PDF Research Skill

LightRAG-based PDF document indexing and semantic search for Claude Code research workflows.

Quick Start (For Claude)

When user invokes /pdf-research, Claude should:

1. Check status first: Run python pdf_research.py status to see current configuration
2. Auto-index if requested: When user provides a PDF directory, run indexing automatically
3. Search queries: Execute searches and return formatted results

Automatic Workflow

# Always run from scripts directory
cd ~/.claude/skills/pdf-research/scripts

# Check current status
python pdf_research.py status

# Index PDFs (when user provides a directory)
python pdf_research.py index /path/to/pdfs

# Search (single query)
python pdf_research.py search "user's question" --mode hybrid

# Interactive search session
python pdf_research.py search

Environment Requirements

Before running commands, ensure:

# Activate Python environment with dependencies
source /path/to/venv/bin/activate  # or use system Python with deps installed

# Ensure OpenAI API key is set
export OPENAI_API_KEY=sk-...

Core Capabilities

1. PDF Indexing (index command)

• Extracts text from PDF documents using PyMuPDF
• Creates semantic chunks with metadata
• Builds knowledge graph with entities and relationships
• Generates vector embeddings for semantic search
• Supports incremental indexing (only new files)

2. Semantic Search (search command)

• naive: Simple keyword matching
• local: Focus on specific entities and details
• global: Focus on broad themes and summaries
• hybrid: Combined local + global (recommended)

3. Status Check (status command)

• Shows current configuration
• Lists indexed documents
• Reports storage statistics

4. Configuration (config command)

• Set default PDF directory
• Set default storage directory
• Set default search mode

Claude Integration Protocol

When User Says "Index PDFs" or Provides a Path

1. Verify the path exists
2. Run: python pdf_research.py index <path>
3. Report results (documents indexed, chunks created, storage size)

When User Asks a Question About Documents

1. Check if storage exists: python pdf_research.py status
2. If not indexed, ask user for PDF directory
3. Run search: python pdf_research.py search "<question>"
4. Format and present results with source references

When User Wants to Configure

1. Run: python pdf_research.py config --pdf-dir <path> --storage-dir <path>
2. Confirm configuration saved

Command Reference

# Configure defaults (run once)
python pdf_research.py config --pdf-dir /path/to/pdfs --storage-dir ./rag_storage

# Index PDFs
python pdf_research.py index [pdf_dir] [--storage <path>]

# Search (single query)
python pdf_research.py search "query" [--mode hybrid|local|global|naive]

# Search (interactive)
python pdf_research.py search

# Check status
python pdf_research.py status

Search Modes

Mode	Best For	Description
hybrid	General queries	Combined local + global (default)
local	Specific facts	Names, numbers, definitions
global	Summaries	Themes, trends, overviews
naive	Exact terms	Simple keyword matching

Storage Structure

After indexing, rag_storage/ contains:

File	Description
config.json	User configuration
kv_store_full_docs.json	Full document text
kv_store_text_chunks.json	Semantic chunks
kv_store_full_entities.json	Extracted entities
vdb_*.json	Vector embeddings
graph_*.graphml	Knowledge graph

Example Session

User: /pdf-research ~/Documents/papers 인덱싱해줘

Claude: [Runs indexing]
        Indexing complete!
        - Documents: 5
        - Chunks: 247
        - Storage: 32.5 MB

User: AI 인재 양성 전략에 대해 알려줘

Claude: [Runs search]
        Based on the indexed documents...
        [Detailed response with references]

Troubleshooting

"OPENAI_API_KEY not set"

export OPENAI_API_KEY=sk-your-key

"No indexed data found"

python pdf_research.py index /path/to/pdfs

"Module not found" errors

pip install lightrag-hku[api] pymupdf python-dotenv

Dependencies

• Python 3.10+
• lightrag-hku[api]>=1.4.9
• pymupdf>=1.24.0
• python-dotenv>=1.0.0
• OpenAI API key