Docker Compose:
- Enable Ollama GPU passthrough (nvidia, count: all) in both dev and prod files
- Add OLLAMA_FLASH_ATTENTION=1 (faster attention on GPU in both files)
- Add OLLAMA_MAX_LOADED_MODELS=2 and OLLAMA_KEEP_ALIVE=30m to prod (was already in dev)
- Remove 8G memory limit from prod Ollama service (CPU-bound constraint, no longer valid)
llm.py:
- Increase num_ctx 16384 → 32768 in stream_chat and stream_chat_with_tools (GPU VRAM allows it)
- Increase num_predict cap 4096 → 8192 for tool-augmented responses
generation_task.py:
- Parallelize build_context, get_tools_for_user, and get_setting all from the start
- As soon as tools list is ready (fast DB call), launch classify_intent as an asyncio.Task
- Await build_context and classify_intent together via asyncio.gather
- Intent result is pre-computed before the generation loop; loop just reads pre_intent on round 0
- intent_ms timing now reflects wall-clock time from intent start to completion
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add GET /api/chat/ps and POST /api/chat/warm endpoints for hot model
visibility and pre-loading
- Extend PATCH /api/chat/conversations/:id to accept model in addition
to title
- Add ModelSelector component with hot/cold indicators from Ollama /api/ps
- Add DashboardChatInput component (model selector + note picker + textarea)
replacing the simple "New Chat" button on the dashboard
- Add model selector dropdown to ChatView header, persisted per-conversation
- Warm default model on dashboard mount via fire-and-forget background task
- Configure Ollama with OLLAMA_MAX_LOADED_MODELS=2 and OLLAMA_KEEP_ALIVE=30m
- Always-visible edit buttons on NoteCard/TaskCard (remove hover-only behavior)
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Phase 4: Full chat system with SSE streaming, note-aware context, and
conversation persistence.
Backend:
- Migration 0005: conversations + messages tables with FKs and indexes
- Conversation/Message SQLAlchemy models with relationships
- LLM service: ensure_model (auto-pull on startup), stream_chat (NDJSON),
generate_completion, fetch_url_content (HTML stripping), build_context
(keyword extraction, related note search, URL content injection)
- Chat service: conversation CRUD, save_response_as_note,
summarize_conversation_as_note
- Chat routes blueprint: 9 endpoints including SSE streaming for messages,
save/summarize as note, Ollama model listing
- Auto-pull llama3.1 model on app startup (non-blocking)
Frontend:
- apiStreamPost: SSE client using fetch + ReadableStream
- Chat Pinia store with streaming state management
- ChatView: dedicated /chat page with conversation sidebar + message thread
- ChatPanel: slide-out panel with contextNoteId from current route
- ChatMessage: markdown-rendered message bubble with "Save as Note" action
- Updated AppHeader with Chat nav link + panel toggle button
- Updated App.vue to mount ChatPanel with route-derived context
- Added /chat and /chat/:id routes
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
bvandeusen