Report OpenVINO devices and whether Intel AI Boost is available.

Transcribe a local audio file on the Intel NPU.

Record the default microphone for 1-60 seconds and transcribe on the NPU.

Extract English and Arabic text from a local image using NPU text models.

Capture the current monitor and extract its text.

Read a screenshot as compact structured text instead of sending an image.

A screenshot costs a vision model roughly 1,600 to 4,800 image tokens. This returns the same screen as a few hundred tokens of text in reading order, with a bounding box per line, so you can see what is on screen and where to click without spending them. Omit image_path to capture the current monitor.

detail is "lines" (default: text plus one box per line), "text" (cheapest, reading order only), or "words" (a box per word — on a dense screen this can cost MORE tokens than the screenshot did, and the reply says so when it has).

Read these limits before relying on it. The text comes from Tesseract OCR, so it is a best-effort transcription and not a user-interface tree: it cannot report widget types, enabled or checked state, focus, scroll position, or anything off-screen, and it misreads small or low-contrast text. If the target is a web page, use Playwright's accessibility tree instead — that is already structured text, it is exact, it includes content scrolled out of view, and it costs no model tokens to produce. This tool is for surfaces with no such tree: native desktop applications, remote desktops, canvas and WebGL, video frames, and scanned documents.

Index a local text file or directory for private semantic search on the Intel NPU.

Search indexed local files by meaning using Intel NPU embeddings.

When the optional reranker model is installed, the top passages are re-scored by a cross-encoder and each hit gains a rerank_score. That score is an unbounded logit, not a cosine, so do not compare it against score. Pass rerank=false to skip the extra second of work.

Extract only the parts of a large local text file that are relevant to a question.

Use this instead of reading a big build log, test output, diff, or data file into your context. Write the output to a file first, then filter it:

make 2>&1 | tee /tmp/build.log
context_filter("/tmp/build.log", "why did the linker step fail")

Returns spans copied verbatim from the file with exact line numbers, never a summary, so quoted text and line numbers can be cited. It also reports how much was dropped and how close the best dropped chunk scored, so you can tell when to widen the query or raise limit rather than assuming full coverage.

Chunks are embedded on the Intel NPU and ranked by cosine similarity; nothing is written to the semantic index and nothing leaves this machine. This is not a substitute for grep: when you know the exact string to look for, grep is faster, free, and exact. Files under 4 KB and over 256 KB are rejected.

Report the local semantic index database, roots, file count, and chunk count.

Open the interactive speech-to-text desktop application.

Open the rectangular screenshot OCR selector.