Phase 03 Plan 01: pgvector Search Backend Summary

Query embedding functions for 3 models with pgvector cosine search returning top-K results with similarity scores

Performance

• Duration: 4 min
• Started: 2026-02-20T12:49:18Z
• Completed: 2026-02-20T12:53:30Z
• Tasks: 2
• Files modified: 2

Accomplishments

• Query embedding functions for all 3 models with correct task types (RETRIEVAL_QUERY)
• pgvector search function with cosine distance and similarity score calculation
• Combined search_all_models function for querying all backends at once
• Proper column validation and HNSW search parameter configuration

Task Commits

Each task was committed atomically:

1. Task 1: Create query embedding functions for all 3 models - 8ed0568c (feat)
2. Task 2: Create pgvector search function with similarity scores - ef19e363 (feat)

Plan metadata: pending (docs: complete plan)

Files Created/Modified

• src/search/__init__.py - Module exports for search functions
• src/search/pgvector_search.py - Query embeddings and pgvector search (229 lines)

Decisions Made

• Removed gross_amount from query results as column doesn't exist in schema
• Set HNSW ef_search=40 for balanced search performance
• Calculate similarity as 1 - distance for intuitive [0,1] range scores

Deviations from Plan

Auto-fixed Issues

1. [Rule 1 - Bug] Removed non-existent gross_amount column

• Found during: Task 2 (pgvector search function)
• Issue: Plan specified gross_amount in query fields, but column doesn't exist in line_item table
• Fix: Removed gross_amount from SELECT query and columns list
• Files modified: src/search/pgvector_search.py
• Verification: Search query executes successfully, returns all existing fields
• Committed in: ef19e363 (Task 2 commit)

Total deviations: 1 auto-fixed (1 bug) Impact on plan: Minor schema mismatch in plan vs actual database. No scope creep.

Issues Encountered

None - all tasks executed smoothly after schema fix.

User Setup Required

None - no external service configuration required. Uses existing Vertex AI and Jina credentials from Phase 2.

Next Phase Readiness

• Search backend complete, ready for LLM matching implementation (Plan 02)
• All 3 embedding models can be queried with proper task types
• Similarity scores in [0,1] range for consistent comparison

Self-Check: PASSED

• FOUND: src/search/init.py
• FOUND: src/search/pgvector_search.py (230 lines)
• FOUND: commit 8ed0568c
• FOUND: commit ef19e363

Phase: 03-search-implementation Completed: 2026-02-20