Semantic Search
Semantic Search lets you find posts using natural language queries, going beyond simple keyword matching.
How It Works
Posts are indexed using AI-generated embeddings (vector representations). When you search:
- Your query is converted to an embedding vector
- The system finds posts with similar vectors using cosine distance
- Results are ranked by semantic relevance
Search Modes
| Mode | Description |
|---|---|
| Semantic (default) | Vector-based similarity search |
| Full-text | Traditional keyword search |
You can switch modes using the searchEngine query parameter:
GET /api/posts?search=your+query
GET /api/posts?search=your+query&searchEngine=fulltext
When to Use Each Mode
- Semantic: Best for conceptual queries, finding related content, natural language questions
- Full-text: Best for exact keyword matches, specific terms, usernames