Captures prompt_eval_count and eval_count from Ollama /api/chat responses
during the agentic loop and returns them in POST /insights/generate/agentic
so the frontend can display context window usage to the user.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Sanitise tool call arguments before re-sending in conversation history: non-object values (bool, string, null) that some models produce are normalised to {} to prevent Ollama 500s
- Map 'error parsing tool call' Ollama 500 to HTTP 400 with a descriptive message listing compatible models (llama3.1, llama3.2, qwen2.5, mistral-nemo)
- Add reverse_geocode tool backed by existing Nominatim helper; description hints model can chain it after get_location_history results
- Make get_sms_messages contact parameter optional (was required, forcing the model to guess); executor now passes None to fall back to all-contacts search
- Log tool result outcomes at warn level for errors/empty results, info for successes; log SMS API errors with full detail; log full request body on Ollama 500
- Strengthen system prompt to require 3-4 tool calls before final answer
- Try fallback server when checking model capabilities (primary-only check caused 500 for models only on fallback)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- cargo fmt applied across all modified source files
- Collapse nested if let Some / if !is_empty into a single let-chain (clippy::collapsible_match)
- All other warnings are pre-existing dead-code lint on unused trait methods
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Verify custom model exists on at least one configured server before starting agentic loop; returns HTTP 400 with descriptive error if not found
- has_tool_calling field auto-serialised in GET /insights/models via existing ModelCapabilities Serialize derive
- model_version stored from OllamaClient.primary_model (already correct in T006 implementation)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Add 6 tool executor methods (search_rag, get_sms_messages, get_calendar_events,
get_location_history, get_file_tags, describe_photo) and the agentic loop that
uses Ollama's chat_with_tools API to let the model decide which context to gather
before writing the final photo insight.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Threads SqliteTagDao through InsightGenerator and AppState (both default
and test_state). Adds Send+Sync bounds to TagDao trait with unsafe impls
for SqliteTagDao (always Mutex-protected) and TestTagDao (single-threaded).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Updated InsightGenerator struct with calendar, location, and search DAOs
- Implemented hybrid context gathering methods:
* gather_calendar_context(): ±7 days with semantic ranking
* gather_location_context(): ±30 min with GPS proximity check
* gather_search_context(): ±30 days semantic search
- Added haversine_distance() utility for GPS calculations
- Updated generate_insight_for_photo_with_model() to use multi-source context
- Combined all context sources (SMS + Calendar + Location + Search) with equal weight
- Initialized new DAOs in AppState (both default and test implementations)
- All contexts are optional (graceful degradation if data missing)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Implements Phase 1 & 2 of Google Takeout RAG integration:
- Database migrations for calendar_events, location_history, search_history
- DAO implementations with hybrid time + semantic search
- Parsers for .ics, JSON, and HTML Google Takeout formats
- Import utilities with batch insert optimization
Features:
- CalendarEventDao: Hybrid time-range + semantic search for events
- LocationHistoryDao: GPS proximity with Haversine distance calculation
- SearchHistoryDao: Semantic-first search (queries are embedding-rich)
- Batch inserts for performance (1M+ records in minutes vs hours)
- OpenTelemetry tracing for all database operations
Import utilities:
- import_calendar: Parse .ics with optional embedding generation
- import_location_history: High-volume GPS data with batch inserts
- import_search_history: Always generates embeddings for semantic search
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
