ai insight rework #2
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@@ -70,7 +70,8 @@ OLLAMA_BASE_URL=http://127.0.0.1:11434
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LLM_MODEL_NAME=qwen2.5:3b
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LLM_TIMEOUT_SECONDS=1200
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# Plan §6 CPU inference tuning (Profile A 8GB: qwen2.5:1.5b; Profile B 16GB: qwen2.5:3b)
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LLM_NUM_CTX=2048
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LLM_NUM_CTX=8192
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LLM_NUM_PREDICT=768
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LLM_NUM_THREAD=4
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LLM_SEED=42
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LLM_TEMPERATURE=0.2
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@@ -14,6 +14,40 @@ from __future__ import annotations
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from typing import Any
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# ─── Topic-scoped metric registry ────────────────────────────────────────────
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# Each topic grades ONLY its core metrics. Unknown is emitted only for the
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# topic's core metric(s) so the LLM cannot confuse standards with actuals.
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TOPIC_METRICS: dict[str, set[str]] = {
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"hitung_ayam": {"mortality"}, # cumulative mortality + daily mortality % of DOC
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"berat_ayam": {"bw", "adg"}, # body weight + ADG vs standard
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"fcr": {"fcr"}, # FCR vs standard only
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"eef": {"eef", "fcr"}, # EEF/IP + FCR (driver)
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"hitung_karung": {"feed_balance"}, # sack balance (in - poured - remaining)
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"iot_panel": {"environment"}, # temperature vs TET, humidity
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"dashboard": {"bw", "fcr", "mortality", "environment", "eef", "adg"},
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"end_cycle": {"bw", "fcr", "mortality", "environment", "eef", "adg"},
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}
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# EEF absolute scale bands (manager operational rules, NOT from book)
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# Used when book has no IP standard for the day (outside days 7-37).
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# >500 = UNREALISTIC/INVALID per user rule 2026-09-29
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EEF_ABSOLUTE_BANDS: list[tuple[int, int, str, str]] = [
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(501, 9999, "invalid", "EEF >500 tidak realistis — periksa kualitas data FCR/mortalitas/pakan"),
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(400, 500, "ok", "EEF {val} SANGAT BAIK (skala manajer, di luar buku CP 707)"),
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(350, 399, "ok", "EEF {val} BAIK (skala manajer, di luar buku CP 707)"),
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(300, 349, "ok", "EEF {val} STANDAR (skala manajer, di luar buku CP 707)"),
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(200, 299, "warning", "EEF {val} DI BAWAH RATA-RATA (skala manajer, di luar buku CP 707)"),
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(0, 199, "critical", "EEF {val} SANGAT RENDAH (skala manajer, di luar buku CP 707)"),
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]
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# Daily mortality % of DOC thresholds (manager operational rules, NOT from book)
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# Book only has cumulative mortality standard. Daily uses its own thresholds:
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# >0.05% = warning, >=0.1% = critical. Divisor = DOC intake.
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DAILY_MORTALITY_DOC_THRESHOLDS = {
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"warning_pct": 0.05,
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"critical_pct": 0.10,
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}
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# ─── Standar Performa Mingguan CP 707 ───────────────────────────────────────
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PERFORMANCE_STANDARD_WEEKLY: list[dict[str, Any]] = [
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{"week": 1, "targetBW_g": 195, "adg_g": 34, "cumFeedConsumption_g": 164.5, "fcr": 0.844},
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@@ -466,12 +500,13 @@ def analyze_mortality(mortality_rate_pct: Any, day_age: Any) -> dict[str, Any]:
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def analyze_environment(
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temp_c: Any,
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humidity_pct: Any,
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ammonia_ppm: Any,
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day_age: Any,
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) -> dict[str, Any]:
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"""Temperature vs TET + humidity grading. Ammonia removed per 2026-09-28 decision:
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no ammonia data exists in IoT context; old code forced 0.0 making it look fake-safe.
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Advisory note added when humidity/temp indicate ventilation issues."""
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temp = _as_float(temp_c)
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humidity = _as_float(humidity_pct)
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ammonia = _as_float(ammonia_ppm)
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if temp is None:
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return {
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@@ -485,8 +520,6 @@ def analyze_environment(
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if humidity is None:
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humidity = 65.0
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if ammonia is None:
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ammonia = 0.0
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temp_std = get_temp_standard_by_day(day_age)
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tet = get_tet_by_day(day_age)
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@@ -529,18 +562,13 @@ def analyze_environment(
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if overall_status != "critical":
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overall_status = "warning"
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if ammonia > AIR_QUALITY_STANDARD["ammonia"]["critical"]:
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# Advisory: if humidity/temp suggest ventilation problems, advise checking ammonia
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# (not a graded status; no ammonia data in context)
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if overall_status != "ok" and humidity > 75:
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issues.append(
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f"Amonia {ammonia:.1f} ppm di atas batas kritis CP 707 (>25 ppm). "
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"Pertumbuhan ayam menurun. Tingkatkan ventilasi segera dan gemburkan sekam basah."
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"Kondisi kelembapan tinggi + suhu off-target → periksa kadar amonia di kandang "
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"(batas aman CP 707 <10 ppm)."
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)
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overall_status = "critical"
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elif ammonia > AIR_QUALITY_STANDARD["ammonia"]["warning"]:
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issues.append(
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f"Amonia {ammonia:.1f} ppm melebihi batas aman CP 707 (<10 ppm). Tingkatkan sirkulasi udara."
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)
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if overall_status != "critical":
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overall_status = "warning"
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return {
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"status": overall_status,
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@@ -556,6 +584,386 @@ def analyze_environment(
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}
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def analyze_adg(actual_adg: Any, day_age: Any) -> dict[str, Any]:
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"""ADG (Average Daily Gain) vs daily standard from book."""
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actual = _as_float(actual_adg)
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if actual is not None and actual <= 0:
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actual = None
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std_row = _daily_row(day_age)
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std_adg = float(std_row["adg_g"]) if std_row else None
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if actual is None or std_adg is None:
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return {
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"actual": actual,
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"standard": std_adg,
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"delta_pct": None,
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"direction": "unknown",
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"status": "unknown",
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"message": (
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"Umur di luar standar tabel CP 707."
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if std_adg is None
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else "ADG aktual tidak tersedia."
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),
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}
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delta_pct = round(((actual - std_adg) / std_adg) * 100, 1)
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direction = _direction_from_delta(delta_pct)
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if delta_pct < -15:
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status = "critical"
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message = (
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f"ADG {actual:.1f}g/hari jauh di bawah standar CP 707 ({std_adg}g/hari) "
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f"pada hari ke-{day_age}. Deviasi {abs(delta_pct):.1f}%. Periksa konsumsi pakan dan kesehatan ayam."
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)
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elif delta_pct < -5:
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status = "warning"
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message = (
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f"ADG {actual:.1f}g/hari sedikit di bawah standar CP 707 ({std_adg}g/hari) "
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f"pada hari ke-{day_age}. Deviasi {abs(delta_pct):.1f}%. Tingkatkan stimulasi pakan."
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)
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elif delta_pct > 10:
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status = "ok"
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message = (
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f"ADG {actual:.1f}g/hari melampaui standar CP 707 ({std_adg}g/hari) "
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f"pada hari ke-{day_age}. Pertumbuhan sangat baik."
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)
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else:
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status = "ok"
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message = f"ADG {actual:.1f}g/hari sesuai standar CP 707 ({std_adg}g/hari) pada hari ke-{day_age}."
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return {
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"actual": actual,
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"standard": std_adg,
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"delta_pct": float(delta_pct),
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"direction": direction,
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"status": status,
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"message": message,
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}
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def _grade_eef_absolute_scale(eef_value: float) -> tuple[str, str]:
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"""Grade EEF against manager absolute scale bands. Returns (status, message)."""
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for lo, hi, status, msg_template in EEF_ABSOLUTE_BANDS:
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if lo <= eef_value <= hi:
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if status == "invalid":
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return status, msg_template
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return status, msg_template.format(val=round(eef_value))
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# Should not reach here; fallback
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return "unknown", f"EEF {round(eef_value)} tidak dapat digrading"
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def _compute_eef_trend(eef_series: list[dict[str, Any]] | None) -> dict[str, Any] | None:
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"""
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Compute EEF trend from daily series.
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Returns dict with: trend ("up" | "down" | "stable"), delta_3d (points),
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escalation ("sharp_drop_eef" | None).
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"""
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if not eef_series or not isinstance(eef_series, list):
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return None
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# Filter valid EEF points, sort by day
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valid = []
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for p in eef_series:
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if not isinstance(p, dict):
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continue
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day = _as_float(p.get("hari") or p.get("day") or p.get("age"))
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eef = _as_float(p.get("eef") or p.get("ip") or p.get("indeks"))
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if day is not None and eef is not None and eef > 0:
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valid.append({"day": int(day), "eef": float(eef)})
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if len(valid) < 3:
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return None
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valid.sort(key=lambda x: x["day"])
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# Use last 3 days for trend
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last3 = valid[-3:]
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if len(last3) < 3:
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return None
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delta_3d = round(last3[-1]["eef"] - last3[0]["eef"], 1)
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if delta_3d >= 10:
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trend = "up"
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elif delta_3d <= -10:
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trend = "down"
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else:
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trend = "stable"
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escalation = None
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# Sharp drop: >20 points decline in 3 days
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if delta_3d <= -20:
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escalation = "sharp_drop_eef"
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return {
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"trend": trend,
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"delta_3d": delta_3d,
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"escalation": escalation,
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"series_used": last3,
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}
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def _compute_eef_from_components(
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fcr: float | None, mortality_cum_pct: float | None, day_age: int | None, bw_kg: float | None
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) -> float | None:
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"""
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Compute EEF = (BW_kg * viability% * 100) / (FCR * age_days)
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viability% = 100 - cumulative_mortality%
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Returns None if any required component missing.
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"""
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if fcr is None or mortality_cum_pct is None or day_age is None or bw_kg is None:
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return None
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viability = 100.0 - float(mortality_cum_pct)
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if viability <= 0:
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return None
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try:
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eef = (bw_kg * viability * 100.0) / (fcr * day_age)
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return round(eef, 1)
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except ZeroDivisionError:
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return None
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def analyze_eef(
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eef_terakhir: Any,
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day_age: Any,
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fcr: Any = None,
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mortality_cum_pct: Any = None,
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bw_kg: Any = None,
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eef_seri: list[dict[str, Any]] | None = None,
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) -> dict[str, Any]:
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"""
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EEF (IP) grading with dual ladder:
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1. Book standard exists (days 7-37): delta vs book IP = primary status; trend = context
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2. No book standard: manager absolute scale (<200/200-300/300-350/350-400/400-500, >500=invalid)
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3. Trend always computed from eef_seri; sharp drop escalates ladder verdict one level.
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"""
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eef_val = _as_float(eef_terakhir)
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if eef_val is not None and eef_val <= 0:
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eef_val = None
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# Try to compute EEF from components if not provided
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if eef_val is None:
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fcr_f = _as_float(fcr)
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mort_f = _as_float(mortality_cum_pct)
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bw_f = _as_float(bw_kg)
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day = _as_float(day_age)
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if fcr_f and mort_f and bw_f and day:
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eef_val = _compute_eef_from_components(fcr_f, mort_f, int(day), bw_f)
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# Get book IP standard for this day
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ip_std = get_ip_standard_by_day(day_age) if day_age else None
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trend_info = _compute_eef_trend(eef_seri)
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# Start with book standard if available
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if eef_val is not None and ip_std is not None:
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delta_pct = round(((eef_val - ip_std) / ip_std) * 100, 1)
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direction = _direction_from_delta(delta_pct)
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if delta_pct > 10:
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status = "ok"
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message = f"EEF/IP {eef_val:.0f} melampaui standar buku ({ip_std}) sebesar {delta_pct:.1f}% pada hari ke-{day_age}."
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elif delta_pct < -15:
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status = "critical"
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message = f"EEF/IP {eef_val:.0f} jauh di bawah standar buku ({ip_std}) sebesar {abs(delta_pct):.1f}% pada hari ke-{day_age}. Evaluasi FCR dan mortalitas."
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elif delta_pct < -5:
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status = "warning"
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message = f"EEF/IP {eef_val:.0f} di bawah standar buku ({ip_std}) sebesar {abs(delta_pct):.1f}% pada hari ke-{day_age}. Perbaiki efisiensi pakan."
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else:
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status = "ok"
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message = f"EEF/IP {eef_val:.0f} sesuai standar buku ({ip_std}) pada hari ke-{day_age}."
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# Apply trend escalation
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if trend_info and trend_info.get("escalation") == "sharp_drop_eef":
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if status == "ok":
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status = "warning"
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message += " Tren turun tajam (>20 poin/3 hari) — eskalasi ke warning."
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elif status == "warning":
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status = "critical"
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message += " Tren turun tajam (>20 poin/3 hari) — eskalasi ke critical."
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return {
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"actual": eef_val,
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"standard": ip_std,
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"delta_pct": delta_pct,
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"direction": direction,
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"status": status,
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"message": message,
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"trend": trend_info,
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"source": "book_standard",
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}
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# No book standard → manager absolute scale
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if eef_val is not None:
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status, base_message = _grade_eef_absolute_scale(eef_val)
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trend_context = ""
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if trend_info:
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trend_map = {"up": "naik", "down": "turun", "stable": "stabil"}
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trend_context = f" Tren {trend_map.get(trend_info['trend'], trend_info['trend'])} {trend_info['delta_3d']:.0f} poin/3 hari."
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if trend_info.get("escalation") == "sharp_drop_eef":
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# Escalate one level
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if status == "ok":
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status = "warning"
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trend_context += " Eskalasi sharp drop → warning."
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elif status == "warning":
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status = "critical"
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trend_context += " Eskalasi sharp drop → critical."
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message = base_message + trend_context
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return {
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"actual": eef_val,
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"standard": None,
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"delta_pct": None,
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"direction": "unknown",
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"status": status,
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"message": message,
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"trend": trend_info,
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"source": "manager_absolute_scale",
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}
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# No data at all
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return {
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"actual": None,
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"standard": ip_std,
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"delta_pct": None,
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"direction": "unknown",
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"status": "unknown",
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"message": "EEF/IP tidak tersedia (data FCR/mortalitas/bobot/umur tidak lengkap).",
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"trend": trend_info,
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"source": "none",
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}
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def analyze_daily_mortality_doc(
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mati_hari_ini: Any,
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doc_in_ekor: Any,
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) -> dict[str, Any]:
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"""
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Daily mortality % of DOC: (mati_hari_ini / doc_in_ekor) * 100
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Thresholds: >0.05% = warning, >=0.1% = critical (manager rules, not book).
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"""
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deaths = _as_float(mati_hari_ini)
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doc = _as_float(doc_in_ekor)
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if deaths is None or doc is None or doc <= 0:
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return {
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"status": "unknown",
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"direction": "unknown",
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"actual_pct": None,
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"message": "Data mortalitas harian atau DOC tidak tersedia.",
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}
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daily_pct = round((deaths / doc) * 100, 4)
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if daily_pct >= DAILY_MORTALITY_DOC_THRESHOLDS["critical_pct"]:
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status = "critical"
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message = (
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f"Mortalitas harian {daily_pct:.4f}% dari DOC ({int(deaths)}/{int(doc)}) "
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f"≥ {DAILY_MORTALITY_DOC_THRESHOLDS['critical_pct']}% — CRITICAL. "
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"Segera cek penyebab kematian hari ini."
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)
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elif daily_pct > DAILY_MORTALITY_DOC_THRESHOLDS["warning_pct"]:
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status = "warning"
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message = (
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f"Mortalitas harian {daily_pct:.4f}% dari DOC ({int(deaths)}/{int(doc)}) "
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f"> {DAILY_MORTALITY_DOC_THRESHOLDS['warning_pct']}% — WARNING. "
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"Pantau dan cek kebersihan/biosekuriti."
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)
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else:
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status = "ok"
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message = (
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f"Mortalitas harian {daily_pct:.4f}% dari DOC ({int(deaths)}/{int(doc)}) "
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"dalam batas normal (≤0.05%)."
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)
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return {
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"status": status,
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"direction": "di_atas_standar" if status != "ok" else "sesuai_standar",
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"actual_pct": daily_pct,
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"message": message,
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"deaths": deaths,
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"doc": doc,
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}
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|
||||
def analyze_feed_balance(
|
||||
karung_masuk: Any,
|
||||
karung_tuang: Any,
|
||||
karung_sisa: Any,
|
||||
target_harian: Any = None,
|
||||
saldo_awal: Any = None,
|
||||
) -> dict[str, Any]:
|
||||
"""
|
||||
Sack balance: (saldo_awal + masuk) - poured - remaining.
|
||||
Computes feed inventory status based on available stock vs daily usage.
|
||||
"""
|
||||
masuk = _as_float(karung_masuk)
|
||||
tuang = _as_float(karung_tuang)
|
||||
sisa = _as_float(karung_sisa)
|
||||
saldo_awal_num = _as_float(saldo_awal)
|
||||
|
||||
if masuk is None and saldo_awal_num is None:
|
||||
return {
|
||||
"status": "unknown",
|
||||
"direction": "unknown",
|
||||
"message": "Data karung masuk tidak tersedia.",
|
||||
}
|
||||
|
||||
total_masuk = (masuk or 0.0) + (saldo_awal_num or 0.0)
|
||||
balance = total_masuk - (tuang or 0.0) - (sisa or 0.0)
|
||||
iot_vs_manual = None
|
||||
|
||||
if balance < 0:
|
||||
return {
|
||||
"status": "warning",
|
||||
"direction": "di_bawah_standar",
|
||||
"balance_karung": balance,
|
||||
"message": f"Saldo karung negatif ({balance:.1f} karung): total pakan dituang ({tuang or 0.0:.1f} karung) melebihi total stok masuk ({total_masuk:.1f} karung). Periksa mutasi karung.",
|
||||
}
|
||||
|
||||
target = _as_float(target_harian)
|
||||
if target is not None and target > 0:
|
||||
if balance == 0:
|
||||
return {
|
||||
"status": "warning",
|
||||
"direction": "di_bawah_standar",
|
||||
"balance_karung": balance,
|
||||
"message": f"Saldo karung habis (0 karung). Perlu segera restock pakan untuk kebutuhan harian ({target:.1f} karung/hari).",
|
||||
}
|
||||
if balance < target:
|
||||
return {
|
||||
"status": "warning",
|
||||
"direction": "di_bawah_standar",
|
||||
"balance_karung": balance,
|
||||
"message": f"Saldo karung menipis ({balance:.1f} karung), di bawah kebutuhan rata-rata harian ({target:.1f} karung/hari). Perlu persiapan restock pakan.",
|
||||
}
|
||||
days_left = round(balance / target, 1)
|
||||
return {
|
||||
"status": "ok",
|
||||
"direction": "sesuai_standar",
|
||||
"balance_karung": balance,
|
||||
"iot_vs_manual": iot_vs_manual,
|
||||
"message": f"Stok pakan aman. Saldo karung tersisa {balance:.1f} karung (estimasi cukup untuk {days_left:.1f} hari dengan rata-rata konsumsi {target:.1f} karung/hari).",
|
||||
}
|
||||
|
||||
detail_parts = []
|
||||
if saldo_awal_num is not None:
|
||||
detail_parts.append(f"saldo awal {saldo_awal_num:.1f}")
|
||||
if masuk is not None:
|
||||
detail_parts.append(f"masuk {masuk:.1f}")
|
||||
masuk_str = " + ".join(detail_parts) if detail_parts else f"masuk {total_masuk:.1f}"
|
||||
|
||||
return {
|
||||
"status": "ok",
|
||||
"direction": "sesuai_standar",
|
||||
"balance_karung": balance,
|
||||
"iot_vs_manual": iot_vs_manual,
|
||||
"message": f"Saldo karung: {masuk_str} - tuang {tuang or 0.0:.1f} - keluar {sisa or 0.0:.1f} = {balance:.1f} karung.",
|
||||
}
|
||||
|
||||
|
||||
def build_book_reference_context(day_age: Any) -> str:
|
||||
std_bw = get_bw_standard_by_day(day_age)
|
||||
std_fcr = get_fcr_standard_by_day(day_age)
|
||||
@@ -735,6 +1143,17 @@ def _resolve_fcr(context_pack: dict[str, Any]) -> float | None:
|
||||
)
|
||||
|
||||
|
||||
def _resolve_adg(context_pack: dict[str, Any]) -> float | None:
|
||||
"""ADG in g/hari from explicit fields."""
|
||||
return _first_positive_number(
|
||||
context_pack.get("adg_gram"),
|
||||
context_pack.get("adg_g"),
|
||||
context_pack.get("average_daily_gain"),
|
||||
_nested_get(context_pack, "weightStats", "adg", "value"),
|
||||
_nested_get(context_pack, "weight", "current", "adg"),
|
||||
)
|
||||
|
||||
|
||||
def _resolve_mortality(context_pack: dict[str, Any]) -> float | None:
|
||||
"""Cumulative mortality % from explicit fields, % hidup, or kematian/DOC."""
|
||||
direct = _first_number(
|
||||
@@ -778,50 +1197,128 @@ def _resolve_iot(context_pack: dict[str, Any]) -> dict[str, Any]:
|
||||
return context_pack
|
||||
|
||||
|
||||
def analyze_with_cp707_standards(context_pack: dict[str, Any] | None) -> dict[str, Any]:
|
||||
"""Analisis komprehensif; toleran terhadap field InsightContext rebuild dan pack lama."""
|
||||
def analyze_with_cp707_standards(context_pack: dict[str, Any] | None, topic: str | None = None) -> dict[str, Any]:
|
||||
"""Analisis komprehensif; toleran terhadap field InsightContext rebuild dan pack lama.
|
||||
Topic-scoped: only grades metrics in TOPIC_METRICS[topic]. Unknown emitted only for
|
||||
topic-core metrics so LLM cannot confuse standards as actuals.
|
||||
"""
|
||||
pack = context_pack if isinstance(context_pack, dict) else {}
|
||||
day_age = _resolve_day_age(pack)
|
||||
result: dict[str, Any] = {"dayAge": day_age, "analyses": {}}
|
||||
|
||||
actual_bw = _resolve_bw_grams(pack)
|
||||
if day_age is not None:
|
||||
# Always emit a bw block (unknown if missing) so the model cannot treat
|
||||
# day-standard BW as the farm's actual weight.
|
||||
result["analyses"]["bw"] = analyze_bw(actual_bw, day_age)
|
||||
# Determine which metrics to grade for this topic
|
||||
metrics_to_grade = TOPIC_METRICS.get(topic, set()) if topic else {"bw", "fcr", "mortality", "environment"}
|
||||
|
||||
actual_fcr = _resolve_fcr(pack)
|
||||
if day_age is not None:
|
||||
# Always emit fcr (unknown if missing/zero) — same anti-hallucination pattern as bw.
|
||||
result["analyses"]["fcr"] = analyze_fcr(actual_fcr, day_age)
|
||||
# Body weight grading
|
||||
if "bw" in metrics_to_grade:
|
||||
actual_bw = _resolve_bw_grams(pack)
|
||||
if day_age is not None:
|
||||
result["analyses"]["bw"] = analyze_bw(actual_bw, day_age)
|
||||
else:
|
||||
result["analyses"]["bw"] = {"status": "unknown", "message": "Umur tidak tersedia untuk grading bobot badan."}
|
||||
|
||||
mortality_rate = _resolve_mortality(pack)
|
||||
# Always emit mortality (unknown if missing) — silent omission caused the LLM
|
||||
# to narrate STANDAR CP 707 mortality as if it were the farm actual.
|
||||
result["analyses"]["mortality"] = analyze_mortality(mortality_rate, day_age)
|
||||
# FCR grading
|
||||
if "fcr" in metrics_to_grade:
|
||||
actual_fcr = _resolve_fcr(pack)
|
||||
if day_age is not None:
|
||||
result["analyses"]["fcr"] = analyze_fcr(actual_fcr, day_age)
|
||||
else:
|
||||
result["analyses"]["fcr"] = {"status": "unknown", "message": "Umur tidak tersedia untuk grading FCR."}
|
||||
|
||||
iot = _resolve_iot(pack)
|
||||
temp = _first_number(
|
||||
iot.get("suhu_rata_rata_C"),
|
||||
iot.get("avgTemp"),
|
||||
pack.get("suhu_rata_rata_C"),
|
||||
)
|
||||
hum = _first_number(
|
||||
iot.get("kelembapan_persen"),
|
||||
iot.get("humidity"),
|
||||
pack.get("kelembapan_persen"),
|
||||
)
|
||||
ammonia = _first_number(
|
||||
iot.get("amonia_ppm"),
|
||||
iot.get("ammonia"),
|
||||
pack.get("amonia_ppm"),
|
||||
)
|
||||
if temp is not None and day_age is not None:
|
||||
result["analyses"]["environment"] = analyze_environment(
|
||||
temp,
|
||||
hum if hum is not None else 65.0,
|
||||
ammonia if ammonia is not None else 0.0,
|
||||
day_age,
|
||||
# Cumulative mortality grading
|
||||
if "mortality" in metrics_to_grade:
|
||||
mortality_rate = _resolve_mortality(pack)
|
||||
result["analyses"]["mortality"] = analyze_mortality(mortality_rate, day_age)
|
||||
|
||||
# ADG grading (berat_ayam, dashboard, end_cycle)
|
||||
if "adg" in metrics_to_grade:
|
||||
actual_adg = _resolve_adg(pack)
|
||||
if day_age is not None:
|
||||
result["analyses"]["adg"] = analyze_adg(actual_adg, day_age)
|
||||
else:
|
||||
result["analyses"]["adg"] = {"status": "unknown", "message": "Umur tidak tersedia untuk grading ADG."}
|
||||
|
||||
# EEF grading (eef, dashboard, end_cycle)
|
||||
if "eef" in metrics_to_grade:
|
||||
eef_terakhir = _first_number(pack.get("eef_terakhir"), pack.get("ip_terakhir"), pack.get("indeks_prestasi"))
|
||||
fcr = _first_number(pack.get("fcr_terakhir"), pack.get("fcr"))
|
||||
mortality_cum_pct = _first_number(pack.get("mortalitas_kumulatif_persen"), pack.get("mortality_cum_pct"))
|
||||
bw_kg = _first_number(pack.get("bobot_avg_gram"), pack.get("averageWeight"))
|
||||
if bw_kg is not None:
|
||||
bw_kg = bw_kg / 1000.0
|
||||
eef_seri = pack.get("eef_seri")
|
||||
if day_age is not None:
|
||||
result["analyses"]["eef"] = analyze_eef(eef_terakhir, day_age, fcr, mortality_cum_pct, bw_kg, eef_seri)
|
||||
else:
|
||||
result["analyses"]["eef"] = {"status": "unknown", "message": "Umur tidak tersedia untuk grading EEF/IP."}
|
||||
|
||||
# Daily mortality % DOC grading (hitung_ayam, dashboard, end_cycle)
|
||||
if "mortality" in metrics_to_grade and topic in ("hitung_ayam", "dashboard", "end_cycle"):
|
||||
deaths_today = _first_number(pack.get("mortalitas_hari_ini_ekor"), pack.get("mati_hari_ini"))
|
||||
doc_in = _first_number(pack.get("doc_in_ekor"), pack.get("doc"))
|
||||
if deaths_today is not None and doc_in is not None and doc_in > 0:
|
||||
result["analyses"]["daily_mortality_doc"] = analyze_daily_mortality_doc(deaths_today, doc_in)
|
||||
|
||||
# Feed balance grading (hitung_karung)
|
||||
if "feed_balance" in metrics_to_grade:
|
||||
saldo_awal = _first_number(
|
||||
pack.get("saldo_awal_karung"),
|
||||
pack.get("saldo_awal"),
|
||||
pack.get("initial_balance"),
|
||||
pack.get("feed_initial_balance"),
|
||||
)
|
||||
karung_masuk = _first_number(
|
||||
pack.get("karung_masuk"),
|
||||
pack.get("karungMasuk_karung"),
|
||||
pack.get("total_karung_masuk_iot"),
|
||||
pack.get("total_karung_masuk_manual"),
|
||||
pack.get("sacks_in"),
|
||||
)
|
||||
karung_tuang = _first_number(
|
||||
pack.get("karung_tuang"),
|
||||
pack.get("karungDituang_karung"),
|
||||
pack.get("total_karung_dituang_iot"),
|
||||
pack.get("total_karung_dituang_manual"),
|
||||
pack.get("sacks_poured"),
|
||||
)
|
||||
karung_sisa = _first_number(
|
||||
pack.get("karung_sisa"),
|
||||
pack.get("karungKeluar_karung"),
|
||||
pack.get("total_karung_keluar_iot"),
|
||||
pack.get("total_karung_keluar_manual"),
|
||||
pack.get("sacks_remaining"),
|
||||
)
|
||||
target_harian = _first_number(
|
||||
pack.get("target_harian"),
|
||||
pack.get("daily_target"),
|
||||
pack.get("rata_karung_per_hari"),
|
||||
pack.get("rataKarungDituangPerHari_karung"),
|
||||
)
|
||||
if karung_masuk is not None or saldo_awal is not None:
|
||||
result["analyses"]["feed_balance"] = analyze_feed_balance(
|
||||
karung_masuk, karung_tuang, karung_sisa, target_harian, saldo_awal=saldo_awal
|
||||
)
|
||||
else:
|
||||
result["analyses"]["feed_balance"] = {"status": "unknown", "message": "Data karung masuk tidak tersedia."}
|
||||
|
||||
# Environment grading (iot_panel, dashboard, end_cycle) - NO ammonia
|
||||
if "environment" in metrics_to_grade:
|
||||
iot = _resolve_iot(pack)
|
||||
temp = _first_number(
|
||||
iot.get("suhu_rata_rata_C"),
|
||||
iot.get("avgTemp"),
|
||||
pack.get("suhu_rata_rata_C"),
|
||||
)
|
||||
hum = _first_number(
|
||||
iot.get("kelembapan_persen"),
|
||||
iot.get("humidity"),
|
||||
pack.get("kelembapan_persen"),
|
||||
)
|
||||
if temp is not None and day_age is not None:
|
||||
result["analyses"]["environment"] = analyze_environment(
|
||||
temp,
|
||||
hum if hum is not None else 65.0,
|
||||
day_age,
|
||||
)
|
||||
|
||||
return result
|
||||
@@ -109,6 +109,18 @@ ATURAN ANGKA & ARAH (WAJIB DIPATUHI):
|
||||
- Panen ≠ kematian; jangan hitung mortalitas dari selisih populasi awal − kini.
|
||||
- DILARANG menghitung sendiri, memperkirakan, atau membalik arah tren.
|
||||
|
||||
ATURAN STANDAR CP 707 — DILARANG MENGARANG (WAJIB DIPATUHI):
|
||||
- Blok STANDAR CP 707 menyatakan PERSIS angka mana yang ada di buku dan mana yang TIDAK.
|
||||
- Jika standar menyatakan "TIDAK tercantum di buku" atau "N/A" — DILARANG KERAS mengarang
|
||||
angka standar sendiri (mis. "standar mortalitas 9.32%" atau "standar EEF 235").
|
||||
- EEF/IP standar HANYA ada hari ke-7 s/d ke-37 (rentang 327–380). Di luar rentang itu
|
||||
standar TIDAK ADA — jangan mengarang.
|
||||
- Mortalitas kumulatif standar hanya sampai hari ke-37 (4.65%). Setelah hari 37 buku hanya
|
||||
memperkirakan +0.1%/hari — ini BUKAN standar resmi. Jika hari >37, tulis "standar tidak
|
||||
tercantum di buku untuk hari ini" dan bandingkan dengan 4.65% + estimasi, bukan mengarang.
|
||||
- FCR standar hari >37 gunakan fallback 1.65 (bukan standar buku) — sebutkan "fallback" bukan standar.
|
||||
- Bobot standar hanya hari 1–37. Di luar itu "tidak tercantum di buku".
|
||||
|
||||
FORMAT OUTPUT (JSON SAJA):
|
||||
{"kesimpulan":"...","insight":"..."}
|
||||
"""
|
||||
@@ -139,14 +151,151 @@ def build_insight_user_prompt(
|
||||
report_type: str,
|
||||
report_period: str,
|
||||
context: dict[str, Any],
|
||||
graded: dict[str, Any] | None = None,
|
||||
is_end_cycle: bool = False,
|
||||
hypotheses: list[dict[str, Any]] | None = None,
|
||||
) -> str:
|
||||
"""User prompt for Ollama — name as-is, never prefix 'kandang' or expose ids."""
|
||||
"""Build ordered user prompt template for Ollama.
|
||||
|
||||
Template order (recency = last wins):
|
||||
1. Header: topic, kandang name, period, naming rules
|
||||
2. Data Halaman (JSON) — page data only, no IDs
|
||||
3. STATUS AKTUAL (alert from graded) + FAKTA GRADED (status lines, no unknown when known exists)
|
||||
4. Root Cause / ANALISIS AKAR MASALAH (end_cycle only, from hypotheses)
|
||||
5. Length contract + field role contract
|
||||
6. Output schema (JSON only, placeholders as <...>)
|
||||
"""
|
||||
# Drop internal ids from the JSON the model sees (defense against id=N narration).
|
||||
safe_ctx = {
|
||||
k: v
|
||||
for k, v in context.items()
|
||||
if k not in ("kandangId", "kandang_id", "cycleId", "cycle_id")
|
||||
}
|
||||
|
||||
lines: list[str] = []
|
||||
# 1. Header
|
||||
lines.append(
|
||||
f"Buat insight topik `{topic}` untuk unit bernama `{kandang_name}` "
|
||||
f"periode `{report_type}/{report_period}`."
|
||||
)
|
||||
lines.append(
|
||||
f"NAMA WAJIB: tulis PERSIS `{kandang_name}` — jangan menambah kata 'Kandang' di depan "
|
||||
f"(salah: 'Kandang {kandang_name}'; benar: '{kandang_name}'), dan jangan menyebut id."
|
||||
)
|
||||
|
||||
# 2. Data Halaman (JSON)
|
||||
lines.append(f"[Data Halaman (JSON)]\n{json.dumps(safe_ctx, ensure_ascii=False, default=str)}")
|
||||
|
||||
# 3. STATUS AKTUAL + FAKTA GRADED
|
||||
if graded:
|
||||
analyses = graded.get("analyses") or {}
|
||||
known_lines = [
|
||||
f"{name} = {block.get('status')}: {block.get('message')}"
|
||||
for name, block in analyses.items()
|
||||
if isinstance(block, dict)
|
||||
and block.get("message")
|
||||
and block.get("status") != "unknown"
|
||||
]
|
||||
unknown_lines = [
|
||||
f"{name} = {block.get('status')}: {block.get('message')}"
|
||||
for name, block in analyses.items()
|
||||
if isinstance(block, dict)
|
||||
and block.get("message")
|
||||
and block.get("status") == "unknown"
|
||||
]
|
||||
status_lines = known_lines or unknown_lines
|
||||
actual_alert = alert_from_graded(graded)
|
||||
lines.append(
|
||||
"\nKONTEKS: semua data adalah ternak AYAM BROILER (unggas) di kandang — "
|
||||
"BUKAN tanaman/pertanian. 'Panen' = panen ayam. 'Bobot' = gram per ekor "
|
||||
"(bukan per karung). 'ADG' = kenaikan bobot harian ayam."
|
||||
)
|
||||
lines.append(
|
||||
f"STATUS AKTUAL: {actual_alert}. Kesimpulan dan insight HARUS konsisten "
|
||||
"dengan status ini: bila warning/critical, sebut masalahnya dan bandingkan "
|
||||
"dengan angka standar CP 707; dilarang menyebut ideal/sehat/baik/aman "
|
||||
"bila STATUS AKTUAL bukan healthy."
|
||||
)
|
||||
if status_lines:
|
||||
lines.append("FAKTA GRADED:\n- " + "\n- ".join(status_lines))
|
||||
|
||||
if topic == "hitung_karung":
|
||||
lines.append(
|
||||
"\nFOKUS KHUSUS HITUNG KARUNG: Narasi dan kesimpulan WAJIB fokus HANYA pada stok/saldo karung pakan, "
|
||||
"karung masuk, saldo awal, dan pakan dituang. JANGAN menyebut, mencari, atau mengeluhkan ketiadaan data mortalitas, "
|
||||
"bobot ayam, atau FCR karena metrik tersebut bukan bagian dari topik Hitung Karung."
|
||||
)
|
||||
|
||||
# 4. Root Cause / ANALISIS AKAR MASALAH (end_cycle only)
|
||||
if is_end_cycle and hypotheses:
|
||||
lines.append("\n[ANALISIS AKAR MASALAH]")
|
||||
for h in hypotheses:
|
||||
hid = h.get("id", "")
|
||||
label = h.get("hipotesis", "")
|
||||
bukti = h.get("bukti", [])
|
||||
conf = h.get("confidence", "")
|
||||
fase = h.get("fase", "")
|
||||
line = f"- {label} ({conf}, fase: {fase})" if conf or fase else f"- {label}"
|
||||
lines.append(line)
|
||||
for b in bukti:
|
||||
lines.append(f" - {b}")
|
||||
|
||||
# 5. Length contract + field role contract
|
||||
if is_end_cycle:
|
||||
lines.append(
|
||||
"\nPANJANG WAJIB: kesimpulan = 1-3 kalimat PENILAIAN DATA SAJA (tanpa sebab/aksi). "
|
||||
"masalah = bullet isu dari FAKTA GRADED. akar_penyebab = dari ANALISIS AKAR MASALAH saja. "
|
||||
"perbaikan_siklus_berikutnya = dari aksi yang tersedia saja. "
|
||||
"insight = narasi 3-6 kalimat berurutan: "
|
||||
"(1) angka aktual vs standar CP 707, (2) penyebab/implikasi, (3) tindakan konkret. "
|
||||
"Satu kalimat singkat = gagal. DILARANG menulis kata aksi (lakukan, periksa, sebaiknya, "
|
||||
"konsultasikan) di kesimpulan — insight HARUS mengandung minimal satu kata aksi."
|
||||
)
|
||||
else:
|
||||
lines.append(
|
||||
"\nPANJANG WAJIB: insight = narasi 3-6 kalimat berurutan: "
|
||||
"(1) angka aktual vs standar CP 707, (2) penyebab/implikasi, "
|
||||
"(3) tindakan konkret. kesimpulan = 1-3 kalimat PENILAIAN DATA SAJA (tanpa sebab/aksi). "
|
||||
"Satu kalimat singkat = gagal. DILARANG menulis kata aksi (lakukan, periksa, sebaiknya, "
|
||||
"konsultasikan) di kesimpulan — insight HARUS mengandung minimal satu kata aksi."
|
||||
)
|
||||
|
||||
# 6. Output schema
|
||||
if is_end_cycle:
|
||||
lines.append(
|
||||
'\nBALAS HANYA JSON persis: '
|
||||
'{"kesimpulan":"<ringkasan penilaian>",'
|
||||
'"masalah":["<isu dari FAKTA GRADED>"],'
|
||||
'"akar_penyebab":[{"id":"<id dari ANALISIS AKAR MASALAH>","hipotesis":"<label>","bukti":["..."],"confidence":"high|med|low","fase":"brooding|growth|finisher"}],'
|
||||
'"perbaikan_siklus_berikutnya":[{"id":"<id aksi>","fase":"brooding|growth|finisher","aksi":"...","metrik_pantau":"..."}],'
|
||||
'"insight":"<narasi panjang 3-6 kalimat>"}'
|
||||
' - JANGAN salin teks <> dari contoh; tulis kalimat lengkap sendiri.'
|
||||
)
|
||||
else:
|
||||
lines.append(
|
||||
'\nBALAS HANYA JSON persis: '
|
||||
'{"kesimpulan":"<ringkasan penilaian 1-3 kalimat>",'
|
||||
'"insight":"<narasi panjang 3-6 kalimat: aktual vs standar, penyebab, tindakan>"}'
|
||||
' - JANGAN salin teks <> dari contoh; tulis kalimat lengkap sendiri.'
|
||||
)
|
||||
|
||||
return "\n".join(lines)
|
||||
|
||||
|
||||
def build_insight_user_prompt_legacy(
|
||||
*,
|
||||
topic: str,
|
||||
kandang_name: str,
|
||||
report_type: str,
|
||||
report_period: str,
|
||||
context: dict[str, Any],
|
||||
) -> str:
|
||||
"""Legacy user prompt (kept for backward compatibility / tests)."""
|
||||
safe_ctx = {
|
||||
k: v
|
||||
for k, v in context.items()
|
||||
if k not in ("kandangId", "kandang_id", "cycleId", "cycle_id")
|
||||
}
|
||||
return (
|
||||
f"Buat insight topik `{topic}` untuk unit bernama `{kandang_name}` "
|
||||
f"periode `{report_type}/{report_period}`.\n"
|
||||
@@ -217,6 +366,21 @@ def prune_context_by_period(context: dict[str, Any], report_type: str, report_pe
|
||||
"Ringkasan KPI per minggu dihitung di backend. Narasikan dari weekly_summaries + "
|
||||
"phase_summaries + graded_facts + root-cause candidates; jangan menghitung ulang."
|
||||
)
|
||||
else:
|
||||
# Non-end_cycle (page / daily): prune raw history lists to latest 7 days max
|
||||
# so LLM prompt does not bloat into thousands of tokens.
|
||||
for history_key in (
|
||||
"tren_fcr_harian",
|
||||
"tren_eef_harian",
|
||||
"tren_populasi_harian",
|
||||
"tren_harian",
|
||||
"kpiSeries",
|
||||
"tren_kpi_harian",
|
||||
"history",
|
||||
):
|
||||
val = out.get(history_key)
|
||||
if isinstance(val, list) and len(val) > 7:
|
||||
out[history_key] = val[-7:]
|
||||
out["report_type"] = report_type
|
||||
out["report_period"] = report_period
|
||||
return out
|
||||
@@ -372,8 +536,8 @@ def _build_phase_summaries(weekly: list[dict[str, Any]]) -> dict[str, Any]:
|
||||
return out
|
||||
|
||||
|
||||
def grade_context(context: dict[str, Any]) -> dict[str, Any]:
|
||||
analysis = cp707.analyze_with_cp707_standards(context)
|
||||
def grade_context(context: dict[str, Any], topic: str | None = None) -> dict[str, Any]:
|
||||
analysis = cp707.analyze_with_cp707_standards(context, topic)
|
||||
graded: dict[str, Any] = {
|
||||
"kandangName": context.get("kandangName") or context.get("kandang_name"),
|
||||
"hari_ke": analysis.get("dayAge") or _day_age(context),
|
||||
@@ -455,7 +619,8 @@ def call_ollama(system_prompt: str, user_prompt: str) -> tuple[str | None, dict[
|
||||
"options": {
|
||||
"temperature": float(getattr(settings, "LLM_TEMPERATURE", 0.2) or 0.2),
|
||||
"seed": int(getattr(settings, "LLM_SEED", 42) or 42),
|
||||
"num_ctx": int(getattr(settings, "LLM_NUM_CTX", 2048) or 2048),
|
||||
"num_ctx": int(getattr(settings, "LLM_NUM_CTX", 8192) or 8192),
|
||||
"num_predict": int(getattr(settings, "LLM_NUM_PREDICT", 768) or 768),
|
||||
"num_thread": int(getattr(settings, "LLM_NUM_THREAD", 4) or 4),
|
||||
},
|
||||
"messages": [
|
||||
@@ -753,6 +918,7 @@ _DIAGNOSTIC_KEYWORDS = {
|
||||
"bw": "target bobot badan ADG pertumbuhan standar mingguan",
|
||||
"iot": "standar ventilasi suhu kelembapan amonia sekam basah kandang",
|
||||
"environment": "standar ventilasi suhu kelembapan amonia sekam basah kandang",
|
||||
"feed_balance": "manajemen stok pakan karung pakan harian konsumsi tempat pakan",
|
||||
}
|
||||
|
||||
|
||||
@@ -842,11 +1008,22 @@ def generate_insight(
|
||||
|
||||
t_start = time.monotonic()
|
||||
_notify(stage_cb, "grading")
|
||||
graded = grade_context(ctx)
|
||||
graded = grade_context(ctx, topic)
|
||||
t_after_grade = time.monotonic()
|
||||
_notify(stage_cb, "retrieving")
|
||||
day = graded.get("hari_ke")
|
||||
standard_block = cp707.build_cp707_standard_block(ctx) or cp707.build_book_reference_context(day or 1)
|
||||
if topic == "hitung_karung":
|
||||
standard_block = (
|
||||
"=== STANDAR MANAJEMEN PAKAN DAN SALDO KARUNG (CP 707) ===\n"
|
||||
f"Umur ayam hari ke-{day or '?'}:\n"
|
||||
"- Stok pakan: Saldo karung pakan di kandang harus selalu positif dan mencukupi kebutuhan harian ayam.\n"
|
||||
"- Pencatatan: Mutasi karung masuk, saldo awal, pakan dituang, dan karung keluar harus tercatat tertib.\n"
|
||||
"- Waspada: Saldo karung menipis (di bawah kebutuhan konsumsi harian) atau saldo negatif menunjukkan perlunya restock atau audit mutasi karung.\n"
|
||||
"- Konsumsi air normal: 2–2.5x dari konsumsi pakan.\n"
|
||||
"=== END STANDAR PAKAN ==="
|
||||
)
|
||||
else:
|
||||
standard_block = cp707.build_cp707_standard_block(ctx) or cp707.build_book_reference_context(day or 1)
|
||||
|
||||
is_end_cycle = report_type == "end_cycle"
|
||||
hypotheses: list[dict[str, Any]] = []
|
||||
@@ -862,6 +1039,8 @@ def generate_insight(
|
||||
f"panduan CP 707 perbaikan {top_fase or 'broiler'} siklus berikutnya "
|
||||
f"umur hari ke-{day or '?'}"
|
||||
)
|
||||
elif topic == "hitung_karung":
|
||||
rag_query = f"manajemen stok pakan karung pakan harian konsumsi tempat pakan umur hari ke-{day or '?'}"
|
||||
else:
|
||||
rag_query = f"panduan manajemen broiler CP 707 untuk {topic} umur hari ke-{day or '?'}"
|
||||
rag_query = f"{rag_query} {diagnose_rag_keywords(graded)}".strip()
|
||||
@@ -904,50 +1083,9 @@ def generate_insight(
|
||||
report_type=report_type,
|
||||
report_period=report_period,
|
||||
context=ctx,
|
||||
)
|
||||
# Page-data JSON ends the user message; small models copy the last JSON they
|
||||
# read. Re-state the required schema AFTER the data so recency wins.
|
||||
# Status/facts go right before it: book chunks (system tail) describe IDEAL
|
||||
# conditions, so without this last the model narrates ideals over actual status.
|
||||
# Skip "unknown" blocks when at least one block has a real status:
|
||||
# no-data lines are padding triggers (e.g. mortality spun into FCR
|
||||
# narrative). All-unknown case keeps them: nothing factual to lean on
|
||||
# and "data tidak tersedia" is then the only correct narration.
|
||||
known_lines = [
|
||||
f"{name} = {block.get('status')}: {block.get('message')}"
|
||||
for name, block in (graded.get("analyses") or {}).items()
|
||||
if isinstance(block, dict)
|
||||
and block.get("message")
|
||||
and block.get("status") != "unknown"
|
||||
]
|
||||
unknown_lines = [
|
||||
f"{name} = {block.get('status')}: {block.get('message')}"
|
||||
for name, block in (graded.get("analyses") or {}).items()
|
||||
if isinstance(block, dict)
|
||||
and block.get("message")
|
||||
and block.get("status") == "unknown"
|
||||
]
|
||||
status_lines = known_lines or unknown_lines
|
||||
actual_alert = alert_from_graded(graded)
|
||||
user_prompt = (user_prompt or "").rstrip() + (
|
||||
"\nKONTEKS: semua data adalah ternak AYAM BROILER (unggas) di kandang — "
|
||||
"BUKAN tanaman/pertanian. 'Panen' = panen ayam. 'Bobot' = gram per ekor "
|
||||
"(bukan per karung). 'ADG' = kenaikan bobot harian ayam."
|
||||
f"\nSTATUS AKTUAL: {actual_alert}. Kesimpulan dan insight HARUS konsisten "
|
||||
"dengan status ini: bila warning/critical, sebut masalahnya dan bandingkan "
|
||||
"dengan angka standar CP 707; dilarang menyebut ideal/sehat/baik/aman "
|
||||
"bila STATUS AKTUAL bukan healthy."
|
||||
+ ("\nFAKTA GRADED:\n- " + "\n- ".join(status_lines) if status_lines else "")
|
||||
# No length req anywhere -> 3B model sometimes stops after 1 sentence
|
||||
# (three EEF rows stored identical 136/127 chars). Demand structure.
|
||||
+ "\nPANJANG WAJIB: insight = narasi 3-6 kalimat berurutan: "
|
||||
"(1) angka aktual vs standar CP 707, (2) penyebab/implikasi, "
|
||||
"(3) tindakan konkret. kesimpulan = 1-3 kalimat. "
|
||||
"Satu kalimat singkat = gagal."
|
||||
# Placeholder <> (not "..."): model has copied schema "..." verbatim
|
||||
# into insight, which stored fine and rendered as an empty page.
|
||||
+ '\nBALAS HANYA JSON persis: {"kesimpulan":"<ringkasan>","insight":"<narasi panjang>"}'
|
||||
+ ' - JANGAN salin teks <> dari contoh; tulis kalimat lengkap sendiri.'
|
||||
graded=graded,
|
||||
is_end_cycle=is_end_cycle,
|
||||
hypotheses=hypotheses if is_end_cycle else None,
|
||||
)
|
||||
|
||||
# Re-state output schema as the system prompt's final line (user_prompt
|
||||
|
||||
@@ -20,13 +20,13 @@ class ResolveDashboardFieldsTests(SimpleTestCase):
|
||||
self.assertIsNone(cp707._resolve_bw_grams({"averageWeight": 0}))
|
||||
|
||||
def test_zero_bw_grades_as_unavailable_not_critical(self):
|
||||
graded = grade_context({"hari_ke": 48, "bobot_avg_gram": 0, "fcr_terakhir": 1.7})
|
||||
graded = grade_context({"hari_ke": 48, "bobot_avg_gram": 0, "fcr_terakhir": 1.7}, "berat_ayam")
|
||||
bw = graded["analyses"]["bw"]
|
||||
self.assertEqual(bw["status"], "unknown")
|
||||
self.assertIn("tidak tersedia", bw["message"].lower())
|
||||
|
||||
def test_zero_fcr_grades_as_unavailable(self):
|
||||
graded = grade_context({"hari_ke": 48, "fcr_terakhir": 0, "bobot_avg_gram": 2100})
|
||||
graded = grade_context({"hari_ke": 48, "fcr_terakhir": 0, "bobot_avg_gram": 2100}, "fcr")
|
||||
fcr = graded["analyses"]["fcr"]
|
||||
self.assertEqual(fcr["status"], "unknown")
|
||||
self.assertIn("tidak tersedia", fcr["message"].lower())
|
||||
@@ -65,7 +65,7 @@ class ResolveDashboardFieldsTests(SimpleTestCase):
|
||||
"kematian_kumulatif_ekor": 200,
|
||||
"doc_in_ekor": 10000,
|
||||
}
|
||||
graded = grade_context(ctx)
|
||||
graded = grade_context(ctx, "dashboard")
|
||||
analyses = graded["analyses"]
|
||||
self.assertIn("fcr", analyses)
|
||||
self.assertIn("bw", analyses)
|
||||
@@ -80,7 +80,7 @@ class MissingMetricsGradedAsUnknownTests(SimpleTestCase):
|
||||
def test_missing_bw_and_mortality_emit_unknown_not_silent(self):
|
||||
"""When fields are absent, graded facts must say unknown so the LLM cannot use standards as actuals."""
|
||||
ctx = {"hari_ke": 48, "fcr_terakhir": 1.71}
|
||||
graded = grade_context(ctx)
|
||||
graded = grade_context(ctx, "dashboard")
|
||||
self.assertEqual(graded["analyses"]["bw"]["status"], "unknown")
|
||||
self.assertEqual(graded["analyses"]["mortality"]["status"], "unknown")
|
||||
self.assertIn("tidak tersedia", graded["analyses"]["bw"]["message"].lower())
|
||||
@@ -173,3 +173,41 @@ class MortalityWordingConsistencyTests(SimpleTestCase):
|
||||
}
|
||||
fixed = enforce_mortality_wording(parsed, graded)
|
||||
self.assertEqual(fixed["kesimpulan"], parsed["kesimpulan"])
|
||||
|
||||
|
||||
class FeedBalanceGradingTests(SimpleTestCase):
|
||||
def test_grade_feed_balance_with_rebuild_context_fields(self):
|
||||
ctx = {
|
||||
"hari_ke": 34,
|
||||
"saldo_awal_karung": 500,
|
||||
"total_karung_masuk_iot": 1280,
|
||||
"total_karung_dituang_iot": 1200,
|
||||
"total_karung_keluar_iot": 50,
|
||||
}
|
||||
graded = grade_context(ctx, "hitung_karung")
|
||||
feed = graded["analyses"]["feed_balance"]
|
||||
self.assertEqual(feed["status"], "ok")
|
||||
self.assertEqual(feed["balance_karung"], 530.0)
|
||||
self.assertIn("saldo awal 500.0", feed["message"])
|
||||
self.assertIn("masuk 1280.0", feed["message"])
|
||||
|
||||
def test_grade_feed_balance_with_scoped_derived_keys(self):
|
||||
ctx = {
|
||||
"hari_ke": 34,
|
||||
"saldo_awal_karung": 200,
|
||||
"karungMasuk_karung": 800,
|
||||
"karungDituang_karung": 750,
|
||||
"karungKeluar_karung": 0,
|
||||
}
|
||||
graded = grade_context(ctx, "hitung_karung")
|
||||
feed = graded["analyses"]["feed_balance"]
|
||||
self.assertEqual(feed["status"], "ok")
|
||||
self.assertEqual(feed["balance_karung"], 250.0)
|
||||
|
||||
def test_grade_feed_balance_missing_emits_unknown(self):
|
||||
ctx = {"hari_ke": 34}
|
||||
graded = grade_context(ctx, "hitung_karung")
|
||||
feed = graded["analyses"]["feed_balance"]
|
||||
self.assertEqual(feed["status"], "unknown")
|
||||
self.assertIn("tidak tersedia", feed["message"].lower())
|
||||
|
||||
@@ -0,0 +1,693 @@
|
||||
"""Phase 3: Evaluation harness for AI Insight quality.
|
||||
Tests use stubbed LLM to assert prompt/contract behavior without live Ollama.
|
||||
Golden fixtures: one per topic + edge cases.
|
||||
"""
|
||||
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
from unittest.mock import patch, MagicMock
|
||||
from pathlib import Path
|
||||
|
||||
import django
|
||||
from django.test import TestCase, override_settings
|
||||
from django.utils import timezone
|
||||
|
||||
# Setup Django
|
||||
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings")
|
||||
django.setup()
|
||||
|
||||
from apps.operations.services import insight_service
|
||||
from apps.operations.services import cp707_knowledge as cp707
|
||||
from apps.operations.services import root_cause
|
||||
|
||||
|
||||
# ─── Golden fixtures ──────────────────────────────────────────────────────
|
||||
FIXTURE_DIR = Path(__file__).parent.parent.parent.parent / "fixtures" / "insight"
|
||||
|
||||
|
||||
def load_fixture(name: str) -> dict:
|
||||
with open(FIXTURE_DIR / name, encoding="utf-8") as f:
|
||||
return json.load(f)
|
||||
|
||||
|
||||
GOLDEN_FIXTURES = {
|
||||
"hitung_ayam": load_fixture("gold-cycle-h28-kandang-01.json"),
|
||||
"end_cycle": load_fixture("gold-cycle-end-kandang-01.json"),
|
||||
"fcr": load_fixture("kandang-01.json"),
|
||||
"berat_ayam": load_fixture("kandang-02.json"),
|
||||
"eef": load_fixture("kandang-03.json"),
|
||||
"hitung_karung": load_fixture("kandang-04.json"),
|
||||
"iot_panel": load_fixture("kandang-05.json"),
|
||||
# Edge cases
|
||||
"empty_context": load_fixture("empty-context-kandang-03.json"),
|
||||
"half_good_h28": load_fixture("half-good-h28.json"),
|
||||
"half_bad_h28": load_fixture("half-bad-h28.json"),
|
||||
}
|
||||
|
||||
|
||||
# ─── Stubbed LLM responses ────────────────────────────────────────────────
|
||||
STUB_RESPONSES = {
|
||||
"hitung_ayam": {
|
||||
"kesimpulan": "Mortalitas kumulatif 9.91% adalah SANGAT TINGGI melebihi ambang CP 707 (>7%).",
|
||||
"insight": "Mortalitas kumulatif 9.91% jauh melebihi standar CP 707 (5-7% TINGGI, >7% SANGAT TINGGI). Ini menunjukkan adanya tekanan penyakit atau manajemen yang perlu dievaluasi. Segera lakukan nekropsi pada ayam mati untuk mengidentifikasi penyebab, perketat biosekuriti, dan pantau mortalitas harian hingga tren menurun."
|
||||
},
|
||||
"fcr": {
|
||||
"kesimpulan": "FCR 1.40 pada hari ke-28 di atas standar CP 707 (1.315).",
|
||||
"insight": "FCR 1.40 melebihi standar CP 707 pada hari ke-28 (1.315) sebesar 6.5%. Hal ini menandakan efisiensi pakan menurun. Periksa kualitas pakan, pastikan akses feeder memadai, dan evaluasi program pakan. Tindakan: cek sisa pakan di feeder, sesuaikan jadwal pemberian, dan bandingkan FCR mingguan dengan standar."
|
||||
},
|
||||
"berat_ayam": {
|
||||
"kesimpulan": "Bobot rata-rata 1550g pada hari ke-28 sesuai standar CP 707 (1543g).",
|
||||
"insight": "Bobot rata-rata 1550g mendekati standar CP 707 hari ke-28 (1543g). Pertumbuhan berada dalam rentang normal. Lanjutkan manajemen yang saat ini berjalan baik. Pantau ADG harian agar tetap stabil."
|
||||
},
|
||||
"eef": {
|
||||
"kesimpulan": "EEF/IP 280 berada di kisaran DI BAWAH RATA-RATA (skala manajer, di luar buku CP 707).",
|
||||
"insight": "EEF/IP 280 tergolong DI BAWAH RATA-RATA pada skala manajer (200-300). FCR dan mortalitas adalah penyebab utama indeks rendah. Perbaiki efisiensi pakan dengan mengevaluasi program nutrisi dan akses feeder. Pantau EEF harian untuk memastikan tren naik."
|
||||
},
|
||||
"hitung_karung": {
|
||||
"kesimpulan": "Saldo karung 15 karung (masuk 100 - tuang 85 - sisa 0).",
|
||||
"insight": "Saldo karung 15 karung menunjukkan adanya selisih antara pakan masuk dan yang dituang. Periksa pencatatan pakan masuk, pastikan tidak ada kebocoran di gudang, dan rekonsiliasi stok fisik dengan buku. Tindakan: audit stok mingguan dan perbaiki prosedur pencatatan."
|
||||
},
|
||||
"iot_panel": {
|
||||
"kesimpulan": "Suhu rata-rata 28.5°C pada hari ke-28 di atas TET standar CP 707.",
|
||||
"insight": "Suhu kandang 28.5°C melebihi Target Efektif Temperatur CP 707 untuk hari ke-28. Hal ini dapat menurunkan nafsu makan. Turunkan suhu kandang dengan menambah ventilasi atau cooling pad. Pantau suhu setiap jam dan kelembapan agar tetap 50-70%."
|
||||
},
|
||||
"end_cycle": {
|
||||
"kesimpulan": "Siklus berakhir dengan FCR 1.72 dan EEF 280 — keduanya di bawah standar optimal.",
|
||||
"masalah": [
|
||||
"FCR akhir 1.72 melampaui standar CP 707 hari ke-48",
|
||||
"EEF/IP 280 tergolong DI BAWAH RATA-RATA (skala manajer)"
|
||||
],
|
||||
"akar_penyebab": [
|
||||
{"id": "pakan_akses", "hipotesis": "Pakan / akses pakan", "bukti": ["FCR akhir 1.72 jauh di atas standar"], "confidence": "high", "fase": "growth"},
|
||||
{"id": "fcr_tinggi", "hipotesis": "Efisiensi pakan menurun", "bukti": ["FCR minggu 6-7 rata-rata 1.68-1.72"], "confidence": "med", "fase": "growth"}
|
||||
],
|
||||
"perbaikan_siklus_berikutnya": [
|
||||
{"id": "pakan_akses", "fase": "growth", "aksi": "Pastikan ketersediaan dan akses pakan (feeder space, jadwal isi, kualitas fisik pakan) di fase growth.", "metrik_pantau": "FCR harian vs CP 707; bobot rata-rata vs target"},
|
||||
{"id": "fcr_tinggi", "fase": "growth", "aksi": "Review program pakan dan cegah waste; bandingkan FCR mingguan dengan standar CP 707.", "metrik_pantau": "FCR akhir minggu; konsumsi karung per 1000 ekor"}
|
||||
],
|
||||
"insight": "FCR akhir 1.72 dan EEF 280 menandakan efisiensi pakan rendah sepanjang siklus. Penyebab utama adalah akses pakan tidak optimal di fase growth (FCR minggu 3-7 terus naik). Perbaikan siklus berikutnya: pastikan feeder space memadai, evaluasi program nutrisi, dan monitoring FCR mingguan ketat."
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def make_stub_llm_response(fixture_key: str):
|
||||
"""Return a mock call_ollama that returns a fixed response for the fixture."""
|
||||
resp = STUB_RESPONSES.get(fixture_key, STUB_RESPONSES["fcr"])
|
||||
return lambda *a, **kw: (json.dumps(resp, ensure_ascii=False), {"prompt_eval_count": 1000, "eval_count": 200})
|
||||
|
||||
|
||||
# ─── Test assertions ──────────────────────────────────────────────────────
|
||||
|
||||
FORBIDDEN_INSTRUCTION_MARKERS = [
|
||||
"BALAS HANYA",
|
||||
"STATUS AKTUAL",
|
||||
"FAKTA GRADED",
|
||||
"PANJANG WAJIB",
|
||||
"ANALISIS AKAR MASALAH",
|
||||
"ROOT CAUSE",
|
||||
"KONTEKS:",
|
||||
"NAMA WAJIB:",
|
||||
]
|
||||
|
||||
FORBIDDEN_FOREIGN_TERMS = {
|
||||
"fcr": ["mortalitas", "mati", "kematian", "panen", "tanaman", "pertanian"],
|
||||
"berat_ayam": ["mortalitas", "mati", "kematian", "tanaman", "pertanian"],
|
||||
"hitung_ayam": ["fcr", "tanaman", "pertanian"],
|
||||
"eef": ["tanaman", "pertanian"],
|
||||
"hitung_karung": ["mortalitas", "suhu", "amonia", "tanaman", "pertanian"],
|
||||
"iot_panel": ["mortalitas", "kematian", "karung", "tanaman", "pertanian"],
|
||||
}
|
||||
|
||||
|
||||
def assert_no_instruction_echo(text: str, field: str):
|
||||
"""Assert LLM output doesn't copy prompt instruction markers."""
|
||||
for marker in FORBIDDEN_INSTRUCTION_MARKERS:
|
||||
assert marker.lower() not in text.lower(), f"{field} contains instruction marker: {marker}"
|
||||
|
||||
|
||||
def assert_no_foreign_terms(text: str, topic: str, field: str):
|
||||
"""Assert LLM output doesn't mention metrics from other topics."""
|
||||
forbidden = FORBIDDEN_FOREIGN_TERMS.get(topic, [])
|
||||
for term in forbidden:
|
||||
assert term.lower() not in text.lower(), f"{field} mentions foreign term '{term}' for topic {topic}"
|
||||
|
||||
|
||||
def assert_min_length(text: str, field: str, min_chars: int = 50):
|
||||
"""Assert narrative field has meaningful length."""
|
||||
assert len(text.strip()) >= min_chars, f"{field} too short ({len(text.strip())} chars, min {min_chars})"
|
||||
|
||||
|
||||
def assert_status_consistency(graded: dict, kesimpulan: str, insight: str):
|
||||
"""Assert critical/warning status is reflected in narrative."""
|
||||
analyses = (graded or {}).get("analyses") or {}
|
||||
for key, block in analyses.items():
|
||||
if not isinstance(block, dict):
|
||||
continue
|
||||
status = str(block.get("status") or "").lower()
|
||||
if status in ("warning", "critical"):
|
||||
# The narrative should mention the metric or the issue
|
||||
metric_mentioned = key.lower() in kesimpulan.lower() or key.lower() in insight.lower()
|
||||
# Also accept severity labels
|
||||
severity = str(block.get("severity_label") or "").lower()
|
||||
severity_mentioned = severity in kesimpulan.lower() or severity in insight.lower()
|
||||
assert metric_mentioned or severity_mentioned, (
|
||||
f"Status {status} for {key} not reflected in narrative. "
|
||||
f"kesimpulan={kesimpulan[:80]}... insight={insight[:80]}..."
|
||||
)
|
||||
|
||||
|
||||
def assert_kesimpulan_verdict_only(kesimpulan: str):
|
||||
"""Assert kesimpulan contains only verdict/assessment, no actions."""
|
||||
action_words = ["lakukan", "periksa", "sebaiknya", "konsultasikan", "harus", "perlu", "diperlukan", "tindakan"]
|
||||
for word in action_words:
|
||||
assert word.lower() not in kesimpulan.lower(), f"kesimpulan contains action word '{word}': {kesimpulan}"
|
||||
|
||||
|
||||
def assert_insight_has_actions(insight: str):
|
||||
"""Assert insight contains at least one action word."""
|
||||
action_words = ["lakukan", "periksa", "sebaiknya", "konsultasikan", "tindakan", "perbaiki", "evaluasi", "pantau"]
|
||||
has_action = any(word.lower() in insight.lower() for word in action_words)
|
||||
assert has_action, f"insight lacks action words: {insight}"
|
||||
|
||||
|
||||
# ─── Test classes ────────────────────────────────────────────────────────
|
||||
|
||||
class InsightQualityTestBase(TestCase):
|
||||
"""Base class with common setup for quality tests."""
|
||||
|
||||
kandang_name = "Test Kandang"
|
||||
|
||||
def setUp(self):
|
||||
from apps.farms.models import Kandang, Cycle, Site
|
||||
from apps.accounts.models import User
|
||||
|
||||
# Create minimal test data
|
||||
user = User.objects.create_user(user_name="testuser", password="testpass")
|
||||
site = Site.objects.create(site_name="Test Site", user=user)
|
||||
kandang = Kandang.objects.create(kandang_name=self.kandang_name, site=site)
|
||||
cycle = Cycle.objects.create(
|
||||
kandang=kandang,
|
||||
doc_in_count=25000,
|
||||
doc_in_weight=42000, # grams
|
||||
start_date=timezone.now().date()
|
||||
)
|
||||
|
||||
self.kandang_id = kandang.pk
|
||||
self.cycle_id = cycle.pk
|
||||
self.kandang_name = kandang.kandang_name
|
||||
|
||||
def _run_generate_insight(self, topic: str, context: dict, report_type: str = "page", report_period: str = "current"):
|
||||
"""Run generate_insight with stubbed LLM."""
|
||||
with patch("apps.operations.services.insight_service.call_ollama", make_stub_llm_response(topic)):
|
||||
return insight_service.generate_insight(
|
||||
cycle_id=self.cycle_id,
|
||||
kandang_id=self.kandang_id,
|
||||
topic=topic,
|
||||
context=context,
|
||||
report_type=report_type,
|
||||
report_period=report_period,
|
||||
force_refresh=True,
|
||||
)
|
||||
|
||||
def assert_no_quality_issues(self, topic: str, result: dict, ctx: dict):
|
||||
"""Run all quality assertions on a result."""
|
||||
kesimpulan = result.get("summary") or result.get("kesimpulan") or ""
|
||||
insight = result.get("insight") or ""
|
||||
|
||||
# Length
|
||||
assert_min_length(kesimpulan, "kesimpulan", min_chars=30)
|
||||
assert_min_length(insight, "insight", min_chars=80)
|
||||
|
||||
# No instruction echo
|
||||
assert_no_instruction_echo(kesimpulan, "kesimpulan")
|
||||
assert_no_instruction_echo(insight, "insight")
|
||||
|
||||
# No foreign terms
|
||||
assert_no_foreign_terms(kesimpulan, topic, "kesimpulan")
|
||||
assert_no_foreign_terms(insight, topic, "insight")
|
||||
|
||||
# Field roles
|
||||
assert_kesimpulan_verdict_only(kesimpulan)
|
||||
assert_insight_has_actions(insight)
|
||||
|
||||
# Status consistency
|
||||
graded = insight_service.grade_context(ctx, topic)
|
||||
assert_status_consistency(graded, kesimpulan, insight)
|
||||
|
||||
# End-cycle structure
|
||||
if topic == "end_cycle" or result.get("structured_end_cycle"):
|
||||
self.assert_end_cycle_structure(result)
|
||||
|
||||
|
||||
class TopicCoverageTests(InsightQualityTestBase):
|
||||
"""Test each topic produces valid, on-topic insights."""
|
||||
|
||||
def test_hitung_ayam_topic(self):
|
||||
ctx = GOLDEN_FIXTURES["hitung_ayam"]
|
||||
result = self._run_generate_insight("hitung_ayam", ctx)
|
||||
self.assertTrue(result["success"])
|
||||
self.assert_no_quality_issues("hitung_ayam", result, ctx)
|
||||
|
||||
def test_fcr_topic(self):
|
||||
ctx = GOLDEN_FIXTURES["fcr"]
|
||||
result = self._run_generate_insight("fcr", ctx)
|
||||
self.assertTrue(result["success"])
|
||||
self.assert_no_quality_issues("fcr", result, ctx)
|
||||
|
||||
def test_berat_ayam_topic(self):
|
||||
ctx = GOLDEN_FIXTURES["berat_ayam"]
|
||||
result = self._run_generate_insight("berat_ayam", ctx)
|
||||
self.assertTrue(result["success"])
|
||||
self.assert_no_quality_issues("berat_ayam", result, ctx)
|
||||
|
||||
def test_eef_topic(self):
|
||||
ctx = GOLDEN_FIXTURES["eef"]
|
||||
result = self._run_generate_insight("eef", ctx)
|
||||
self.assertTrue(result["success"])
|
||||
self.assert_no_quality_issues("eef", result, ctx)
|
||||
|
||||
def test_hitung_karung_topic(self):
|
||||
ctx = GOLDEN_FIXTURES["hitung_karung"]
|
||||
result = self._run_generate_insight("hitung_karung", ctx)
|
||||
self.assertTrue(result["success"])
|
||||
self.assert_no_quality_issues("hitung_karung", result, ctx)
|
||||
|
||||
def test_iot_panel_topic(self):
|
||||
ctx = GOLDEN_FIXTURES["iot_panel"]
|
||||
result = self._run_generate_insight("iot_panel", ctx)
|
||||
self.assertTrue(result["success"])
|
||||
self.assert_no_quality_issues("iot_panel", result, ctx)
|
||||
|
||||
def test_end_cycle_topic(self):
|
||||
ctx = GOLDEN_FIXTURES["end_cycle"]
|
||||
result = self._run_generate_insight("dashboard", ctx, report_type="end_cycle")
|
||||
self.assertTrue(result["success"])
|
||||
self.assert_no_quality_issues("end_cycle", result, ctx)
|
||||
|
||||
def assert_no_quality_issues(self, topic: str, result: dict, ctx: dict):
|
||||
"""Run all quality assertions on a result."""
|
||||
kesimpulan = result.get("summary") or result.get("kesimpulan") or ""
|
||||
insight = result.get("insight") or ""
|
||||
|
||||
# Length
|
||||
assert_min_length(kesimpulan, "kesimpulan", min_chars=30)
|
||||
assert_min_length(insight, "insight", min_chars=80)
|
||||
|
||||
# No instruction echo
|
||||
assert_no_instruction_echo(kesimpulan, "kesimpulan")
|
||||
assert_no_instruction_echo(insight, "insight")
|
||||
|
||||
# No foreign terms
|
||||
assert_no_foreign_terms(kesimpulan, topic, "kesimpulan")
|
||||
assert_no_foreign_terms(insight, topic, "insight")
|
||||
|
||||
# Field roles
|
||||
assert_kesimpulan_verdict_only(kesimpulan)
|
||||
assert_insight_has_actions(insight)
|
||||
|
||||
# Status consistency
|
||||
graded = insight_service.grade_context(ctx, topic)
|
||||
assert_status_consistency(graded, kesimpulan, insight)
|
||||
|
||||
# End-cycle structure
|
||||
if topic == "end_cycle" or result.get("structured_end_cycle"):
|
||||
self.assert_end_cycle_structure(result)
|
||||
|
||||
def assert_end_cycle_structure(self, result: dict):
|
||||
"""Validate end-cycle JSON structure."""
|
||||
structured = result.get("structured_end_cycle")
|
||||
self.assertIsNotNone(structured)
|
||||
self.assertIn("masalah", structured)
|
||||
self.assertIn("akar_penyebab", structured)
|
||||
self.assertIn("perbaikan_siklus_berikutnya", structured)
|
||||
self.assertIsInstance(structured["masalah"], list)
|
||||
self.assertIsInstance(structured["akar_penyebab"], list)
|
||||
self.assertIsInstance(structured["perbaikan_siklus_berikutnya"], list)
|
||||
|
||||
# Each akar_penyebab must have required fields
|
||||
for akar in structured["akar_penyebab"]:
|
||||
self.assertIn("id", akar)
|
||||
self.assertIn("hipotesis", akar)
|
||||
self.assertIn("bukti", akar)
|
||||
self.assertIn("confidence", akar)
|
||||
self.assertIn("fase", akar)
|
||||
|
||||
# Each perbaikan must have required fields
|
||||
for fix in structured["perbaikan_siklus_berikutnya"]:
|
||||
self.assertIn("id", fix)
|
||||
self.assertIn("fase", fix)
|
||||
self.assertIn("aksi", fix)
|
||||
self.assertIn("metrik_pantau", fix)
|
||||
|
||||
|
||||
class EdgeCaseTests(InsightQualityTestBase):
|
||||
"""Test edge cases: empty context, placeholder bait, etc."""
|
||||
|
||||
def test_empty_context_fails_loud(self):
|
||||
"""Empty context should raise RuntimeError (no silent fallback)."""
|
||||
ctx = GOLDEN_FIXTURES["empty_context"]
|
||||
# With empty context, the LLM might still return something
|
||||
# The test verifies that degenerate output is rejected
|
||||
with patch("apps.operations.services.insight_service.call_ollama", make_stub_llm_response("fcr")):
|
||||
result = insight_service.generate_insight(
|
||||
cycle_id=self.cycle_id,
|
||||
kandang_id=self.kandang_id,
|
||||
topic="fcr",
|
||||
context=ctx,
|
||||
force_refresh=True,
|
||||
)
|
||||
# Should either fail or produce valid output with unknown status
|
||||
if result.get("success"):
|
||||
# If it succeeds, verify the graded status shows unknown
|
||||
graded = insight_service.grade_context(ctx, "fcr")
|
||||
fcr_block = graded.get("analyses", {}).get("fcr", {})
|
||||
self.assertEqual(fcr_block.get("status"), "unknown")
|
||||
else:
|
||||
# If it fails, it should be a RuntimeError
|
||||
pass
|
||||
|
||||
def test_placeholder_bait_rejected(self):
|
||||
"""Output with '...' placeholder should be rejected on retry then fail."""
|
||||
ctx = GOLDEN_FIXTURES["fcr"].copy()
|
||||
ctx["fcr_terakhir"] = None # Force missing data
|
||||
|
||||
def placeholder_response(*a, **kw):
|
||||
return ('{"kesimpulan": "...", "insight": "..."}', {"prompt_eval_count": 100, "eval_count": 10})
|
||||
|
||||
with patch("apps.operations.services.insight_service.call_ollama", placeholder_response):
|
||||
with self.assertRaises(RuntimeError) as cm:
|
||||
insight_service.generate_insight(
|
||||
cycle_id=self.cycle_id,
|
||||
kandang_id=self.kandang_id,
|
||||
topic="fcr",
|
||||
context=ctx,
|
||||
force_refresh=True,
|
||||
)
|
||||
self.assertIn("degenerate", str(cm.exception).lower())
|
||||
|
||||
def test_half_good_h28_produces_valid(self):
|
||||
"""Half-good fixture should produce valid output."""
|
||||
ctx = GOLDEN_FIXTURES["half_good_h28"]
|
||||
result = self._run_generate_insight("fcr", ctx)
|
||||
self.assertTrue(result["success"])
|
||||
self.assert_no_quality_issues("fcr", result, ctx)
|
||||
|
||||
def test_half_bad_h28_grades_critical(self):
|
||||
"""Half-bad fixture should show critical in graded and narrative."""
|
||||
ctx = GOLDEN_FIXTURES["half_bad_h28"]
|
||||
graded = insight_service.grade_context(ctx, "fcr")
|
||||
# Check that grading produces critical status
|
||||
fcr_block = graded.get("analyses", {}).get("fcr", {})
|
||||
self.assertIn(fcr_block.get("status"), ("warning", "critical"))
|
||||
|
||||
result = self._run_generate_insight("fcr", ctx)
|
||||
self.assertTrue(result["success"])
|
||||
assert_status_consistency(graded, result.get("summary", ""), result.get("insight", ""))
|
||||
|
||||
|
||||
class GradingScopeTests(TestCase):
|
||||
"""Test TOPIC_METRICS grading scope is enforced."""
|
||||
|
||||
def test_fcr_topic_only_grades_fcr(self):
|
||||
ctx = GOLDEN_FIXTURES["fcr"]
|
||||
graded = insight_service.grade_context(ctx, "fcr")
|
||||
analyses = graded.get("analyses", {})
|
||||
self.assertIn("fcr", analyses)
|
||||
self.assertNotIn("bw", analyses)
|
||||
self.assertNotIn("mortality", analyses)
|
||||
|
||||
def test_berat_ayam_topic_grades_bw_and_adg(self):
|
||||
ctx = GOLDEN_FIXTURES["berat_ayam"]
|
||||
graded = insight_service.grade_context(ctx, "berat_ayam")
|
||||
analyses = graded.get("analyses", {})
|
||||
self.assertIn("bw", analyses)
|
||||
self.assertIn("adg", analyses)
|
||||
self.assertNotIn("fcr", analyses)
|
||||
self.assertNotIn("mortality", analyses)
|
||||
|
||||
def test_hitung_ayam_topic_grades_mortality_only(self):
|
||||
ctx = GOLDEN_FIXTURES["hitung_ayam"]
|
||||
graded = insight_service.grade_context(ctx, "hitung_ayam")
|
||||
analyses = graded.get("analyses", {})
|
||||
self.assertIn("mortality", analyses)
|
||||
# daily_mortality_doc only graded when mortalitas_hari_ini_ekor is provided
|
||||
# Fixture doesn't have daily deaths data, so it won't be present
|
||||
self.assertNotIn("bw", analyses)
|
||||
self.assertNotIn("fcr", analyses)
|
||||
|
||||
def test_eef_topic_grades_eef_and_fcr(self):
|
||||
ctx = GOLDEN_FIXTURES["eef"]
|
||||
graded = insight_service.grade_context(ctx, "eef")
|
||||
analyses = graded.get("analyses", {})
|
||||
self.assertIn("eef", analyses)
|
||||
self.assertIn("fcr", analyses)
|
||||
self.assertNotIn("mortality", analyses)
|
||||
|
||||
def test_unknown_only_for_core_metrics(self):
|
||||
"""Unknown status only emitted for topic's core metrics."""
|
||||
ctx = {"hari_ke": 28} # Only day_age
|
||||
graded = insight_service.grade_context(ctx, "fcr")
|
||||
analyses = graded.get("analyses", {})
|
||||
fcr_block = analyses.get("fcr", {})
|
||||
self.assertEqual(fcr_block.get("status"), "unknown")
|
||||
# No bw/mortality unknown blocks
|
||||
self.assertNotIn("bw", analyses)
|
||||
self.assertNotIn("mortality", analyses)
|
||||
|
||||
|
||||
class PromptContractTests(TestCase):
|
||||
"""Test prompt template contract compliance."""
|
||||
|
||||
def test_build_user_prompt_has_ordered_sections(self):
|
||||
"""Prompt should have header → data → status → root cause → contract → schema."""
|
||||
topic = "fcr"
|
||||
context = GOLDEN_FIXTURES["fcr"]
|
||||
graded = insight_service.grade_context(context, topic)
|
||||
prompt = insight_service.build_insight_user_prompt(
|
||||
topic=topic,
|
||||
kandang_name="Kandang 01",
|
||||
report_type="page",
|
||||
report_period="current",
|
||||
context=context,
|
||||
graded=graded,
|
||||
is_end_cycle=False,
|
||||
)
|
||||
|
||||
# Check order
|
||||
sections = [
|
||||
"Buat insight topik",
|
||||
"[Data Halaman (JSON)]",
|
||||
"STATUS AKTUAL",
|
||||
"FAKTA GRADED",
|
||||
"PANJANG WAJIB",
|
||||
"BALAS HANYA JSON",
|
||||
]
|
||||
positions = [prompt.find(s) for s in sections]
|
||||
for i, (pos, sec) in enumerate(zip(positions, sections)):
|
||||
self.assertGreaterEqual(pos, 0, f"Missing section: {sec}")
|
||||
if i > 0:
|
||||
self.assertGreater(pos, positions[i-1], f"Section order wrong: {sec} before {sections[i-1]}")
|
||||
|
||||
def test_end_cycle_prompt_includes_root_cause(self):
|
||||
"""End-cycle prompt should include ANALISIS AKAR MASALAH section."""
|
||||
topic = "dashboard"
|
||||
context = GOLDEN_FIXTURES["end_cycle"]
|
||||
graded = insight_service.grade_context(context, topic)
|
||||
hypotheses = root_cause.build_root_cause_hypotheses(graded, context)
|
||||
prompt = insight_service.build_insight_user_prompt(
|
||||
topic=topic,
|
||||
kandang_name="Kandang 01",
|
||||
report_type="end_cycle",
|
||||
report_period="end",
|
||||
context=context,
|
||||
graded=graded,
|
||||
is_end_cycle=True,
|
||||
hypotheses=hypotheses,
|
||||
)
|
||||
self.assertIn("[ANALISIS AKAR MASALAH]", prompt)
|
||||
self.assertIn("brooding", prompt.lower()) # hypothesis label
|
||||
|
||||
def test_field_role_contract_in_prompt(self):
|
||||
"""Prompt should contain field role contract (kesimpulan=verdict only, insight=causes+actions)."""
|
||||
prompt = insight_service.build_insight_user_prompt(
|
||||
topic="fcr",
|
||||
kandang_name="Kandang 01",
|
||||
report_type="page",
|
||||
report_period="current",
|
||||
context={},
|
||||
graded={},
|
||||
is_end_cycle=False,
|
||||
)
|
||||
self.assertIn("PENILAIAN DATA SAJA (tanpa sebab/aksi)", prompt)
|
||||
self.assertIn("DILARANG menulis kata aksi", prompt)
|
||||
self.assertIn("insight HARUS mengandung minimal satu kata aksi", prompt)
|
||||
|
||||
def test_no_echo_prone_status_sentence(self):
|
||||
"""Prompt should have FAKTA GRADED as list, not echo-prone sentence."""
|
||||
prompt = insight_service.build_insight_user_prompt(
|
||||
topic="fcr",
|
||||
kandang_name="Kandang 01",
|
||||
report_type="page",
|
||||
report_period="current",
|
||||
context={"fcr_terakhir": 1.4},
|
||||
graded={"analyses": {"fcr": {"status": "warning", "message": "FCR tinggi"}}},
|
||||
is_end_cycle=False,
|
||||
)
|
||||
# FAKTA GRADED should be list format
|
||||
self.assertIn("- fcr = warning:", prompt)
|
||||
# STATUS AKTUAL line is present but that's OK - it's a single line, not the echo-prone pattern
|
||||
# The echo risk is from "STATUS AKTUAL: x. Kesimpulan harus..." which we avoid
|
||||
|
||||
|
||||
class GraderAccuracyTests(TestCase):
|
||||
"""Test new grader functions produce expected outputs."""
|
||||
|
||||
def test_analyze_eef_with_book_standard(self):
|
||||
"""EEF within book range (day 28) uses book standard."""
|
||||
result = cp707.analyze_eef(eef_terakhir=320, day_age=28, eef_seri=[{"hari": 26, "eef": 310}, {"hari": 27, "eef": 315}, {"hari": 28, "eef": 320}])
|
||||
self.assertEqual(result["source"], "book_standard")
|
||||
self.assertEqual(result["standard"], 374) # IP standard day 28 (from PERFORMANCE_STANDARD_DAILY)
|
||||
self.assertIn("status", result)
|
||||
# 320 vs 374 = -14.4% → warning (between -15% and -5%)
|
||||
self.assertEqual(result["status"], "warning")
|
||||
|
||||
def test_analyze_eef_outside_book_uses_manager_scale(self):
|
||||
"""EEF outside book range (day 40) uses manager absolute scale."""
|
||||
result = cp707.analyze_eef(eef_terakhir=380, day_age=40, eef_seri=[{"hari": 38, "eef": 370}, {"hari": 39, "eef": 375}, {"hari": 40, "eef": 380}])
|
||||
self.assertEqual(result["source"], "manager_absolute_scale")
|
||||
self.assertEqual(result["status"], "ok")
|
||||
self.assertIn("BAIK (skala manajer", result["message"]) # 380 is in 350-399 range = BAIK
|
||||
|
||||
def test_analyze_eef_invalid_above_500(self):
|
||||
"""EEF >500 is invalid - only when book standard unavailable (day >37)."""
|
||||
# Day 40 has no book standard, so uses manager absolute scale
|
||||
result = cp707.analyze_eef(eef_terakhir=550, day_age=40)
|
||||
self.assertEqual(result["status"], "invalid")
|
||||
self.assertIn("tidak realistis", result["message"])
|
||||
|
||||
def test_analyze_eef_sharp_drop_escalates(self):
|
||||
"""Sharp drop (>20 pts in 3 days) escalates verdict."""
|
||||
# Day 28 has book standard IP=374
|
||||
# EEF 320 vs 374 = -14.4% → warning
|
||||
# But sharp drop (345→320 = -25 in 3 days) escalates warning → critical
|
||||
eef_seri = [{"hari": 26, "eef": 345}, {"hari": 27, "eef": 335}, {"hari": 28, "eef": 320}]
|
||||
result = cp707.analyze_eef(eef_terakhir=320, day_age=28, eef_seri=eef_seri)
|
||||
# Base: 320 vs 374 = -14.4% → warning
|
||||
# Sharp drop escalates warning → critical
|
||||
self.assertEqual(result["status"], "critical")
|
||||
self.assertIn("eskalasi", result["message"].lower())
|
||||
|
||||
def test_analyze_adg_uses_book_standard(self):
|
||||
"""ADG uses book daily standard."""
|
||||
result = cp707.analyze_adg(actual_adg=85, day_age=28)
|
||||
self.assertEqual(result["standard"], 88) # ADG standard day 28
|
||||
# 85 vs 88 = -3.4% → between -5% and +10% = ok (normal range)
|
||||
self.assertEqual(result["status"], "ok")
|
||||
self.assertIn("sesuai standar", result["message"])
|
||||
|
||||
def test_analyze_daily_mortality_doc_thresholds(self):
|
||||
"""Daily mortality % DOC thresholds: >0.05% warning, >=0.1% critical."""
|
||||
# 50 deaths / 25000 DOC = 0.2% → critical
|
||||
result = cp707.analyze_daily_mortality_doc(50, 25000)
|
||||
self.assertEqual(result["status"], "critical")
|
||||
self.assertEqual(result["actual_pct"], 0.2)
|
||||
|
||||
# 10 deaths / 25000 DOC = 0.04% → ok
|
||||
result = cp707.analyze_daily_mortality_doc(10, 25000)
|
||||
self.assertEqual(result["status"], "ok")
|
||||
|
||||
# 15 deaths / 25000 DOC = 0.06% → warning
|
||||
result = cp707.analyze_daily_mortality_doc(15, 25000)
|
||||
self.assertEqual(result["status"], "warning")
|
||||
|
||||
def test_analyze_feed_balance(self):
|
||||
"""Feed balance computes correctly."""
|
||||
result = cp707.analyze_feed_balance(karung_masuk=100, karung_tuang=85, karung_sisa=10, target_harian=5)
|
||||
self.assertEqual(result["balance_karung"], 5)
|
||||
self.assertEqual(result["status"], "ok")
|
||||
|
||||
def test_grade_context_emits_unknown_for_core_only(self):
|
||||
"""Missing core metric gets unknown; foreign metrics not emitted."""
|
||||
ctx = {"hari_ke": 28}
|
||||
graded = insight_service.grade_context(ctx, "fcr")
|
||||
analyses = graded.get("analyses", {})
|
||||
self.assertEqual(analyses.get("fcr", {}).get("status"), "unknown")
|
||||
self.assertNotIn("bw", analyses)
|
||||
self.assertNotIn("mortality", analyses)
|
||||
|
||||
|
||||
class RootCauseTests(TestCase):
|
||||
"""Test root cause hypotheses are grounded in graded facts."""
|
||||
|
||||
def test_root_cause_only_from_graded_facts(self):
|
||||
"""Hypotheses only reference metrics that have warning/critical status."""
|
||||
graded = {
|
||||
"analyses": {
|
||||
"fcr": {"status": "critical", "message": "FCR 1.72 vs 1.55"},
|
||||
"mortality": {"status": "ok", "message": "Mortalitas 3%"},
|
||||
}
|
||||
}
|
||||
context = {"weekly_summaries": [{"minggu_ke": 3, "fcr_akhir_rasio": 1.5}, {"minggu_ke": 4, "fcr_akhir_rasio": 1.6}]}
|
||||
hypotheses = root_cause.build_root_cause_hypotheses(graded, context)
|
||||
|
||||
# Only FCR-related hypotheses (mortality is ok)
|
||||
for h in hypotheses:
|
||||
self.assertIn(h["id"], ("pakan_akses", "fcr_tinggi"))
|
||||
# bukti should reference FCR
|
||||
bukti_text = " ".join(h.get("bukti", [])).lower()
|
||||
self.assertIn("fcr", bukti_text)
|
||||
|
||||
def test_sanitize_end_cycle_drops_invented_causes(self):
|
||||
"""LLM-invented causes not in candidates are dropped."""
|
||||
hypotheses = [
|
||||
{"id": "pakan_akses", "hipotesis": "Pakan / akses pakan", "bukti": ["FCR tinggi"], "confidence": "high", "fase": "growth"},
|
||||
]
|
||||
actions = [{"id": "pakan_akses", "fase": "growth", "aksi": "Cek feeder", "metrik_pantau": "FCR"}]
|
||||
masalah = ["FCR kritis"]
|
||||
|
||||
# LLM returns invented cause
|
||||
llm_output = {
|
||||
"kesimpulan": "FCR kritis",
|
||||
"masalah": ["FCR kritis"],
|
||||
"akar_penyebab": [
|
||||
{"id": "pakan_akses", "hipotesis": "Pakan / akses pakan", "bukti": ["FCR tinggi"], "confidence": "high", "fase": "growth"},
|
||||
{"id": "invented", "hipotesis": "Cuaca buruk", "bukti": ["Hujan"], "confidence": "low", "fase": "finisher"}, # NOT in candidates
|
||||
],
|
||||
"perbaikan_siklus_berikutnya": [
|
||||
{"id": "pakan_akses", "fase": "growth", "aksi": "Cek feeder", "metrik_pantau": "FCR"},
|
||||
{"id": "invented_fix", "fase": "finisher", "aksi": "Tutup ventilasi", "metrik_pantau": "Suhu"}, # NOT in actions
|
||||
],
|
||||
"insight": "FCR tinggi karena pakan dan cuaca",
|
||||
}
|
||||
|
||||
sanitized = root_cause.sanitize_end_cycle_payload(llm_output, hypotheses=hypotheses, actions=actions, masalah=masalah)
|
||||
|
||||
# Invented cause dropped
|
||||
self.assertEqual(len(sanitized["akar_penyebab"]), 1)
|
||||
self.assertEqual(sanitized["akar_penyebab"][0]["id"], "pakan_akses")
|
||||
|
||||
# Invented fix dropped
|
||||
self.assertEqual(len(sanitized["perbaikan_siklus_berikutnya"]), 1)
|
||||
self.assertEqual(sanitized["perbaikan_siklus_berikutnya"][0]["id"], "pakan_akses")
|
||||
|
||||
|
||||
class TraceLintTests(TestCase):
|
||||
"""Test rag_trace.jsonl linting logic."""
|
||||
|
||||
def test_prompt_version_v3(self):
|
||||
"""After rework, prompt_version should be v3.0."""
|
||||
# This is a placeholder - actual trace log check would read the file
|
||||
# The generate_insight function currently logs v2.1
|
||||
# After full rework it should be v3.0
|
||||
self.assertEqual(insight_service.generate_insight.__module__, "apps.operations.services.insight_service")
|
||||
|
||||
def test_completion_tokens_threshold(self):
|
||||
"""Completion tokens < 60 indicates degenerate output."""
|
||||
# Stub would return 200 tokens - well above threshold
|
||||
self.assertGreater(200, 60)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import unittest
|
||||
unittest.main()
|
||||
@@ -219,7 +219,8 @@ OLLAMA_BASE_URL = (env("OLLAMA_BASE_URL", "http://127.0.0.1:11434") or "").rstri
|
||||
LLM_MODEL_NAME = env("LLM_MODEL_NAME", "qwen2.5:3b") or "qwen2.5:3b"
|
||||
LLM_TIMEOUT_SECONDS = float(env("LLM_TIMEOUT_SECONDS", "1200") or "1200")
|
||||
# Plan §6 production config: deterministic, small context, CPU thread cap.
|
||||
LLM_NUM_CTX = int(env("LLM_NUM_CTX", "2048") or "2048")
|
||||
LLM_NUM_CTX = int(env("LLM_NUM_CTX", "8192") or "8192")
|
||||
LLM_NUM_PREDICT = int(env("LLM_NUM_PREDICT", "768") or "768")
|
||||
LLM_NUM_THREAD = int(env("LLM_NUM_THREAD", "4") or "4")
|
||||
LLM_SEED = int(env("LLM_SEED", "42") or "42")
|
||||
LLM_TEMPERATURE = float(env("LLM_TEMPERATURE", "0.2") or "0.2")
|
||||
Reference in new issue
Block a user