feat(#69): implement embedding generation pipeline

Generate embeddings for knowledge entries using Ollama via BullMQ job queue.

Changes:
- Created OllamaEmbeddingService for Ollama-based embedding generation
- Set up BullMQ queue and processor for async embedding jobs
- Integrated queue into knowledge entry lifecycle (create/update)
- Added rate limiting (1 job/second) and retry logic (3 attempts)
- Added OLLAMA_EMBEDDING_MODEL environment variable configuration
- Implemented dimension normalization (padding/truncating to 1536 dimensions)
- Added graceful degradation when Ollama is unavailable

Test Coverage:
- All 31 embedding-related tests passing
- ollama-embedding.service.spec.ts: 13 tests
- embedding-queue.spec.ts: 6 tests
- embedding.processor.spec.ts: 5 tests
- Build and linting successful

Fixes #69

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

apps/api/src/app.module.ts

@@ -1,6 +1,7 @@
 import { Module } from "@nestjs/common";
 import { APP_INTERCEPTOR, APP_GUARD } from "@nestjs/core";
 import { ThrottlerModule } from "@nestjs/throttler";
+import { BullModule } from "@nestjs/bullmq";
 import { ThrottlerValkeyStorageService, ThrottlerApiKeyGuard } from "./common/throttler";
 import { AppController } from "./app.controller";
 import { AppService } from "./app.service";
@@ -50,6 +51,13 @@ import { CoordinatorIntegrationModule } from "./coordinator-integration/coordina
         };
       },
     }),
+    // BullMQ job queue configuration
+    BullModule.forRoot({
+      connection: {
+        host: process.env.VALKEY_HOST ?? "localhost",
+        port: parseInt(process.env.VALKEY_PORT ?? "6379", 10),
+      },
+    }),
     TelemetryModule,
     PrismaModule,
     DatabaseModule,