---
phase: "03"
plan: "02"
subsystem: "memory"
tags: ["faiss", "vector-store", "cosine-similarity", "persistence"]
dependency-graph:
requires: ["01-01"]
provides: ["faiss-vector-store", "embedding-search"]
affects: ["03-03", "04-01"]
tech-stack:
added: ["faiss-cpu>=1.13.2"]
patterns: ["IndexFlatIP + IndexIDMap", "L2 normalize for cosine similarity", "JSON ID mapping alongside FAISS binary"]
key-files:
created:
- "src/skill_retriever/memory/vector_store.py"
- "tests/test_vector_store.py"
modified:
- "pyproject.toml"
- "src/skill_retriever/memory/__init__.py"
decisions:
- "IndexFlatIP over IVF/HNSW — brute-force sufficient for <50k vectors"
- "pyright: ignore comments for faiss calls — faiss-cpu lacks type stubs"
metrics:
duration: "~5 minutes"
completed: "2026-02-03"
---
# Phase 03 Plan 02: Vector Store (FAISS IndexFlatIP with ID Mapping) Summary
FAISS vector store with L2-normalized IndexFlatIP for cosine similarity, string-to-int ID mapping, JSON persistence, and full CRUD operations.
## What Was Built
### FAISSVectorStore (`src/skill_retriever/memory/vector_store.py`)
- `__init__(dimensions)` — Creates `IndexFlatIP` wrapped in `IndexIDMap`, defaults to 384 from `EMBEDDING_CONFIG`
- `add(component_id, embedding)` — Single vector insert with L2 normalization
- `add_batch(ids, embeddings)` — Bulk insert in single FAISS call
- `search(query_embedding, top_k)` — Returns `(component_id, similarity)` tuples, filters out idx == -1
- `remove(component_id)` — Removes by string ID, raises `KeyError` if absent
- `save(directory)` / `load(directory)` — FAISS binary index + JSON ID mapping
- `count` property, `contains(component_id)` method
### Tests (`tests/test_vector_store.py`)
9 test cases all passing:
1. `test_add_and_count` — Add 3 vectors, verify count
2. `test_add_and_search` — Exact vector query returns similarity > 0.99
3. `test_search_returns_cosine_similarity` — Orthogonal vectors verify correct similarity scores
4. `test_search_filters_negative_one` — top_k > count returns only valid results
5. `test_add_batch` — Bulk add 10 vectors, verify search
6. `test_remove` — Add 3, remove 1, verify count and contains
7. `test_remove_nonexistent_raises` — KeyError for unknown ID
8. `test_save_and_load` — Persistence round-trip with search verification
9. `test_contains` — True for added, False for unknown
## Decisions Made
| Decision | Rationale |
|----------|-----------|
| IndexFlatIP (brute-force) | Sufficient for <50k vectors, no training needed |
| L2 normalize before add/search | Inner product on unit vectors = cosine similarity |
| JSON for ID mapping | Human-readable, debuggable, small file |
| pyright: ignore on faiss calls | faiss-cpu has no type stubs; same pattern as networkx in graph_store |
## Deviations from Plan
### Auto-fixed Issues
**1. [Rule 3 - Blocking] Added pyright ignore comments for faiss-cpu**
- **Found during:** Task 1 verification
- **Issue:** faiss-cpu lacks type stubs, causing 20 pyright errors in strict mode
- **Fix:** Added `# pyright: ignore[...]` comments on each faiss call, matching the project's existing pattern with networkx
- **Files modified:** `src/skill_retriever/memory/vector_store.py`
- **Commit:** 4348082
## Verification Results
```
pytest tests/test_vector_store.py -v → 9 passed
pyright src/skill_retriever/memory/vector_store.py → 0 errors
ruff check src/skill_retriever/memory/ → All checks passed
```
## Commits
| Hash | Message |
|------|---------|
| `4348082` | feat(03-02): FAISS vector store with cosine similarity and persistence |
