": "cooking/grilling", "prefLabel": "Grilling" }
  ],
  "hierarchy": [
    { "conceptId": "cooking/baking", "broaderId": "cooking" },
    { "conceptId": "cooking/grilling", "broaderId": "cooking" }
  ]
}

pdf-brain taxonomy seed --file my-taxonomy.json

Configuration

Config File

pdf-brain stores configuration in $PDF_LIBRARY_PATH/config.json:

# Show all config
pdf-brain config show

# Get a specific value
pdf-brain config get enrichment.provider

# Set a value
pdf-brain config set enrichment.model anthropic/claude-haiku-4-5

Config Options

{
  "ollama": {
    "host": "http://localhost:11434"
  },
  "embedding": {
    "provider": "ollama",
    "model": "mxbai-embed-large"
  },
  "enrichment": {
    "provider": "gateway",
    "model": "anthropic/claude-haiku-4-5"
  },
  "judge": {
    "provider": "gateway",
    "model": "anthropic/claude-haiku-4-5"
  }
}

Environment Variables

AI Gateway

For cloud LLM providers (Anthropic, OpenAI, etc.), use the AI Gateway:

# Set your API key
export AI_GATEWAY_API_KEY=your-key

# Configure to use gateway
pdf-brain config set enrichment.provider gateway
pdf-brain config set enrichment.model anthropic/claude-haiku-4-5

# Other supported models:
# - anthropic/claude-sonnet-4-20250514
# - openai/gpt-4o-mini
# - openai/gpt-4o

Storage

~/Documents/.pdf-library/
├── library.db          # libSQL database (vectors, FTS, metadata, taxonomy)
├── library.db-shm      # Shared memory (WAL mode)
├── library.db-wal      # Write-ahead log
└── downloads/          # PDFs downloaded from URLs

Database Size

The database can get large due to vector index overhead. For ~500k chunks:

The *_idx_shadow tables store HNSW neighbor graphs for approximate nearest neighbor search. Each row averages ~100KB.

libSQL quirk: SELECT COUNT(*) FROM embeddings returns 0. Always count a specific column:

SELECT COUNT(chunk_id) FROM embeddings  -- correct

How It Works

1. Extract - PDF text via pdf-parse, Markdown parsed directly
2. Enrich (optional) - LLM extracts metadata, matches taxonomy concepts
3. Chunk - Text split into ~512 token chunks with overlap
4. Embed - Each chunk embedded via Ollama (1024 dimensions)
5. Store - libSQL with vector index (HNSW) + FTS5
6. Search - Query embedded, compared via cosine similarity

MCP Integration

pdf-brain ships as an MCP server for AI coding assistants:

{
  "mcpServers": {
    "pdf-brain": {
      "command": "npx",
      "args": ["pdf-brain", "mcp"]
    }
  }
}

Document Tools

Taxonomy Tools

Config Tools

Utility Tools

Troubleshooting

“Ollama not available”

# Check if Ollama is running
curl http://localhost:11434/api/tags

# Start Ollama
ollama serve

# Check models
ollama list

“Model not found”

# Pull required models
ollama pull mxbai-embed-large
ollama pull llama3.2:3b

“Database locked”

The database uses WAL mode. If you see lock errors:

# Check for zombie processes
lsof ~/Documents/.pdf-library/library.db*

# Force checkpoint
sqlite3 ~/Documents/.pdf-library/library.db "PRAGMA wal_checkpoint(TRUNCATE);"

Slow enrichment

Enrichment is CPU-intensive. For large batches:

• Use --auto-tag instead of --enrich for faster processing
• Run overnight for large libraries
• Consider GPU acceleration for Ollama

Development

# Clone
git clone https://github.com/joelhooks/pdf-brain
cd pdf-brain

# Install
bun install

# Run CLI
bun run src/cli.ts <command>

# Run tests
bun test

# Type check
bun run typecheck

License

MIT