Documentation Index Fetch the complete documentation index at: https://docs.reducto.ai/llms.txt
Use this file to discover all available pages before exploring further.
Reducto’s APIs have various configuration options that let you control how your documents are processed. This section covers all available configurations across the platform.
Configuration by Endpoint Parse
Split
Classify
Edit
Parse converts documents into structured content. Options are grouped by purpose: Group Purpose Pages enhanceAI-powered accuracy Agentic Modes , Chart Extraction retrievalRAG optimization Chunking Methods formattingDetecting styling & output format Table Formats , Additional Document Data spreadsheetExcel/CSV handling Spreadsheet Processing settingsProcessing controls Processing Settings , Page Ranges
result = client.parse.run( input = upload, enhance = { ... }, retrieval = { ... }, formatting = { ... }, spreadsheet = { ... }, settings = { ... } )
Split divides documents into logical sections. Group Purpose Pages split_descriptionSection definitions Split Configuration split_rulesSplitting logic prompt Split Configuration settingsTable handling Split Configuration parsingDocument processing All Parse options
result = client.split.run( input = upload, split_description = [{ "name" : "..." , "description" : "..." }], split_rules = "..." , settings = { "table_cutoff" : "truncate" } )
Classify determines document type based on natural language criteria. Group Purpose Pages classification_schemaCategory definitions Classify Configuration page_rangePages used as context Classify Configuration
response = client.classify.run( input = upload, classification_schema = [ { "category" : "invoice" , "criteria" : [ "billing info" , "itemized charges" ]}, { "category" : "contract" , "criteria" : [ "legal terms" , "signatures" ]}, ] )
Edit fills forms and modifies documents. Option Purpose Pages edit_instructionsNatural language instructions (base config) form_schemaPre-defined field locations Form Schema edit_optionsHighlight color, overflow (base config)
result = client.edit.run( document_url = upload, edit_instructions = "Fill name: John Doe, date: 2024-01-15" , form_schema = [ ... ], edit_options = { "color" : "#FF0000" } )
Common Patterns
Variable chunking with embedding optimization for vector search: result = client.parse.run( input = upload, retrieval = { "chunking" : { "chunk_mode" : "variable" , "chunk_size" : 1000 }, "embedding_optimized" : True }, formatting = { "table_output_format" : "dynamic" } )
Enable agentic mode for both text and tables: result = client.parse.run( input = upload, enhance = { "agentic" : [{ "scope" : "text" }, { "scope" : "table" }] } )
Complete extraction with citations
Migrating from v2 If you’re using the legacy configuration format, use this converter to transform your v2 config to v3:
See the Migration Guide for complete mapping tables and examples. 
