> ## 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. # Configuration Overview > How Reducto's configuration system works 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 converts documents into structured content. Options are grouped by purpose: | Group | Purpose | Pages | | ------------- | --------------------------------- | ------------------------------------------------------------------------------------------------------------------------- | | `enhance` | AI-powered accuracy | [Agentic Modes](/configs/parse/agentic-modes), [Chart Extraction](/configs/parse/chart-extraction) | | `retrieval` | RAG optimization | [Chunking Methods](/configs/parse/chunking-methods) | | `formatting` | Detecting styling & output format | [Table Formats](/configs/parse/table-output-formats), [Additional Document Data](/configs/parse/additional-document-data) | | `spreadsheet` | Excel/CSV handling | [Spreadsheet Processing](/configs/parse/spreadsheet) | | `settings` | Processing controls | [Processing Settings](/configs/parse/ocr-settings), [Page Ranges](/configs/parse/page-ranges) | ```python theme={null} result = client.parse.run( input=upload, enhance={...}, retrieval={...}, formatting={...}, spreadsheet={...}, settings={...} ) ``` Extract pulls structured data from documents using a JSON schema. | Group | Purpose | Pages | | -------------- | --------------------------- | ---------------------------------------------------------------------------------------------- | | `instructions` | Schema and system prompt | (base config) | | `settings` | Citations, array extraction | [Array Extraction](/configs/extract/array-extraction), [Citations](/configs/extract/citations) | | `parsing` | Document processing | All Parse options | ```python theme={null} result = client.extract.run( input=upload, instructions={"schema": {...}, "system_prompt": "..."}, settings={"array_extract": True, "citations": {"enabled": True}}, parsing={...} ) ``` Split divides documents into logical sections. | Group | Purpose | Pages | | ------------------- | ---------------------- | --------------------------------------------------- | | `split_description` | Section definitions | [Split Configuration](/configs/split/configuration) | | `split_rules` | Splitting logic prompt | [Split Configuration](/configs/split/configuration) | | `settings` | Table handling | [Split Configuration](/configs/split/configuration) | | `parsing` | Document processing | All Parse options | ```python theme={null} 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_schema` | Category definitions | [Classify Configuration](/configs/classify/configuration) | | `page_range` | Pages used as context | [Classify Configuration](/configs/classify/configuration) | ```python theme={null} 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_instructions` | Natural language instructions | (base config) | | `form_schema` | Pre-defined field locations | [Form Schema](/configs/edit/form-schema) | | `edit_options` | Highlight color, overflow | (base config) | ```python theme={null} 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: ```python theme={null} 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: ```python theme={null} result = client.parse.run( input=upload, enhance={ "agentic": [{"scope": "text"}, {"scope": "table"}] } ) ``` Array extraction with source locations for long documents: ```python theme={null} result = client.extract.run( input=upload, instructions={"schema": schema}, settings={ "array_extract": True, "citations": {"enabled": True} } ) ``` ## Migrating from v2 If you're using the legacy configuration format, use this converter to transform your v2 config to v3: