Extract Information to Table¶

Parse material, extract fields, and write structured results to a database table. Use it to turn forms, resumes, or business documents into records.

Prepare your resources¶

Prepare source material, JSON Schema, a result database, table name, and output volume. Check columns and types when using an existing table. The template creates a missing table from field definitions or reuses an existing table with the same name.

This template saves one combined extraction into a table. If each resume or receipt should become one row, validate a single file first, then design batch processing.

Use the template¶

On the workflow creation page, start from a template, search for Extract Information to Table, and load it onto the canvas.

Complete the required fields in the parameter form.

Setting	Purpose
Source material	Documents, forms, email, or folders to parse
Parsing tier	Controls parsing speed, quality, and cost
Extraction schema	JSON Schema defining fields to extract and write
Result database	Database for extracted records
Result table name	Reuse an existing table with this name in the selected database, or create it if absent
Output location	Catalog location for extraction results

Save the parameters, then start a manual run. The system saves the current workflow before submitting execution.

Complete processing flow¶

Arrows show execution order; configured bindings supply each step’s input.

Extract Information to Table

Configure fields and the table¶

Setting	What to check
Extraction schema	Field names and types match target columns
Instructions	Meanings, formats, and missing-value rules
Layout-sensitive fields	Names of fields whose position matters
Result database and table	Correct destination; avoid an incompatible existing table
Output location	File destination, checked separately from table records

Extraction uses n_to_1. It combines inputs into one JSON result and binds result to the table node’s raw_json. The template has no automatic one-file-per-row loop.

Define structures for multi-value fields, such as education arrays. A schema accepting arrays does not mean any existing column can store them. Check the target and actual write result; see Save to MOI Table.

Check table and file results¶

Inspect parsed content using one source file.
Verify extracted fields and values.
Check database, table_name, written_columns, and affected_rows on the writing node.
Compare records in the result table with the original.
Inspect saved results, parsed files, and final source registration.

Table writing precedes file saving and registration. If a later step fails, records may already exist; inspect them before rerunning.

Example: store one resume¶

Define name, education, and work experience fields, including the structure of multiple entries. Select one resume, configure a dedicated result table, and run. Check completeness, written columns, and affected rows. See Extract resume information.

Common problems¶

Symptom	Check and action
Extraction succeeds but writing fails	Check the database, columns, and field types
Several files yield one result	Extraction combines inputs; per-file processing needs a different flow
Nested fields cannot be written	Check schema, column types, and accepted node inputs
A failed run left table records	Locate whether failure occurred after writing before repeating it
Only files are needed	Use Document Information Extraction

Last updated on Sep 15, 2026