kapi exec entity-extract

$ kapi entity-extract [files...]

Detekter navngitte enheter (personer, organisasjoner, produkter, steder) med en LLM, en lokal NER-modell, eller begge

Commandexec entity-extract

Try it (demo)

Demo mode — illustrative output from a built-in stub, not a real model. Install the CLI to run with your own API key.

$ kapi exec entity-extract

Flags

Flag	Type	Default	Description
--api-key	string		API key for the AI provider
--batch-concurrency	int	1	Number of concurrent batch calls (0 or 1 = sequential)
--batch-size	int	1	Number of blocks per LLM call (0 or 1 = one block per call)
--concurrency , -j	int	0	max parallel files (0 = auto)
--credential	string		saved credential name to use (see 'kapi credentials list')
--encoding , -e	string		input file encoding
--engine	string	llm	llm (AI provider; default) / ner (local on-device model: nothing leaves the machine) / hybrid (both) (LLM (AI provider), Local NER (on-device), Hybrid (NER + LLM))
--fail-on-unknown	bool	false	exit with error if any file cannot be processed (default: skip with warning)
--format , -f	string		override input format detection
--json	bool	false	output results as JSON
--known-terms	stringSlice		Terms to exclude from extraction (already in the terms store)
--locale	string		Locale of the source content
--map , -m	stringArray		map glob pattern to format (e.g. '*.docx=okf_openxml:test')
--model	string		AI model name
--no-warn	bool	false	suppress warnings for skipped files
--pack	bool	false	when transforming a .kpz, also eject the result to the .kpz (auto-pack)
--parallel-blocks	int	0	fan out block processing across N goroutines (0 = off)
--progress , -p	bool	false	show progress bar
--provider	string	anthropic	AI provider (Anthropic, OpenAI, Gemini, Azure OpenAI, Claude Code, Ollama, Demo (illustrative))
--source-lang	string		source language (e.g. en, en-US; defaults to the project's source_language, else en)
--strict	bool	false	alias for --fail-on-unknown
--target-lang	string		target language (e.g. fr, de-DE)
--trace	string		write flow trace JSON to file (for flow visualization)

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