v0.4.0

Changes

• semantic message search now ranks across the full compatible local vector set instead of only the newest candidate window. (#36) Thanks @GaosCode.
• hybrid message search now fuses FTS with local semantic vectors while avoiding embedding-provider calls when no local vectors exist. (#37) Thanks @GaosCode.
• local embedding providers now support OpenAI-compatible endpoints, Ollama, and llama.cpp, and doctor can probe the configured provider before you queue vectors
• embed now drains the queued embedding backlog in bounded batches, requeues safely on provider throttling, and drops stale stored vectors when messages no longer have embeddable content
• Git snapshot publishing can now opt in to backing up generated embedding vectors with --with-embeddings while still keeping embedding queue state local.
• Git-backed snapshot imports are now much faster on large archives by using import-only SQLite pragmas and bulk-load FTS5 settings during search index rebuilds
• messages and mentions now use composite read-path indexes so larger archives spend less time sorting/filtering common guild, channel, and author queries

Fixes

• normalized message text is now sanitized before it reaches SQLite and FTS5, repairing malformed UTF-8 and stripping invisible/control-character noise that can poison search content
• Git-backed snapshots now keep embedding queue state and generated vectors local to each archive, so subscribers no longer inherit misleading embedding backlog metadata. (#38) Thanks @GaosCode.

Docs

• docs now cover semantic and hybrid search setup, embedding privacy, Git snapshot behavior, and local vector rebuilds. (#39) Thanks @GaosCode.

Tests

• Git embedding snapshot export/import now has CLI, share-package, and Docker E2E coverage.
• total Go test coverage now reaches the 85% line.