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In deze zelfstudie ziet u een end-to-end IDP-pijplijn (Intelligent Document Processing) met behulp van drie Databricks AI-functies.
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ 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, ...) │
│ │
└─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────┘
De pijplijn verwerkt juridische overeenkomsten met SEC, waarbij elk wordt geclassificeerd in een van de vijf categorieën (affiliate, marketing, consulting, hosting, borg) en relevante termen zoals partijnamen, datums en compensatiegegevens worden geëxtraheerd.
Vereisten
- Serverloze rekenkracht of een SQL-warehouse waarvoor AI Functions is ingeschakeld
- Toegang tot het voorbeeldgegevensvolume op
/Volumes/samples/sec/contracts/
Note
De samples.sec.contracts gegevensset is standaard beschikbaar in alle werkruimten. Als u uw eigen PDF-bestanden wilt verwerken, wijzigt u SOURCE_PATH in de volgende cel naar een Unity Catalog-volume dat uw bestanden bevat. Voor het opnemen van PDF-bestanden uit externe bronnen, zoals SharePoint of Google Drive, raadt Databricks Lakeflow Connect aan.
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]
Configuratie
Classificatielabels geven aan ai_classify uit welke categorieën u kunt kiezen. Elk extractieschema definieert de korte, gespecificeerde velden die ai_extract zal ophalen voor dat overeenkomsttype.
Wijzig deze om de pijplijn aan uw eigen documenttypen aan te passen.
# 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
Bronslaag - Onbewerkte PDF-bestanden opnemen
PDF-bestanden lezen als binaire gegevens met het binaryFile-formaat van Spark. Elke rij bevat het bestandspad, ruwe inhoudsbytes, lengte en wijzigingstijdstempel.
Productietip: Voor incrementele opname vervangt u
spark.readdoor Auto Loader (cloudFilesformaat), zodat alleen nieuwe bestanden worden verwerkt tijdens elke uitvoering.
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"))
Silver Layer - Parseren en classificeren
Parseren : ai_parse_document converteert onbewerkte PDF-bytes naar een gestructureerde VARIANT met documentelementen, metagegevens van indeling en bestandsgegevens.
Classificeren - ai_classify accepteert de VARIANT-uitvoer rechtstreeks van ai_parse_document - u hoeft niet naar een tekenreeks te casten. Documenten met parseringsfouten worden gefilterd vóór classificatie. De classificatie geeft sterk gewicht aan trefwoorden voor bestandsnaam, tenzij de inhoud van het document duidelijk in strijd is met deze trefwoorden.
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"))
Gouden laag - Gestructureerde velden extraheren
Elk overeenkomsttype heeft een toegewezen extractieschema met drie tot vier korte velden.
ai_extract haalt partijnamen, datums, dollarbedragen en korte woordgroepen op uit elk geclassificeerd document. De onderstaande lus verwerkt alle vijf typen en geeft resultaten inline weer.
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)
(Optioneel) Opslaan in Delta-tabellen
Als u resultaten voor downstreamwerkstromen, analyses of dashboards wilt opslaan, verwijdert u de opmerkingen in de onderstaande cel en stelt u de doelcatalogus en het schema in. Het behouden van de geparseerde documenten wordt aanbevolen. Hiermee kunnen toekomstige uitvoeringen de parseringsstap overslaan en rechtstreeks vanuit de tabel lezen.
# 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.")