⚖ Legal Knowledge Wiki

Transform legal documents into a living knowledge graph. Feed in contracts, regulations, playbooks or guidance documents — the tool extracts legal concepts, maps their relationships, identifies knowledge gaps, and produces a full research environment: interactive graph, markdown wiki, source pages, gap-bridge prompts, project context files, incremental ontology, and prioritised to-do lists.

Inspired by Karpathy's LLM Wiki pattern and adapted for professional legal work.

Terminal preview

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✦ Full output on each run

sources/                         Output:
  ├── GDPR.pdf            →      output/knowledge_graph.html     ← interactive D3.js graph
  ├── DPA_template.docx   →      output/graph_prompts.md         ← ready-to-paste Claude prompts
  └── DORA_guide.md       →      wiki/                           ← one article per concept
                                 wiki/sources/                   ← one page per source document
                                 gaps/gap_analysis.json          ← structured gap analysis
                                 todo/                           ← prioritised action lists
                                 .infranodus/ontology.json       ← living memory (incremental)
                                 CLAUDE.md                       ← project context for Claude Code
                                 agents.md                       ← agent workflow configuration

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✦ Five phases

Phase	What happens
1. Extract	PDF/DOCX/TXT → Claude extracts concepts, definitions, types, importance
2. Connect	Cross-document connection mapping + structured gap analysis
3. Source pages	One rich wiki page per ingested document (summary, takeaways, gaps)
4. Wiki articles	One markdown article per concept with wikilinks
5. Outputs	Graph + gap prompts + todos + ontology + CLAUDE.md + agents.md

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✦ Key features

Interactive knowledge graph (output/knowledge_graph.html)

Self-contained HTML — open with any browser, no server needed:

• Force-directed D3.js graph with colour-coded node types
• Node size reflects importance; dashed rings mark knowledge gaps
• Click any node for definition, connections, related concepts
• Gap analysis panel with missing definitions and research questions
• Search bar, zoom/pan/drag

Gap-bridge prompts (output/graph_prompts.md)

The graph structure is exported as ready-to-paste Claude prompts. Each prompt includes the underlying JSON graph so Claude reasons structurally — not just by retrieval. Paste directly into Claude Code terminal to bridge gaps.

Source pages (wiki/sources/)

One structured page per ingested document: summary, key takeaways, evidence/data, notable provisions, relevant concepts (as wikilinks), and open questions.

Living ontology (.infranodus/)

Incrementally updated across runs — new concepts and connections are merged, not overwritten. Historical snapshots in .infranodus/history.jsonl.

Project context (CLAUDE.md + agents.md)

Auto-generated on every run. Open the project in Claude Code and it immediately understands the knowledge base structure, active gaps, and available workflows.

Prioritised todos (todo/)

Three files: sources to add, concepts to develop, research questions to answer — all derived from the gap analysis and updated automatically.

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✦ Concept types

Type	Description	Example
regulation	Laws and regulations	GDPR, DORA, EU AI Act
principle	Legal principles	Accountability, Proportionality
obligation	Legal duties	Data Breach Notification
right	Legal rights	Right to Erasure
risk	Legal risks	Unlimited Liability
term	Defined terms	Personal Data, Controller
standard	Technical/compliance standards	ISO 27001
entity	Organisations/parties	Data Subject, DPA

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✦ Setup

git clone https://github.com/marcoderoni/legal-knowledge-wiki
cd legal-knowledge-wiki

python3 -m venv venv
source venv/bin/activate        # Windows: venv\Scripts\activate

pip install -r requirements.txt

cp env.example .env
# Edit .env and add: ANTHROPIC_API_KEY=your_key_here

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✦ Usage

# Full pipeline — process all docs in sources/
python main.py

# Custom sources folder
python main.py --sources /path/to/legal/docs

# Fast run: graph + prompts + todos only (no wiki articles, no API calls for wiki)
python main.py --no-wiki --no-source-pages

# Regenerate graph/prompts/todos from saved extractions (no API calls at all)
python main.py --mode graph

# Regenerate wiki articles only
python main.py --mode wiki

# Don't auto-open browser
python main.py --no-browser

Input formats

• PDF (.pdf) — via pdfplumber
• Word (.docx, .doc) — via python-docx
• Plain text / Markdown (.txt, .md)

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✦ Working with the knowledge base in Claude Code

Once built, open the project folder in Claude Code. CLAUDE.md gives Claude full context. Example prompts:

"Summarise the main legal obligations in this knowledge base"
"What are the biggest gaps between GDPR and DORA coverage?"
"Use the prompt in output/graph_prompts.md to bridge the gap between X and Y"
"Draft a risk memo on data transfer based on the wiki"
"Which source documents cover controller obligations?"

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✦ Project structure

legal-knowledge-wiki/
├── main.py                      # CLI entry point
├── CLAUDE.md                    # auto-generated — Claude Code context
├── agents.md                    # auto-generated — agent workflow config
├── requirements.txt
├── config/settings.yaml
├── extractor/document.py        # PDF/DOCX/TXT extraction + chunking
├── analyzer/
│   ├── concepts.py              # Claude API: concept extraction
│   └── connections.py           # Claude API: cross-doc connections + gaps
├── reporter/
│   ├── wiki.py                  # Concept wiki article generator
│   ├── source_wiki.py           # Source document page generator  ← NEW
│   ├── visualizer.py            # D3.js HTML graph generator
│   ├── prompt_export.py         # Gap-bridge prompt exporter       ← NEW
│   ├── project_docs.py          # CLAUDE.md + agents.md generator  ← NEW
│   ├── ontology.py              # Incremental .infranodus/ manager  ← NEW
│   └── todo.py                  # Prioritised todo generator        ← NEW
├── sources/                     # ← drop documents here
├── wiki/                        # generated concept articles
│   └── sources/                 # generated source pages
├── concepts/                    # raw JSON extractions
├── connections/                 # connection JSON
├── gaps/                        # gap analysis JSON
├── output/                      # HTML graph + gap prompts
├── todo/                        # action lists
└── .infranodus/                 # living ontology (incremental)

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✦ Configuration (config/settings.yaml)

Model choice: Opus vs Sonnet

This project defaults to claude-opus-4-5 — the most capable Claude model, producing richer concept extraction, more precise relationship mapping, and deeper gap analysis. The tradeoff is speed and cost: expect ~45–60 minutes for a typical set of 5 legal documents.

Switch to claude-sonnet-4-5 for faster, cheaper runs (~5–10 minutes) with only a minor reduction in extraction quality. Recommended for iterative testing; use Opus for final knowledge base builds.

anthropic:
  model: claude-opus-4-5       # or claude-sonnet-4-5 for faster/cheaper
  max_tokens: 4096

extraction:
  max_chunk_size: 8000
  overlap: 200

graph:
  max_nodes: 150
  open_browser: true

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✦ Part of the Legal AI Toolkit

Project	Description
Legal AI Toolkit	Claude Code agent + Make automation
Contract Scanner	Single-contract R/Y/G risk assessment
Contract Bulk Analyzer	Cross-portfolio analysis
Legal GPT Reviewer	Provider-agnostic reviewer (Claude + OpenAI/Groq)
Legal Knowledge Wiki	This project — knowledge graph from legal docs

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License

MIT © Marco De Roni 2026