From b789e0a0fae315e1bed2a4d29821123cce0c6f97 Mon Sep 17 00:00:00 2001 From: Yovan Date: Mon, 2 Nov 2026 10:51:23 +0700 Subject: [PATCH] tugas10 --- Lab1_Eksperimen_Chunking.pdf | 175 +++++++++++++++++++ Lab2_Indeks_dan_Pencarian.pdf | 156 +++++++++++++++++ Lab3_Hit_at_k.pdf | 195 ++++++++++++++++++++++ README.md | 145 ++++++++++++++++ __pycache__/laporan_utils.cpython-314.pyc | Bin 0 -> 8494 bytes __pycache__/rag_utils.cpython-314.pyc | Bin 0 -> 10758 bytes buat_dokumen_contoh.py | 144 ++++++++++++++++ dokumen/etika_keamanan_ai.pdf | 93 +++++++++++ dokumen/machine_learning_dasar.pdf | 93 +++++++++++ dokumen/pengantar_rag.pdf | 93 +++++++++++ lab1_chunking.py | 155 +++++++++++++++++ lab2_indeks_cari.py | 143 ++++++++++++++++ lab3_hit_at_k.py | 171 +++++++++++++++++++ laporan_utils.py | 76 +++++++++ rag_utils.py | 153 +++++++++++++++++ 15 files changed, 1792 insertions(+) create mode 100644 Lab1_Eksperimen_Chunking.pdf create mode 100644 Lab2_Indeks_dan_Pencarian.pdf create mode 100644 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Alurnya berurutan: **memotong dokumen (Lab 1) → membangun indeks dan mencari (Lab 2) → mengukur kualitas pencarian (Lab 3)**. + +--- + +## Lab 1 – Eksperimen Chunking + +### Tujuan +Memahami bagaimana dokumen PDF dipotong menjadi potongan kecil (*chunk*), dan bagaimana **ukuran chunk** serta **overlap** memengaruhi jumlah dan isi chunk. + +### Langkah 1 – Membaca 500 karakter pertama +Output menampilkan awal teks `pengantar_rag.pdf` (judul "Pengantar Retrieval-Augmented Generation (RAG)" dan definisi RAG). Baris ringkasan kualitas teks: + +``` +{'File': 'pengantar_rag.pdf', 'Total karakter': 4061, + 'Karakter rusak (U+FFFD)': 0, 'Persentase huruf': '83%'} +``` + +| Metrik | Nilai | Arti | +|---|---|---| +| Total karakter | 4061 | Panjang seluruh teks hasil ekstraksi PDF | +| Karakter rusak (U+FFFD) | 0 | Tidak ada karakter korup, jadi ekstraksi PDF berhasil bersih | +| Persentase huruf | 83% | Sebagian besar isi adalah huruf; sisanya spasi, angka, tanda baca. Ini wajar untuk teks normal | + +**Kesimpulan:** PDF dapat dibaca dengan baik sehingga aman dipakai sebagai bahan chunking. + +### Langkah 2 – Jumlah chunk untuk setiap kombinasi ukuran dan overlap + +| Ukuran | Overlap 10% | Overlap 15% | Overlap 20% | +|---|---|---|---| +| 500 | 9 chunk | 10 chunk | 10 chunk | +| 1000 | 5 chunk | 5 chunk | 5 chunk | +| 1500 | 3 chunk | 4 chunk | 4 chunk | + +Pola yang terlihat: + +1. **Ukuran chunk makin besar → jumlah chunk makin sedikit.** Pada ukuran 500 ada 9–10 chunk, pada 1500 hanya 3–4 chunk. +2. **Overlap makin besar → jumlah chunk cenderung naik atau tetap.** Overlap membuat jarak geser antar chunk lebih pendek sehingga dibutuhkan lebih banyak chunk untuk menutup teks yang sama. Pada ukuran 1000 jumlahnya tetap 5 karena teks hanya 4061 karakter, jadi perubahan overlap kecil belum menambah chunk. +3. Ukuran chunk lebih berpengaruh daripada overlap terhadap jumlah chunk. + +### Langkah 3 – Memeriksa 3 chunk acak (ukuran 500) +Untuk tiap chunk ditampilkan penanda seperti: + +``` +chunk #1 [0:500] awal_tengah=False akhir_tengah=True potong_tabel=False +``` + +| Penanda | Arti | +|---|---| +| `[0:500]` | Chunk mengambil karakter ke-0 sampai ke-500 dari teks | +| `awal_tengah` | `True` jika chunk **dimulai di tengah kata/kalimat** | +| `akhir_tengah` | `True` jika chunk **berakhir di tengah kata/kalimat** | +| `potong_tabel` | `True` jika chunk **memotong tabel** | + +Pada chunk #1, `akhir_tengah=True` terlihat dari teks yang berhenti di "...memungkinkan j" (kata terpotong). Ini menunjukkan **kelemahan chunking berbasis jumlah karakter**: batas chunk tidak mempedulikan batas kata atau kalimat, sehingga makna bisa terpotong. Overlap berguna untuk mengurangi dampak ini karena potongan yang terpotong muncul lagi di awal chunk berikutnya. + +--- + +## Lab 2 – Membangun Indeks dan Mencari + +### Tujuan +Membangun indeks dari seluruh dokumen, lalu menguji apakah pertanyaan menemukan chunk yang benar. + +### Langkah 1 – Membangun indeks +``` +etika_keamanan_ai.pdf: 4 chunk +machine_learning_dasar.pdf: 4 chunk +pengantar_rag.pdf: 5 chunk +Total 13 chunk | ukuran=1000 overlap=15% | backend=lsa +``` + +- Ada 3 dokumen, total **13 chunk**, dengan konfigurasi **ukuran 1000, overlap 15%**. +- `backend=lsa` berarti pencarian memakai **LSA (Latent Semantic Analysis)**, yaitu representasi vektor berbasis statistik kata. Ini bukan model embedding neural seperti MiniLM. + +### Langkah 2 – 5 pertanyaan uji +Setiap pertanyaan menampilkan 3 hasil teratas dengan format `skor`, `dokumen#nomor_chunk`, dan `relevan`. + +- **skor** adalah kemiripan (cosine similarity) antara pertanyaan dan chunk. Makin dekat ke 1, makin mirip. +- **relevan=True** berarti chunk tersebut memuat **kata kunci jawaban** yang sudah ditentukan. + +| Pertanyaan | Kata kunci | Hasil peringkat 1 | Penilaian | +|---|---|---|---| +| Siapa yang memperkenalkan RAG dan tahun berapa? | `Lewis`, `2020` | `pengantar_rag.pdf#0`, skor 0.741, **relevan** | Benar. Chunk berisi info Lewis dan 2020 | +| Berapa dimensi embedding yang dihasilkan model MiniLM? | `384` | `pengantar_rag.pdf#1`, skor 0.969, **relevan** | Benar dan skornya sangat tinggi | +| Apa itu overfitting dan apa cirinya? | `overfitting`, `data latih` | `machine_learning_dasar.pdf#0`, skor 0.939, **relevan** | Benar. Peringkat 2 juga relevan | +| Undang-undang apa yang mengatur pelindungan data pribadi di Indonesia? | `27 Tahun 2022` | (terpotong di screenshot) | Cek hasilnya di terminal | + +Hal yang bisa dibaca dari hasil: + +- Jawaban yang benar umumnya berada di **peringkat 1** dengan selisih skor yang jelas dari hasil lainnya. Contoh: pertanyaan MiniLM, 0.969 dibanding 0.351. +- Pada pertanyaan pertama, peringkat 2 (0.736, dokumen etika AI) skornya hampir sama dengan peringkat 1 (0.741) walaupun **tidak relevan**. Ini tanda LSA kadang bingung pada pertanyaan yang kosakatanya umum. +- Hasil yang `relevan=False` di peringkat bawah itu normal. Yang penting jawaban benar ada di posisi atas. + +--- + +## Lab 3 – Mengukur Kualitas dengan Hit@k + +### Tujuan +Mengukur secara kuantitatif apakah chunk jawaban benar muncul di **k hasil teratas**, lalu membandingkan dua ukuran chunk. + +### Definisi +**Hit@k** = proporsi pertanyaan yang chunk jawabannya muncul dalam k peringkat teratas. + +``` +Hit@k = (jumlah pertanyaan yang jawabannya ada di top-k) / (total pertanyaan) +``` + +Contoh: Hit@5 = 0.933 berarti 93,3% pertanyaan berhasil. Karena nilainya kelipatan 1/15, kemungkinan ada **15 pertanyaan uji**, dan **14 di antaranya berhasil**, sedangkan 1 gagal. + +### Hasil perbandingan + +| Koleksi | Total chunk | Hit@1 | Hit@3 | Hit@5 | +|---|---|---|---|---| +| Ukuran 500, overlap 15% | 25 | 0.933 | 0.933 | 0.933 | +| Ukuran 1000, overlap 15% | 13 | **1.0** | **1.0** | **1.0** | + +### Diagnosis kegagalan (k=5) +``` +ukuran 500, no. 7 (Bagaimana menyatukan ketepatan dan kelengkapan menjadi satu angka?): +chunk jawaban ada tetapi baru muncul di peringkat 18 (> 5) +``` + +- Pertanyaan no. 7 gagal pada ukuran 500. Chunk jawabannya **ada** di indeks, tetapi **berada di peringkat 18**, jauh di luar top-5. +- Pada ukuran 1000, tidak ada pertanyaan yang gagal. +- Perhatikan bahwa Hit@1, Hit@3, dan Hit@5 pada ukuran 500 sama-sama 0.933. Artinya pertanyaan yang gagal tidak sekadar "hampir masuk", tetapi benar-benar jauh dari top-5. Memperbesar k tidak menolong. + +### Dugaan penyebab (sesuai kesimpulan program) +1. **Ketidakcocokan kosakata.** Pertanyaan memakai istilah berbeda dari dokumen. Contoh: pertanyaan memakai "ketepatan dan kelengkapan", sedangkan dokumen memakai istilah Inggris seperti *precision* dan *recall* atau *F1*. LSA berbasis kata sehingga sulit menjembatani parafrase ini. +2. **Chunk kecil memuat sedikit konteks.** Chunk 500 karakter hanya memuat sedikit kata konteks, sehingga skor kemiripannya kalah dari chunk lain yang topiknya lebih umum. + +### Kesimpulan dan catatan penting +- **Ukuran 1000 karakter lebih baik** daripada 500 karakter pada eksperimen ini (Hit@k 1.00 dibanding 0.93). +- Skor sempurna **jangan dianggap bukti sistem sudah sangat baik**. Korpusnya hanya 3 dokumen kecil (13–25 chunk) dengan topik yang jelas berbeda, sehingga pencarian relatif mudah. Pada PDF yang lebih besar dan topiknya lebih mirip antar dokumen, Hit@k diperkirakan turun dan perbedaan antar ukuran chunk akan lebih terlihat. +- Laporan akhir tersimpan ke `Lab3_Hit_at_k.pdf`. + +--- + +## Ringkasan Keseluruhan + +| Lab | Pertanyaan yang dijawab | Temuan utama | +|---|---|---| +| Lab 1 | Bagaimana ukuran dan overlap memengaruhi chunk? | Ukuran besar menghasilkan chunk lebih sedikit. 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Baca satu PDF dengan baca_pdf() dan cetak 500 karakter pertama. +2. Potong dengan potong() memakai tiga ukuran (500, 1000, 1500) dan overlap 10-20%. +3. Lihat 3 chunk acak tiap ukuran: terpotong di tengah kalimat atau tabel? +""" +import os +import random +import sys + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) +from laporan_utils import Laporan +from rag_utils import FOLDER_DOKUMEN, baca_pdf, norm, potong, potong_dengan_posisi + +BASE = os.path.dirname(os.path.abspath(__file__)) +PDF = os.path.join(FOLDER_DOKUMEN, "pengantar_rag.pdf") # ganti dengan PDF Anda +UKURAN = [500, 1000, 1500] +OVERLAP = [0.10, 0.15, 0.20] +OVERLAP_UTAMA = 0.15 +SEED = 42 + + +def wilayah_tabel(teks): + """Cari rentang karakter tabel ("Tabel N." sampai baris "Sumber:").""" + hasil, pos = [], 0 + while True: + a = teks.find("Tabel ", pos) + if a < 0: + return hasil + b = teks.find("Sumber:", a) + b = len(teks) if b < 0 else teks.find("\n", b) + hasil.append((a, b)) + pos = b + + +def awal_di_tengah_kalimat(teks, mulai): + """Chunk dimulai di tengah kalimat bila karakter bermakna sebelumnya bukan akhir kalimat/baris.""" + sebelum = teks[:mulai].rstrip(" ") + return bool(sebelum) and sebelum[-1] not in ".!?:\n" + + +def akhir_di_tengah_kalimat(teks, akhir): + ujung = teks[:akhir].rstrip() + return bool(ujung) and ujung[-1] not in ".!?" + + +def memotong_tabel(wil, mulai, akhir): + """True bila chunk beririsan dengan tabel tetapi tidak memuat seluruh tabel.""" + for a, b in wil: + beririsan = mulai < b and akhir > a + utuh = mulai <= a and akhir >= b + if beririsan and not utuh: + return True + return False + + +def main(): + # ---- Langkah 1 + teks = baca_pdf(PDF) + n_huruf = sum(c.isalpha() for c in teks) + kualitas = { + "File": os.path.basename(PDF), + "Total karakter": len(teks), + "Karakter rusak (U+FFFD)": teks.count("\ufffd"), + "Persentase huruf": f"{n_huruf / len(teks):.0%}", + } + print("=== Langkah 1: 500 karakter pertama ===") + print(teks[:500]) + print(kualitas) + + # ---- Langkah 2 + print("\n=== Langkah 2: jumlah chunk ===") + tabel_jumlah = [["Ukuran (karakter)"] + [f"overlap {int(o * 100)}%" for o in OVERLAP]] + for u in UKURAN: + baris = [u] + for o in OVERLAP: + n = len(potong(teks, u, o)) + baris.append(n) + print(f"ukuran={u:5d} overlap={o:.0%} -> {n} chunk") + tabel_jumlah.append(baris) + + # ---- Langkah 3 + wil = wilayah_tabel(teks) + rnd = random.Random(SEED) + cuplikan, statistik = {}, [["Ukuran", "Jumlah chunk", "Awal di tengah kalimat", + "Akhir di tengah kalimat", "Memotong tabel"]] + for u in UKURAN: + pot = potong_dengan_posisi(teks, u, OVERLAP_UTAMA) + n = len(pot) + a = sum(awal_di_tengah_kalimat(teks, m) for m, _, _ in pot) + z = sum(akhir_di_tengah_kalimat(teks, e) for _, e, _ in pot) + t = sum(memotong_tabel(wil, m, e) for m, e, _ in pot) + statistik.append([u, n, f"{a}/{n} ({a / n:.0%})", f"{z}/{n} ({z / n:.0%})", f"{t}/{n}"]) + pilih = sorted(rnd.sample(range(n), min(3, n))) + cuplikan[u] = [(i + 1, pot[i][0], pot[i][1], pot[i][2], + awal_di_tengah_kalimat(teks, pot[i][0]), + akhir_di_tengah_kalimat(teks, pot[i][1]), + memotong_tabel(wil, pot[i][0], pot[i][1])) for i in pilih] + print(f"\n=== Langkah 3: ukuran {u} (3 chunk acak) ===") + for no, m, e, isi, aw, ak, tb in cuplikan[u]: + print(f"--- chunk #{no} [{m}:{e}] awal_tengah={aw} akhir_tengah={ak} potong_tabel={tb}") + print(isi) + + # ---- Kesimpulan otomatis dari angka + rincian = "; ".join( + f"ukuran {r[0]}: awal {r[2]}, akhir {r[3]}, memotong tabel {r[4]}" for r in statistik[1:]) + kesimpulan = ( + "Pemotongan per karakter tidak mengenal batas kalimat maupun tabel. Hasil seluruh chunk (overlap " + f"{int(OVERLAP_UTAMA * 100)}%) - {rincian}. Jadi pada setiap ukuran, mayoritas chunk berakhir di tengah " + "kalimat dan sebagian besar juga dimulai di tengah kalimat (hanya chunk pertama yang selalu bersih). " + "Persentasenya tidak turun banyak saat ukuran diperbesar; yang berubah adalah jumlah chunk dan " + "banyaknya topik per chunk. Tabel 1 terpotong oleh sedikitnya satu chunk pada tiap ukuran, dan teks " + "tabel hasil ekstraksi sudah bercampur antar kolom, sehingga baris tabel yang terpisah dari baris " + "judulnya sulit dipahami. Perbaikan yang layak dicoba: pemotongan berbasis kalimat/paragraf dan " + "memperlakukan tabel sebagai satu chunk utuh." + ) + print("\nKesimpulan:", kesimpulan) + + # ---- Laporan PDF + L = Laporan(os.path.join(BASE, "Lab1_Eksperimen_Chunking.pdf"), + "Lab 1 - Eksperimen Chunking", "Dikerjakan dengan Python (pdfplumber + fungsi potong)") + L.h1("Langkah 1. Membaca PDF dan memeriksa teks") + L.p(f"File yang dibaca: {os.path.basename(PDF)}. Berikut 500 karakter pertama hasil baca_pdf():") + L.kode(teks[:500]) + L.spasi() + L.tabel([["Pemeriksaan", "Hasil"]] + [[k, v] for k, v in kualitas.items()], [6 * 28.35, 8 * 28.35]) + L.p("Teks terbaca bersih: tidak ada karakter rusak, kata utuh, dan urutan paragraf benar. " + "Satu catatan: teks Tabel 1 hasil ekstraksi tercampur antar kolom karena sel yang lebih dari satu baris " + "digabung per baris tampilan, bukan per sel.") + L.h1("Langkah 2. Jumlah chunk tiap pengaturan") + L.p("Fungsi potong(teks, ukuran, overlap) memakai jendela geser per karakter; langkah = ukuran x (1 - overlap).") + L.tabel(tabel_jumlah) + L.cap("Tabel 1. Jumlah chunk untuk 3 ukuran x 3 overlap. Makin besar ukuran, makin sedikit chunk; " + "makin besar overlap, makin banyak chunk.") + L.h1(f"Langkah 3. Tiga chunk acak per ukuran (overlap {int(OVERLAP_UTAMA * 100)}%, seed {SEED})") + L.tabel(statistik) + L.cap("Tabel 2. Statistik seluruh chunk pada tiap ukuran (bukan hanya yang dicuplik).") + for u in UKURAN: + L.h2(f"Ukuran {u} karakter") + for no, m, e, isi, aw, ak, tb in cuplikan[u]: + L.p(f"Chunk #{no} (karakter {m}-{e}) | awal di tengah kalimat: {'ya' if aw else 'tidak'} | " + f"akhir di tengah kalimat: {'ya' if ak else 'tidak'} | memotong tabel: {'ya' if tb else 'tidak'}") + L.kode(isi) + L.spasi(4) + L.h1("Kesimpulan") + L.p(kesimpulan) + L.halaman_baru() + L.h1("Lampiran: kode lab1_chunking.py") + L.kode(open(__file__, encoding="utf-8").read()) + L.simpan() + print("\nLaporan: Lab1_Eksperimen_Chunking.pdf") + + +if __name__ == "__main__": + main() diff --git a/lab2_indeks_cari.py b/lab2_indeks_cari.py new file mode 100644 index 0000000..4529d40 --- /dev/null +++ b/lab2_indeks_cari.py @@ -0,0 +1,143 @@ +"""Lab 2 - Bangun indeks & cari (25 menit). + +1. Letakkan 2-3 PDF di folder dokumen/, jalankan indeks, pastikan tiap file melaporkan jumlah chunk. +2. Ajukan 5 pertanyaan dengan query(); periksa apakah chunk teratas benar-benar relevan. +3. Ajukan 2 pertanyaan berparafrase; apakah pencarian semantik tetap menemukannya? +""" +import io +import os +import sys +from contextlib import redirect_stdout + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) +from laporan_utils import Laporan +from rag_utils import bangun_indeks, cocok, query + +BASE = os.path.dirname(os.path.abspath(__file__)) +UKURAN, OVERLAP = 1000, 0.15 + +# kw = kata kunci yang HARUS ada di chunk teratas agar dianggap relevan +PERTANYAAN = [ + {"q": "Siapa yang memperkenalkan RAG dan pada tahun berapa?", "kw": ["Lewis", "2020"]}, + {"q": "Berapa dimensi embedding yang dihasilkan model MiniLM?", "kw": ["384"]}, + {"q": "Apa itu overfitting dan apa cirinya?", "kw": ["overfitting", "data latih"]}, + {"q": "Undang-undang apa yang mengatur pelindungan data pribadi di Indonesia?", "kw": ["27 Tahun 2022"]}, + {"q": "Bagaimana cara memitigasi prompt injection?", "kw": ["memisahkan instruksi"]}, +] +PARAFRASE = [ + {"q": "Bagaimana mencegah model bahasa mengarang jawaban yang keliru?", "kw": ["halusinasi"], + "catatan": "Teks memakai kata 'halusinasi'; pertanyaan memakai 'mengarang jawaban yang keliru'."}, + {"q": "Kapan sebuah model dianggap terlalu menghafal data latihan?", "kw": ["overfitting"], + "catatan": "Teks memakai 'overfitting' dan 'data latih'; pertanyaan memakai 'menghafal' dan 'data latihan'."}, +] + + +def ringkas(t, n=150): + t = " ".join(t.split()) + return t if len(t) <= n else t[:n] + "..." + + +def jalankan(indeks, daftar, k=3): + hasil = [] + for s in daftar: + atas = query(indeks, s["q"], k) + rel = [cocok(a["teks"], s["kw"]) for a in atas] + hasil.append((s, atas, rel)) + return hasil + + +def main(): + # ---- Langkah 1 + print("=== Langkah 1: membangun indeks ===") + buf = io.StringIO() + with redirect_stdout(buf): + idx = bangun_indeks(ukuran=UKURAN, overlap=OVERLAP, backend="auto") + log_indeks = buf.getvalue() + print(log_indeks) + idx_leksikal = bangun_indeks(ukuran=UKURAN, overlap=OVERLAP, backend="tfidf", verbose=False) + per_file = {} + for m in idx.meta: + per_file[m["file"]] = per_file.get(m["file"], 0) + 1 + + # ---- Langkah 2 + print("=== Langkah 2: 5 pertanyaan ===") + h2 = jalankan(idx, PERTANYAAN) + for s, atas, rel in h2: + print(f"\nQ: {s['q']} (kata kunci: {s['kw']})") + for r, (a, ok) in enumerate(zip(atas, rel), 1): + print(f" #{r} skor={a['skor']:.3f} {a['file']}#{a['no']} relevan={ok} | {ringkas(a['teks'])}") + n_top1 = sum(rel[0] for _, _, rel in h2) + n_top3 = sum(any(rel) for _, _, rel in h2) + + # ---- Langkah 3 + print("\n=== Langkah 3: parafrase ===") + sem = jalankan(idx, PARAFRASE) + lek = jalankan(idx_leksikal, PARAFRASE) + for (s, a1, r1), (_, a2, r2) in zip(sem, lek): + print(f"\nQ: {s['q']}") + print(f" {idx.backend}: top1 relevan={r1[0]} top3 relevan={any(r1)} | {a1[0]['file']}#{a1[0]['no']}") + print(f" tfidf : top1 relevan={r2[0]} top3 relevan={any(r2)} | {a2[0]['file']}#{a2[0]['no']}") + + # ---- Kesimpulan dari angka + sem_top3 = sum(any(r) for _, _, r in sem) + lek_top3 = sum(any(r) for _, _, r in lek) + sem_top1 = sum(r[0] for _, _, r in sem) + lek_top1 = sum(r[0] for _, _, r in lek) + kes = (f"Pada 5 pertanyaan langsung, chunk peringkat 1 relevan pada {n_top1}/5 pertanyaan dan salah satu dari " + f"3 chunk teratas relevan pada {n_top3}/5 pertanyaan. Pada 2 pertanyaan parafrase, backend " + f"'{idx.backend}' menemukan chunk relevan di peringkat 1 pada {sem_top1}/2 dan di 3 teratas pada " + f"{sem_top3}/2, sedangkan pembanding TF-IDF (kecocokan bentuk kata) {lek_top1}/2 dan {lek_top3}/2. ") + gagal_top1 = [s["q"] for s, atas, rel in h2 if not rel[0]] + if gagal_top1: + kes += ("Pertanyaan yang chunk peringkat 1-nya belum relevan: " + "; ".join(f'"{q}"' for q in gagal_top1) + + ". Penyebab yang paling mungkin: kalimat jawaban terbelah di batas chunk sehingga kata kuncinya " + "terpisah, sementara chunk di sebelahnya memuat topik yang mirip. ") + if sem_top1 == lek_top1 and sem_top3 == lek_top3: + kes += ("Pada dua parafrase ini, semantik dan TF-IDF memberi hasil yang sama, jadi tes ini belum bisa " + "membedakan keduanya: pertanyaan masih berbagi akar kata dengan teks (model, jawaban, data latih) " + "dan TF-IDF n-gram karakter menangkap variasi bentuk kata. ") + if idx.backend == "lsa": + kes += ("Catatan: backend LSA hanya semantik ringan (hubungan antar kata dipelajari dari korpus yang kecil), " + "sehingga parafrase yang benar-benar tidak berbagi kata akan sulit. Untuk hasil lebih kuat, pasang " + "sentence-transformers; kode ini otomatis memakainya.") + print("\nKesimpulan:", kes) + + # ---- Laporan PDF + L = Laporan(os.path.join(BASE, "Lab2_Indeks_dan_Pencarian.pdf"), "Lab 2 - Bangun Indeks & Cari", + f"ukuran chunk {UKURAN}, overlap {int(OVERLAP * 100)}%, backend: {idx.backend}") + L.h1("Langkah 1. Membangun indeks") + L.p("Tiga PDF diletakkan di folder dokumen/. Setiap file melaporkan jumlah chunk-nya:") + L.tabel([["File", "Jumlah chunk"]] + [[f, n] for f, n in per_file.items()] + [["Total", len(idx.chunks)]], + [9 * 28.35, 4 * 28.35]) + L.kode(log_indeks.rstrip()) + L.h1("Langkah 2. Lima pertanyaan dan pemeriksaan chunk teratas") + L.p("Sebuah chunk dianggap relevan bila memuat semua kata kunci jawaban. Tabel menunjukkan 3 chunk teratas.") + for s, atas, rel in h2: + L.h2(s["q"]) + L.tabel([["Peringkat", "Skor", "Sumber", "Relevan?", "Cuplikan"]] + + [[r, f"{a['skor']:.3f}", f"{a['file']} #{a['no']}", "ya" if ok else "tidak", ringkas(a["teks"], 140)] + for r, (a, ok) in enumerate(zip(atas, rel), 1)], + [1.6 * 28.35, 1.3 * 28.35, 3.8 * 28.35, 1.6 * 28.35, 8.7 * 28.35]) + L.p(f"Ringkasan: chunk peringkat 1 relevan pada {n_top1}/5 pertanyaan; ada chunk relevan di 3 teratas pada {n_top3}/5.") + L.h1("Langkah 3. Pertanyaan berparafrase") + L.p("Dua pertanyaan memakai kata yang berbeda dari teks dokumen. Dibandingkan: pencarian semantik " + f"({idx.backend}) vs TF-IDF.") + baris = [["Pertanyaan", "Catatan", f"{idx.backend}: top1 / top3", "TF-IDF: top1 / top3"]] + for (s, a1, r1), (_, a2, r2) in zip(sem, lek): + baris.append([s["q"], s["catatan"], f"{'ya' if r1[0] else 'tidak'} / {'ya' if any(r1) else 'tidak'}", + f"{'ya' if r2[0] else 'tidak'} / {'ya' if any(r2) else 'tidak'}"]) + L.tabel(baris, [5 * 28.35, 6 * 28.35, 3 * 28.35, 3 * 28.35]) + for (s, a1, r1), _ in zip(sem, lek): + L.p(f"Chunk teratas untuk \"{s['q']}\": {a1[0]['file']} #{a1[0]['no']} (skor {a1[0]['skor']:.3f})") + L.kode(ringkas(a1[0]["teks"], 400)) + L.h1("Kesimpulan") + L.p(kes) + L.halaman_baru() + L.h1("Lampiran: kode lab2_indeks_cari.py") + L.kode(open(__file__, encoding="utf-8").read()) + L.simpan() + print("\nLaporan: Lab2_Indeks_dan_Pencarian.pdf") + + +if __name__ == "__main__": + main() diff --git a/lab3_hit_at_k.py b/lab3_hit_at_k.py new file mode 100644 index 0000000..270cb6b --- /dev/null +++ b/lab3_hit_at_k.py @@ -0,0 +1,171 @@ +"""Lab 3 - Ukur Hit@k (25 menit). + +1. 15 pertanyaan uji beserta kata kunci jawaban dari dokumen. +2. hit_at_k untuk k = 1, 3, 5 pada dua koleksi dengan ukuran chunk berbeda. +3. Kesimpulan 3-4 kalimat: ukuran terbaik, pertanyaan yang gagal, dugaan penyebab. +""" +import os +import sys + +import matplotlib +matplotlib.use("Agg") +import matplotlib.pyplot as plt + +sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) +from laporan_utils import Laporan +from rag_utils import FOLDER_DOKUMEN, baca_pdf, bangun_indeks, cocok, hit_at_k, norm, peringkat_hit + +BASE = os.path.dirname(os.path.abspath(__file__)) +OVERLAP = 0.15 +UKURAN = (500, 1000) # dua koleksi yang dibandingkan +KS = (1, 3, 5) + +SOAL = [ # q = pertanyaan, kw = kata kunci yang harus ada di chunk jawaban, jenis = langsung / parafrase + {"q": "Siapa yang memperkenalkan RAG?", "kw": ["Lewis"], "jenis": "langsung"}, + {"q": "Berapa dimensi embedding yang dihasilkan model MiniLM?", "kw": ["384"], "jenis": "langsung"}, + {"q": "Seberapa besar tumpang tindih antar potongan teks yang disarankan?", "kw": ["10 sampai 20 persen"], "jenis": "parafrase"}, + {"q": "Pustaka apa buatan Meta untuk pencarian tetangga terdekat?", "kw": ["FAISS"], "jenis": "langsung"}, + {"q": "Jika 12 dari 15 soal terjawab pada lima hasil teratas, berapa skornya?", "kw": ["0,8"], "jenis": "parafrase"}, + {"q": "Berapa rentang nilai cosine similarity?", "kw": ["-1 dan 1"], "jenis": "langsung"}, + {"q": "Bagaimana menyatukan ketepatan dan kelengkapan menjadi satu angka?", "kw": ["rata-rata harmonik"], "jenis": "parafrase"}, + {"q": "Mana yang diutamakan untuk skrining pasien: tidak melewatkan kasus atau menghindari alarm palsu?", "kw": ["deteksi penyakit"], "jenis": "parafrase"}, + {"q": "Berapa banyak lipatan yang biasa dipakai pada validasi silang?", "kw": ["5 atau 10"], "jenis": "parafrase"}, + {"q": "Teknik apa yang membuat sebagian bobot menjadi tepat nol?", "kw": ["Lasso"], "jenis": "parafrase"}, + {"q": "Undang-undang nomor berapa yang mengatur pelindungan data pribadi?", "kw": ["27 Tahun 2022"], "jenis": "langsung"}, + {"q": "Bagaimana melindungi sistem dari instruksi jahat yang disusupkan ke dokumen?", "kw": ["memisahkan instruksi"], "jenis": "parafrase"}, + {"q": "Kasus perekrutan yang merendahkan pelamar perempuan dialami perusahaan apa?", "kw": ["Amazon"], "jenis": "parafrase"}, + {"q": "Siapa yang memperkenalkan model card?", "kw": ["Mitchell"], "jenis": "langsung"}, + {"q": "Apa kekurangan chunk berukuran 1500 karakter?", "kw": ["boros"], "jenis": "langsung"}, # jawaban ada di tabel +] + + +def diagnosis(idx, soal, k): + """Tebak penyebab gagal secara otomatis dari data (bukan menebak sembarang).""" + r = peringkat_hit(idx, soal) + if r is None: + return "kata kunci terbelah di batas chunk (tidak ada satu chunk pun yang memuatnya utuh)" + return f"chunk jawaban ada tetapi baru muncul di peringkat {r} (> {k})" + + +def main(): + # sanity check: setiap kata kunci memang ada di dokumen asli + korpus = norm(" ".join(baca_pdf(os.path.join(FOLDER_DOKUMEN, f)) + for f in sorted(os.listdir(FOLDER_DOKUMEN)) if f.endswith(".pdf"))) + for s in SOAL: + assert all(norm(k) in korpus for k in s["kw"]), f"kata kunci tidak ada di dokumen: {s}" + + koleksi, hasil = {}, {} + for u in UKURAN: + print(f"\n=== Koleksi ukuran {u} ===") + koleksi[u] = bangun_indeks(ukuran=u, overlap=OVERLAP, backend="auto") + hasil[u] = {k: hit_at_k(koleksi[u], SOAL, k) for k in KS} + print({f"Hit@{k}": round(v, 3) for k, v in hasil[u].items()}) + backend = koleksi[UKURAN[0]].backend + + ranks = {u: [peringkat_hit(koleksi[u], s) for s in SOAL] for u in UKURAN} + + # ---- kesimpulan otomatis + kunci = lambda u: (hasil[u][5], hasil[u][3], hasil[u][1]) + a, b = UKURAN + terbaik = a if kunci(a) > kunci(b) else b if kunci(b) > kunci(a) else None + fmt = lambda u: "/".join(f"{hasil[u][k]:.2f}" for k in KS) + if terbaik: + lain = b if terbaik == a else a + s1 = (f"Ukuran {terbaik} karakter lebih baik: Hit@1/3/5 = {fmt(terbaik)} dibanding {fmt(lain)} pada " + f"ukuran {lain}.") + else: + s1 = f"Kedua ukuran seimbang: Hit@1/3/5 = {fmt(a)} (ukuran {a}) dan {fmt(b)} (ukuran {b})." + gagal = {u: [i for i, r in enumerate(ranks[u], 1) if r is None or r > 5] for u in UKURAN} + s2 = "Pada k=5, pertanyaan yang gagal: " + "; ".join( + f"ukuran {u} -> " + (", ".join(f"no. {i}" for i in gagal[u]) if gagal[u] else "tidak ada") for u in UKURAN) + "." + penyebab = {} + for u in UKURAN: + for i in gagal[u]: + penyebab[(u, i)] = diagnosis(koleksi[u], SOAL[i - 1], 5) + print("\nDiagnosis gagal (k=5):") + for (u, i), c in penyebab.items(): + print(f" ukuran {u}, no. {i} ({SOAL[i-1]['q']}): {c}") + + # hit@k per jenis soal (langsung vs parafrase) + per_jenis = {} + for jenis in ("langsung", "parafrase"): + sub = [s for s in SOAL if s["jenis"] == jenis] + per_jenis[jenis] = (len(sub), {u: hit_at_k(koleksi[u], sub, 5) for u in UKURAN}) + + # kalimat 3: dugaan penyebab berdasarkan jenis diagnosis + if penyebab: + terbelah = [x for x in penyebab.values() if x.startswith("kata kunci terbelah")] + peringkat = [x for x in penyebab.values() if x.startswith("chunk jawaban ada")] + dugaan = [] + if peringkat: + dugaan.append("kosakata pertanyaan berbeda dari kosakata dokumen (parafrase, mis. istilah Indonesia " + "vs istilah Inggris di teks), dan chunk kecil memuat lebih sedikit kata konteks sehingga " + "skor kemiripannya kalah dari chunk lain yang topiknya lebih umum") + if terbelah: + dugaan.append("kata kunci jawaban terbelah di batas chunk dan overlap belum cukup menyelamatkannya") + s3 = "Dugaan penyebab: " + "; serta ".join(dugaan) + "." + else: + s3 = "Tidak ada kegagalan pada k=5, sehingga tidak ada penyebab yang perlu dianalisis." + s4 = ("Catatan: skor mendekati sempurna karena korpus hanya 3 dokumen kecil (13-25 chunk) dengan topik yang " + "jelas berbeda; pada PDF yang lebih besar dan lebih mirip antar dokumen, Hit@k diperkirakan turun dan " + "perbedaan antar ukuran chunk akan lebih terlihat.") + kesimpulan = " ".join([s1, s2, s3, s4]) + print("\nKesimpulan:", kesimpulan) + + # grafik + fig, ax = plt.subplots(figsize=(6.4, 3.2), dpi=150) + lebar = 0.35 + for j, u in enumerate(UKURAN): + xs = [i + (j - 0.5) * lebar for i in range(len(KS))] + bars = ax.bar(xs, [hasil[u][k] for k in KS], lebar, label=f"ukuran {u}") + ax.bar_label(bars, fmt="%.2f", fontsize=8) + ax.set_xticks(range(len(KS)), [f"Hit@{k}" for k in KS]) + ax.set_ylim(0, 1.12) + ax.set_ylabel("proporsi soal") + ax.set_title(f"Hit@k pada 15 soal (overlap {int(OVERLAP * 100)}%, backend {backend})", fontsize=10) + ax.legend(loc="lower right", fontsize=8) + fig.tight_layout() + gambar = os.path.join(BASE, "_grafik_hit_at_k.png") + fig.savefig(gambar) + plt.close(fig) + + # laporan PDF + W = 28.35 + L = Laporan(os.path.join(BASE, "Lab3_Hit_at_k.pdf"), "Lab 3 - Ukur Hit@k", + f"backend: {backend} | overlap {int(OVERLAP * 100)}% | koleksi ukuran {UKURAN[0]} dan {UKURAN[1]}") + L.h1("Langkah 1. Lima belas pertanyaan uji") + L.p("Soal dibuat dari tiga dokumen di folder dokumen/. Sebuah chunk dianggap benar bila memuat semua kata kunci " + "jawaban. Sebanyak 7 soal bernada langsung dan 8 soal diparafrase (kata-katanya sengaja berbeda dari teks).") + L.tabel([["No", "Jenis", "Pertanyaan", "Kata kunci"]] + + [[i, s["jenis"], s["q"], ", ".join(s["kw"])] for i, s in enumerate(SOAL, 1)], + [1 * W, 2 * W, 10 * W, 4 * W]) + L.h1("Langkah 2. Hasil Hit@k") + L.tabel([["Koleksi"] + [f"Hit@{k}" for k in KS] + ["Jumlah chunk"]] + + [[f"ukuran {u}"] + [f"{hasil[u][k]:.3f}" for k in KS] + [len(koleksi[u].chunks)] for u in UKURAN], + [4 * W, 2.5 * W, 2.5 * W, 2.5 * W, 3 * W]) + L.gambar(gambar, 13) + L.h2("Hit@5 menurut jenis soal") + L.tabel([["Jenis soal", "Jumlah"] + [f"ukuran {u}" for u in UKURAN]] + + [[j, n] + [f"{v[u]:.3f}" for u in UKURAN] for j, (n, v) in per_jenis.items()], + [4 * W, 2.5 * W, 3 * W, 3 * W]) + L.h2("Peringkat chunk pertama yang memuat jawaban, per soal") + L.p("Angka 1 berarti chunk teratas sudah benar. Angka > 5 berarti gagal pada Hit@5.") + L.tabel([["No", "Jenis", f"Peringkat (ukuran {UKURAN[0]})", f"Peringkat (ukuran {UKURAN[1]})"]] + + [[i, s["jenis"], ranks[UKURAN[0]][i - 1], ranks[UKURAN[1]][i - 1]] for i, s in enumerate(SOAL, 1)], + [1.2 * W, 3 * W, 5 * W, 5 * W]) + if penyebab: + L.h2("Diagnosis soal yang gagal (k=5)") + L.tabel([["Ukuran", "No", "Pertanyaan", "Diagnosis"]] + + [[u, i, SOAL[i - 1]["q"], c] for (u, i), c in penyebab.items()], [1.5 * W, 1 * W, 7 * W, 8 * W]) + L.h1("Langkah 3. Kesimpulan") + L.p(kesimpulan) + L.halaman_baru() + L.h1("Lampiran: kode lab3_hit_at_k.py") + L.kode(open(__file__, encoding="utf-8").read()) + L.simpan() + os.remove(gambar) + print("\nLaporan: Lab3_Hit_at_k.pdf") + + +if __name__ == "__main__": + main() diff --git a/laporan_utils.py b/laporan_utils.py new file mode 100644 index 0000000..42b40ad --- /dev/null +++ b/laporan_utils.py @@ -0,0 +1,76 @@ +"""Pembuat laporan PDF sederhana (reportlab) untuk ketiga lab.""" +import textwrap +from xml.sax.saxutils import escape + +from reportlab.lib import colors +from reportlab.lib.pagesizes import A4 +from reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet +from reportlab.lib.units import cm +from reportlab.platypus import (Image, PageBreak, Paragraph, Preformatted, + SimpleDocTemplate, Spacer, Table, TableStyle) + + +class Laporan: + def __init__(self, path, judul, subjudul=""): + st = getSampleStyleSheet() + self.s = { + "judul": ParagraphStyle("j", parent=st["Title"], fontSize=19, spaceAfter=4), + "sub": ParagraphStyle("sj", parent=st["BodyText"], alignment=1, textColor=colors.grey, spaceAfter=14), + "h1": ParagraphStyle("h1", parent=st["Heading1"], fontSize=14, spaceBefore=12, spaceAfter=6), + "h2": ParagraphStyle("h2", parent=st["Heading2"], fontSize=11.5, spaceBefore=9, spaceAfter=4), + "p": ParagraphStyle("p", parent=st["BodyText"], fontSize=10, leading=14, spaceAfter=5), + "sel": ParagraphStyle("sel", parent=st["BodyText"], fontSize=8.5, leading=10.5), + "selb": ParagraphStyle("selb", parent=st["BodyText"], fontSize=8.5, leading=10.5, fontName="Helvetica-Bold"), + "kode": ParagraphStyle("k", fontName="Courier", fontSize=7.2, leading=8.6, + backColor=colors.whitesmoke, borderPadding=4), + "cap": ParagraphStyle("cap", parent=st["BodyText"], fontSize=8.5, textColor=colors.grey, spaceAfter=8), + } + self.doc = SimpleDocTemplate(path, pagesize=A4, leftMargin=2 * cm, rightMargin=2 * cm, + topMargin=2 * cm, bottomMargin=2 * cm, title=judul) + self.c = [Paragraph(escape(judul), self.s["judul"])] + if subjudul: + self.c.append(Paragraph(escape(subjudul), self.s["sub"])) + + def h1(self, t): self.c.append(Paragraph(escape(t), self.s["h1"])) + def h2(self, t): self.c.append(Paragraph(escape(t), self.s["h2"])) + def p(self, t): self.c.append(Paragraph(escape(t), self.s["p"])) + def spasi(self, h=6): self.c.append(Spacer(1, h)) + def halaman_baru(self): self.c.append(PageBreak()) + + def cap(self, t): self.c.append(Paragraph(escape(t), self.s["cap"])) + + def kode(self, teks, lebar=105): + baris = [] + for b in teks.expandtabs(4).split("\n"): + baris += textwrap.wrap(b, lebar, subsequent_indent=" ", drop_whitespace=False) or [""] + self.c.append(Preformatted("\n".join(baris), self.s["kode"])) + + def teks_chunk(self, teks, maks=700): + t = teks if len(teks) <= maks else teks[:maks] + " ..." + self.c.append(Preformatted(t, self.s["kode"], maxLineLength=100, newLineChars="↵")) + self.c.append(Spacer(1, 4)) + + def tabel(self, data, lebar=None, header=True): + d = [[Paragraph(escape(str(x)), self.s["selb"] if (header and i == 0) else self.s["sel"]) + for x in baris] for i, baris in enumerate(data)] + tb = Table(d, colWidths=lebar, repeatRows=1 if header else 0, hAlign="LEFT") + gaya = [("GRID", (0, 0), (-1, -1), 0.4, colors.grey), ("VALIGN", (0, 0), (-1, -1), "TOP")] + if header: + gaya.append(("BACKGROUND", (0, 0), (-1, 0), colors.HexColor("#e6e6e6"))) + tb.setStyle(TableStyle(gaya)) + self.c.append(tb) + self.c.append(Spacer(1, 6)) + + def gambar(self, path, lebar_cm=15): + from PIL import Image as PI + w, h = PI.open(path).size + self.c.append(Image(path, width=lebar_cm * cm, height=lebar_cm * cm * h / w)) + + def simpan(self): + def kaki(canvas, doc): + canvas.saveState() + canvas.setFont("Helvetica", 8) + canvas.setFillColor(colors.grey) + canvas.drawCentredString(A4[0] / 2, 1.1 * cm, f"Halaman {doc.page}") + canvas.restoreState() + self.doc.build(self.c, onFirstPage=kaki, onLaterPages=kaki) diff --git a/rag_utils.py b/rag_utils.py new file mode 100644 index 0000000..4f3e460 --- /dev/null +++ b/rag_utils.py @@ -0,0 +1,153 @@ +"""Fungsi inti RAG sederhana: baca_pdf, potong, bangun_indeks, query, hit_at_k. + +Backend embedding: + - "neural": sentence-transformers (kalau terpasang & modelnya bisa diunduh) + - "lsa" : TF-IDF n-gram karakter + TruncatedSVD (semantik ringan, offline) + - "tfidf" : TF-IDF n-gram karakter saja (pembanding berbasis kata/leksikal) + - "auto" : coba "neural", kalau gagal pakai "lsa" +""" +import glob +import os +import re + +import numpy as np +import pdfplumber +from sklearn.decomposition import TruncatedSVD +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.preprocessing import normalize + +FOLDER_DOKUMEN = os.path.join(os.path.dirname(os.path.abspath(__file__)), "dokumen") +MODEL_NEURAL = "paraphrase-multilingual-MiniLM-L12-v2" + + +# ---------------------------------------------------------------- baca & potong +def baca_pdf(path): + """Baca seluruh teks sebuah PDF (semua halaman digabung).""" + halaman = [] + with pdfplumber.open(path) as pdf: + for p in pdf.pages: + halaman.append(p.extract_text() or "") + return "\n".join(halaman) + + +def potong_dengan_posisi(teks, ukuran=1000, overlap=0.15): + """Sliding window per karakter. Mengembalikan list (mulai, akhir, teks).""" + if not 0 <= overlap < 1: + raise ValueError("overlap harus 0 <= overlap < 1 (mis. 0.15 = 15%)") + langkah = max(1, int(ukuran * (1 - overlap))) + hasil, mulai = [], 0 + while mulai < len(teks): + akhir = min(mulai + ukuran, len(teks)) + potongan = teks[mulai:akhir].strip() + if potongan: + hasil.append((mulai, akhir, potongan)) + if akhir == len(teks): + break + mulai += langkah + return hasil + + +def potong(teks, ukuran=1000, overlap=0.15): + """Potong teks menjadi chunk berukuran `ukuran` karakter dengan overlap pecahan.""" + return [t for _, _, t in potong_dengan_posisi(teks, ukuran, overlap)] + + +def norm(s): + """Normalisasi untuk pencocokan kata kunci: huruf kecil, spasi/baris baru jadi satu spasi.""" + return re.sub(r"\s+", " ", s).lower().strip() + + +# ---------------------------------------------------------------------- indeks +class Indeks: + def __init__(self, chunks, meta, backend="auto"): + self.chunks, self.meta = chunks, meta + self.backend = self._pilih_backend(backend) + self._fit() + + def _pilih_backend(self, backend): + if backend in ("auto", "neural"): + try: + from sentence_transformers import SentenceTransformer + self._st = SentenceTransformer(MODEL_NEURAL) + return "neural" + except Exception: + if backend == "neural": + raise + return "lsa" + return backend + + def _fit(self): + if self.backend == "neural": + self.Z = np.asarray(self._st.encode(self.chunks, normalize_embeddings=True)) + return + self.vec = TfidfVectorizer(analyzer="char_wb", ngram_range=(3, 5), sublinear_tf=True) + X = self.vec.fit_transform(self.chunks) + if self.backend == "tfidf": + self.Z = normalize(X) + else: # lsa + nc = max(2, min(64, X.shape[0] - 1, X.shape[1] - 1)) + self.svd = TruncatedSVD(n_components=nc, random_state=0) + self.Z = normalize(self.svd.fit_transform(X)) + + def skor(self, pertanyaan): + if self.backend == "neural": + q = np.asarray(self._st.encode([pertanyaan], normalize_embeddings=True)) + return np.asarray(self.Z @ q.T).ravel() + q = self.vec.transform([pertanyaan]) + if self.backend == "tfidf": + q = normalize(q) + return np.asarray((self.Z @ q.T).todense()).ravel() + q = normalize(self.svd.transform(q)) + return (self.Z @ q.T).ravel() + + +def bangun_indeks(folder=FOLDER_DOKUMEN, ukuran=1000, overlap=0.15, backend="auto", verbose=True): + """Baca semua PDF di `folder`, potong, lalu bangun indeks. Mencetak jumlah chunk per file.""" + chunks, meta = [], [] + for path in sorted(glob.glob(os.path.join(folder, "*.pdf"))): + nama = os.path.basename(path) + bagian = potong(baca_pdf(path), ukuran, overlap) + if verbose: + print(f" {nama}: {len(bagian)} chunk") + for i, c in enumerate(bagian): + chunks.append(c) + meta.append({"file": nama, "no": i}) + if not chunks: + raise FileNotFoundError(f"Tidak ada PDF di {folder}") + idx = Indeks(chunks, meta, backend) + if verbose: + print(f" Total {len(chunks)} chunk | ukuran={ukuran} overlap={overlap:.0%} | backend={idx.backend}") + return idx + + +def query(indeks, pertanyaan, k=3): + """Kembalikan k chunk teratas: list dict(skor, file, no, teks).""" + s = indeks.skor(pertanyaan) + urut = np.argsort(-s)[:k] + return [{"skor": float(s[i]), "file": indeks.meta[i]["file"], "no": indeks.meta[i]["no"], + "teks": indeks.chunks[i]} for i in urut] + + +# ------------------------------------------------------------------- evaluasi +def cocok(teks, kata_kunci): + """True bila SEMUA kata kunci muncul dalam teks (tanpa peduli huruf besar/kecil).""" + t = norm(teks) + return all(norm(k) in t for k in kata_kunci) + + +def peringkat_hit(indeks, soal): + """Peringkat (mulai dari 1) chunk pertama yang memuat kata kunci; None bila tidak ada.""" + s = indeks.skor(soal["q"]) + for rank, i in enumerate(np.argsort(-s), start=1): + if cocok(indeks.chunks[i], soal["kw"]): + return rank + return None + + +def hit_at_k(indeks, daftar_soal, k): + """Proporsi soal yang chunk berisi kata kunci jawabannya masuk k teratas.""" + hit = 0 + for soal in daftar_soal: + atas = query(indeks, soal["q"], k) + hit += any(cocok(a["teks"], soal["kw"]) for a in atas) + return hit / len(daftar_soal)