11 isomorphic performance fixes from deep audit (no behavior changes):
- Eliminate double serialization: store_payload now accepts pre-serialized
bytes (&[u8]) instead of re-serializing from serde_json::Value. Uses
Cow<[u8]> for zero-copy when compression is disabled.
- Add SQLite cache_size (64MB) and mmap_size (256MB) pragmas
- Replace SELECT-then-INSERT label upserts with INSERT...ON CONFLICT
RETURNING in both issues.rs and merge_requests.rs
- Replace INSERT + SELECT milestone upsert with RETURNING
- Use prepare_cached for 5 hot-path queries in extractor.rs
- Optimize compute_list_hash: index-sort + incremental SHA-256 instead
of clone+sort+join+hash
- Pre-allocate embedding float-to-bytes buffer with Vec::with_capacity
- Replace RandomState::new() in rand_jitter with atomic counter XOR nanos
- Remove redundant per-note payload storage (discussion payload contains
all notes already)
- Change transform_issue to accept &GitLabIssue (avoids full struct clone)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Automated formatting and lint corrections from parallel agent work:
- cargo fmt: import reordering (alphabetical), line wrapping to respect
max width, trailing comma normalization, destructuring alignment,
function signature reformatting, match arm formatting
- clippy (pedantic): Range::contains() instead of manual comparisons,
i64::from() instead of `as i64` casts, .clamp() instead of
.max().min() chains, let-chain refactors (if-let with &&),
#[allow(clippy::too_many_arguments)] and
#[allow(clippy::field_reassign_with_default)] where warranted
- Removed trailing blank lines and extra whitespace
No behavioral changes. All existing tests pass unmodified.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Reduces CHUNK_MAX_BYTES from 32KB to 6KB and CHUNK_OVERLAP_CHARS from
500 to 200 to stay within nomic-embed-text's 8,192-token context
window. This commit addresses all downstream consequences of that
reduction:
- Config drift detection: find_pending_documents and
count_pending_documents now take model_name and compare
chunk_max_bytes, model, and dims against stored metadata. Documents
embedded with stale config are automatically re-queued.
- Overflow guard: documents producing >= CHUNK_ROWID_MULTIPLIER chunks
are skipped with a sentinel error recorded in embedding_metadata,
preventing both rowid collision and infinite re-processing loops.
- Deferred clearing: old embeddings are no longer cleared before
attempting new ones. clear_document_embeddings is deferred until the
first successful chunk embedding, so if all chunks fail the document
retains its previous embeddings rather than losing all data.
- Savepoints: each page of DB writes is wrapped in a SQLite savepoint
so a crash mid-page rolls back atomically instead of leaving partial
state (cleared embeddings with no replacements).
- Per-chunk retry on context overflow: when a batch fails with a
context-length error, each chunk is retried individually so one
oversized chunk doesn't poison the entire batch.
- Adaptive dedup in vector search: replaces the static 3x over-fetch
multiplier with a dynamic one based on actual max chunks per document
(using the new chunk_count column with a fallback COUNT query for
pre-migration data). Also replaces partial_cmp with total_cmp for
f64 distance sorting.
- Stores chunk_max_bytes and chunk_count (on sentinel rows) in
embedding_metadata to support config drift detection and adaptive
dedup without runtime queries.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Implements the embedding module that generates vector representations
of documents using a local Ollama instance with the nomic-embed-text
model. These embeddings enable semantic (vector) search and the hybrid
search mode that fuses lexical and semantic results via RRF.
Key components:
- embedding::ollama: HTTP client for the Ollama /api/embeddings
endpoint. Handles connection errors with actionable error messages
(OllamaUnavailable, OllamaModelNotFound) and validates response
dimensions.
- embedding::chunking: Splits long documents into overlapping
paragraph-aware chunks for embedding. Uses a configurable max token
estimate (8192 default for nomic-embed-text) with 10% overlap to
preserve cross-chunk context.
- embedding::chunk_ids: Encodes chunk identity as
doc_id * 1000 + chunk_index for the embeddings table rowid. This
allows vector search to map results back to documents and
deduplicate by doc_id efficiently.
- embedding::change_detector: Compares document content_hash against
stored embedding hashes to skip re-embedding unchanged documents,
making incremental embedding runs fast.
- embedding::pipeline: Orchestrates the full embedding flow: detect
changed documents, chunk them, call Ollama in configurable
concurrency (default 4), store results. Supports --retry-failed
to re-attempt previously failed embeddings.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
