Use the SSRF-safe HTTP client for file_url downloads and add redirect-aware
guards to the docreader Playwright path, closing GHSA-44fp, GHSA-rv6v, and
GHSA-c7m6.
WPS embeds images in cells using =DISPIMG("ID",mode) which may appear as plain text prefixed with "_xlfn." when processed by openpyxl. These function strings pollute the RAG-indexed document content, causing incorrect AI recall.
Add a regex filter (_is_image_function) that detects _xlfn.DISPIMG(...) and _xlfn.IMAGE(...) patterns and excludes them during row processing in ExcelParser.parse_into_text().
Refs: https://github.com/Tencent/WeKnora/issues/1779
Introduce opendataloader and PaddleOCR-VL parser engines with tenant-level
settings UI, replace liteparse, and harden Excel/PPT/Markdown parsing.
Optional odl-hybrid sidecar stays local-build only and is excluded from
default dev-start and full profiles.
Large scanned PDFs (hundreds of pages) were slow and fragile end-to-end.
This change addresses the parse, transport, and task-scheduling layers:
docreader (parse + transport):
- Parallelize per-page scanned rendering across processes (forkserver/fork),
with serial fallback. ~4-7x faster on large scanned PDFs; pdfium is not
thread-safe so we fan out across processes. Configurable via
DOCREADER_PDF_RENDER_PARALLELISM.
- Add server-streaming ReadStream RPC: emit one meta frame then one frame per
image, so documents with many page images are no longer capped by the unary
gRPC message-size limit (a 874-page PDF produced ~193MiB of images, far over
the 50MB cap) and memory is bounded on both ends. Unary Read is kept for
backward compatibility; the Go production reader switches to ReadStream.
VLM:
- Make the VLM HTTP timeout configurable (VLM_HTTP_TIMEOUT_SECONDS) and raise
the default 90s -> 180s so dense scanned-page OCR does not time out with
"context deadline exceeded".
Async task queues:
- Isolate high-volume, model-heavy fan-out tasks into dedicated asynq queues so
a single large document cannot saturate the shared worker pool and block
user-facing document parsing:
image:multimodal -> "multimodal"
chunk:extract -> "graph"
question:generation -> "question"
- Register the new queues in the server weight map and the cancel inspector's
scanned-queue set (so cancelling a knowledge still purges its pending tasks).
wizardchen