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/src/knowledge/services/embedding.service.spec.ts

@@ -0,0 +1,115 @@
+import { describe, it, expect, beforeEach, vi } from "vitest";
+import { EmbeddingService } from "./embedding.service";
+import { PrismaService } from "../../prisma/prisma.service";
+
+describe("EmbeddingService", () => {
+  let service: EmbeddingService;
+  let prismaService: PrismaService;
+
+  beforeEach(() => {
+    prismaService = {
+      $executeRaw: vi.fn(),
+      knowledgeEmbedding: {
+        deleteMany: vi.fn(),
+      },
+    } as unknown as PrismaService;
+
+    service = new EmbeddingService(prismaService);
+  });
+
+  describe("isConfigured", () => {
+    it("should return false when OPENAI_API_KEY is not set", () => {
+      const originalEnv = process.env["OPENAI_API_KEY"];
+      delete process.env["OPENAI_API_KEY"];
+
+      expect(service.isConfigured()).toBe(false);
+
+      if (originalEnv) {
+        process.env["OPENAI_API_KEY"] = originalEnv;
+      }
+    });
+
+    it("should return true when OPENAI_API_KEY is set", () => {
+      const originalEnv = process.env["OPENAI_API_KEY"];
+      process.env["OPENAI_API_KEY"] = "test-key";
+
+      expect(service.isConfigured()).toBe(true);
+
+      if (originalEnv) {
+        process.env["OPENAI_API_KEY"] = originalEnv;
+      } else {
+        delete process.env["OPENAI_API_KEY"];
+      }
+    });
+  });
+
+  describe("prepareContentForEmbedding", () => {
+    it("should combine title and content with title weighting", () => {
+      const title = "Test Title";
+      const content = "Test content goes here";
+
+      const result = service.prepareContentForEmbedding(title, content);
+
+      expect(result).toContain(title);
+      expect(result).toContain(content);
+      // Title should appear twice for weighting
+      expect(result.split(title).length - 1).toBe(2);
+    });
+
+    it("should handle empty content", () => {
+      const title = "Test Title";
+      const content = "";
+
+      const result = service.prepareContentForEmbedding(title, content);
+
+      expect(result).toBe(`${title}\n\n${title}`);
+    });
+  });
+
+  describe("generateAndStoreEmbedding", () => {
+    it("should skip generation when not configured", async () => {
+      const originalEnv = process.env["OPENAI_API_KEY"];
+      delete process.env["OPENAI_API_KEY"];
+
+      await service.generateAndStoreEmbedding("test-id", "test content");
+
+      expect(prismaService.$executeRaw).not.toHaveBeenCalled();
+
+      if (originalEnv) {
+        process.env["OPENAI_API_KEY"] = originalEnv;
+      }
+    });
+  });
+
+  describe("deleteEmbedding", () => {
+    it("should delete embedding for entry", async () => {
+      const entryId = "test-entry-id";
+
+      await service.deleteEmbedding(entryId);
+
+      expect(prismaService.knowledgeEmbedding.deleteMany).toHaveBeenCalledWith({
+        where: { entryId },
+      });
+    });
+  });
+
+  describe("batchGenerateEmbeddings", () => {
+    it("should return 0 when not configured", async () => {
+      const originalEnv = process.env["OPENAI_API_KEY"];
+      delete process.env["OPENAI_API_KEY"];
+
+      const entries = [
+        { id: "1", content: "content 1" },
+        { id: "2", content: "content 2" },
+      ];
+
+      const result = await service.batchGenerateEmbeddings(entries);
+
+      expect(result).toBe(0);
+
+      if (originalEnv) {
+        process.env["OPENAI_API_KEY"] = originalEnv;
+      }
+    });
+  });
+});