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0.3.0 — Ask a question, and check the answer
Open a Microsoft GraphRAG index in your browser, ask it a question, and follow the answer back to the exact records it used.
Try it now → Ask → See a saved run. No API key needed: the sample ships with a recorded run, so the whole answer-to-evidence path is one click away.
The Ask tab
- Local and Global search against any OpenAI-compatible provider, following GraphRAG's two methods. The key stays in your browser, never in a saved run and never in a log line.
- Every citation is a button. Picking one opens that record beside the answer, with the exact text that went into the prompt, the links the run carried and the source text behind it.
- What the model ignored is as visible as what it used. The evidence graph outlines the cited records in red; the table under it lists everything retrieved, with scores.
- The pipeline is on screen. Retrieval, context window, model call and response, with the counts and milliseconds the run actually spent. "Show the prompt sent to the model" prints the messages verbatim.
- The embedding space plots the question and the entity vectors (PCA, 2D or 3D), marking what went into the prompt and what the token budget cut.
- Runs are portable. Save one as a trace file and read it back on a machine with no key at all.
Also in this release
tools/embed_indexwrites theembeddings.parquetsidecar local search needs, with the model, the dimension and a SHA-256 of every source file in its metadata. A sidecar from a different index is detected and local search says so.- A folder may carry an
example-run.json; the Ask tab offers it as one click. - The app opens on the schema view: entity types, the relationships between them, and the Parquet tables with their key and reference columns.
- Korean documentation (README.ko.md) and a security note on what leaves the tab and what does not.
- Browser back and forward move between views and selections.
Full list in the changelog.
Checked before release
426 unit tests, 32 Playwright end-to-end tests against the production build, and two guards that keep private data and provider keys out of what is published.