- 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
Prevent API key and sensitive info leakage through exception messages:
- Replace str(exc) with generic error messages in all catch-all handlers
- Add server-side exception logging via logger.exception()
- Add ALLOWED_KWARGS per-tool allowlist to filter untrusted LLM kwargs
- Bound tool execution loop to MAX_TOOL_ITERATIONS=10
- Fix tool_call delta merge to use tool_call index
Implement a full chat-based travel agent using LiteLLM for multi-provider
LLM support (OpenAI, Anthropic, Gemini, Ollama, Groq, Mistral, etc.).
Backend:
- New 'chat' Django app with ChatConversation and ChatMessage models
- Streaming SSE endpoint via StreamingHttpResponse
- 5 agent tools: search_places, list_trips, get_trip_details,
add_to_itinerary, get_weather
- LiteLLM client wrapper with per-user API key retrieval
- System prompt with user preference context injection
Frontend:
- New /chat route with full-page chat UI (DaisyUI + Tailwind)
- Collapsible conversation sidebar with CRUD
- SSE streaming response display with tool call visualization
- Provider selector dropdown
- SSE proxy fix to stream text/event-stream without buffering
- Navbar link and i18n keys
