Build a Cross-Border Supplier Document Schema Mapper

Map localized vendor credential formats to standardized Frictionless Data schemas based on jurisdiction. Translate divergent operational license data into a unified global compliance format.

Third-Party Risk Operations Manager

Ingest localized tabular supplier compliance data containing jurisdiction-specific license and tax identifiers.

Use a reasoning agent to map local header names and data types to a canonical Frictionless Data schema.

Apply Frictionless schema validation to ensure the mapped data conforms to global corporate compliance standards.

Output a normalized schema definition file and a transformation report detailing field adaptations.

Terminal CLI and local web queue displaying mapped credential records, canonical schema comparisons, and validation reports.

Create synthetic supplier onboarding CSVs with disparate column headers and test mapping them to a defined Frictionless resource schema. Starter examples below are synthetic. They are not downloaded records.

Frictionless data validation Applies the Frictionless Data validation framework and tabular resource schema conventions described in the frictionless-py documentation.

agno, frictionless, pandas, pydantic. Agno orchestrates intelligent LLM mapping of tabular headers into a unified canonical schema before standard procedural validation. Local or hosted LLM with tool calling capability and low temperature for deterministic header translation.

Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.

Successfully maps disparate local table headers to a standard Frictionless schema using the AI agent.

Validates the transformed data against the generated canonical schema without uncaught exceptions.

Generates a JSON transformation report documenting mapping decisions and validation status.

```json [ { "input": { "jurisdiction": "DE", "local_data": [ { "firmenname": "GmbH Tech", "steuernummer": "123456789", "ablaufdatum": "2025-12-31" } ], "canonical_target_fields": [ "supplier_name", "tax_id", "expiry_date" ] }, "expected_output": { "jurisdiction": "DE", "mapping": { "firmenname": "supplier_name", "steuernummer": "tax_id", "ablaufdatum": "expiry_date" }, "schema_compliant": true, "normalized_records": [ { "supplier_name": "GmbH Tech", "tax_id": "123456789", "expiry_date": "2025-12-31" } ] } }, { "input": { "jurisdiction": "FR", "local_data": [ { "nom_entreprise": "Societe SA", "num_tva": "FR987654321", "date_fin": "2024-10-15" } ], "canonical_target_fields": [ "supplier_name", "tax_id", "expiry_date" ] }, "expected_output": { "jurisdiction": "FR", "mapping": { "nom_entreprise": "supplier_name", "num_tva": "tax_id", "date_fin": "expiry_date" }, "schema_compliant": true, "normalized_records": [ { "supplier_name": "Societe SA", "tax_id": "FR987654321", "expiry_date": "2024-10-15" } ] } } ] ```

Operates entirely on synthetic simplified supplier data schema definitions; produces internal transformation reports and does not execute actual commercial mapping. Use mock external writes. Require human review before any real deployment.