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Documentation Index

Fetch the complete documentation index at: https://docs.okrapdf.com/llms.txt

Use this file to discover all available pages before exploring further.

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Chat

okra chat is the unified entry point for all chat. It routes automatically based on what you pass: 
# General question (no document)
okra chat "what is a 10-K filing?"

# Single document
okra chat "how many pages?" --doc ocr-abc123

# Multi-document comparison
okra chat "compare revenue" --doc ocr-abc123,ocr-def456

# Collection
okra chat "summarize key metrics" -c "TSMC 2024 Filings"
FlagDescription
--doc <ids>Comma-separated job IDs (ocr-*)
-c, --collection <name>Collection name or ID
-m, --message <text>Message (alternative to positional)
--schema <json>JSON Schema for structured output
--model <model>Model override
--shareGenerate a public share link
--agent <type>Agent type: canvas (LLM-driven canvas manipulation)
--tools <type>Tool mode (coming soon)
-q, --quietSuppress spinners (just the answer)

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Routing

What you passModeBackend
No --doc, no -cGeneral chatFireworks (no document context)
--doc ocr-XXX (single)Single-docDocumentAgent DO /completion
--doc ocr-XXX,ocr-YYYMulti-docLead agent + DO fan-out
-c my-collectionMulti-docSame as above, resolves collection first
--doc ocr-XXX --agent canvasCanvas agentCanvasAgent DO /canvas/completion

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General chat

Ask anything without a document — helpful for finance questions, OCR concepts, or quick lookups. 
okra chat "what is EBITDA?"
okra chat "explain the difference between 10-K and 10-Q filings"

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Single-document chat

Query a specific extracted document. The agent has tools to search text, query tables, and retrieve pages. 
okra chat "what was total revenue?" --doc ocr-abc123
okra chat "list all tables on page 5" --doc ocr-abc123

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Multi-document chat

Compare across documents. The lead agent fans out queries to each document and synthesizes results. 
okra chat "compare the risk factors" --doc ocr-abc123,ocr-def456

# Or use a collection
okra chat "what are the common themes?" -c "Q4 Reports"

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Canvas agent (--agent canvas)

Use --agent canvas for LLM-driven canvas manipulation — the agent can read and modify the document canvas using tool calls. 
okra chat "highlight all revenue figures" --doc ocr-abc123 --agent canvas
okra chat "redact PII on page 3" --doc ocr-abc123 --agent canvas
Requires DOCUMENT_AGENT_SHARED_SECRET to be set. Streams tool calls and text deltas to the terminal, with cost and tool-call count reported at the end.
--agent canvas only supports single-document mode. The first --doc ID is used if multiple are provided.

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Structured output (--schema)

Pass a JSON Schema to get structured JSON responses. Works with single-doc mode. 
# Inline schema
okra chat "get company info" --doc ocr-abc123 \
  --schema '{"type":"object","properties":{"companyName":{"type":"string"}}}'

# Quiet mode — pure JSON, no spinners
okra chat "get company info" --doc ocr-abc123 \
  --schema '{"type":"object","properties":{"companyName":{"type":"string"}}}' \
  --quiet

{
  "companyName": "Stock Code 00700 - Hong Kong Listed Equity Issuer"
}
Combine --schema with --quiet for pipeline-friendly output you can pipe straight into jq.

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Parallel sessions

Run multiple single-doc queries in parallel — each runs independently. 
QUESTION="What was total revenue for FY2023?"

for jobId in ocr-abc1 ocr-abc2 ocr-abc3; do
  okra chat "$QUESTION" --doc "$jobId" > "results/${jobId}.json" &
done
wait

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Legacy syntax

The old send subcommand still works: 
okra chat send ocr-abc123 -m "What tables are in this document?"
Prefer the new syntax: okra chat "question" --doc ocr-abc123