fix(api-proxy): add embedding model pricing to resolve unknown model rejection

containers/api-proxy/ai-credits-pricing.js

@@ -26,6 +26,9 @@ module.exports = Object.freeze({
   'gemini-3.1-flash':  { input: 0.75,  cachedInput: 0.075, cacheWrite: null, output: 4.50 },
   'gemini-3.1-pro':    { input: 2.00,  cachedInput: 0.20,  cacheWrite: null, output: 12.00 },
   'gemini-3.5-flash':  { input: 1.50,  cachedInput: 0.15,  cacheWrite: null, output: 9.00 },
-  'mai-code-1-flash':  { input: 0.75,  cachedInput: 0.075, cacheWrite: null, output: 4.50 },
-  'raptor-mini':       { input: 0.25,  cachedInput: 0.025, cacheWrite: null, output: 2.00 },
+  'mai-code-1-flash':           { input: 0.75,  cachedInput: 0.075, cacheWrite: null, output: 4.50 },
+  'raptor-mini':                { input: 0.25,  cachedInput: 0.025, cacheWrite: null, output: 2.00 },
+  // Embedding models (output tokens are not produced; output cost is 0)
+  'text-embedding-3-small':     { input: 0.02,  cachedInput: 0,     cacheWrite: null, output: 0.00 },
+  'text-embedding-ada-002':     { input: 0.10,  cachedInput: 0,     cacheWrite: null, output: 0.00 },
 });

containers/api-proxy/guards/ai-credits-guard.test.js

@@ -121,6 +121,40 @@ describe('ai-credits-guard', () => {
     }));
   });
 
+  it('resolves text-embedding-3-small with input-only pricing', () => {
+    const usage = applyAiCreditsUsage({ input_tokens: 1_000_000, output_tokens: 500 }, 'text-embedding-3-small');
+    // input: 1M × $0.02/M / 10_000 denominator = 2.0 AIC
+    // output: 500 tokens × $0.00/M = 0 AIC (embedding models produce no output cost)
+    expect(usage).not.toBeNull();
+    expect(usage.aiCreditsThisResponse).toBeCloseTo(2.0, 5);
+    expect(usage.outputCreditsThisResponse).toBe(0);
+  });
+
+  it('resolves text-embedding-3-small-inference via prefix match', () => {
+    const usage = applyAiCreditsUsage({ input_tokens: 1_000_000, output_tokens: 0 }, 'text-embedding-3-small-inference');
+    // Same pricing as text-embedding-3-small via prefix match
+    expect(usage).not.toBeNull();
+    expect(usage.aiCreditsThisResponse).toBeCloseTo(2.0, 5);
+  });
+
+  it('resolves text-embedding-ada-002 with input-only pricing', () => {
+    const usage = applyAiCreditsUsage({ input_tokens: 1_000_000, output_tokens: 1000 }, 'text-embedding-ada-002');
+    // input: 1M × $0.10/M / 10_000 denominator = 10.0 AIC
+    // output: 1000 tokens × $0.00/M = 0 AIC (embedding models produce no output cost)
+    expect(usage).not.toBeNull();
+    expect(usage.aiCreditsThisResponse).toBeCloseTo(10.0, 5);
+    expect(usage.outputCreditsThisResponse).toBe(0);
+  });
+
+  it('does not reject embedding models when maxAiCredits is active', () => {
+    process.env.AWF_MAX_AI_CREDITS = '10';
+    resetAiCreditsGuardForTests();
+
+    expect(checkUnknownModelRejection('text-embedding-3-small')).toBeNull();
+    expect(checkUnknownModelRejection('text-embedding-3-small-inference')).toBeNull();
+    expect(checkUnknownModelRejection('text-embedding-ada-002')).toBeNull();
+  });
+
   it('uses bundled models.dev pricing for known catalog models', () => {
     const usage = applyAiCreditsUsage({
       input_tokens: 100,