Dokumentintelligenz, unterstützt von KI-Funktionen

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In diesem Tutorial wird eine End-to-End-Intelligent-Document-Processing-(IDP)-Pipeline mit drei Databricks-AI-Funktionen veranschaulicht.

┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│  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, ...)      │
│                                                                                                                                                     │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘

Die Pipeline verarbeitet von SEC eingereichte Rechtsvereinbarungen, klassifizieren die einzelnen in eine von fünf Kategorien (Partner, Marketing, Beratung, Hosting, Escrow) und extrahieren relevante Begriffe wie Parteinamen, Datumsangaben und Vergütungsdetails.

Voraussetzungen

  • Serverlose Berechnung oder ein SQL Warehouse mit aktivierten KI-Funktionen
  • Zugriff auf das Beispieldatenvolume unter /Volumes/samples/sec/contracts/

Note

Der samples.sec.contracts Datensatz ist standardmäßig in allen Arbeitsbereichen verfügbar. Um Ihre eigenen PDF-Dateien zu verarbeiten, ändern Sie SOURCE_PATH in der nächsten Zelle in ein Unity-Katalogvolume, das Ihre Dateien enthält. Für das Aufnehmen von PDF-Dateien aus externen Quellen wie SharePoint oder Google Drive empfiehlt Databricks 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]

Konfiguration

Klassifizierungsbezeichnungen geben an ai_classify , aus welchen Kategorien Sie wählen können. Jedes Extraktionsschema definiert die kurzen, typisierten Felder, die ai_extract für diesen Vereinbarungstyp abgerufen werden sollen.

Ändern Sie diese, um die Pipeline an Ihre eigenen Dokumenttypen anzupassen.

# 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

Bronzeschicht – Roh-PDFs einlesen

Lesen von PDF-Dateien als binär im Spark-FormatbinaryFile. Jede Zeile enthält den Dateipfad, unformatierte Inhaltsbytes, Länge und Änderungszeitstempel.

Produktionstipp: Ersetzen Sie für die inkrementelle Aufnahme spark.read durch das automatische Ladeprogramm (cloudFiles Format), sodass nur neue Dateien bei jeder Ausführung verarbeitet werden.

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"))

Silberschicht – Analysieren und Klassifizieren

Analysieren : ai_parse_document Wandelt unformatierte PDF-Bytes in eine strukturierte VARIANT mit Dokumentelementen, Layoutmetadaten und Dateiinformationen um.

Klassifizieren – ai_classify akzeptiert die VARIANT-Ausgabe direkt aus ai_parse_document – keine Umwandlung in eine Zeichenfolge erforderlich. Dokumente mit Analysefehlern werden vor der Klassifizierung herausgefiltert. Der Klassifizierer verleiht Dateinamenstichwörtern eine starke Gewichtung, es sei denn, der Dokumentinhalt widerspricht ihnen eindeutig.

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"))

Goldschicht – Extrahieren strukturierter Felder

Jeder Vereinbarungstyp verfügt über ein dediziertes Extraktionsschema mit drei bis vier kurzen Feldern. ai_extract Ruft Partynamen, Datumsangaben, Dollarbeträge und kurze Ausdrücke aus jedem klassifizierten Dokument ab. Die folgende Schleife verarbeitet alle fünf Typen und zeigt Ergebnisse inline an.

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)

(Optional) In Delta-Tabellen speichern

Um Ergebnisse für nachgeschaltete Workflows, Analysen oder Dashboards zu speichern, entkommentieren Sie die Zelle unten und legen Sie Ihren Zielkatalog und Ihr Schema fest. Es wird empfohlen, die analysierten Dokumente beizubehalten– es ermöglicht zukünftigen Läufen, den Analyseschritt zu überspringen und direkt aus der Tabelle zu lesen.

# 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.")

Beispiel-Notebook

Dokumentintelligenz, unterstützt von KI-Funktionen

Notebook abrufen

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