k-extract 0.6.0

General-purpose knowledge graph extraction framework

k-extract

Extract knowledge graphs from any codebase or documentation. Point it at your repos, describe what you're trying to understand, and get a graph out.

Quick Start

1. Define what to extract

uvx k-extract init ./my-repo ./another-repo

This walks you through:

• Describing your problem ("I need to understand my testing inventory and coverage gaps")
• Reviewing a proposed ontology (entity types, relationship types)
• Refining until you're satisfied

Produces extraction.yaml — your complete extraction config.

2. Run the extraction

uvx k-extract run --config extraction.yaml

Outputs graph.jsonl. Ctrl-C anytime — re-run to resume where you left off.

3. Load into kartograph

The output is kartograph-compatible JSONL. Feed it to kartograph's mutation endpoint to query your graph.

Requirements

• uv (or Python 3.12+ with pip install k-extract)
• An Anthropic API key (or Vertex AI credentials) — set via environment variables
• Model configured via environment (e.g., ANTHROPIC_MODEL=claude-sonnet-4-6)

Configuration

extraction.yaml is human-readable and fully editable. It contains:

• problem_statement — what you're trying to understand
• data_sources — paths to your repos/data
• ontology — entity and relationship types to extract
• prompts — the exact instructions agents receive (generated, but editable)
• output — where results go (graph.jsonl, extraction.db)

Edit any field, re-run. Changing the config invalidates previous results — use --force to start fresh.

CLI Reference

uvx k-extract init <path> [<path> ...]           # Interactive ontology design
uvx k-extract run --config <yaml>                # Run extraction (resumes by default)
uvx k-extract run --config <yaml> --force        # Discard previous results, start fresh
uvx k-extract jobs --config <yaml>               # Inspect job state
uvx k-extract jobs --config <yaml> --status failed  # See failed jobs

How It Works

1. init scans your data, proposes an ontology based on your problem statement, and generates agent prompts
2. run batches files into jobs sized to the model's context window, then launches parallel agents
3. Each agent reads source files, extracts entities/relationships via tool calls, and commits to a shared store
4. Results stream to graph.jsonl as jobs complete

License

Apache-2.0