Data Extraction Pipeline
Structured JSON from any unstructured text
Extract structured data from invoices, receipts, emails, forms, and PDFs at scale — outputting clean, validated JSON ready for database ingestion. Handles format variance and missing fields that rule-based parsers fail on.
Gemini 1.5 Pro
for 5,000K tokens/mes · 88% entrada / 12% salida
¿POR QUÉ ESTE MODELO?
Gemini 1.5 Pro is built for production data pipelines: its enormous context window handles long documents in a single call, its structured output mode produces schema-valid JSON reliably, and its pricing scales favorably at the millions-of-tokens volumes that extraction pipelines generate daily.
MODELOS ALTERNATIVOS
CONSEJOS DE IMPLEMENTACIÓN
- 1
Define your JSON schema using JSON Schema Draft-07 and pass it directly in the system prompt — models with native JSON mode output schema-valid JSON on the first attempt 95%+ of the time, eliminating parsing failures.
- 2
Add a 'confidence' field to every extracted value: instruct the model to output 'high', 'medium', or 'low' confidence per field, and route 'low' confidence extractions to a human review queue rather than auto-ingesting them.
- 3
Process documents in parallel workers to maximize throughput — extraction jobs are embarrassingly parallel, and splitting a 10,000-document batch across 20 concurrent workers reduces wall-clock time by 20x without changing cost.