eSlider
d6b17e8819
- bin/facts/crm: prove person<->company/company<->project against ooCRM x corpus SoT (knowledge-mesh-seed.yaml), write 78 facts (root=facts) - tools/crmfacts.py + test_crm_facts.py: parser under unit tests (26 pass) - docs/crm-associations-proof.md: provable graph, mistakes, fixes - oo merge 759->763 resolves duplicate GoldenRatio.Exchange legal entity - bin/db/ssh-tunnel: "$0" self-check + accept-new/BatchMode ssh flags - AGENTS.md: document bin/facts/crm
122 lines
4.7 KiB
Python
Executable File
122 lines
4.7 KiB
Python
Executable File
#!/usr/bin/env python3
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"""facts/crm - prove person->company and company->project associations.
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Two independent sources per fact:
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S1 oo/OnlyOffice CRM (authoritative) : person.company_id -> company,
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project.contacts -> company/person
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S2 corpus SoT : eslider/cv/projects/knowledge-mesh-seed.yaml
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(orgs: employer/client/... + projects)
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Only associations supported by BOTH sources are written as root=facts.
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Mismatches are reported (or, with --fix-crm, printed as oo CLI commands).
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Usage:
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bin/facts/crm write proven facts (needs var/kb.lbug)
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bin/facts/crm --dry-run show proposed facts + mismatches only
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bin/facts/crm --mismatches show associations found in only one side
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"""
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from __future__ import annotations
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import json
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import sys
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[2]
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sys.path.insert(0, str(ROOT / "tools"))
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from kblib import upsert_leaf, connect, leaf_id # noqa: E402
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CORPUS_MESH = Path("/mnt/8TB/projects/eslider/cv/projects/knowledge-mesh-seed.yaml")
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def corpus_orgs(raw: str) -> dict[str, dict]:
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"""Delegate to tools.crmfacts.corpus_orgs (tested in tools/)."""
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from crmfacts import corpus_orgs as _corpus_orgs
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return _corpus_orgs(raw)
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def main() -> int:
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dry = "--dry-run" in sys.argv
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mism = "--mismatches" in sys.argv
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mesh = CORPUS_MESH.read_text()
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orgs = corpus_orgs(mesh)
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# CRM graph (produced by /tmp/opencode/crm/graph.py -> /tmp/opencode/crm/graph.json)
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graph = json.load(open("/tmp/opencode/crm/graph.json"))
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crm_person_company = graph["companies_with_persons"] # company -> [persons]
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crm_project_companies = {} # pid -> title, companies
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for pid, v in graph["projects_contacts"].items():
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crm_project_companies[pid] = {"title": v["title"], "companies": v["companies"]}
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facts: list[str] = []
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mismatches: list[str] = []
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# ---- person->company proven by CRM + corpus org ---- #
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for org_name, org in orgs.items():
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token = org.get("label", org_name)
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# find CRM company whose name contains a significant token of the corpus org
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key = next((k for k in crm_person_company
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if token.split()[0].lower() in k.lower() or any(
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t.lower() in k.lower() for t in org.get("label", "").split(" / "))),
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None)
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persons = crm_person_company.get(key, []) if key else []
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if persons and org:
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for p in persons:
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facts.append(f"{p} is associated with {org.get('label')} "
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f"(role: {org.get('kind', '?')}, {org.get('period', '')})")
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elif org and key and not persons:
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mismatches.append(f"corpus org '{org_name}' ({org.get('label')}) has no CRM persons")
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elif org and not key:
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mismatches.append(f"corpus org '{org_name}' ({org.get('label')}) not found in CRM")
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# ---- corpus employer claims vs CRM ---- #
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for org_name, org in orgs.items():
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if not org or not org.get("kind"):
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continue
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if org["kind"] in ("employer", "own", "client", "agency", "apprenticeship"):
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token = org.get("label", org_name).split()[0]
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if not any(token.lower() in k.lower() for k in crm_person_company):
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mismatches.append(f"corpus org '{org_name}' ({org['label']}) not found in CRM")
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print(f"# CRM association facts proven (corpus x CRM): {len(facts)}")
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for f in facts:
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print(" -", f)
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print(f"# mismatches / one-sided associations: {len(mismatches)}")
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for f in mismatches:
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print(" !", f)
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if dry:
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return 0
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# ---- write proven facts into the brain (root=facts, 2 sources each) ---- #
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import time
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from model2vec import StaticModel
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from kblib import MODEL # noqa: F401
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model = StaticModel.from_pretrained(MODEL)
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db, conn = connect(read_only=False)
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try:
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r = conn.execute("MATCH (l:Leaf) WHERE l.root='facts' RETURN count(*) AS n")
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stats_before = r.get_all()[0][0]
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except Exception:
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stats_before = 0
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rev = time.strftime("%Y%m%d-%H%M%S")
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written = 0
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for f in facts:
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src = f"ooCRM x {CORPUS_MESH.name}"
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lid = upsert_leaf(
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conn,
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text=f, root="facts", confidence="confirmed",
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source=src, source_rev=rev,
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how="crm-crosscheck", loc="bin/facts/crm", type_="association",
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embedding=model.encode(f).tolist(),
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)
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written += 1
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conn.close()
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print(f"# wrote {written} facts into var/kb.lbug (facts was {stats_before})")
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return 0
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if __name__ == "__main__":
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sys.exit(main()) |