a022a3d15d
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>
159 lines
4.8 KiB
Rust
159 lines
4.8 KiB
Rust
use anyhow::{Context, Result};
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use chrono::Utc;
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use clap::Parser;
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use image_api::ai::LocalLlm;
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use image_api::bin_progress;
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use image_api::database::calendar_dao::{InsertCalendarEvent, SqliteCalendarEventDao};
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use image_api::parsers::ical_parser::parse_ics_file;
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use log::{error, info};
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// Import the trait to use its methods
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use image_api::database::CalendarEventDao;
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#[derive(Parser, Debug)]
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#[command(author, version, about = "Import Google Takeout Calendar data", long_about = None)]
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struct Args {
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/// Path to the .ics calendar file
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#[arg(short, long)]
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path: String,
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/// Generate embeddings for calendar events (slower but enables semantic search)
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#[arg(long, default_value = "false")]
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generate_embeddings: bool,
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/// Skip events that already exist in the database
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#[arg(long, default_value = "true")]
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skip_existing: bool,
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/// Batch size for embedding generation
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#[arg(long, default_value = "128")]
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batch_size: usize,
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}
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#[tokio::main]
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async fn main() -> Result<()> {
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dotenv::dotenv().ok();
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env_logger::init();
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let args = Args::parse();
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info!("Parsing calendar file: {}", args.path);
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let events = parse_ics_file(&args.path).context("Failed to parse .ics file")?;
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info!("Found {} calendar events", events.len());
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let context = opentelemetry::Context::current();
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// LocalLlm dispatches per LLM_BACKEND, so embeddings written here land
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// in the same vector space the query side searches.
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let llm = if args.generate_embeddings {
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Some(LocalLlm::from_env())
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} else {
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None
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};
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let mut inserted_count = 0usize;
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let mut skipped_count = 0usize;
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let mut error_count = 0usize;
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let pb = bin_progress::determinate(events.len() as u64, "importing");
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// Process events in batches
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// Can't use rayon with async, so process sequentially
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for event in &events {
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let mut dao_instance = SqliteCalendarEventDao::new();
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// Check if event exists
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if args.skip_existing
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&& let Ok(exists) = dao_instance.event_exists(
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&context,
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event.event_uid.as_deref().unwrap_or(""),
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event.start_time,
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)
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&& exists
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{
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skipped_count += 1;
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pb.inc(1);
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continue;
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}
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// Generate embedding if requested (blocking call)
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let embedding = if let Some(ref llm) = llm {
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let text = format!(
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"{} {} {}",
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event.summary,
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event.description.as_deref().unwrap_or(""),
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event.location.as_deref().unwrap_or("")
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);
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match tokio::task::block_in_place(|| {
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tokio::runtime::Handle::current().block_on(async { llm.embed(&text).await })
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}) {
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Ok(emb) => Some(emb),
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Err(e) => {
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pb.println(format!("embedding failed for '{}': {}", event.summary, e));
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None
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}
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}
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} else {
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None
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};
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// Insert into database
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let insert_event = InsertCalendarEvent {
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event_uid: event.event_uid.clone(),
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summary: event.summary.clone(),
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description: event.description.clone(),
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location: event.location.clone(),
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start_time: event.start_time,
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end_time: event.end_time,
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all_day: event.all_day,
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organizer: event.organizer.clone(),
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attendees: if event.attendees.is_empty() {
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None
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} else {
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Some(serde_json::to_string(&event.attendees).unwrap_or_default())
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},
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embedding,
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created_at: Utc::now().timestamp(),
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source_file: Some(args.path.clone()),
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};
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match dao_instance.store_event(&context, insert_event) {
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Ok(_) => inserted_count += 1,
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Err(e) => {
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pb.println(format!("store failed for '{}': {:?}", event.summary, e));
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error_count += 1;
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}
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}
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pb.set_message(format!(
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"inserted={} skipped={} errors={}",
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inserted_count, skipped_count, error_count
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));
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pb.inc(1);
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}
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pb.finish_and_clear();
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info!("=== Import Summary ===");
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info!("Total events found: {}", events.len());
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info!("Successfully inserted: {}", inserted_count);
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info!("Skipped (already exist): {}", skipped_count);
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info!("Errors: {}", error_count);
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if args.generate_embeddings {
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info!("Embeddings were generated for semantic search");
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} else {
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info!("No embeddings generated (use --generate-embeddings to enable semantic search)");
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}
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if error_count > 0 {
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error!(
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"Completed with {} errors — review log output above",
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error_count
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);
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}
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Ok(())
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}
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