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

pnpm-lock.yaml

@@ -113,6 +113,9 @@ importers:
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+      openai:
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+        version: 6.17.0([email protected])([email protected])
       reflect-metadata:
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         version: 0.2.2
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     engines: {node: '>=18'}
 
+  [email protected]:
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+      zod: ^3.25 || ^4.0
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+      zod:
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+
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@@ -9134,6 +9149,11 @@ snapshots:
       is-inside-container: 1.0.0
       wsl-utils: 0.1.0
 
+  [email protected]([email protected])([email protected]):
+    optionalDependencies:
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+      zod: 4.3.6
+
   [email protected]:
     dependencies:
       deep-is: 0.1.4