feat: add semantic search with pgvector (closes #68, #69, #70)

Issues resolved:
- #68: pgvector Setup
  * Added pgvector vector index migration for knowledge_embeddings
  * Vector index uses HNSW algorithm with cosine distance
  * Optimized for 1536-dimension OpenAI embeddings

- #69: Embedding Generation Pipeline
  * Created EmbeddingService with OpenAI integration
  * Automatic embedding generation on entry create/update
  * Batch processing endpoint for existing entries
  * Async generation to avoid blocking API responses
  * Content preparation with title weighting

- #70: Semantic Search API
  * POST /api/knowledge/search/semantic - pure vector search
  * POST /api/knowledge/search/hybrid - RRF combined search
  * POST /api/knowledge/embeddings/batch - batch generation
  * Comprehensive test coverage
  * Full documentation in docs/SEMANTIC_SEARCH.md

Technical details:
- Uses OpenAI text-embedding-3-small model (1536 dims)
- HNSW index for O(log n) similarity search
- Reciprocal Rank Fusion for hybrid search
- Graceful degradation when OpenAI not configured
- Async embedding generation for performance

Configuration:
- Added OPENAI_API_KEY to .env.example
- Optional feature - disabled if API key not set
- Falls back to keyword search in hybrid mode

apps/api/prisma/migrations/20260130002000_add_knowledge_embeddings_vector_index/migration.sql

@@ -0,0 +1,8 @@
+-- Add HNSW index for fast vector similarity search on knowledge_embeddings table
+-- Using cosine distance operator for semantic similarity
+-- Parameters: m=16 (max connections per layer), ef_construction=64 (build quality)
+
+CREATE INDEX IF NOT EXISTS knowledge_embeddings_embedding_idx
+ON knowledge_embeddings
+USING hnsw (embedding vector_cosine_ops)
+WITH (m = 16, ef_construction = 64);