# Issue #65: [KNOW-013] Full-Text Search Setup

## Objective

Set up PostgreSQL full-text search for entries in the knowledge module with weighted fields and proper indexing.

## Approach

1. Examine current Prisma schema for knowledge entries
2. Write tests for full-text search functionality (TDD)
3. Add tsvector column to knowledge entries table
4. Create GIN index for performance
5. Implement trigger to maintain tsvector on insert/update
6. Weight fields: title (A), summary (B), content (C)
7. Verify with sample queries

## Progress

- [x] Create scratchpad
- [x] Read Prisma schema
- [x] Examine existing search service
- [x] Write failing tests for tsvector column (RED)
- [x] Create migration with tsvector column, GIN index, and triggers
- [x] Update Prisma schema to include tsvector
- [x] Update search service to use precomputed tsvector (GREEN)
- [x] Run tests and verify coverage (8/8 integration tests pass, 205/225 knowledge module tests pass)
- [x] Run quality checks (typecheck and lint pass)
- [x] Commit changes (commit 24d59e7)

## Current State

The search service already implements full-text search using `to_tsvector` and `ts_rank`
in raw SQL queries, but it calculates tsvector on-the-fly. This is inefficient for large
datasets. The migration will:

1. Add a `search_vector` tsvector column to knowledge_entries
2. Create a GIN index on search_vector for fast lookups
3. Add a trigger to automatically update search_vector on insert/update
4. Use weighted fields: title (A), summary (B), content (C)

## Testing

- Unit tests for search query generation
- Integration tests with actual database queries
- Performance verification with sample data

## Notes

- Using PostgreSQL's built-in full-text search capabilities
- GIN index for fast text search
- Automatic maintenance via triggers
- Field weights: A (title) > B (summary) > C (content)