Intelligent Document Processing (IDP)
Overview of the Intelligent Document Processing (IDP) integration.
Overview
Intelligent Document Processing (IDP) extracts structured data from documents using AI. This integration enables automated document analysis, data extraction, and intelligent processing workflows.
Setup
The following integrations need to be connected to your Kognitos workspace:
Intelligent Document Processing (IDP)
Steps
Follow these steps to connect the integration in Kognitos:
Configure
Add a name for the connection. You'll be prompted for authentication details if needed. Then, click on Connect.
Credentials
1. Anthropic API Key
Follow these steps to obtain your Anthropic API key:
Log in to the Anthropic Console
Go to the Anthropic Console and log in with your credentials.
Navigate to API Keys
Go to Settings > API Keys. Then click + Create Key in the top right.
Configuration
Select a workspace and give your key a descriptive name (e.g., "Development Key" or "Production App"). Then, click Add to generate your API key.
Copy and Store Your Key
Copy your API key immediately and store it securely. You won't be able to view it again after closing the dialog.
2. OpenAI API Key
Follow these steps to obtain your OpenAI API key:
Log In to OpenAI
Navigate to the OpenAI Platform and log in with your credentials.
API Keys
Open Account Settings, then navigate to API Keys.
Generate a New API Key
Click Create new secret key. Copy the key immediately — it will only be shown once.
Authentication
Use one of the following authentication methods to connect this integration in Kognitos. Each method has its own configuration requirements.
Connect using API Key
Connect to Anthropic API for document processing.
API Key
The Anthropic API key
sensitive
Connect using Service Account Credentials and Region
Connect to Google Vertex AI (Gemini) API for document processing.
Service Account Credentials
The Google service account credentials JSON as string
sensitive
Region
The Google Cloud region
text
Connect using API Key
Connect to OpenAI API for document processing.
API Key
The OpenAI API key
sensitive
Actions
The following actions are available in the Intelligent Document Processing (IDP) integration:
1. Classify documents
Classify text or documents against user-defined topics.
2. Extract fields from documents
Extract structured fields from text or documents.
3. Extract subdocuments from a document
Extract subdocuments from a document.
4. Extract table records from documents
Extract one recurring table across all pages into a merged dataset.
5. Extract tables from documents
Extract structured tables from text or documents.
6. Merge subdocuments into a document
Merge multiple subdocuments/pages into a single PDF document.
7. Parse layout from a document
Parse the structural layout of a document.
8. Read content from documents
Read text content from documents.
Concepts
Idp classification options
Options for classify_text_or_documents.At least one source of classification topics must be provided: topics (explicit list), classification_rules (file or string to parse), or prompt (natural language description).
Explicit list of ClassificationTopic objects. Each topic has topic_name (required), topic_description (what the topic represents), and topic_criteria (specific criteria to evaluate). Highest priority if provided. Example:: topics=[ ClassificationTopic( topic_name="Invoice", topic_description="A billing document", topic_criteria="Contains line items, totals, " "and payment terms", ), ] Tip: More specific topic_criteria produce better classification accuracy. Vague criteria lead to false positives.
optional[list of idp classification topic]
prompt
Classification instructions or natural language context. If no topics are provided, the LLM will extract topics from this prompt automatically.
optional[text]
classification_rules
Domain-specific instructions as a file (.txt, .md, .docx) or plain string. The LLM parses these rules to extract classification topics and criteria. Injected into the system prompt; benefits from prompt caching across multiple calls. Use when topics are easier to express as prose rules than structured ClassificationTopic objects.
optional[file or text?]
examples
File (.txt, .md, .docx) or plain string with sample classification decisions. Useful when the LLM consistently misclassifies borderline cases.
optional[file or text?]
llm_model
LLM model override. The model must belong to the provider configured in the IDP connection (e.g., an OpenAI model requires openai credentials). See SupportedModel for valid values. If None, uses the provider's default model.
optional[enum[CLAUDE_HAIKU_4_5, CLAUDE_OPUS_4_6, CLAUDE_OPUS_4_8, CLAUDE_OPUS_5, CLAUDE_SONNET_4_5, CLAUDE_SONNET_4_6, CLAUDE_SONNET_5, GEMINI_2_5_FLASH, GEMINI_2_5_PRO, GEMINI_3_1_FLASH_LITE, GEMINI_3_1_PRO_PREVIEW, GEMINI_3_5_FLASH, GEMINI_3_6_FLASH, GEMINI_3_FLASH_PREVIEW, GPT_5_2, GPT_5_4, GPT_5_4_MINI, GPT_5_5, GPT_5_6_LUNA, GPT_5_6_SOL, GPT_5_6_TERRA]?]
confidence_threshold
Minimum average confidence score (0-100). Classifications below this threshold raise ClassificationError.
optional[number]
Idp classification topic
A topic definition for document classification.
topic_name
Name of the classification topic.
optional[text]
topic_description
Description of what this topic represents.
optional[text]
topic_criteria
Specific criteria to evaluate for this topic.
optional[text]
Idp classification result
Result of classify_text_or_documents.Contains classification results for each input document or text. For document inputs, elements are DocumentClassification. For text inputs, elements are TextClassification.
result_type
Discriminator constant ("classification") that clients use for UI rendering and response routing — enables distinguishing this result type from other IDP procedure results.
optional[enum[CLASSIFICATION, FIELDS_EXTRACTION, LAYOUT_PARSING, MERGE_SUBDOCUMENTS, READ_CONTENT, SUBDOCUMENTS_EXTRACTION, TABLES_EXTRACTION, TABLE_RECORDS_EXTRACTION]?]
classifications
Per-input classification results. Each element carries its own element_type discriminator for further UI dispatch.
list of idp document classification] or optional[list of idp text classification?
Idp fields extraction options
Options for extract_fields_from_text_or_documents.Specify what to extract using either fields (structured field definitions) or prompt (natural language description). These are mutually exclusive — provide one or the other, not both.
List of FieldDefinition objects describing each field to extract. Each definition has name (required), format ("string", "number", "date", "boolean", "list"), rule (per-field extraction instruction), and default_value (fallback when the field is missing or below confidence). Mutually exclusive with prompt. Max 300 fields per extraction.
optional[list of json]
prompt
Natural language description of what to extract. The LLM parses this into FieldDefinition objects automatically. Mutually exclusive with fields. Example: "Extract the invoice number, date, and total amount".
optional[text]
business_rules
Domain-specific instructions that guide the LLM during extraction. Accepts a file (.txt, .md, .docx) or a plain string — any text works, no prescribed format. Rules are injected into the system prompt with highest precedence over default behavior. Because they are part of the system prompt, they benefit from prompt caching across multiple LLM calls on the same document set. Use business rules to enforce formatting conventions, domain constraints, or handling instructions for ambiguous content.
optional[file or text?]
examples
File (.txt, .md, .docx) or plain string with sample input/output pairs that demonstrate expected extraction behavior. Injected into the system prompt alongside business rules. Particularly useful when the LLM consistently misinterprets a field's format or value.
optional[file or text?]
llm_model
LLM model override. The model must belong to the provider configured in the IDP connection (e.g., an OpenAI model requires openai credentials). See SupportedModel for valid values. If None, uses the provider's default model.
optional[enum[CLAUDE_HAIKU_4_5, CLAUDE_OPUS_4_6, CLAUDE_OPUS_4_8, CLAUDE_OPUS_5, CLAUDE_SONNET_4_5, CLAUDE_SONNET_4_6, CLAUDE_SONNET_5, GEMINI_2_5_FLASH, GEMINI_2_5_PRO, GEMINI_3_1_FLASH_LITE, GEMINI_3_1_PRO_PREVIEW, GEMINI_3_5_FLASH, GEMINI_3_6_FLASH, GEMINI_3_FLASH_PREVIEW, GPT_5_2, GPT_5_4, GPT_5_4_MINI, GPT_5_5, GPT_5_6_LUNA, GPT_5_6_SOL, GPT_5_6_TERRA]?]
confidence_threshold
Minimum confidence score (0-100). Fields below this threshold raise ExtractionError. Lower this for noisy or OCR-heavy documents where perfect confidence is unrealistic.
optional[number]
verification
Post-extraction verification level. When enabled, the LLM cross-checks extracted values against the source document. Business rules act as corroborating evidence during verification — values conforming to rules receive a confidence boost, while violations are flagged for review. None disables verification.
optional[enum[HIGH, LOW, MODERATE]?]
Idp fields extraction result
Result of extract_fields_from_text_or_documents.Contains all extracted field occurrences. For document inputs, fields are DocumentField instances with page_number and bounding_box (normalized 0-1 coordinates) that clients can use to render overlays, highlight extracted values on the original document, or build customizable review UIs. For text inputs, fields are TextField instances with location (character offsets) for text highlighting.
result_type
Discriminator constant ("fields_extraction").
optional[enum[CLASSIFICATION, FIELDS_EXTRACTION, LAYOUT_PARSING, MERGE_SUBDOCUMENTS, READ_CONTENT, SUBDOCUMENTS_EXTRACTION, TABLES_EXTRACTION, TABLE_RECORDS_EXTRACTION]?]
fields
Extracted field results — one per unique field occurrence.
list of idp document field] or optional[list of idp text field?
Idp subdocuments extraction options
Options for extract_subdocuments_from_a_document.Supports three extraction strategies: 1. Page range: Set page_range to extract a specific range. 2. Page marker: Set page_marker to split at pages matching a text pattern (uses LLM to evaluate each page). 3. Fixed size: Set subdocument_size to split into N-page chunks with optional overlap.
page_range
Tuple of (start_page, end_page) for range extraction. Pages are 1-indexed. Example: (1, 5) extracts pages 1-5.
optional[list of number]
page_marker
Text pattern to identify subdocument boundaries. Example: "INVOICE NUMBER". The LLM evaluates each page.
optional[text]
subdocument_size
Number of pages per subdocument for chunking.
optional[number]
subdocument_overlap_size
Number of overlapping pages between consecutive chunks. Requires subdocument_size.
optional[number]
llm_model
LLM model override (used for marker-based splitting only — page-range and fixed-size modes do not use an LLM). The model must belong to the provider configured in the IDP connection. See SupportedModel for valid values. If None, uses the provider's default model.
optional[enum[CLAUDE_HAIKU_4_5, CLAUDE_OPUS_4_6, CLAUDE_OPUS_4_8, CLAUDE_OPUS_5, CLAUDE_SONNET_4_5, CLAUDE_SONNET_4_6, CLAUDE_SONNET_5, GEMINI_2_5_FLASH, GEMINI_2_5_PRO, GEMINI_3_1_FLASH_LITE, GEMINI_3_1_PRO_PREVIEW, GEMINI_3_5_FLASH, GEMINI_3_6_FLASH, GEMINI_3_FLASH_PREVIEW, GPT_5_2, GPT_5_4, GPT_5_4_MINI, GPT_5_5, GPT_5_6_LUNA, GPT_5_6_SOL, GPT_5_6_TERRA]?]
Idp subdocuments extraction result
Result of extract_subdocuments_from_a_document.Contains the extracted subdocument pages as separate IO objects.
result_type
Discriminator constant ("subdocuments_extraction").
optional[enum[CLASSIFICATION, FIELDS_EXTRACTION, LAYOUT_PARSING, MERGE_SUBDOCUMENTS, READ_CONTENT, SUBDOCUMENTS_EXTRACTION, TABLES_EXTRACTION, TABLE_RECORDS_EXTRACTION]?]
subdocuments
List of subdocument IO objects (PDF format).
optional[list of file]
source_document
Filename of the source document.
optional[text]
Idp table records extraction options
Options for extract_table_records_from_documents.Describes a single recurring table schema (via table) to extract from every page of the input document(s). The document is split into page windows that are extracted in parallel and stitched back into one merged dataset. Use this when the same table layout repeats across many pages (e.g. a multi-page timesheet or line-item packet) and you want all rows in one result. To locate distinct, individual tables with page and bounding-box identity, use TablesExtractionOptions instead.
table
Description of the recurring table to extract. Should be descriptive enough for the LLM to identify it on each page — include the table title or expected column headers. Example: "Daily labor grid with columns Date, Employee, " "Job, Hours".
optional[text]
business_rules
Domain-specific instructions that guide the LLM during extraction. Accepts a file (.txt, .md, .docx) or a plain string — any text works. Injected into the system prompt with highest precedence; benefits from prompt caching across the per-chunk calls. Use to enforce column naming, row filtering, or handling of merged cells and annotations.
optional[file or text?]
examples
File (.txt, .md, .docx) or plain string with sample extraction pairs. Useful when the LLM misidentifies headers or splits rows incorrectly.
optional[file or text?]
llm_model
LLM model override. The model must belong to the provider configured in the IDP connection (e.g., an OpenAI model requires openai credentials). See SupportedModel for valid values. If None, uses the provider's default model.
optional[enum[CLAUDE_HAIKU_4_5, CLAUDE_OPUS_4_6, CLAUDE_OPUS_4_8, CLAUDE_OPUS_5, CLAUDE_SONNET_4_5, CLAUDE_SONNET_4_6, CLAUDE_SONNET_5, GEMINI_2_5_FLASH, GEMINI_2_5_PRO, GEMINI_3_1_FLASH_LITE, GEMINI_3_1_PRO_PREVIEW, GEMINI_3_5_FLASH, GEMINI_3_6_FLASH, GEMINI_3_FLASH_PREVIEW, GPT_5_2, GPT_5_4, GPT_5_4_MINI, GPT_5_5, GPT_5_6_LUNA, GPT_5_6_SOL, GPT_5_6_TERRA]?]
confidence_threshold
Minimum confidence score (0-100). The aggregated per-column confidence is averaged and compared against this threshold. Set to 0 (default) to disable threshold checking and capture all rows.
optional[number]
pages_per_call
Number of pages sent to the LLM per extraction call. 1 (default) maximizes parallelism and accuracy for dense single-page tables; increase it when a logical row spans page breaks so the model sees both pages at once.
optional[number]
page_overlap
Number of pages shared between consecutive windows. 0 (default) means no overlap. Use a small overlap (e.g. 1) when rows straddle page boundaries; duplicate rows from the shared pages are de-duplicated during stitching. Must be less than pages_per_call.
optional[number]
Idp table records extraction result
Result of extract_table_records_from_documents.Contains every row of one recurring table schema, merged across all pages of the input document(s) into a single dataset. Unlike TablesExtractionResult (which returns located table artifacts, one per description, each with its own page and bounding box), this returns one merged records table with per-chunk page provenance carried in segments.
result_type
Discriminator constant ("table_records_extraction").
optional[enum[CLASSIFICATION, FIELDS_EXTRACTION, LAYOUT_PARSING, MERGE_SUBDOCUMENTS, READ_CONTENT, SUBDOCUMENTS_EXTRACTION, TABLES_EXTRACTION, TABLE_RECORDS_EXTRACTION]?]
The merged table — canonical headers plus all rows in document order. None only if no input pages were processed.
optional[idp table]
Per-chunk provenance. Each TableRecordSegment maps a half-open row range in records back to the source document and page range that produced it.
optional[list of idp table record segment]
confidence
Aggregated per-column confidence scores (0-100), row-count-weighted across the contributing chunks.
optional[list of number]
Idp table
A structured table extracted from a document or text.Contains the table data (headers + rows) and an optional PyArrow representation for efficient downstream processing.
element_type
Discriminator constant ("idp_table").
optional[enum[DOCUMENT_CLASSIFICATION, DOCUMENT_CONTENT, DOCUMENT_FIELD, DOCUMENT_IDP_TABLE, DOCUMENT_KEY_VALUE_PAIR, DOCUMENT_LAYOUT, DOCUMENT_TEXT, IDP_TABLE, TEXT_CLASSIFICATION, TEXT_FIELD, TEXT_IDP_TABLE]?]
title
Table title or caption, if detected.
optional[text]
headers
Column header names.
optional[list of text]
rows
Table body as a list of rows, each row a list of cell values.
optional[list of text]
num_rows
Number of data rows (excluding header).
optional[number]
num_cols
Number of columns.
optional[number]
arrow_table
PyArrow Table for efficient columnar access.
optional[table?]
Idp table record segment
Provenance for one contiguous block of rows in a merged record table.Returned as part of TableRecordsExtractionResult.segments. Each segment maps a half-open row range in the merged records table back to the source document and page range that produced it, so callers can trace any row to its origin without a parallel per-row list.
source_document
Filename of the source document.
optional[text]
source_document_index
0-based index into the input document list.
optional[number]
page_start
1-indexed first page of the chunk that produced these rows (inclusive).
optional[number]
page_end
1-indexed last page of the chunk that produced these rows (inclusive).
optional[number]
row_start
0-indexed offset of the first row of this segment within the merged records.rows (inclusive).
optional[number]
row_end
0-indexed offset one past the last row of this segment within the merged records.rows (exclusive).
optional[number]
confidence
Per-column confidence scores (0-100) for this chunk. Each entry corresponds to a column in the merged table.
optional[list of number]
Idp tables extraction options
Options for extract_tables_from_text_or_documents.Specify which tables to extract via tables — a list of table descriptions. Each description tells the LLM what table to look for.
tables
Descriptions of tables to extract. Each string should be descriptive enough for the LLM to identify the right table — include the table's title or header names. Max 5 tables per extraction. Example: ["Previous Employment History table", "Education and Certifications table"]. Tip: if a table is not found, make the description more specific by referencing the table's title or column headers (e.g., "Line items table with columns Item, Qty, Price" instead of "table").
optional[list of text]
business_rules
Domain-specific instructions that guide the LLM during table extraction. Accepts a file (.txt, .md, .docx) or a plain string — any text works. Injected into the system prompt with highest precedence. Benefits from prompt caching across multiple LLM calls. Use to enforce column naming, row filtering, or handling of merged cells and annotations.
optional[file or text?]
examples
File (.txt, .md, .docx) or plain string with sample extraction pairs. Useful when the LLM misidentifies headers or splits rows incorrectly.
optional[file or text?]
llm_model
LLM model override. The model must belong to the provider configured in the IDP connection (e.g., an OpenAI model requires openai credentials). See SupportedModel for valid values. If None, uses the provider's default model.
optional[enum[CLAUDE_HAIKU_4_5, CLAUDE_OPUS_4_6, CLAUDE_OPUS_4_8, CLAUDE_OPUS_5, CLAUDE_SONNET_4_5, CLAUDE_SONNET_4_6, CLAUDE_SONNET_5, GEMINI_2_5_FLASH, GEMINI_2_5_PRO, GEMINI_3_1_FLASH_LITE, GEMINI_3_1_PRO_PREVIEW, GEMINI_3_5_FLASH, GEMINI_3_6_FLASH, GEMINI_3_FLASH_PREVIEW, GPT_5_2, GPT_5_4, GPT_5_4_MINI, GPT_5_5, GPT_5_6_LUNA, GPT_5_6_SOL, GPT_5_6_TERRA]?]
confidence_threshold
Minimum confidence score (0-100). Table confidence is per-column; the average is compared against this threshold.
optional[number]
verification
Post-extraction verification level. None disables verification.
optional[enum[HIGH, LOW, MODERATE]?]
Idp tables extraction result
Result of extract_tables_from_text_or_documents.Contains all extracted tables. For document inputs, tables are DocumentIDPTable instances with page_number and bounding_box (normalized 0-1 coordinates) that clients can use to render overlays or highlight table regions on the original document. For text inputs, tables are TextIDPTable instances with location (character offsets) for text highlighting.
result_type
Discriminator constant ("tables_extraction").
optional[enum[CLASSIFICATION, FIELDS_EXTRACTION, LAYOUT_PARSING, MERGE_SUBDOCUMENTS, READ_CONTENT, SUBDOCUMENTS_EXTRACTION, TABLES_EXTRACTION, TABLE_RECORDS_EXTRACTION]?]
tables
Extracted table results.
list of idp document table] or optional[list of idp text table?
Idp merge subdocuments options
Options for merge_subdocuments_into_a_document.
document_name
Filename for the merged output document.
optional[text]
Idp merge subdocuments result
Result of merge_subdocuments_into_a_document.Contains the merged document as a single IO object.
result_type
Discriminator constant ("merge_subdocuments").
optional[enum[CLASSIFICATION, FIELDS_EXTRACTION, LAYOUT_PARSING, MERGE_SUBDOCUMENTS, READ_CONTENT, SUBDOCUMENTS_EXTRACTION, TABLES_EXTRACTION, TABLE_RECORDS_EXTRACTION]?]
document
The merged document (PDF format).
optional[file]
document_name
Filename of the merged document.
optional[text]
source_count
Number of input documents/pages that were merged.
optional[number]
Idp layout parsing options
Options for parse_layout_from_a_document.Controls how the document's structural layout is extracted, including tables, key-value pairs, and text segments.
business_rules
Domain-specific instructions that guide the LLM during layout extraction. Accepts a file (.txt, .md, .docx) or a plain string — any text works. Injected into the system prompt with highest precedence. Benefits from prompt caching across multiple LLM calls.
optional[file or text?]
examples
File (.txt, .md, .docx) or plain string with sample extraction pairs demonstrating expected output structure.
optional[file or text?]
llm_model
LLM model override. The model must belong to the provider configured in the IDP connection (e.g., an OpenAI model requires openai credentials). See SupportedModel for valid values. If None, uses the provider's default model.
optional[enum[CLAUDE_HAIKU_4_5, CLAUDE_OPUS_4_6, CLAUDE_OPUS_4_8, CLAUDE_OPUS_5, CLAUDE_SONNET_4_5, CLAUDE_SONNET_4_6, CLAUDE_SONNET_5, GEMINI_2_5_FLASH, GEMINI_2_5_PRO, GEMINI_3_1_FLASH_LITE, GEMINI_3_1_PRO_PREVIEW, GEMINI_3_5_FLASH, GEMINI_3_6_FLASH, GEMINI_3_FLASH_PREVIEW, GPT_5_2, GPT_5_4, GPT_5_4_MINI, GPT_5_5, GPT_5_6_LUNA, GPT_5_6_SOL, GPT_5_6_TERRA]?]
confidence_threshold
Minimum confidence score (0-100). Elements below this threshold raise AnalysisError. Set to 0 (default) to disable threshold checking.
optional[number]
verification
Post-extraction verification level. None disables verification.
optional[enum[HIGH, LOW, MODERATE]?]
analysis_mode
Analysis strategy: - "single_pass": Processes the entire document in one LLM call. Fastest and cheapest — best for simple, short documents. - "parallel": Per-page extraction with smart deduplication. Best quality for multi-page documents — handles cross-page tables and repeated headers. - "plan_based": A planning LLM first analyzes the document, then creates focused extraction tasks that run in parallel. Best for complex documents with varied structure.
optional[enum[PARALLEL, PLAN_BASED, SINGLE_PASS]?]
Idp layout parsing result
Result of parse_layout_from_a_document.Contains the complete structural layout of the document. All layout elements (tables, key-value pairs, texts) carry page_number and bounding_box (normalized 0-1 coordinates) that clients can use to render overlays, build interactive document viewers, or create customizable review UIs.
result_type
Discriminator constant ("layout_parsing").
optional[enum[CLASSIFICATION, FIELDS_EXTRACTION, LAYOUT_PARSING, MERGE_SUBDOCUMENTS, READ_CONTENT, SUBDOCUMENTS_EXTRACTION, TABLES_EXTRACTION, TABLE_RECORDS_EXTRACTION]?]
The parsed document layout with tables, key-value pairs, and text segments.
optional[idp document layout]
source_document
Filename of the source document.
optional[text]
Idp document layout
Complete parsed layout of a document.Returned by parse_layout_from_a_document. Contains the document's structural elements organized by type: tables, key-value pairs, and text segments. All child elements carry page_number and bounding_box (normalized 0-1 coordinates) that clients can use to render overlays, build interactive document viewers, or create customizable review UIs.
element_type
Discriminator constant ("document_layout").
optional[enum[DOCUMENT_CLASSIFICATION, DOCUMENT_CONTENT, DOCUMENT_FIELD, DOCUMENT_IDP_TABLE, DOCUMENT_KEY_VALUE_PAIR, DOCUMENT_LAYOUT, DOCUMENT_TEXT, IDP_TABLE, TEXT_CLASSIFICATION, TEXT_FIELD, TEXT_IDP_TABLE]?]
document_type
Detected document type (e.g., "invoice").
optional[text]
document_title
Detected document title, if any.
optional[text]
source_document
Filename of the source document.
optional[text]
analysis_mode
The analysis mode that was used.
optional[enum[PARALLEL, PLAN_BASED, SINGLE_PASS]?]
confidence
Overall confidence score (0-100).
optional[number]
verified
Whether verification was applied.
optional[boolean]
hallucinations_detected
Whether any hallucinations were detected during verification.
optional[boolean]
Idp read content options
Options for read_content_from_documents.Controls how document content is extracted and returned. Digital PDFs use fast PyMuPDF extraction; scanned PDFs and images trigger LLM-based OCR automatically.
conversion
Output format. "as_markdown" preserves headings, lists, and tables as markdown (best for structured documents). "as_text" returns plain text (best for downstream text processing or search indexing). Defaults to "as_markdown".
optional[text]
max_pages_per_document
Maximum pages to process per document. Pages beyond this limit are silently skipped.
optional[number]
llm_model
LLM model override (used only for scanned/image documents that require OCR — digital PDFs ignore this setting). The model must belong to the provider configured in the IDP connection. See SupportedModel for valid values. If None, uses the provider's default model.
optional[enum[CLAUDE_HAIKU_4_5, CLAUDE_OPUS_4_6, CLAUDE_OPUS_4_8, CLAUDE_OPUS_5, CLAUDE_SONNET_4_5, CLAUDE_SONNET_4_6, CLAUDE_SONNET_5, GEMINI_2_5_FLASH, GEMINI_2_5_PRO, GEMINI_3_1_FLASH_LITE, GEMINI_3_1_PRO_PREVIEW, GEMINI_3_5_FLASH, GEMINI_3_6_FLASH, GEMINI_3_FLASH_PREVIEW, GPT_5_2, GPT_5_4, GPT_5_4_MINI, GPT_5_5, GPT_5_6_LUNA, GPT_5_6_SOL, GPT_5_6_TERRA]?]
Idp read content result
Result of read_content_from_documents.Contains extracted text/markdown content for each input document.
result_type
Discriminator constant ("read_content").
optional[enum[CLASSIFICATION, FIELDS_EXTRACTION, LAYOUT_PARSING, MERGE_SUBDOCUMENTS, READ_CONTENT, SUBDOCUMENTS_EXTRACTION, TABLES_EXTRACTION, TABLE_RECORDS_EXTRACTION]?]
Idp document content
Text content extracted from a single document.Returned as part of ReadContentResult.content.
element_type
Discriminator constant ("document_content").
optional[enum[DOCUMENT_CLASSIFICATION, DOCUMENT_CONTENT, DOCUMENT_FIELD, DOCUMENT_IDP_TABLE, DOCUMENT_KEY_VALUE_PAIR, DOCUMENT_LAYOUT, DOCUMENT_TEXT, IDP_TABLE, TEXT_CLASSIFICATION, TEXT_FIELD, TEXT_IDP_TABLE]?]
content
The extracted text or markdown content.
optional[text]
is_handwritten
Whether the document appears handwritten.
optional[boolean]
source_document
Filename of the source document.
optional[text]
source_document_index
0-based index into the input document list.
optional[number]
Concept attribute specifications
metrics (idp classification result)
num_documents
optional[number]
num_pages
optional[number]
input_tokens
optional[number]
output_tokens
optional[number]
llm_model
optional[text]
data_processed
optional[number]
processing_time
optional[number]
prepass_input_tokens
optional[number]
prepass_output_tokens
optional[number]
fields (idp fields extraction options)
name
text
format
optional[text]
rule
optional[text]
default_value
optional[any?]
metrics (idp fields extraction result)
num_documents
optional[number]
num_pages
optional[number]
input_tokens
optional[number]
output_tokens
optional[number]
llm_model
optional[text]
data_processed
optional[number]
processing_time
optional[number]
prepass_input_tokens
optional[number]
prepass_output_tokens
optional[number]
metrics (idp subdocuments extraction result)
num_documents
optional[number]
num_pages
optional[number]
input_tokens
optional[number]
output_tokens
optional[number]
llm_model
optional[text]
data_processed
optional[number]
processing_time
optional[number]
prepass_input_tokens
optional[number]
prepass_output_tokens
optional[number]
metrics (idp table records extraction result)
num_documents
optional[number]
num_pages
optional[number]
input_tokens
optional[number]
output_tokens
optional[number]
llm_model
optional[text]
data_processed
optional[number]
processing_time
optional[number]
prepass_input_tokens
optional[number]
prepass_output_tokens
optional[number]
metrics (idp tables extraction result)
num_documents
optional[number]
num_pages
optional[number]
input_tokens
optional[number]
output_tokens
optional[number]
llm_model
optional[text]
data_processed
optional[number]
processing_time
optional[number]
prepass_input_tokens
optional[number]
prepass_output_tokens
optional[number]
metrics (idp merge subdocuments result)
num_documents
optional[number]
num_pages
optional[number]
input_tokens
optional[number]
output_tokens
optional[number]
llm_model
optional[text]
data_processed
optional[number]
processing_time
optional[number]
prepass_input_tokens
optional[number]
prepass_output_tokens
optional[number]
metrics (idp layout parsing result)
num_documents
optional[number]
num_pages
optional[number]
input_tokens
optional[number]
output_tokens
optional[number]
llm_model
optional[text]
data_processed
optional[number]
processing_time
optional[number]
prepass_input_tokens
optional[number]
prepass_output_tokens
optional[number]
metrics (idp read content result)
num_documents
optional[number]
num_pages
optional[number]
input_tokens
optional[number]
output_tokens
optional[number]
llm_model
optional[text]
data_processed
optional[number]
processing_time
optional[number]
prepass_input_tokens
optional[number]
prepass_output_tokens
optional[number]
Last updated
Was this helpful?