portable-ai-runner / scripts /cortex_knowledge_graph.py
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knowledge graph builder — validated end-to-end (24 nodes, 34 edges)
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#!/usr/bin/env python3
# CORTEX knowledge graph builder — the model's map of its own knowledge.
#
# Reads the drive's actual artifacts (vaults, manifests, cores registry) and emits:
# - knowledge_graph.json {nodes: [...], edges: [...]}
# - knowledge_graph.mmd a mermaid rendering for humans
#
# Nodes: phases, reserved symbols, tiers/cores, models, drives, skills, stores, app.
# Edges: directional pointers only — every edge says WHERE, never what to think.
#
# Usage: python3 cortex_knowledge_graph.py [--root <DRIVE-ROOT>] [--out <path>]
# Deps: stdlib only. Run on the drive after any REPAIR to refresh the graph.
import json, os, re, sys
HERE = os.path.dirname(os.path.abspath(__file__))
ROOT = os.path.abspath(sys.argv[sys.argv.index("--root") + 1]) if "--root" in sys.argv else os.path.dirname(HERE)
OUT = sys.argv[sys.argv.index("--out") + 1] if "--out" in sys.argv else os.path.join(ROOT, "models", "knowledge_graph.json")
def load_json(path):
try:
with open(path, encoding="utf-8") as f:
return json.load(f)
except (OSError, json.JSONDecodeError):
return None
def load_jsonl(path):
out = []
try:
with open(path, encoding="utf-8") as f:
for line in f:
if line.strip():
out.append(json.loads(line))
except OSError:
pass
return out
nodes, edges = [], []
_node_ids = set()
def node(nid, ntype, label, **props):
if nid in _node_ids: # dedupe (stores recur across maps.jsonl entries)
return
_node_ids.add(nid)
nodes.append({"id": nid, "type": ntype, "label": label, **props})
def edge(src, dst, rel):
edges.append({"src": src, "dst": dst, "rel": rel})
# ---- phases ----
for p in ["awake", "repair", "rest"]:
node(f"phase:{p}", "phase", p)
edges += [("phase:awake", "phase:repair", "files proposals via 'proposals' symbol"),
("phase:repair", "phase:rest", "hands over for integrity/consolidation"),
("phase:rest", "phase:awake", "re-indexed map")]
# ---- reserved symbols ----
for s, desc in [("knowledge", "where to LOOK: federated index"),
("proposals", "where to PROPOSE: owner-gated queue")]:
node(f"symbol:{s}", "symbol", s, desc=desc)
edges += [("phase:awake", "symbol:knowledge", "looks up via"),
("phase:awake", "symbol:proposals", "files into when it cannot answer"),
("phase:repair", "symbol:proposals", "consumes after owner gate")]
# ---- tiers (models/cores.json) ----
cores = load_json(os.path.join(ROOT, "models", "cores.json")) or {}
tier_ids = []
for tier, info in (cores.get("tiers") or {}).items():
node(f"tier:{tier}", "tier", tier, awake_core=info.get("awake_core", ""))
tier_ids.append(tier)
edges.append((f"tier:{tier}", "phase:awake", "runs the same three phases"))
# ---- models per tier (handles both manifest layouts) ----
MANIFESTS = [("models/models.json", "browser"), ("models/heavy.json", "heavy"), ("models/heavy70.json", "heavy70")]
for rel, tier in MANIFESTS:
m = load_json(os.path.join(ROOT, rel))
if not m:
continue
models = m.get("models") or m.get("heavy_models") or ([m["model"]] if m.get("model") else [])
for mod in models:
if not mod:
continue
mid = mod.get("gguf_repo") or mod.get("repo") or mod.get("id") or mod.get("name", "unknown")
props = {}
for k_src, k_out in (("gguf_file", "file"), ("file", "file"), ("size_on_disk_gb", "size_gb"),
("size_gb", "size_gb"), ("dtype", "dtype"), ("task", "task"),
("measured_gen_tok_s", "measured_gen_tok_s")):
if mod.get(k_src):
props[k_out] = mod[k_src]
node(f"model:{mid}", "model", mid, tier=tier, **props)
edges.append((f"tier:{tier}", f"model:{mid}", "awake core option"))
# ---- drives, skills, stores ----
for dname in ["memories", "memories-studio"]:
dpath = os.path.join(ROOT, dname)
if not os.path.isdir(dpath):
continue
node(f"drive:{dname}", "drive", dname)
edges.append(("symbol:knowledge", f"drive:{dname}", "federated index covers"))
skills_dir = os.path.join(dpath, "skills")
skill_files = os.listdir(skills_dir) if os.path.isdir(skills_dir) else []
for fn in skill_files:
if fn.endswith(".md"):
sid = f"skill:{fn[:-3]}"
node(sid, "skill", fn[:-3], drive=dname)
edges.append((f"drive:{dname}", sid, "holds"))
edges.append(("phase:repair", sid, "acquired/patched via owner gate"))
try:
with open(os.path.join(dpath, "maps.jsonl"), encoding="utf-8") as f:
map_entries = [json.loads(l) for l in f if l.strip()]
except OSError:
map_entries = []
for e in map_entries:
store = e.get("store", "github-cache")
node(f"store:{store}", "store", store)
edges.append((f"skill:{e['skill']}", f"store:{store}", f"points_to {str(e.get('source', '?'))[:60]}"))
# ---- app ----
node("app:runner", "app", "Portable AI Runner (index.html + serve.py)")
for t in tier_ids:
edges.append(("app:runner", f"tier:{t}", "serves"))
# ---- emit ----
edges = [e if isinstance(e, dict) else {"src": e[0], "dst": e[1], "rel": e[2]} for e in edges]
graph = {"generated_by": "scripts/cortex_knowledge_graph.py", "nodes": nodes, "edges": edges}
os.makedirs(os.path.dirname(os.path.abspath(OUT)), exist_ok=True)
with open(OUT, "w", encoding="utf-8") as f:
json.dump(graph, f, indent=2)
def mmd_id(nid):
return re.sub(r"[^A-Za-z0-9_]", "_", nid.replace(":", "_", 1).replace(".", "_"))
mmd = ["graph LR"]
seen = set()
for e in edges:
a, b = mmd_id(e["src"]), mmd_id(e["dst"])
if (a, b) not in seen:
mmd.append(f" {a}[{e['src']}] -->|{e['rel'][:40]}| {b}[{e['dst']}]")
seen.add((a, b))
open(OUT[:-5] + ".mmd", "w", encoding="utf-8").write("\n".join(mmd) + "\n")
print(f"knowledge graph: {len(nodes)} nodes, {len(edges)} edges -> {OUT}")