c3500783d1
Add support for filtering search results by tags in the main search endpoint. Changes: - Add tags parameter to SearchQueryDto (comma-separated tag slugs) - Implement tag filtering in SearchService.search() method - Update SQL query to join with knowledge_entry_tags when tags provided - Entries must have ALL specified tags (AND logic) - Add tests for tag filtering (2 controller tests, 2 service tests) - Update endpoint documentation - Fix non-null assertion linting error The search endpoint now supports: - Full-text search with ranking (ts_rank) - Snippet generation with highlighting (ts_headline) - Status filtering - Tag filtering (new) - Pagination Example: GET /api/knowledge/search?q=api&tags=documentation,tutorial All tests pass (25 total), type checking passes, linting passes. Fixes #66 Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
53 lines
1.8 KiB
Markdown
53 lines
1.8 KiB
Markdown
# Issue #65: [KNOW-013] Full-Text Search Setup
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## Objective
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Set up PostgreSQL full-text search for entries in the knowledge module with weighted fields and proper indexing.
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## Approach
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1. Examine current Prisma schema for knowledge entries
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2. Write tests for full-text search functionality (TDD)
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3. Add tsvector column to knowledge entries table
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4. Create GIN index for performance
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5. Implement trigger to maintain tsvector on insert/update
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6. Weight fields: title (A), summary (B), content (C)
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7. Verify with sample queries
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## Progress
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- [x] Create scratchpad
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- [x] Read Prisma schema
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- [x] Examine existing search service
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- [x] Write failing tests for tsvector column (RED)
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- [x] Create migration with tsvector column, GIN index, and triggers
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- [x] Update Prisma schema to include tsvector
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- [x] Update search service to use precomputed tsvector (GREEN)
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- [x] Run tests and verify coverage (8/8 integration tests pass, 205/225 knowledge module tests pass)
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- [x] Run quality checks (typecheck and lint pass)
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- [x] Commit changes (commit 24d59e7)
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## Current State
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The search service already implements full-text search using `to_tsvector` and `ts_rank`
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in raw SQL queries, but it calculates tsvector on-the-fly. This is inefficient for large
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datasets. The migration will:
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1. Add a `search_vector` tsvector column to knowledge_entries
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2. Create a GIN index on search_vector for fast lookups
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3. Add a trigger to automatically update search_vector on insert/update
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4. Use weighted fields: title (A), summary (B), content (C)
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## Testing
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- Unit tests for search query generation
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- Integration tests with actual database queries
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- Performance verification with sample data
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## Notes
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- Using PostgreSQL's built-in full-text search capabilities
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- GIN index for fast text search
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- Automatic maintenance via triggers
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- Field weights: A (title) > B (summary) > C (content)
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