The conversations sidebar was force-shown via 'lg:flex' at large
breakpoints even when sidebarOpen was false, causing a broken split-
layout in the w-96 drawer panel (~144px left for actual chat).
Fixes:
- Sidebar no longer force-applies lg:flex in panelMode
- In panelMode the sidebar goes full-width and hides the chat area
entirely (stacked panel pattern instead of split-column)
- Hamburger toggle is always visible in panelMode (was lg:hidden)
- Selecting a conversation or creating a new one auto-closes the
sidebar in panelMode, returning to chat view
- Welcome screen and header title use compact sizing in panelMode
- search_places: detect HTTP 429 and mark retryable=False to stop the
retry loop immediately instead of spiraling until MAX_ITERATIONS
- get_weather: extract collection coordinates (lat/lng from first
location with coords) and retry when LLM omits required params;
uses sync_to_async for the DB query in the async view
- AITravelChat: deduplicate context-only tools (get_trip_details,
get_weather) in the render pipeline to prevent duplicate place cards
from appearing when the retry loop causes multiple get_trip_details calls
- Tests: 5 new tests covering 429 non-retryable path and weather
coord fallback; all 39 chat tests pass
- Add _COMMAND_VERBS guard to _is_likely_location_reply() so messages
starting with imperative verbs (find, search, show, get, ...) are not
mistakenly treated as user location replies. This prevented 'Find
good places' from being used as a retry location, which was causing
the clarification path to never fire and the tool loop to exhaust
MAX_ALL_FAILURE_ROUNDS instead.
- Extract city from comma-delimited fallback address strings when
city/country FKs are absent, e.g. 'Little Turnstile 6, London'
→ 'London', so context-based location retry works for manually-
entered itinerary stops without geocoded FK data.
- Add attempted_location_retry flag: if retry was attempted but all
retry attempts failed, convert result to an execution failure rather
than emitting a clarification prompt (user already provided context
via their itinerary).
- Fix test assertion ordering in test_collection_context_retry_extracts_
city_from_fallback_address: streaming_content must be consumed before
checking mock call counts since StreamingHttpResponse is lazy.
Replaced `node:22-alpine` + `npm install -g bun@1.2.22` with
`oven/bun:1.2.22-alpine` as the builder stage base image.
The npm-based bun install was failing on linux/arm64 with:
"Failed to find package @oven/bun-linux-aarch64"
The official oven/bun Docker image supports both linux/amd64 and
linux/arm64 natively, eliminating the need to install bun via npm.
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
- Fetch models dynamically from OpenCode Zen API (36 models vs 5 hardcoded)
- Add function calling support check before using tools
- Add retry logic (num_retries=2) for transient failures
- Improve logging for debugging API calls and errors
- Update system prompt for multi-stop itinerary context
- Clean up unused imports in frontend components
- Remove deleted views.py (moved to views/__init__.py)
- Connection error: wrap sync get_llm_api_key() in sync_to_async in
stream_chat_completion() to fix SynchronousOnlyOperation raised when
the async SSE generator calls a synchronous Django ORM function
- Models not loading: add opencode_zen handler to models endpoint
returning its default model; fix frontend to show 'Default' instead
of 'Loading...' indefinitely when no model list is returned
- Location in header: remove destination subtitle from Travel Assistant
header — collection-wide chat has no single meaningful location
Fix 1: Provider/Model Selection (Critical - unblocks LLM)
- Add /api/chat/providers/{id}/models/ endpoint to fetch available models
- Auto-select first configured provider instead of hardcoded 'openai'
- Add model dropdown populated from provider API
- Filter provider list to only show configured providers
- Show helpful error when no providers configured
Fix 2: Auto-Learn Preferences (Replaces manual input)
- Create auto_profile.py utility to infer preferences from user data
- Learn interests from Activity sport types and Location categories
- Learn trip style from Lodging types (hostel=budget, resort=luxury, etc.)
- Learn geographic preferences from VisitedRegion/VisitedCity
- Call auto-learn on every chat start (send_message)
- System prompt now indicates preferences are auto-inferred
Fix 3: Remove Manual Preference UI
- Remove travel_preferences section from Settings
- Remove preference form fields and save logic
- Remove preference fetch from server-side load
- Keep UserRecommendationPreferenceProfile type for backend use
The LLM should now work correctly:
- Users with any configured provider will have it auto-selected
- Model list is fetched dynamically from provider API
- Preferences are learned from actual travel history
