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/search.controller.ts

@@ -1,9 +1,10 @@
-import { Controller, Get, Query, UseGuards } from "@nestjs/common";
+import { Controller, Get, Post, Body, Query, UseGuards } from "@nestjs/common";
 import { SearchService, PaginatedSearchResults } from "./services/search.service";
 import { SearchQueryDto, TagSearchDto, RecentEntriesDto } from "./dto";
 import { AuthGuard } from "../auth/guards/auth.guard";
 import { WorkspaceGuard, PermissionGuard } from "../common/guards";
 import { Workspace, Permission, RequirePermission } from "../common/decorators";
+import { EntryStatus } from "@prisma/client";
 import type {
   PaginatedEntries,
   KnowledgeEntryWithTags,
@@ -97,4 +98,55 @@ export class SearchController {
       count: entries.length,
     };
   }
+
+  /**
+   * POST /api/knowledge/search/semantic
+   * Semantic search using vector similarity
+   * Requires: Any workspace member, OpenAI API key configured
+   *
+   * @body query - The search query string (required)
+   * @body status - Filter by entry status (optional)
+   * @query page - Page number (default: 1)
+   * @query limit - Results per page (default: 20, max: 100)
+   */
+  @Post("semantic")
+  @RequirePermission(Permission.WORKSPACE_ANY)
+  async semanticSearch(
+    @Workspace() workspaceId: string,
+    @Body() body: { query: string; status?: EntryStatus },
+    @Query("page") page?: number,
+    @Query("limit") limit?: number
+  ): Promise<PaginatedSearchResults> {
+    return this.searchService.semanticSearch(body.query, workspaceId, {
+      status: body.status,
+      page,
+      limit,
+    });
+  }
+
+  /**
+   * POST /api/knowledge/search/hybrid
+   * Hybrid search combining vector similarity and full-text search
+   * Uses Reciprocal Rank Fusion to merge results
+   * Requires: Any workspace member
+   *
+   * @body query - The search query string (required)
+   * @body status - Filter by entry status (optional)
+   * @query page - Page number (default: 1)
+   * @query limit - Results per page (default: 20, max: 100)
+   */
+  @Post("hybrid")
+  @RequirePermission(Permission.WORKSPACE_ANY)
+  async hybridSearch(
+    @Workspace() workspaceId: string,
+    @Body() body: { query: string; status?: EntryStatus },
+    @Query("page") page?: number,
+    @Query("limit") limit?: number
+  ): Promise<PaginatedSearchResults> {
+    return this.searchService.hybridSearch(body.query, workspaceId, {
+      status: body.status,
+      page,
+      limit,
+    });
+  }
 }