codician-dev
988358ad1a
Implements the knowledge-graph playbook (extraction → resolution → assembly → querying + evaluation loop) as a plug & play CLI + .growmos/ JSONL store: task packets for Claude Code / Codex / Grok / Cursor / Gemini, MCP stdio server, git hooks, CI, optional headless provider mode, Apollo example corpus, 18 tests, docs and methodology. Repo dogfoods its own graph.
2.2 KiB
Headless mode: cron, CI, overnight loops
Agent-native mode needs no API key. When no agent is at the keyboard — a nightly job, a CI step, a batch backfill — growmos can call an LLM API itself. It is stdlib-only and provider-neutral.
Configure
export ANTHROPIC_API_KEY=sk-ant-… # provider auto-detected from whichever key is set
# or
export OPENAI_API_KEY=… # OpenAI
export XAI_API_KEY=… # xAI Grok
# or any OpenAI-compatible server:
export GROWMOS_PROVIDER=openai GROWMOS_BASE_URL=http://localhost:11434/v1 GROWMOS_API_KEY=x
# optional per-stage overrides:
export GROWMOS_EXTRACT_MODEL=… GROWMOS_REASON_MODEL=…
Or set provider.name / base_url / extract_model / reason_model in .growmos/config.json
(never commit an API key there — use env vars).
Model split follows the playbook's Table IV: a fast, cheap model for high-volume extraction
(claude-haiku-4-5, gpt-4o-mini, grok-3-mini by default) and a stronger reasoning model for
resolution, summarization and answering (claude-sonnet-5, gpt-4o, grok-3).
Structured outputs are requested (output_config.format json_schema on Anthropic,
response_format json_schema on OpenAI-compatible APIs) so payloads validate by construction.
Run
growmos ingest --scan --limit 25 # extraction → resolution → hub profiles, respecting caps
growmos query "…" --auto # grounded answer via the reasoning model
Cron / GitHub Actions
- run: pip install growmos
- run: growmos ingest --scan
env: { ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }} }
- run: growmos doctor && growmos eval
- run: git add .growmos && git commit -m "growmos: overnight growth" || true
Cost notes (from the playbook's scaling guidance)
- Extraction dominates for large corpora — cache the fixed prompt prefix and batch when your provider offers it; growmos already caps documents per run.
- Resolution is one call per entity type per block (blocks of ≤
resolve_batch_size), not per document. - Summarization is per hub node and only when its source set changed.
- Querying cost is proportional to subgraph size — tune
--hopsand--max-triples.