More planning

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teernisse
2026-01-23 10:03:40 -05:00
parent 1f36fe6a21
commit e846a39ce6

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SPEC.md
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@@ -115,7 +115,8 @@ npm link # Makes `gi` available globally
│ - Normalize artifacts to unified schema │
│ - Extract searchable documents (canonical text + metadata) │
│ - Content hashing for change detection │
│ - Build relationship graph (issue↔MR↔note↔file)
│ - MVP relationships: parent-child FKs + label/path associations
│ (full cross-entity "decision graph" is post-MVP scope) │
└─────────────────────────────────────────────────────────────────┘
@@ -159,10 +160,16 @@ npm link # Makes `gi` available globally
Issues and MRs support efficient bulk fetching with incremental sync:
```
GET /projects/:id/issues?updated_after=X&order_by=updated_at&sort=asc&per_page=100
GET /projects/:id/merge_requests?updated_after=X&order_by=updated_at&sort=asc&per_page=100
GET /projects/:id/issues?scope=all&state=all&updated_after=X&order_by=updated_at&sort=asc&per_page=100
GET /projects/:id/merge_requests?scope=all&state=all&updated_after=X&order_by=updated_at&sort=asc&per_page=100
```
**Required query params for completeness:**
- `scope=all` - include all issues/MRs, not just authored by current user
- `state=all` - include closed items (GitLab defaults may exclude them)
Without these params, the 2+ years of historical data would be incomplete.
### Dependent Resources (Per-Parent Fetch)
Discussions must be fetched per-issue and per-MR. There is no bulk endpoint:
@@ -178,10 +185,25 @@ GET /projects/:id/merge_requests/:iid/discussions?per_page=100&page=N
**Initial sync:**
1. Fetch all issues (paginated, ~60 calls for 6K issues at 100/page)
2. For EACH issue → fetch all discussions (~3K calls)
2. For EACH issue → fetch all discussions (≥ issues_count calls + pagination overhead)
3. Fetch all MRs (paginated, ~60 calls)
4. For EACH MR → fetch all discussions (~3K calls)
5. Total: ~6,100+ API calls for initial sync
4. For EACH MR → fetch all discussions (≥ mrs_count calls + pagination overhead)
5. Total: thousands of API calls for initial sync
**API Call Estimation Formula:**
```
total_calls ≈ ceil(issues/100) + issues × avg_discussion_pages_per_issue
+ ceil(mrs/100) + mrs × avg_discussion_pages_per_mr
```
Example: 3K issues, 3K MRs, average 1.2 discussion pages per parent:
- Issue list: 30 calls
- Issue discussions: 3,000 × 1.2 = 3,600 calls
- MR list: 30 calls
- MR discussions: 3,000 × 1.2 = 3,600 calls
- **Total: ~7,260 calls**
This matters for rate limit planning and setting realistic "10-20 minutes" expectations.
**Incremental sync:**
1. Fetch issues where `updated_after=cursor` (bulk)
@@ -235,10 +257,15 @@ tests/unit/db.test.ts
✓ enables foreign keys
tests/integration/gitlab-client.test.ts
✓ authenticates with valid PAT
✓ returns 401 for invalid PAT
✓ fetches project by path
✓ handles rate limiting (429) with retry
(mocked) authenticates with valid PAT
(mocked) returns 401 for invalid PAT
(mocked) fetches project by path
(mocked) handles rate limiting (429) with retry
tests/live/gitlab-client.live.test.ts (optional, gated by GITLAB_LIVE_TESTS=1, not in CI)
✓ authenticates with real PAT against configured baseUrl
✓ fetches real project by path
✓ handles actual rate limiting behavior
tests/integration/app-lock.test.ts
✓ acquires lock successfully
@@ -256,6 +283,7 @@ tests/integration/init.test.ts
✓ fails if any project path not found
✓ prompts before overwriting existing config
✓ respects --force to skip confirmation
✓ generates gi.config.json with sensible defaults
```
**Manual CLI Smoke Tests:**
@@ -269,6 +297,7 @@ tests/integration/init.test.ts
| `gi init` (config exists) | Confirmation prompt | Warns before overwriting |
| `gi --help` | Command list | Shows all available commands |
| `gi version` | Version number | Shows installed version |
| `gi sync-status` | Last sync time, cursor positions | Shows successful last run |
**Data Integrity Checks:**
- [ ] `projects` table contains rows for each configured project path
@@ -332,6 +361,13 @@ tests/integration/init.test.ts
}
```
**Raw Payload Compression:**
- When `storage.compressRawPayloads: true` (default), raw JSON payloads are gzip-compressed before storage
- `raw_payloads.content_encoding` indicates `'identity'` (uncompressed) or `'gzip'` (compressed)
- Compression typically reduces storage by 70-80% for JSON payloads
- Decompression is handled transparently when reading payloads
- Tradeoff: Slightly higher CPU on write/read, significantly lower disk usage
**DB Runtime Defaults (Checkpoint 0):**
- On every connection:
- `PRAGMA journal_mode=WAL;`
@@ -387,13 +423,20 @@ CREATE TABLE raw_payloads (
source TEXT NOT NULL, -- 'gitlab'
project_id INTEGER REFERENCES projects(id), -- nullable for instance-level resources
resource_type TEXT NOT NULL, -- 'project' | 'issue' | 'mr' | 'note' | 'discussion'
gitlab_id INTEGER NOT NULL,
gitlab_id TEXT NOT NULL, -- TEXT because discussion IDs are strings; numeric IDs stored as strings
fetched_at INTEGER NOT NULL,
content_encoding TEXT NOT NULL DEFAULT 'identity', -- 'identity' | 'gzip'
payload BLOB NOT NULL -- raw JSON or gzip-compressed JSON
);
CREATE INDEX idx_raw_payloads_lookup ON raw_payloads(project_id, resource_type, gitlab_id);
CREATE INDEX idx_raw_payloads_history ON raw_payloads(project_id, resource_type, gitlab_id, fetched_at);
-- Schema version tracking for migrations
CREATE TABLE schema_version (
version INTEGER PRIMARY KEY,
applied_at INTEGER NOT NULL,
description TEXT
);
```
---
@@ -411,7 +454,8 @@ tests/unit/issue-transformer.test.ts
tests/unit/pagination.test.ts
✓ fetches all pages when multiple exist
✓ respects per_page parameter
stops when empty page returned
follows X-Next-Page header until empty/absent
✓ falls back to empty-page stop if headers missing (robustness)
tests/unit/discussion-transformer.test.ts
✓ transforms discussion payload to normalized schema
@@ -450,7 +494,7 @@ tests/integration/sync-runs.test.ts
| `gi list issues --limit=10` | Table of 10 issues | Shows iid, title, state, author |
| `gi list issues --project=group/project-one` | Filtered list | Only shows issues from that project |
| `gi count issues` | `Issues: 1,234` (example) | Count matches GitLab UI |
| `gi show issue 123` | Issue detail view | Shows title, description, labels, discussions, URL |
| `gi show issue 123` | Issue detail view | Shows title, description, labels, discussions, URL. If multiple projects have issue #123, prompts for clarification or use `--project=PATH` |
| `gi count discussions --type=issue` | `Issue Discussions: 5,678` | Non-zero count |
| `gi count notes --type=issue` | `Issue Notes: 12,345 (excluding 2,345 system)` | Non-zero count |
| `gi sync-status` | Last sync time, cursor positions | Shows successful last run |
@@ -543,7 +587,7 @@ CREATE TABLE discussions (
gitlab_discussion_id TEXT NOT NULL, -- GitLab's string ID (e.g. "6a9c1750b37d...")
project_id INTEGER NOT NULL REFERENCES projects(id),
issue_id INTEGER REFERENCES issues(id),
merge_request_id INTEGER REFERENCES merge_requests(id),
merge_request_id INTEGER, -- FK added in CP2 via ALTER TABLE
noteable_type TEXT NOT NULL, -- 'Issue' | 'MergeRequest'
individual_note BOOLEAN NOT NULL, -- standalone comment vs threaded discussion
first_note_at INTEGER, -- for ordering discussions
@@ -686,6 +730,15 @@ CREATE INDEX idx_mr_labels_label ON mr_labels(label_id);
-- Additional indexes for DiffNote queries (tables created in CP1)
CREATE INDEX idx_notes_type ON notes(type);
CREATE INDEX idx_notes_new_path ON notes(position_new_path);
-- Migration: Add FK constraint to discussions table (was deferred from CP1)
-- SQLite doesn't support ADD CONSTRAINT, so we recreate the table with FK
-- This is handled by the migration system; pseudocode for clarity:
-- 1. CREATE TABLE discussions_new with REFERENCES merge_requests(id)
-- 2. INSERT INTO discussions_new SELECT * FROM discussions
-- 3. DROP TABLE discussions
-- 4. ALTER TABLE discussions_new RENAME TO discussions
-- 5. Recreate indexes
```
**MR Discussion Processing Rules:**
@@ -696,8 +749,8 @@ CREATE INDEX idx_notes_new_path ON notes(position_new_path);
---
### Checkpoint 3: Document + Embedding Generation with Lexical Search
**Deliverable:** Documents and embeddings generated; `gi search --mode=lexical` works end-to-end
### Checkpoint 3A: Document Generation + FTS (Lexical Search)
**Deliverable:** Documents generated + FTS5 index; `gi search --mode=lexical` works end-to-end (no Ollama required)
**Automated Tests (Vitest):**
```
@@ -707,76 +760,67 @@ tests/unit/document-extractor.test.ts
✓ extracts discussion document with full thread context
✓ includes parent issue/MR title in discussion header
✓ formats notes with author and timestamp
truncates content exceeding 8000 tokens
excludes system notes from discussion documents by default
✓ includes system notes only when --include-system-notes enabled (debug)
✓ truncates content exceeding 8000 tokens at note boundaries
✓ preserves first and last notes when truncating middle
✓ computes SHA-256 content hash consistently
tests/unit/embedding-client.test.ts
✓ connects to Ollama API
✓ generates embedding for text input
✓ returns 768-dimension vector
✓ handles Ollama connection failure gracefully
✓ batches requests (32 documents per batch)
tests/integration/document-creation.test.ts
✓ creates document for each issue
✓ creates document for each MR
✓ creates document for each discussion
✓ populates document_labels junction table
✓ computes content_hash for each document
✓ excludes system notes from discussion content
tests/integration/embedding-storage.test.ts
stores embedding in sqlite-vss
embedding rowid matches document id
creates embedding_metadata record
✓ skips re-embedding when content_hash unchanged
✓ re-embeds when content_hash changes
tests/integration/fts-index.test.ts
documents_fts row count matches documents
FTS triggers fire on insert/update/delete
updates propagate via triggers
tests/integration/fts-search.test.ts
✓ returns exact keyword matches
✓ porter stemming works (search/searching)
✓ returns empty for non-matching query
```
**Manual CLI Smoke Tests:**
| Command | Expected Output | Pass Criteria |
|---------|-----------------|---------------|
| `gi embed --all` | Progress bar with ETA | Completes without error |
| `gi embed --all` (re-run) | `0 documents to embed` | Skips already-embedded docs |
| `gi stats` | Embedding coverage stats | Shows 100% coverage |
| `gi stats --json` | JSON stats object | Valid JSON with document/embedding counts |
| `gi embed --all` (Ollama stopped) | Clear error message | Non-zero exit, actionable error |
| `gi search "authentication" --mode=lexical` | FTS results | Returns matching documents, no embeddings required |
| `gi generate-docs` | Progress bar, final count | Completes without error |
| `gi generate-docs` (re-run) | `0 documents to regenerate` | Skips unchanged docs |
| `gi search "authentication" --mode=lexical` | FTS results | Returns matching documents, works without Ollama |
| `gi stats` | Document count stats | Shows document coverage |
**Data Integrity Checks:**
- [ ] `SELECT COUNT(*) FROM documents` = issues + MRs + discussions
- [ ] `SELECT COUNT(*) FROM embeddings` = `SELECT COUNT(*) FROM documents`
- [ ] `SELECT COUNT(*) FROM embedding_metadata` = `SELECT COUNT(*) FROM documents`
- [ ] All `embedding_metadata.content_hash` matches corresponding `documents.content_hash`
- [ ] `SELECT COUNT(*) FROM documents_fts` = `SELECT COUNT(*) FROM documents` (via FTS triggers)
- [ ] `SELECT COUNT(*) FROM documents WHERE LENGTH(content_text) > 32000` logs truncation warnings
- [ ] Discussion documents include parent title in content_text
- [ ] Discussion documents exclude system notes
**Scope:**
- Ollama integration (nomic-embed-text model)
- Embedding generation pipeline:
- Batch size: 32 documents per batch
- Concurrency: configurable (default 4 workers)
- Retry with exponential backoff for transient failures (max 3 attempts)
- Per-document failure recording to enable targeted re-runs
- Vector storage in SQLite (sqlite-vss extension)
- Progress tracking and resumability
- Document extraction layer:
- Canonical "search documents" derived from issues/MRs/discussions
- Stable content hashing for change detection (SHA-256 of content_text)
- Single embedding per document (chunking deferred to post-MVP)
- Truncation: content_text capped at 8000 tokens (nomic-embed-text limit is 8192)
- Truncation: content_text capped at 8000 tokens at NOTE boundaries
- **Implementation:** Use character budget, not exact token count
- `maxChars = 32000` (conservative 4 chars/token estimate)
- Drop whole notes from middle, never cut mid-note
- `approxTokens = ceil(charCount / 4)` for reporting/logging only
- This avoids tokenizer dependency while preventing embedding failures
- System notes excluded from discussion documents (stored in DB for audit, but not in embeddings/search)
- Denormalized metadata for fast filtering (author, labels, dates)
- Fast label filtering via `document_labels` join table
- FTS5 index for lexical search (enables `gi search --mode=lexical` without Ollama)
- FTS5 index for lexical search
- `gi search --mode=lexical` CLI command (works without Ollama)
**Schema Additions:**
This checkpoint delivers a working search experience before introducing embedding infrastructure risk.
**Schema Additions (CP3A):**
```sql
-- Unified searchable documents (derived from issues/MRs/discussions)
-- Note: Full documents table schema is in CP3B section for continuity with embeddings
CREATE TABLE documents (
id INTEGER PRIMARY KEY,
source_type TEXT NOT NULL, -- 'issue' | 'merge_request' | 'discussion'
@@ -806,7 +850,122 @@ CREATE TABLE document_labels (
);
CREATE INDEX idx_document_labels_label ON document_labels(label_name);
-- sqlite-vss virtual table
-- Fast path filtering for documents (extracted from DiffNote positions)
CREATE TABLE document_paths (
document_id INTEGER NOT NULL REFERENCES documents(id),
path TEXT NOT NULL,
PRIMARY KEY(document_id, path)
);
CREATE INDEX idx_document_paths_path ON document_paths(path);
-- Track sources that require document regeneration (populated during ingestion)
CREATE TABLE dirty_sources (
source_type TEXT NOT NULL, -- 'issue' | 'merge_request' | 'discussion'
source_id INTEGER NOT NULL, -- local DB id
queued_at INTEGER NOT NULL,
PRIMARY KEY(source_type, source_id)
);
-- Resumable dependent fetches (discussions are per-parent resources)
CREATE TABLE pending_discussion_fetches (
project_id INTEGER NOT NULL REFERENCES projects(id),
noteable_type TEXT NOT NULL, -- 'Issue' | 'MergeRequest'
noteable_iid INTEGER NOT NULL, -- parent iid (stable human identifier)
queued_at INTEGER NOT NULL,
attempt_count INTEGER NOT NULL DEFAULT 0,
last_attempt_at INTEGER,
last_error TEXT,
PRIMARY KEY(project_id, noteable_type, noteable_iid)
);
CREATE INDEX idx_pending_discussions_retry
ON pending_discussion_fetches(attempt_count, last_attempt_at)
WHERE last_error IS NOT NULL;
-- Full-text search for lexical retrieval
-- Using porter stemmer for better matching of word variants
CREATE VIRTUAL TABLE documents_fts USING fts5(
title,
content_text,
content='documents',
content_rowid='id',
tokenize='porter unicode61'
);
-- Triggers to keep FTS in sync
CREATE TRIGGER documents_ai AFTER INSERT ON documents BEGIN
INSERT INTO documents_fts(rowid, title, content_text)
VALUES (new.id, new.title, new.content_text);
END;
CREATE TRIGGER documents_ad AFTER DELETE ON documents BEGIN
INSERT INTO documents_fts(documents_fts, rowid, title, content_text)
VALUES('delete', old.id, old.title, old.content_text);
END;
CREATE TRIGGER documents_au AFTER UPDATE ON documents BEGIN
INSERT INTO documents_fts(documents_fts, rowid, title, content_text)
VALUES('delete', old.id, old.title, old.content_text);
INSERT INTO documents_fts(rowid, title, content_text)
VALUES (new.id, new.title, new.content_text);
END;
```
**FTS5 Tokenizer Notes:**
- `porter` enables stemming (searching "authentication" matches "authenticating", "authenticated")
- `unicode61` handles Unicode properly
- Code identifiers (snake_case, camelCase, file paths) may not tokenize ideally; post-MVP consideration for custom tokenizer
---
### Checkpoint 3B: Embedding Generation (Semantic Search)
**Deliverable:** Embeddings generated + `gi search --mode=semantic` works; graceful fallback if Ollama unavailable
**Automated Tests (Vitest):**
```
tests/unit/embedding-client.test.ts
✓ connects to Ollama API
✓ generates embedding for text input
✓ returns 768-dimension vector
✓ handles Ollama connection failure gracefully
✓ batches requests (32 documents per batch)
tests/integration/embedding-storage.test.ts
✓ stores embedding in sqlite-vss
✓ embedding rowid matches document id
✓ creates embedding_metadata record
✓ skips re-embedding when content_hash unchanged
✓ re-embeds when content_hash changes
```
**Manual CLI Smoke Tests:**
| Command | Expected Output | Pass Criteria |
|---------|-----------------|---------------|
| `gi embed --all` | Progress bar with ETA | Completes without error |
| `gi embed --all` (re-run) | `0 documents to embed` | Skips already-embedded docs |
| `gi stats` | Embedding coverage stats | Shows 100% coverage |
| `gi stats --json` | JSON stats object | Valid JSON with document/embedding counts |
| `gi embed --all` (Ollama stopped) | Clear error message | Non-zero exit, actionable error |
| `gi search "authentication" --mode=semantic` | Vector results | Returns semantically similar documents |
**Data Integrity Checks:**
- [ ] `SELECT COUNT(*) FROM embeddings` = `SELECT COUNT(*) FROM documents`
- [ ] `SELECT COUNT(*) FROM embedding_metadata` = `SELECT COUNT(*) FROM documents`
- [ ] All `embedding_metadata.content_hash` matches corresponding `documents.content_hash`
**Scope:**
- Ollama integration (nomic-embed-text model)
- Embedding generation pipeline:
- Batch size: 32 documents per batch
- Concurrency: configurable (default 4 workers)
- Retry with exponential backoff for transient failures (max 3 attempts)
- Per-document failure recording to enable targeted re-runs
- Vector storage in SQLite (sqlite-vss extension)
- Progress tracking and resumability
- `gi search --mode=semantic` CLI command
**Schema Additions (CP3B):**
```sql
-- sqlite-vss virtual table for vector search
-- Storage rule: embeddings.rowid = documents.id
CREATE VIRTUAL TABLE embeddings USING vss0(
embedding(768)
@@ -828,22 +987,6 @@ CREATE TABLE embedding_metadata (
-- Index for finding failed embeddings to retry
CREATE INDEX idx_embedding_metadata_errors ON embedding_metadata(last_error) WHERE last_error IS NOT NULL;
-- Track sources that require document regeneration (populated during ingestion)
CREATE TABLE dirty_sources (
source_type TEXT NOT NULL, -- 'issue' | 'merge_request' | 'discussion'
source_id INTEGER NOT NULL, -- local DB id
queued_at INTEGER NOT NULL,
PRIMARY KEY(source_type, source_id)
);
-- Fast path filtering for documents (extracted from DiffNote positions)
CREATE TABLE document_paths (
document_id INTEGER NOT NULL REFERENCES documents(id),
path TEXT NOT NULL,
PRIMARY KEY(document_id, path)
);
CREATE INDEX idx_document_paths_path ON document_paths(path);
```
**Storage Rule (MVP):**
@@ -879,6 +1022,12 @@ Agreed. What about refresh token strategy?
Short-lived access tokens (15min), longer refresh (7 days). Here's why...
```
**System Notes Exclusion Rule:**
- System notes (is_system=1) are stored in the DB for audit purposes
- System notes are EXCLUDED from discussion documents by default
- This prevents semantic noise ("changed assignee", "added label", "mentioned in") from polluting embeddings
- Debug flag `--include-system-notes` available for troubleshooting
This format preserves:
- Parent context (issue/MR title and number)
- Project path for scoped search
@@ -889,14 +1038,28 @@ This format preserves:
- Temporal ordering of the conversation
- Full thread semantics for decision traceability
**Truncation:**
If content exceeds 8000 tokens:
**Note:** Token count is approximate (`ceil(charCount / 4)`). Enforce `maxChars = 32000`.
**Truncation (Note-Boundary Aware):**
If content exceeds 8000 tokens (~32000 chars):
1. Truncate from the middle (preserve first + last notes for context)
2. Set `documents.is_truncated = 1`
3. Set `documents.truncated_reason = 'token_limit_middle_drop'`
4. Log a warning with document ID and original token count
**Algorithm:**
1. Count non-system notes in the discussion
2. If total chars ≤ maxChars, no truncation needed
3. Otherwise, drop whole notes from the MIDDLE:
- Preserve first N notes and last M notes
- Never cut mid-note (produces unreadable snippets and worse embeddings)
- Continue dropping middle notes until under maxChars
4. Insert marker: `\n\n[... N notes omitted for length ...]\n\n`
5. Set `documents.is_truncated = 1`
6. Set `documents.truncated_reason = 'token_limit_middle_drop'`
7. Log a warning with document ID and original/truncated token count
**Why note-boundary truncation:**
- Cutting mid-note produces unreadable snippets ("...the authentication flow because--")
- Keeping whole notes preserves semantic coherence for embeddings
- First notes contain context/problem statement; last notes contain conclusions
- Middle notes are often back-and-forth that's less critical
**Token estimation:** `approxTokens = ceil(charCount / 4)`. No tokenizer dependency.
This metadata enables:
- Monitoring truncation frequency in production
@@ -954,14 +1117,14 @@ tests/e2e/golden-queries.test.ts
| `gi search "authentication" --author=johndoe` | Filtered by author | All results have @johndoe |
| `gi search "authentication" --after=2024-01-01` | Date filtered | All results after date |
| `gi search "authentication" --label=bug` | Label filtered | All results have bug label |
| `gi search "redis" --mode=lexical` | FTS results only | Works without Ollama |
| `gi search "redis" --mode=lexical` | FTS results only | Shows FTS results, no embeddings |
| `gi search "auth" --path=src/auth/` | Path-filtered results | Only results referencing files in src/auth/ |
| `gi search "authentication" --json` | JSON output | Valid JSON matching stable schema |
| `gi search "authentication" --explain` | Rank breakdown | Shows vector/FTS/RRF contributions |
| `gi search "authentication" --limit=5` | 5 results max | Returns at most 5 results |
| `gi search "xyznonexistent123"` | No results message | Graceful empty state |
| `gi search "auth"` (no data synced) | No data message | Shows "Run gi sync first" |
| `gi search "auth"` (Ollama stopped) | FTS results + warning | Shows warning, still returns results |
| `gi search "authentication"` (no data synced) | No data message | Shows "Run gi sync first" |
| `gi search "authentication"` (Ollama stopped) | FTS results + warning | Shows warning, still returns results |
**Golden Query Test Suite:**
Create `tests/fixtures/golden-queries.json` with 10 queries and expected URLs:
@@ -991,8 +1154,12 @@ Each query must have at least one expected URL appear in top 10 results.
- Result ranking and scoring (document-level)
- Search filters: `--type=issue|mr|discussion`, `--author=username`, `--after=date`, `--label=name`, `--project=path`, `--path=file`, `--limit=N`
- `--limit=N` controls result count (default: 20, max: 100)
- `--path` filters documents by referenced file paths (from DiffNote positions)
- MVP: substring/exact match; glob patterns deferred
- `--path` filters documents by referenced file paths (from DiffNote positions):
- If `--path` ends with `/`: prefix match (`path LIKE 'src/auth/%'`)
- Otherwise: exact match OR prefix on directory boundary
- Examples: `--path=src/auth/` matches `src/auth/login.ts`, `src/auth/utils/helpers.ts`
- Examples: `--path=src/auth/login.ts` matches only that exact file
- Glob patterns deferred to post-MVP
- Label filtering operates on `document_labels` (indexed, exact-match)
- Filters work identically in hybrid and lexical modes
- Debug: `--explain` returns rank contributions from vector + FTS + RRF
@@ -1005,51 +1172,25 @@ Each query must have at least one expected URL appear in top 10 results.
- Filters exclude all results: `No results match the specified filters.`
- Helpful hints shown in non-JSON mode (e.g., "Try broadening your search")
**Schema Additions:**
```sql
-- Full-text search for hybrid retrieval
-- Using porter stemmer for better matching of word variants
CREATE VIRTUAL TABLE documents_fts USING fts5(
title,
content_text,
content='documents',
content_rowid='id',
tokenize='porter unicode61'
);
-- Triggers to keep FTS in sync
CREATE TRIGGER documents_ai AFTER INSERT ON documents BEGIN
INSERT INTO documents_fts(rowid, title, content_text)
VALUES (new.id, new.title, new.content_text);
END;
CREATE TRIGGER documents_ad AFTER DELETE ON documents BEGIN
INSERT INTO documents_fts(documents_fts, rowid, title, content_text)
VALUES('delete', old.id, old.title, old.content_text);
END;
CREATE TRIGGER documents_au AFTER UPDATE ON documents BEGIN
INSERT INTO documents_fts(documents_fts, rowid, title, content_text)
VALUES('delete', old.id, old.title, old.content_text);
INSERT INTO documents_fts(rowid, title, content_text)
VALUES (new.id, new.title, new.content_text);
END;
```
**FTS5 Tokenizer Notes:**
- `porter` enables stemming (searching "authentication" matches "authenticating", "authenticated")
- `unicode61` handles Unicode properly
- Code identifiers (snake_case, camelCase, file paths) may not tokenize ideally; post-MVP consideration for custom tokenizer
**Hybrid Search Algorithm (MVP) - Reciprocal Rank Fusion:**
1. Query both vector index (top 50) and FTS5 (top 50)
2. Merge results by document_id
3. Combine with Reciprocal Rank Fusion (RRF):
1. Determine recall size (adaptive based on filters):
- `baseTopK = 50`
- If any filters present (--project, --type, --author, --label, --path, --after): `topK = 200`
- This prevents "no results" when relevant docs exist outside top-50 unfiltered recall
2. Query both vector index (top topK) and FTS5 (top topK)
- Apply SQL-expressible filters during retrieval when possible (project_id, author_username, source_type)
3. Merge results by document_id
4. Combine with Reciprocal Rank Fusion (RRF):
- For each retriever list, assign ranks (1..N)
- `rrf_score = Σ 1 / (k + rank)` with k=60 (tunable)
- `rrfScore = Σ 1 / (k + rank)` with k=60 (tunable)
- RRF is simpler than weighted sums and doesn't require score normalization
4. Apply filters (type, author, date, label)
5. Return top K
5. Apply remaining filters (date ranges, labels, paths that weren't applied in SQL)
6. Return top K results
**Why Adaptive Recall:**
- Fixed top-50 + filter can easily return 0 results even when relevant docs exist
- Increasing recall when filters are present catches more candidates before filtering
- SQL-level filtering is preferred (faster, uses indexes) but not always possible
**Why RRF over Weighted Sums:**
- FTS5 BM25 scores and vector distances use different scales
@@ -1125,17 +1266,23 @@ interface SearchResult {
author: string | null;
createdAt: string; // ISO 8601
updatedAt: string; // ISO 8601
score: number; // 0-1 normalized RRF score
score: number; // normalized 0-1 (rrfScore / maxRrfScore in this result set)
snippet: string; // truncated content_text
labels: string[];
// Only present with --explain flag
explain?: {
vectorRank?: number; // null if not in vector results
ftsRank?: number; // null if not in FTS results
rrfScore: number;
rrfScore: number; // raw RRF score (rank-based, comparable within a query)
};
}
// Note on score normalization:
// - `score` is normalized 0-1 for UI display convenience
// - Normalization is per-query (score = rrfScore / max(rrfScore) in this result set)
// - Use `explain.rrfScore` for raw scores when comparing across queries
// - Scores are NOT comparable across different queries
interface SearchResponse {
query: string;
mode: "hybrid" | "lexical" | "semantic";
@@ -1186,7 +1333,7 @@ tests/integration/sync-recovery.test.ts
| `gi sync` (no changes) | `0 issues, 0 MRs updated` | Fast completion, no API calls beyond cursor check |
| `gi sync` (after GitLab change) | `1 issue updated, 3 discussions refetched` | Detects and syncs the change |
| `gi sync --full` | Full sync progress | Resets cursors, fetches everything |
| `gi sync-status` | Cursor positions, last sync time | Shows current state |
| `gi sync-status` | Last sync time, cursor positions | Shows current state |
| `gi sync` (with rate limit) | Backoff messages | Respects rate limits, completes eventually |
| `gi search "new content"` (after sync) | Returns new content | New content is searchable |
@@ -1262,13 +1409,25 @@ gi sync-status
**Orchestration steps (in order):**
1. Acquire app lock with heartbeat
2. Ingest delta (issues, MRs, discussions) based on cursors
- During ingestion, INSERT into `dirty_sources` for each upserted entity
3. Apply rolling backfill window
4. Regenerate documents for entities in `dirty_sources` (process + delete from queue)
5. Embed documents with changed content_hash
6. FTS triggers auto-sync (no explicit step needed)
7. Release lock, record sync_run as succeeded
2. Ingest delta (issues, MRs) based on cursors
- For each upserted issue/MR, enqueue into `pending_discussion_fetches`
- INSERT into `dirty_sources` for each upserted issue/MR
3. Process `pending_discussion_fetches` queue (bounded per run, retryable):
- Fetch discussions for each queued parent
- On success: upsert discussions/notes, INSERT into `dirty_sources`, DELETE from queue
- On failure: increment `attempt_count`, record `last_error`, leave in queue for retry
- Bound processing: max N parents per sync run to avoid unbounded API calls
4. Apply rolling backfill window
5. Regenerate documents for entities in `dirty_sources` (process + delete from queue)
6. Embed documents with changed content_hash
7. FTS triggers auto-sync (no explicit step needed)
8. Release lock, record sync_run as succeeded
**Why queue-based discussion fetching:**
- One pathological MR thread (huge pagination, 5xx errors, permission issues) shouldn't block the entire sync
- Primary resource cursors can advance independently
- Discussions can be retried without re-fetching all issues/MRs
- Bounded processing prevents unbounded API calls per sync run
Individual commands remain available for checkpoint testing and debugging:
- `gi ingest --type=issues`
@@ -1298,7 +1457,8 @@ All commands support `--help` for detailed usage information.
|---------|-----|-------------|
| `gi ingest --type=issues` | 1 | Fetch issues from GitLab |
| `gi ingest --type=merge_requests` | 2 | Fetch MRs and discussions |
| `gi embed --all` | 3 | Generate embeddings for all documents |
| `gi generate-docs` | 3A | Extract documents from issues/MRs/discussions |
| `gi embed --all` | 3B | Generate embeddings for all documents |
| `gi embed --retry-failed` | 3 | Retry failed embeddings |
| `gi sync` | 5 | Full sync orchestration (ingest + docs + embed) |
| `gi sync --full` | 5 | Force complete re-sync (reset cursors) |
@@ -1310,7 +1470,7 @@ All commands support `--help` for detailed usage information.
| Command | CP | Description |
|---------|-----|-------------|
| `gi list issues [--limit=N] [--project=PATH]` | 1 | List issues |
| `gi list mrs [--limit=N]` | 2 | List merge requests |
| `gi list mrs --limit=N` | 2 | List merge requests |
| `gi count issues` | 1 | Count issues |
| `gi count mrs` | 2 | Count merge requests |
| `gi count discussions --type=issue` | 1 | Count issue discussions |
@@ -1318,8 +1478,8 @@ All commands support `--help` for detailed usage information.
| `gi count discussions --type=mr` | 2 | Count MR discussions |
| `gi count notes --type=issue` | 1 | Count issue notes (excluding system) |
| `gi count notes` | 2 | Count all notes (excluding system) |
| `gi show issue <iid>` | 1 | Show issue details |
| `gi show mr <iid>` | 2 | Show MR details with discussions |
| `gi show issue <iid> [--project=PATH]` | 1 | Show issue details (prompts if iid ambiguous across projects) |
| `gi show mr <iid> [--project=PATH]` | 2 | Show MR details with discussions |
| `gi stats` | 3 | Embedding coverage statistics |
| `gi stats --json` | 3 | JSON stats for scripting |
| `gi sync-status` | 1 | Show cursor positions and last sync |
@@ -1401,6 +1561,8 @@ Common errors and their resolutions:
| **Disk full during write** | Fails with clear error. Cursor preserved at last successful commit. Free space and resume. |
| **Stale lock detected** | Lock held > 10 minutes without heartbeat is considered stale. Next sync auto-recovers. |
| **Network interruption** | Retries with exponential backoff. After max retries, sync fails but cursor is preserved. |
| **Embedding permanent failure** | After 3 retries, document stays in `embedding_metadata` with `last_error` populated. Use `gi embed --retry-failed` to retry later, or `gi stats` to see failed count. Documents with failed embeddings are excluded from vector search but included in FTS. |
| **Orphaned records** | MVP: No automatic cleanup. `last_seen_at` field enables future detection of items deleted in GitLab. Post-MVP: `gi gc --dry-run` to identify orphans, `gi gc --confirm` to remove. |
---
@@ -1506,7 +1668,7 @@ CREATE TABLE note_positions (
new_line INTEGER,
position_type TEXT -- 'text' | 'image' | etc.
);
CREATE INDEX idx_note_positions_new_path ON note_positions(position_new_path);
CREATE INDEX idx_note_positions_new_path ON note_positions(new_path);
```
---
@@ -1535,6 +1697,8 @@ Each checkpoint includes:
| Search quality | Hybrid (vector + FTS5) retrieval with RRF, golden query test suite |
| Concurrent sync corruption | DB lock + heartbeat + rolling backfill, automatic stale lock recovery |
| Embedding failures | Per-document error tracking, retry with backoff, targeted re-runs |
| Pathological discussions | Queue-based discussion fetching; one bad thread doesn't block entire sync |
| Empty search results with filters | Adaptive recall (topK 50→200 when filtered) |
**SQLite Performance Defaults (MVP):**
- Enable `PRAGMA journal_mode=WAL;` on every connection
@@ -1552,23 +1716,24 @@ Each checkpoint includes:
| sync_runs | 0 | Audit trail of sync operations (with heartbeat) |
| app_locks | 0 | Crash-safe single-flight lock |
| sync_cursors | 0 | Resumable sync state per primary resource |
| raw_payloads | 0 | Decoupled raw JSON storage (with project_id) |
| raw_payloads | 0 | Decoupled raw JSON storage (gitlab_id as TEXT) |
| schema_version | 0 | Database migration version tracking |
| issues | 1 | Normalized issues (unique by project+iid) |
| labels | 1 | Label definitions (unique by project + name) |
| issue_labels | 1 | Issue-label junction |
| merge_requests | 2 | Normalized MRs (unique by project+iid) |
| discussions | 1 | Discussion threads (issue discussions in CP1, MR discussions in CP2) |
| notes | 1 | Individual comments with is_system flag (DiffNote paths added in CP2) |
| merge_requests | 2 | Normalized MRs (unique by project+iid) |
| mr_labels | 2 | MR-label junction |
| documents | 3 | Unified searchable documents with truncation metadata |
| document_labels | 3 | Document-label junction for fast filtering |
| document_paths | 3 | Fast path filtering for documents (DiffNote file paths) |
| dirty_sources | 3 | Queue for incremental document regeneration |
| embeddings | 3 | Vector embeddings (sqlite-vss, rowid=document_id) |
| embedding_metadata | 3 | Embedding provenance + error tracking |
| documents_fts | 4 | Full-text search index (fts5 with porter stemmer) |
| mr_files | 6 | MR file changes (deferred to File History feature) |
| documents | 3A | Unified searchable documents with truncation metadata |
| document_labels | 3A | Document-label junction for fast filtering |
| document_paths | 3A | Fast path filtering for documents (DiffNote file paths) |
| dirty_sources | 3A | Queue for incremental document regeneration |
| pending_discussion_fetches | 3A | Resumable queue for dependent discussion fetching |
| documents_fts | 3A | Full-text search index (fts5 with porter stemmer) |
| embeddings | 3B | Vector embeddings (sqlite-vss, rowid=document_id) |
| embedding_metadata | 3B | Embedding provenance + error tracking |
| mr_files | 6 | MR file changes (deferred to post-MVP) |
---
@@ -1584,10 +1749,10 @@ Each checkpoint includes:
| Labels uniqueness | **By (project_id, name)** | GitLab API returns labels as strings |
| Sync method | **Polling only for MVP** | Webhooks add complexity; polling every 10 min is sufficient |
| Sync safety | **DB lock + heartbeat + rolling backfill** | Prevents race conditions and missed updates |
| Discussions sync | **Dependent resource model** | Discussions API is per-parent; refetch all when parent updates |
| Discussions sync | **Resumable queue model** | Queue-based fetching allows one pathological thread to not block entire sync |
| Hybrid ranking | **RRF over weighted sums** | Simpler, no score normalization needed |
| Embedding rowid | **rowid = documents.id** | Eliminates fragile rowid mapping |
| Embedding truncation | **8000 tokens, truncate middle** | Preserve first/last notes for context |
| Embedding truncation | **Note-boundary aware middle drop** | Never cut mid-note; preserves semantic coherence |
| Embedding batching | **32 docs/batch, 4 concurrent workers** | Balance throughput, memory, and error isolation |
| FTS5 tokenizer | **porter unicode61** | Stemming improves recall |
| Ollama unavailable | **Graceful degradation to FTS5** | Search still works without semantic matching |
@@ -1596,6 +1761,14 @@ Each checkpoint includes:
| `gi init` validation | **Validate GitLab before writing config** | Fail fast, better UX |
| Ctrl+C handling | **Graceful shutdown** | Finish page, commit cursor, exit cleanly |
| Empty state UX | **Actionable messages** | Guide user to next step |
| raw_payloads.gitlab_id | **TEXT not INTEGER** | Discussion IDs are strings; numeric IDs stored as strings |
| GitLab list params | **Always scope=all&state=all** | Ensures all historical data including closed items |
| Pagination | **X-Next-Page headers with empty-page fallback** | Headers are more robust than empty-page detection |
| Integration tests | **Mocked by default, live tests optional** | Deterministic CI; live tests gated by GITLAB_LIVE_TESTS=1 |
| Search recall with filters | **Adaptive topK (50→200 when filtered)** | Prevents "no results" when relevant docs exist outside top-50 |
| RRF score normalization | **Per-query normalized 0-1** | score = rrfScore / max(rrfScore); raw score in explain |
| --path semantics | **Trailing / = prefix match** | `--path=src/auth/` does prefix; otherwise exact match |
| CP3 structure | **Split into 3A (FTS) and 3B (embeddings)** | Lexical search works before embedding infra risk |
---