Kecerdasan Dokumen didukung oleh Fungsi AI

Buka versi buku catatan halaman ini

Tutorial ini menunjukkan alur kerja Pemrosesan Dokumen Cerdas (IDP) secara menyeluruh menggunakan tiga fungsi AI dari Databricks.

┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│  BRONZE                SILVER                                                           GOLD                                                        │
│                                                                                                                                                     │
│                                                                                         ┌── gold_consulting_agreements (scope, compensation, ...)   │
│                                                                                         ├── gold_marketing_agreements (territory, campaign, ...)    │
│  raw_contracts ──▶ parsed_contracts ──▶ classified_contracts ──▶ extracted fields ──▶   ├── gold_hosting_agreements (SLA, uptime, fees, ...)        │
│  (Auto Loader)    (ai_parse_document)   (ai_classify)           (ai_extract)            └── gold_affiliate_agreements (commission, terms, ...)      │
│                                                                                                                                                     │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

Alur memproses perjanjian hukum yang diajukan SEC, mengklasifikasikan masing-masing ke dalam salah satu dari lima kategori (afiliasi, pemasaran, konsultasi, hosting, escrow) dan mengekstrak istilah yang relevan seperti nama pihak, tanggal, dan detail kompensasi.

Prasyarat

  • Komputasi tanpa server atau gudang SQL dengan Fungsi AI diaktifkan
  • Akses ke volume data sampel di /Volumes/samples/sec/contracts/

Note

Himpunan samples.sec.contracts data tersedia di semua ruang kerja secara default. Untuk memproses PDF Anda sendiri, ubah SOURCE_PATH di sel berikutnya menjadi volume Katalog Unity yang berisi file Anda. Untuk menyerap PDF dari sumber eksternal seperti SharePoint atau Google Drive, Databricks merekomendasikan Lakeflow Connect.

from pyspark.sql import functions as F
import json
import uuid

# Source path — point this at a Unity Catalog volume containing your PDF files.
# The sample path below contains SEC-filed legal agreements.
SOURCE_PATH = "/Volumes/samples/sec/contracts/"

# Serverless compute does not support .cache(), so intermediate results are
# materialized to temp tables instead. A random suffix avoids collisions
# if multiple users run the tutorial concurrently.
_TMP_SUFFIX = uuid.uuid4().hex[:8]

Konfigurasi

Label klasifikasi memberi tahu ai_classify kategori mana yang harus dipilih. Setiap skema ekstraksi mendefinisikan bidang pendek yang diketik yang ai_extract akan menarik untuk jenis perjanjian tersebut.

Ubah ini untuk menyesuaikan alur dengan jenis dokumen Anda sendiri.

# One-line descriptions used by ai_classify to pick the best label per document.
CLASSIFICATION_LABELS = json.dumps(
    {
        "affiliate_agreement": "One party refers customers or resells products for commissions or revenue share.",
        "marketing_agreement": "One party provides marketing, promotion, distribution, or advertising services.",
        "consulting_agreement": "An individual or firm provides advisory or professional services as an independent contractor.",
        "hosting_agreement": "One party provides web hosting, server hosting, application hosting, or managed infrastructure.",
        "escrow_agreement": "A third-party agent holds materials (source code, documentation) with defined release triggers.",
    }
)

# Instructions passed to ai_classify. Filenames carry strong signal for these
# SEC filings, so the classifier is told to trust them unless content disagrees.
CLASSIFICATION_INSTRUCTIONS = """
You are classifying SEC-filed legal agreements into exactly one of five labels.
Read the contract and assign exactly one category:
affiliate_agreement, marketing_agreement, consulting_agreement, hosting_agreement, escrow_agreement.

Decision rules:
* Give strong weight to the contract title and filename when they contain explicit type keywords such as Affiliate Agreement, Marketing Agreement, Consulting Agreement, Hosting Agreement, or Escrow Agreement.
* Only override the filename when the document content clearly and unambiguously describes a different category.

Return only the single best label.
""".strip().replace("\n", " ")
CLASSIFICATION_INSTRUCTIONS_SQL = CLASSIFICATION_INSTRUCTIONS.replace("'", "\\'")

# Shared extraction instructions appended to every per-type prompt.
EXTRACTION_BASE_INSTRUCTIONS = (
    "The input is an SEC-filed legal agreement. "
    "Use all available context in the input, including any document metadata. "
    "Do not extract full sentences, clauses, or paragraph-length descriptions. "
    "If a dollar amount is redacted (e.g., [***]), extract the surrounding structure (e.g., [***]% of revenue above [***] threshold). "
)

# Per-type extraction schemas. Each schema lists short fields that ai_extract
# will populate for every document classified into that type.
EXTRACTION_CONFIGS = {
    "affiliate_agreement": {
        "schema": {
            "party_1_name": {"type": "string", "description": "Legal name of the first party \u2014 must be an actual company or legal entity name, not a role or generic label (e.g., not 'Affiliate', 'Company', or 'Licensor'). Extract from the preamble, recitals, signature block, or document title. Every affiliate agreement involves exactly two parties \u2014 if not found in the body text, infer from the SEC filing entity or any other available context."},
            "party_2_name": {"type": "string", "description": "Legal name of the second party \u2014 must be an actual company or legal entity name, not a role or generic label (e.g., not 'Affiliate', 'Company', or 'Licensor'). Extract from the preamble, recitals, signature block, or document title. Every affiliate agreement involves exactly two parties \u2014 if not found in the body text, infer from the SEC filing entity or any other available context."},
            "commission_rate": {"type": "string", "description": "Primary rate or structure in a short phrase (e.g., 50/50 revenue share, 15-25% tiered discount, $55/referral)."},
            "payment_frequency": {"type": "string", "description": "How often payments are made (e.g., Monthly, Net 30, Quarterly)."},
        },
        "instructions": f"{EXTRACTION_BASE_INSTRUCTIONS} This is an affiliate agreement.",
    },
    "marketing_agreement": {
        "schema": {
            "party_1_name": {"type": "string", "description": "Legal name of the first party."},
            "party_2_name": {"type": "string", "description": "Legal name of the second party."},
            "effective_date": {"type": "string", "description": "Contract start date (e.g., January 30, 2000)."},
            "territory": {"type": "string", "description": "Geographic scope as a place name only (e.g., United States, Texas, New York). Must be an actual geographic location. If the territory references an exhibit or schedule, or no specific place is named, return null."},
        },
        "instructions": f"{EXTRACTION_BASE_INSTRUCTIONS} This is a marketing agreement.",
    },
    "consulting_agreement": {
        "schema": {
            "company_name": {"type": "string", "description": "Legal name of the company engaging the consultant."},
            "consultant_name": {"type": "string", "description": "Legal name of the consultant or consulting firm."},
            "compensation_amount": {"type": "string", "description": "Rate or total with currency and period (e.g., EUR 500/hour, $18,000/month, $250,000 lump sum)."},
            "effective_date": {"type": "string", "description": "Contract start date (e.g., May 1, 2019)."},
        },
        "instructions": f"{EXTRACTION_BASE_INSTRUCTIONS} This is a consulting agreement.",
    },
    "hosting_agreement": {
        "schema": {
            "provider_name": {"type": "string", "description": "Legal name of the hosting provider."},
            "customer_name": {"type": "string", "description": "Legal name of the customer."},
            "effective_date": {"type": "string", "description": "Contract start date (e.g., March 1, 2005)."},
            "term_length": {"type": "string", "description": "Duration or term condition as a short phrase. May be a fixed period (e.g., 12 months, 2 years) or an event-dependent term (e.g., coterminous with License Agreement, until termination of Service Agreement). Always use digits for numbers, never words."},
        },
        "instructions": f"{EXTRACTION_BASE_INSTRUCTIONS} This is a hosting agreement.",
    },
    "escrow_agreement": {
        "schema": {
            "owner_name": {"type": "string", "description": "Legal name of the depositor or software developer."},
            "licensee_name": {"type": "string", "description": "Legal name of the beneficiary or licensee."},
            "escrow_agent_name": {"type": "string", "description": "Legal name of the escrow agent."},
            "software_name": {"type": "string", "description": "Name of the escrowed software or materials."},
        },
        "instructions": f"{EXTRACTION_BASE_INSTRUCTIONS} This is an escrow agreement.",
    },
}

print(f"Configured {len(json.loads(CLASSIFICATION_LABELS))} classification labels")
print(f"Configured {len(EXTRACTION_CONFIGS)} extraction schemas: {', '.join(EXTRACTION_CONFIGS.keys())}")

def _flatten_extraction(contract_type: str):
    """Return a transform that filters to `contract_type`, calls ai_extract on
    the batch, and flattens the JSON response into typed columns."""
    config = EXTRACTION_CONFIGS[contract_type]
    schema_json = json.dumps(config["schema"]).replace("'", "\\'")
    instructions = config["instructions"].replace("'", "\\'")

    def transform(df):
        # ai_extract runs once per batch — each row gets its own extraction,
        # but Spark pushes the whole batch to the AI function in parallel.
        extracted = (
            df.filter(F.col("contract_type") == contract_type)
            .select(
                F.col("path"),
                F.col("contract_type"),
                F.col("parsed_content"),
                F.expr(
                    f"""
                    ai_extract(
                        parsed_content,
                        '{schema_json}',
                        MAP('instructions', '{instructions}')
                    )
                    """
                ).alias("extracted"),
            )
        )

        # Flatten the nested JSON response into top-level STRING columns.
        select_cols = [F.col("path"), F.col("contract_type")]
        for field_name in config["schema"]:
            select_cols.append(F.expr(f"extracted:response.{field_name}::STRING").alias(field_name))

        return extracted.select(*select_cols)

    return transform

Lapisan Perunggu — Mengolah PDF Mentah

Baca file PDF sebagai biner menggunakan format Spark binaryFile . Setiap baris berisi jalur file, byte konten mentah, panjang, dan tanda waktu modifikasi.

Tips produksi: Untuk penyerapan bertahap, ganti spark.read dengan Auto Loader (cloudFiles format) sehingga hanya file baru yang diproses pada setiap proses.

raw_contracts_df = spark.read.format("binaryFile").load(SOURCE_PATH)

print(f"Loaded {raw_contracts_df.count()} documents from {SOURCE_PATH}")
display(raw_contracts_df.select("path", "length", "modificationTime"))

Lapisan Perak — Menganalisis & Klasifikasi

Uraikan — ai_parse_document mengonversi byte PDF mentah menjadi VARIAN terstruktur yang berisi elemen dokumen, metadata tata letak, dan informasi file.

Mengklasifikasikan — ai_classify menerima output VARIAN langsung dari ai_parse_document - tidak perlu mentransmisian ke string. Dokumen dengan kesalahan penguraian difilter sebelum klasifikasi. Pengklasifikasi memberikan bobot yang kuat untuk kata kunci nama file kecuali konten dokumen dengan jelas bertentangan dengan kata kunci tersebut.

parsed_contracts_df = raw_contracts_df.select(
    F.col("path"),
    F.expr("ai_parse_document(content, MAP('version', '2.0'))").alias("parsed_content"),
)

# Materialize parsed results to a temp table so downstream steps
# read from the table rather than re-invoking ai_parse_document.
_parsed_table = f"_tmp_idp_parsed_{_TMP_SUFFIX}"
parsed_contracts_df.write.mode("overwrite").saveAsTable(_parsed_table)
parsed_contracts_df = spark.table(_parsed_table)

num_parsed = parsed_contracts_df.count()
print(f"Parsed {num_parsed} documents")
display(parsed_contracts_df.limit(5))

classified_contracts_df = (
    parsed_contracts_df
    .filter("TRY_CAST(parsed_content:error_status AS STRING) IS NULL")
    .select(
        F.col("path"),
        F.col("parsed_content"),
        F.expr(
            f"""
            ai_classify(
                parsed_content,
                '{CLASSIFICATION_LABELS}',
                MAP('instructions', '{CLASSIFICATION_INSTRUCTIONS_SQL}')
            )
            """
        ).alias("classification"),
    )
    .select(
        F.col("path"),
        F.col("parsed_content"),
        F.col("classification"),
        F.expr("classification:response[0]::STRING").alias("contract_type"),
    )
)

# Materialize classified results to a temp table so each gold-layer
# extraction reads from the table rather than re-invoking ai_classify.
_classified_table = f"_tmp_idp_classified_{_TMP_SUFFIX}"
classified_contracts_df.write.mode("overwrite").saveAsTable(_classified_table)
classified_contracts_df = spark.table(_classified_table)

num_classified = classified_contracts_df.count()
print(f"Classified {num_classified} documents")
display(classified_contracts_df.select("path", "contract_type"))

Lapisan Emas — Ekstraksi Bidang Terstruktur

Setiap jenis perjanjian memiliki skema ekstraksi khusus dengan tiga hingga empat bidang pendek. ai_extract menarik nama pihak, tanggal, jumlah dolar, dan frasa singkat dari setiap dokumen rahasia. Perulangan di bawah ini memproses kelima tipe dan menampilkan hasil dalam baris yang sama.

gold_dfs = {}

for contract_type in EXTRACTION_CONFIGS:
    transform = _flatten_extraction(contract_type)
    gold_df = transform(classified_contracts_df)
    gold_dfs[contract_type] = gold_df

    print(f"\n{'=' * 60}")
    print(f"  {contract_type.replace('_', ' ').title()}")
    print(f"{'=' * 60}")
    display(gold_df)

(Opsional) Simpan ke Tabel Delta

Untuk menyimpan hasil untuk alur kerja hilir, analitik, atau dasbor, hapus komentar sel di bawah ini dan atur katalog dan skema target Anda. Menyimpan dokumen yang telah diurai disarankan — ini memungkinkan proses di masa mendatang untuk menghindari proses penguraian ulang dan membaca langsung dari tabel.

# Uncomment and configure to persist tables
# TARGET_CATALOG = "your_catalog"
# TARGET_SCHEMA = "your_schema"
#
# # Parsed documents — persist to avoid re-running ai_parse_document
# parsed_contracts_df.write.mode("overwrite").saveAsTable(
#     f"{TARGET_CATALOG}.{TARGET_SCHEMA}.parsed_contracts"
# )
# print(f"Wrote parsed contracts to {TARGET_CATALOG}.{TARGET_SCHEMA}.parsed_contracts")
#
# # Classified documents
# classified_contracts_df.select("path", "contract_type").write.mode("overwrite").saveAsTable(
#     f"{TARGET_CATALOG}.{TARGET_SCHEMA}.classified_contracts"
# )
# print(f"Wrote classifications to {TARGET_CATALOG}.{TARGET_SCHEMA}.classified_contracts")
#
# # Gold tables — one per agreement type
# for contract_type, gold_df in gold_dfs.items():
#     table_name = f"{TARGET_CATALOG}.{TARGET_SCHEMA}.gold_{contract_type}s"
#     gold_df.write.mode("overwrite").saveAsTable(table_name)
#     print(f"Wrote to {table_name}")
#
# print("Done — all tables persisted.")

Contoh buku catatan

Kecerdasan Dokumen didukung oleh Fungsi AI

Dapatkan buku catatan

  • Last updated on