requirements-amd.txt/requirements-nvidia.txt were pulling a multi-GB
torch wheel by default even though nothing in synapse/ imports torch,
transformers, accelerate, bitsandbytes, or PySide6 - dead weight that
made the pip batch fragile (one failed download could take unrelated
base deps down with it on a slow connection). Split the unused ML/GUI
stack out of requirements-base.txt into a new opt-in requirements-ml.txt,
and dropped the torch lines from the AMD/NVIDIA overlays and generator.
Also: recreate the venv if it exists but pip is missing, instead of
silently reusing a half-built one (ensurepip can fail during venv
creation and leave an interpreter with no pip).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Models page redesign: tabs (Required/Recommended/Installed) with a
multi-column card grid, required models (memory curator + embeddings)
surfaced first and gating the rest until both are installed. Adds a
qwen2.5:14b tier to the hardware-fit catalog for high-VRAM machines.
Installer and restore messaging updated to match. Also declares pytest
in requirements-base.txt so bin/check.sh's test suite is reproducible
on a fresh venv.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
- Projects/workspaces: documents grouped into projects; chat RAG scopes to the
active project. Switcher in the Documents page.
- Agentic action tools: web_search, fetch_url, and remember (first write tool),
allowlist-gated per playbook.
- Local Whisper STT (faster-whisper, no torch): on-device dictation replacing
the browser Web Speech API. POST /stt + GET /stt/status; browser fallback.
- Vector index extended to conversation recall (message_vectors), with the
brute-force cosine scan kept as the fallback.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- 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>
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