756f77730f
Seed app/data/operatii-etichetate.json regenerat cu subagenti Haiku pe TOATE cele 17181 operatii distincte (ordine frecventa, 100%), inlocuind seed-ul Groq (3758). Validare Haiku vs Groq pe 157 op etichetate: la dezacorduri Haiku corect ~22/30, Groq ~0. Haiku prinde gunoiul ratat de Groq (ITP, chirie anvelope, nume piese fara actiune): NUL 2200 (12.8%) vs ~7.6% Groq; adaptare electronica OE-7 (nu OE-5), placute frana uzura OE-1 (nu OE-F avarie). US-001..006: prefiltru NUL determinist, etichetator offline, generator seed, seeder mapping_suggestions (in init_db, gated seed_operatii_enabled), embeddings indexeaza corpus etichetat, enrich NUL+kNN. Distributie seed: OE-1 80.1%, NUL 12.8%, OE-2 3.5%, restul rar (OE-4/3/7/8/R/I/5, AITLV, R-ODO). config: seed_operatii_enabled=True + embeddings_enabled=True implicit (SILVER populat + sugestii semantice; ambele suggestion-only, dezactivabile prin env). Suita: 1387 passed, 1 deselected (live). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
60 lines
2.2 KiB
Python
60 lines
2.2 KiB
Python
"""Seeder corpus operatii etichetate -> mapping_suggestions (SILVER, PRD 5.18 US-004).
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Artefactul `app/data/operatii-etichetate.json` e produs offline de
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`tools/mapare-llm/genereaza_seed.py` (etichetare LM Studio, o singura data) si comis
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in repo. La `init_db` il incarcam in `mapping_suggestions` cu INSERT OR IGNORE, ca
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SILVER sa nu mai fie gol in productie (sugestii exact-match + corpus k-NN reale).
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Format seed: [{denumire, denumire_normalizata, cod, is_nul, source, confidence}].
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Reutilizeaza `shared_store.seed_suggestions` (normalizeaza cheia + impune NUL->cod NULL,
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INSERT OR IGNORE). NB (F10): confirmarile UMANE stau in `shared_mappings`, NU aici —
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deci INSERT OR IGNORE pastreaza codul LLM existent la re-seed (v1 = ignore, nu upsert).
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SUGGESTION-ONLY (invariant #13): nimic din SILVER nu intra in resolve_prestatii/load_mapping.
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"""
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from __future__ import annotations
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import json
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import os
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import sqlite3
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from .shared_store import seed_suggestions
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SEED_PATH = os.path.join(os.path.dirname(__file__), "data", "operatii-etichetate.json")
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def load_seed_file(path: str = SEED_PATH) -> list[dict]:
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"""Citeste artefactul seed. Lipsa / invalid -> [] (degradare gratioasa)."""
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if not path or not os.path.exists(path):
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return []
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try:
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with open(path, encoding="utf-8") as fh:
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data = json.load(fh)
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except (ValueError, OSError):
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return []
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return data if isinstance(data, list) else []
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def seed_operatii_etichetate(conn: sqlite3.Connection, path: str = SEED_PATH) -> int:
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"""Incarca seedul in mapping_suggestions (INSERT OR IGNORE). Intoarce nr. randuri inserate.
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Mapeaza cheia seedului `cod` -> `cod_prestatie` (forma asteptata de seed_suggestions);
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`is_nul=True` forteaza cod NULL acolo. Idempotent: re-rularea nu dubleaza randuri.
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"""
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raw = load_seed_file(path)
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if not raw:
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return 0
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items = [
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{
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"denumire": e.get("denumire") or e.get("denumire_normalizata") or "",
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"cod_prestatie": e.get("cod"),
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"is_nul": bool(e.get("is_nul")),
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"source": e.get("source") or "llm_seed",
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"confidence": e.get("confidence") or 0.0,
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}
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for e in raw
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if isinstance(e, dict)
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]
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return seed_suggestions(conn, items)
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