feat: implement Tier 3 features — lane queue, credential redaction, token dashboard, xAI, Voyage AI

- Lane Queue: per-session FIFO queue in gateway replacing reject-when-busy (9 tests)
- Credential Redaction: redactConfig() expanded to cover 18+ secret fields (16 tests)
- Web UI Token Dashboard: system.tokenUsage endpoint + Usage page with summary cards
- xAI (Grok) Provider: OpenAI-compatible client with model pricing
- Voyage AI Embeddings: new embedding provider with configurable dimensions (5 tests)
- Update gap analysis: 90→95 match (70%→74%), Tier 3 section marked DONE
- Update state.json: test count 1001→1034, add tier3_completion entry

Total: 1034 tests passing across 85 files, typecheck clean

src/memory/embeddings.test.ts

@@ -5,6 +5,7 @@ import {
   GeminiEmbeddingProvider,
   OllamaEmbeddingProvider,
   LlamaCppEmbeddingProvider,
+  VoyageAIEmbeddingProvider,
 } from './embeddings.js';
 import type { EmbeddingConfig } from '../config/schema.js';
 
@@ -39,6 +40,11 @@ describe('createEmbeddingProvider', () => {
     expect(provider).toBeInstanceOf(LlamaCppEmbeddingProvider);
   });
 
+  it('creates Voyage provider', () => {
+    const provider = createEmbeddingProvider({ ...baseConfig, provider: 'voyage' });
+    expect(provider).toBeInstanceOf(VoyageAIEmbeddingProvider);
+  });
+
   it('throws on unknown provider', () => {
     expect(() => createEmbeddingProvider({ ...baseConfig, provider: 'unknown' as never })).toThrow('Unknown embedding provider');
   });
@@ -157,3 +163,67 @@ describe('LlamaCppEmbeddingProvider', () => {
     expect(provider.dimensions).toBe(768);
   });
 });
+
+describe('VoyageAIEmbeddingProvider', () => {
+  it('defaults to 1024 dimensions', () => {
+    const config: EmbeddingConfig = {
+      enabled: true,
+      provider: 'voyage',
+      model: 'voyage-3',
+      chunk_size: 512,
+      chunk_overlap: 50,
+      top_k: 5,
+      hybrid_weight: 0.7,
+    };
+    const provider = new VoyageAIEmbeddingProvider(config);
+    expect(provider.dimensions).toBe(1024);
+  });
+
+  it('reports configured dimensions', () => {
+    const config: EmbeddingConfig = {
+      enabled: true,
+      provider: 'voyage',
+      model: 'voyage-3-lite',
+      dimensions: 512,
+      chunk_size: 512,
+      chunk_overlap: 50,
+      top_k: 5,
+      hybrid_weight: 0.7,
+    };
+    const provider = new VoyageAIEmbeddingProvider(config);
+    expect(provider.dimensions).toBe(512);
+  });
+
+  it('uses custom endpoint if provided', () => {
+    const config: EmbeddingConfig = {
+      enabled: true,
+      provider: 'voyage',
+      model: 'voyage-3',
+      endpoint: 'https://custom.proxy.example.com/v1',
+      api_key: 'test-key',
+      chunk_size: 512,
+      chunk_overlap: 50,
+      top_k: 5,
+      hybrid_weight: 0.7,
+    };
+    // Should not throw when constructing with custom endpoint
+    const provider = new VoyageAIEmbeddingProvider(config);
+    expect(provider.dimensions).toBe(1024);
+  });
+
+  it('uses api_key from config', () => {
+    const config: EmbeddingConfig = {
+      enabled: true,
+      provider: 'voyage',
+      model: 'voyage-3',
+      api_key: 'voy-test-key-123',
+      chunk_size: 512,
+      chunk_overlap: 50,
+      top_k: 5,
+      hybrid_weight: 0.7,
+    };
+    // Should construct without error when api_key is provided
+    const provider = new VoyageAIEmbeddingProvider(config);
+    expect(provider.dimensions).toBe(1024);
+  });
+});

src/memory/embeddings.ts

@@ -159,6 +159,48 @@ export class LlamaCppEmbeddingProvider implements EmbeddingProvider {
   }
 }
 
+// ---------------------------------------------------------------------------
+// Voyage AI
+// ---------------------------------------------------------------------------
+
+export class VoyageAIEmbeddingProvider implements EmbeddingProvider {
+  private _model: string;
+  private _dimensions: number;
+  private _apiKey: string;
+  private _endpoint: string;
+
+  constructor(config: EmbeddingConfig) {
+    this._model = config.model;
+    this._dimensions = config.dimensions ?? 1024;
+    this._apiKey = config.api_key ?? process.env.VOYAGE_API_KEY ?? '';
+    this._endpoint = config.endpoint ?? 'https://api.voyageai.com/v1';
+  }
+
+  get dimensions(): number {
+    return this._dimensions;
+  }
+
+  async embed(texts: string[]): Promise<number[][]> {
+    // Voyage AI's API is OpenAI-compatible for embeddings
+    const { default: OpenAI } = await import('openai');
+    const client = new OpenAI({
+      apiKey: this._apiKey,
+      baseURL: this._endpoint,
+    });
+
+    const response = await client.embeddings.create({
+      model: this._model,
+      input: texts,
+      // Note: Voyage AI does not support the `dimensions` parameter.
+      // Dimensions are model-dependent (voyage-3: 1024, voyage-3-lite: 512, voyage-code-3: 1024).
+    });
+
+    // Sort by index to ensure order matches input
+    const sorted = response.data.sort((a, b) => a.index - b.index);
+    return sorted.map((item) => item.embedding);
+  }
+}
+
 // ---------------------------------------------------------------------------
 // Factory
 // ---------------------------------------------------------------------------
@@ -176,6 +218,8 @@ export function createEmbeddingProvider(config: EmbeddingConfig): EmbeddingProvi
       return new OllamaEmbeddingProvider(config);
     case 'llamacpp':
       return new LlamaCppEmbeddingProvider(config);
+    case 'voyage':
+      return new VoyageAIEmbeddingProvider(config);
     default:
       throw new Error(`Unknown embedding provider: ${(config as Record<string, unknown>).provider}`);
   }