Phase 02 Plan 03: MiniLM Embeddings and Query Variations Summary

Local MiniLM embeddings (384-dim) for all 6078 items, HNSW indexes for 3 embedding types, and 3648 synthetic query variations for test set evaluation

Performance

• Duration: 17 min
• Started: 2026-02-20T11:33:19Z
• Completed: 2026-02-20T11:50:42Z
• Tasks: 3
• Files modified: 8

Accomplishments

• Generated 6078 MiniLM embeddings (384 dimensions, normalized for cosine similarity)
• Created HNSW indexes for Google, Jina, and MiniLM embedding columns
• Built query variation module with typo (QWERTZ), reorder, and paraphrase generators
• Generated 3648 synthetic variations (1216 test items x 3 variation types)

Task Commits

Each task was committed atomically:

1. Task 1: Create MiniLM embedding module and generate embeddings - 35066034 (feat)
2. Task 2: Create HNSW indexes for all embedding columns - 66913833 (feat)
3. Task 3: Create query variation module and generate test set variations - 9a19c58f (feat)

Files Created/Modified

• src/embeddings/minilm_embed.py - Local MiniLM embedding with batch processing
• src/embeddings/__init__.py - Updated exports for minilm module
• migrations/003_hnsw_indexes.sql - HNSW index creation for all 3 embedding columns
• migrations/002_test_query_variation.sql - Query variation table schema
• src/evaluation/query_variations.py - Typo, reorder, and paraphrase generation
• src/evaluation/__init__.py - Updated exports for variation functions
• init.sql - Added commented HNSW index definitions for fresh setup

Decisions Made

• MiniLM embeddings are normalized at encode time (normalize_embeddings=True) for efficient cosine similarity
• HNSW indexes use m=16 (connections per node) and ef_construction=64 for quality/performance balance
• German QWERTZ keyboard layout used for typo variations via nlpaug
• Gemini 2.0 Flash used for paraphrasing German accounting text

Deviations from Plan

None - plan executed exactly as written.

Issues Encountered

None - embedding infrastructure (text_prep.py, batch_processor.py) was already in place from a partial 02-02 run.

User Setup Required

None - MiniLM is a local model (sentence-transformers downloads automatically). Paraphrase generation uses existing Vertex AI credentials.

Next Phase Readiness

• All 3 embedding types populated for 6078 line items
• HNSW indexes enable fast similarity search (<10ms for 6K items)
• Query variations ready for robustness testing in Phase 4
• Ready for Phase 3: Search API implementation

Phase: 02-embedding-generation Completed: 2026-02-20

Self-Check: PASSED

• src/embeddings/minilm_embed.py exists
• migrations/003_hnsw_indexes.sql exists
• migrations/002_test_query_variation.sql exists
• src/evaluation/query_variations.py exists
• Commit 35066034 exists (Task 1)
• Commit 66913833 exists (Task 2)
• Commit 9a19c58f exists (Task 3)
• All 6078 items have MiniLM embeddings
• 3 HNSW indexes created
• 3648 query variations generated