Queries embedded via llama-swap were searching corpora embedded via
Ollama (measured: spaces diverged). Introduce LocalLlm — the local
Ollama + llama-swap pair with LLM_BACKEND dispatch baked in — and route
all embedding writers through it; anything embedding via a concrete
client reintroduces the bug.
- search_rag: embed the model's query verbatim (no metadata boilerplate),
make date optional — no time-decay when omitted, so "when did X
happen?" queries rank purely by similarity across all time
- reembed_embeddings bin: re-embed summaries / calendar / search /
knowledge entities via the active backend, with old-new cosine report
per table and truncate-and-retry for inputs over the embed server's
physical batch size
- import_calendar, import_search_history: embed through LocalLlm
- search_messages / get_sms_messages: render sender → recipient so sent
messages are attributable to a conversation
- insight job failures: store the one-line anyhow context chain ({:#})
instead of the Debug dump the client was shown verbatim
- serialize env_dispatch tests behind a lock (parallel-runner flake)
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
populate_knowledge now loads real libraries from the DB instead of
fabricating a single library_id=1 row from BASE_PATH. Adds --library
<id|name> to restrict the walk and validates --path against the selected
library roots. The full library set is still passed to InsightGenerator so
resolve_full_path can probe every root when an insight resolves to a
different library than the one being walked.
Adds indicatif progress bars across the long-running utility binaries via
a shared src/bin_progress.rs helper (determinate bar + open-ended spinner
with consistent styling). Per-batch info! noise is replaced by the bar's
throughput/ETA; warnings and errors route through pb.println so they
scroll above the bar instead of fighting with it.
populate_knowledge spinner during scan, determinate bar over all libs
backfill_hashes spinner with running hashed/missing/errors counts
import_calendar determinate bar; embedding/store failures inline
import_location_* determinate bar advancing by chunk size
import_search_* determinate bar; pb cloned into the spawn task
cleanup_files P1 determinate bar over DB paths
cleanup_files P2 determinate bar; pb.suspend() around y/n/a/s prompt
Co-Authored-By: Claude Opus 4.7 (1M context) <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>
Cameron Cordes
Cameron