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6.1.6. Semantic and Vector Search
- Concept: Advanced search techniques beyond keyword matching
- Purpose: Find conceptually related content
- Benefit: Better results for natural language queries
Comparative Table: Search Types
| Type | How It Works | Best For |
|---|---|---|
| Keyword | Exact term matching | Precise, known-item queries |
| Semantic | Meaning-based ranking | Natural language questions |
| Vector | Embedding similarity | Conceptual matching |
| Hybrid | Combined approaches | Best overall relevance |
Key Trade-Offs:
- Enrichment Depth vs. Cost: More skills provide richer data but increase processing cost
- Index Size vs. Query Performance: More fields enable more queries but slow down indexing
Reflection Question: Your search results show low relevance for natural language queries but work well for keyword searches. What feature would most improve results?