- Chunker: char overlap across boundaries + hard-split of oversized paragraphs.
- Retrieval knobs: rag_top_k / rag_min_score in settings + Settings UI.
- Vector index: sqlite-vec ANN over document embeddings, dual-written and
backfilled, with brute-force cosine as the guaranteed fallback.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- Upload endpoint (base64 JSON, no multipart dep): extracts text from
pdf/docx/txt/md via pypdf + python-docx, then runs the existing
chunk/embed pipeline. Documents page uploads files straight through.
- Citations: the chat stream emits an SSE `sources` event listing the
documents that fed the answer; the UI shows them as chips under the reply.
- Deps: pypdf, python-docx (both pure-Python, Windows-safe).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
The backend logged 200 OK for polls the client had already timed out on.
is_running() waited 2s for an Ollama that ships OFF (now 0.5s), is_available()
spawned 'ollama --version' every call and that command blocks ~5s when Ollama is
wedged (now cached), and both ran synchronously inside an async def, stalling the
event loop on every poll while the UI polls continuously (now to_thread).
Measured with Ollama's port blackholed: /status 5.89s -> 0.64s, concurrent GET /
stalled -> 0.06s. Poll timeout in nexus_window.py raised 2s -> 5s for margin.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>