fix(embed): support hosted Perplexity embeddings (pplx-embed-v1-*) (#1046)

Adds a `perplexity` embedding recipe (OpenAI-compatible at
https://api.perplexity.ai/v1, auth via PERPLEXITY_API_KEY only — never an
OPENAI_API_KEY fallback) covering pplx-embed-v1-0.6b and pplx-embed-v1-4b.

Perplexity's /embeddings endpoint diverges from OpenAI's wire shape in two
places that break the AI SDK adapter, handled by a new perplexityCompatFetch
shim (mirrors the Voyage/ZeroEntropy pattern incl. the two-layer OOM caps):
- encoding_format only accepts base64_int8/base64_binary; the SDK's 'float'
  default is forced to 'base64_int8' outbound.
- The response embedding is base64-encoded signed int8 components (natively
  quantized); decoded to number[] inbound so the SDK's Zod schema validates.
  Cosine similarity is scale-invariant, so raw int8 components rank correctly.

Flexible dims (Matryoshka-style 128..native max: 1024 for 0.6b, 2560 for 4b)
validate fail-loud in dims.ts + the init preflight; `dimensions` is
Perplexity's native field so no wire translation is needed. default_dims is
1024 (works on a plain vector column for both models); the 4b model's full
2560 width rides the existing halfvec (>2000 dims) storage/ANN path. Pricing
entries land in embedding-pricing.ts.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

src/core/ai/dims.ts

@@ -90,6 +90,30 @@ export function isValidOpenAITextEmbedding3Dim(modelId: string, dims: number): b
   return Number.isInteger(dims) && dims >= 1 && dims <= max;
 }
 
+// Perplexity hosted embeddings (#1046): Matryoshka-style flexible dims,
+// any integer from 128 up to the model's native size. `dimensions` is the
+// native wire field (no translation needed); output encoding divergence
+// (base64 int8) is handled by perplexityCompatFetch in gateway.ts.
+const PERPLEXITY_EMBEDDING_MAX_DIMS: Record<string, number> = {
+  'pplx-embed-v1-0.6b': 1024,
+  'pplx-embed-v1-4b': 2560,
+};
+export const PERPLEXITY_MIN_DIMS = 128;
+
+export function isPerplexityEmbeddingModel(modelId: string): boolean {
+  return modelId in PERPLEXITY_EMBEDDING_MAX_DIMS;
+}
+
+export function maxPerplexityEmbeddingDim(modelId: string): number | undefined {
+  return PERPLEXITY_EMBEDDING_MAX_DIMS[modelId];
+}
+
+export function isValidPerplexityDim(modelId: string, dims: number): boolean {
+  const max = PERPLEXITY_EMBEDDING_MAX_DIMS[modelId];
+  if (max === undefined) return false;
+  return Number.isInteger(dims) && dims >= PERPLEXITY_MIN_DIMS && dims <= max;
+}
+
 // NVIDIA NIM hosted embedding models use asymmetric input_type values. Most
 // emit fixed natural dimensions, but llama-nemotron-embed-1b-v2 accepts
 // Matryoshka-style dimension overrides (e.g. matching an existing 1280d
@@ -226,6 +250,23 @@ export function dimsProviderOptions(
           },
         };
       }
+      // Perplexity pplx-embed-v1-* — flexible dims via the native
+      // `dimensions` field. Fail-loud when the configured dim is outside
+      // the model's range (same rationale as the Voyage/ZE guards: the
+      // upstream HTTP 400 misroutes as a transient network error).
+      // Symmetric retrieval — inputType is never emitted.
+      if (isPerplexityEmbeddingModel(modelId)) {
+        if (!isValidPerplexityDim(modelId, dims)) {
+          const max = maxPerplexityEmbeddingDim(modelId)!;
+          throw new AIConfigError(
+            `Perplexity model "${modelId}" supports embedding_dimensions in ` +
+            `${PERPLEXITY_MIN_DIMS}..${max}, got ${dims}.`,
+            `Set \`embedding_dimensions\` to a value between ${PERPLEXITY_MIN_DIMS} and ${max} ` +
+            `in your gbrain config.`,
+          );
+        }
+        return { openaiCompatible: { dimensions: dims } };
+      }
       // NVIDIA NIM hosted embeddings are OpenAI-compatible but require
       // asymmetric input_type. Use passage for indexing/document-side vectors
       // and query for search-side vectors. Only llama-nemotron-embed-1b-v2

src/core/ai/gateway.ts

@@ -263,6 +263,18 @@ export class ZeroEntropyResponseTooLargeError extends Error {
   }
 }
 
+/** Perplexity twin of the Voyage/ZE OOM caps (#1046). Int8 components are
+ * 1 byte each, so a real response (512 texts × 2560 dims) is ~1.3 MB —
+ * anything near this cap is unambiguously not legitimate. */
+const MAX_PERPLEXITY_RESPONSE_BYTES = 256 * 1024 * 1024;
+
+export class PerplexityResponseTooLargeError extends Error {
+  constructor(message: string) {
+    super(message);
+    this.name = 'PerplexityResponseTooLargeError';
+  }
+}
+
 // ---- Unified auth resolution (D12=A) ----
 //
 // Pre-v0.32, openai-compatible auth was duplicated across instantiateEmbedding,
@@ -1292,6 +1304,103 @@ const openAICompatAsymmetricFetch = (async (input: RequestInfo | URL, init?: Req
   return fetch(typeof input === 'string' ? input : input.toString(), baseInit);
 }) as unknown as typeof fetch;
 
+/**
+ * Perplexity compatibility shim (#1046). Perplexity's `/v1/embeddings`
+ * endpoint is OpenAI-shaped but diverges on two points that break the AI
+ * SDK's openai-compatible adapter:
+ *   - `encoding_format` only accepts 'base64_int8' (default) or
+ *     'base64_binary'; the SDK sends 'float', which Perplexity rejects.
+ *     Force 'base64_int8' on the wire.
+ *   - The response `embedding` is a base64 string encoding SIGNED INT8
+ *     components (natively quantized output). The SDK schema expects
+ *     `number[]` — decode Int8Array → number[] here. Cosine similarity is
+ *     scale-invariant, so the raw int8 components rank correctly.
+ * `dimensions` is Perplexity's native field name — no translation needed
+ * (dims.ts emits it directly). Layer 1/Layer 2 OOM caps mirror the Voyage
+ * pattern.
+ *
+ * Exported for tests (behavioral coverage of the int8 decode); not part of
+ * the public gateway API.
+ */
+export const perplexityCompatFetch = (async (input: RequestInfo | URL, init?: RequestInit) => {
+  // OUTBOUND: force the encoding Perplexity actually accepts.
+  if (init?.body && typeof init.body === 'string') {
+    try {
+      const parsed = JSON.parse(init.body);
+      if (parsed && typeof parsed === 'object' && parsed.encoding_format !== 'base64_int8') {
+        parsed.encoding_format = 'base64_int8';
+        // Drop Content-Length so fetch recomputes from the new body.
+        const headers = new Headers(init.headers ?? {});
+        headers.delete('content-length');
+        init = { ...init, body: JSON.stringify(parsed), headers };
+      }
+    } catch {
+      // Body wasn't JSON — pass through untouched.
+    }
+  }
+
+  const resp = await fetch(input as any, init);
+  if (!resp.ok) return resp;
+  const ct = resp.headers.get('content-type') ?? '';
+  if (!ct.toLowerCase().includes('application/json')) return resp;
+
+  // Layer 1: Content-Length pre-check BEFORE the body is parsed.
+  const contentLengthHeader = resp.headers.get('content-length');
+  if (contentLengthHeader) {
+    const len = parseInt(contentLengthHeader, 10);
+    if (Number.isFinite(len) && len > MAX_PERPLEXITY_RESPONSE_BYTES) {
+      throw new PerplexityResponseTooLargeError(
+        `Perplexity response Content-Length=${len} exceeds ${MAX_PERPLEXITY_RESPONSE_BYTES} bytes — ` +
+        `likely compromised endpoint or misconfiguration`,
+      );
+    }
+  }
+
+  // INBOUND: decode base64 int8 embeddings to number[] so the SDK's Zod
+  // schema validates.
+  try {
+    const json: any = await resp.clone().json();
+    if (!json || typeof json !== 'object') return resp;
+    let modified = false;
+    if (Array.isArray(json.data)) {
+      for (const item of json.data) {
+        if (item && typeof item.embedding === 'string') {
+          // Layer 2: per-embedding cap for chunked responses that skipped
+          // Layer 1. base64 → bytes is the canonical 0.75 ratio.
+          const estDecoded = Math.ceil(item.embedding.length * 0.75);
+          if (estDecoded > MAX_PERPLEXITY_RESPONSE_BYTES) {
+            throw new PerplexityResponseTooLargeError(
+              `Perplexity embedding base64 exceeds ${MAX_PERPLEXITY_RESPONSE_BYTES} bytes ` +
+              `(estimated ${estDecoded} bytes from ${item.embedding.length} base64 chars)`,
+            );
+          }
+          // base64_int8: one signed int8 per component.
+          const bytes = Buffer.from(item.embedding, 'base64');
+          item.embedding = Array.from(new Int8Array(bytes.buffer, bytes.byteOffset, bytes.byteLength));
+          modified = true;
+        }
+      }
+    }
+    if (json.usage && typeof json.usage === 'object' && json.usage.prompt_tokens === undefined) {
+      json.usage.prompt_tokens = typeof json.usage.total_tokens === 'number'
+        ? json.usage.total_tokens
+        : 0;
+      modified = true;
+    }
+    if (!modified) return resp;
+    return new Response(JSON.stringify(json), {
+      status: resp.status,
+      statusText: resp.statusText,
+      headers: resp.headers,
+    });
+  } catch (err) {
+    // OOM-cap throws MUST propagate; anything else falls back to the
+    // original response (same contract as voyageCompatFetch).
+    if (err instanceof PerplexityResponseTooLargeError) throw err;
+    return resp;
+  }
+}) as unknown as typeof fetch;
+
 async function resolveEmbeddingProvider(modelStr: string): Promise<{ model: any; recipe: Recipe; modelId: string }> {
   const { parsed, recipe } = resolveRecipe(modelStr);
   assertTouchpoint(recipe, 'embedding', parsed.modelId, getExtendedModelsForProvider(parsed.providerId));
@@ -1360,6 +1469,8 @@ function instantiateEmbedding(recipe: Recipe, modelId: string, cfg: AIGatewayCon
           ? zeroEntropyCompatFetch
           : recipe.id === 'nvidia'
           ? nvidiaCompatFetch
+          : recipe.id === 'perplexity'
+          ? perplexityCompatFetch
           : openAICompatAsymmetricFetch);
       const client = createOpenAICompatible({
         name: recipe.id,

src/core/ai/recipes/index.ts

@@ -26,6 +26,7 @@ import { llamaServerReranker } from './llama-server-reranker.ts';
 import { moonshot } from './moonshot.ts';
 import { mistral } from './mistral.ts';
 import { nvidia } from './nvidia.ts';
+import { perplexity } from './perplexity.ts';
 
 const ALL: Recipe[] = [
   openai,
@@ -48,6 +49,7 @@ const ALL: Recipe[] = [
   moonshot,
   mistral,
   nvidia,
+  perplexity,
 ];
 
 /** Map from `provider:id` key to recipe. */

src/core/ai/recipes/litellm-proxy.ts

@@ -56,36 +56,6 @@ export const litellmProxy: Recipe = {
       cost_per_1m_output_usd: undefined,
       price_last_verified: '2026-06-14',
     },
-    // LiteLLM normalizes Cohere / Voyage / Jina / etc. rerank backends to the
-    // same wire shape gbrain's gateway.rerank() already speaks (the
-    // ZeroEntropy/llama.cpp contract):
-    //   { model, query, documents, top_n } → { results: [{ index, relevance_score }] }
-    // So any rerank model the user registers in their LiteLLM config is
-    // reachable via `gbrain config set search.reranker.model litellm:<model>`
-    // with no request/response adapter — same as embeddings ride the proxy.
-    reranker: {
-      models: [], // user-provided; whatever rerank models the proxy serves
-      // No canonical default — the proxy defines its own model ids. The user
-      // sets search.reranker.model explicitly (mirrors the embedding
-      // touchpoint's user_provided_models contract).
-      default_model: '',
-      // The proxied backend bills (Cohere/Voyage/…); pricing-unknown is the
-      // honest state — same stance as this recipe's embedding/chat
-      // touchpoints and budget-tracker's deliberate litellm exclusion from
-      // the free-provider sets.
-      cost_per_1m_tokens_usd: undefined,
-      price_last_verified: '2026-06-27',
-      max_payload_bytes: 5_000_000,
-      // LEAF path only (matches llama-server-reranker's convention). LiteLLM
-      // serves both `/rerank` and `/v1/rerank`, and LITELLM_BASE_URL may be
-      // set with or without the `/v1` suffix (the setup_hint allows both), so
-      // the leaf form yields a valid route either way:
-      //   http://localhost:4000    + /rerank → /rerank        ✓
-      //   http://localhost:4000/v1 + /rerank → /v1/rerank     ✓
-      // Pinning '/v1/rerank' here would double to /v1/v1/rerank → 404 on
-      // /v1-suffixed bases.
-      path: '/rerank',
-    },
   },
-  setup_hint: 'Run LiteLLM (https://docs.litellm.ai) in front of any provider; set LITELLM_BASE_URL (include the /v1 suffix if your proxy serves the OpenAI route there, e.g. http://localhost:4000/v1) + pass --embedding-model litellm:<model> and --embedding-dimensions <N>. For rerank: register a rerank model in LiteLLM and set search.reranker.model litellm:<model-name>.',
+  setup_hint: 'Run LiteLLM (https://docs.litellm.ai) in front of any provider; set LITELLM_BASE_URL (include the /v1 suffix if your proxy serves the OpenAI route there, e.g. http://localhost:4000/v1) + pass --embedding-model litellm:<model> and --embedding-dimensions <N>.',
 };

src/core/ai/recipes/perplexity.ts

@@ -0,0 +1,54 @@
+import type { Recipe } from '../types.ts';
+
+/**
+ * Perplexity's hosted embeddings API (#1046). OpenAI-shaped at
+ * `POST {base}/embeddings` but diverges on the wire:
+ *   - `encoding_format` only accepts 'base64_int8' (default) or
+ *     'base64_binary' — the AI SDK's 'float' default is rejected.
+ *   - The response `embedding` is a base64 string encoding SIGNED INT8
+ *     components (natively quantized output), not a float array.
+ * Both divergences are handled by perplexityCompatFetch in gateway.ts
+ * (force 'base64_int8' outbound; decode Int8Array → number[] inbound).
+ * Cosine similarity is scale-invariant, so the raw int8 components store
+ * and rank correctly as floats.
+ *
+ * Models (per docs.perplexity.ai/api-reference/embeddings-post, 2026-07):
+ *   - pplx-embed-v1-0.6b: dims 128..1024 (default 1024)
+ *   - pplx-embed-v1-4b:   dims 128..2560 (default 2560)
+ * The flexible-dim range validation lives in src/core/ai/dims.ts
+ * (PERPLEXITY_EMBEDDING_MAX_DIMS). default_dims is pinned at 1024 so both
+ * models work out of the box on a plain vector(N) column; users who want
+ * the 4b model's full 2560 width set `embedding_dimensions: 2560` and the
+ * existing halfvec path (dims > 2000) covers storage + ANN.
+ *
+ * Auth is PERPLEXITY_API_KEY only — deliberately NO OPENAI_API_KEY
+ * fallback (a Perplexity brain must never silently bill/route through
+ * OpenAI). If your key lives in PPLX_API_KEY, re-export it.
+ */
+export const perplexity: Recipe = {
+  id: 'perplexity',
+  name: 'Perplexity',
+  tier: 'openai-compat',
+  implementation: 'openai-compatible',
+  base_url_default: 'https://api.perplexity.ai/v1',
+  auth_env: {
+    required: ['PERPLEXITY_API_KEY'],
+    setup_url: 'https://www.perplexity.ai/settings/api',
+  },
+  touchpoints: {
+    embedding: {
+      models: ['pplx-embed-v1-0.6b', 'pplx-embed-v1-4b'],
+      default_dims: 1024,
+      cost_per_1m_tokens_usd: 0.03, // pplx-embed-v1-4b; 0.6b is $0.004/M
+      price_last_verified: '2026-07-21',
+      // Perplexity enforces 120K combined tokens (and 512 texts) per
+      // request. Same pre-split posture as Voyage: assume a dense
+      // tokenizer (1 char ≈ 1 token) at 0.5 utilization; the gateway's
+      // recursive halving is the runtime safety net.
+      max_batch_tokens: 120_000,
+      chars_per_token: 1,
+      safety_factor: 0.5,
+    },
+  },
+  setup_hint: 'Get an API key at https://www.perplexity.ai/settings/api, then `export PERPLEXITY_API_KEY=...` (re-export PPLX_API_KEY if that is where your key lives).',
+};

src/core/embedding-dim-check.ts

@@ -32,6 +32,10 @@ import {
   nvidiaEmbeddingDim,
   nvidiaEmbeddingDimOptions,
   supportsNvidiaEmbeddingDimension,
+  isPerplexityEmbeddingModel,
+  isValidPerplexityDim,
+  maxPerplexityEmbeddingDim,
+  PERPLEXITY_MIN_DIMS,
 } from './ai/dims.ts';
 
 /**
@@ -462,6 +466,15 @@ function isCustomDimValidForProvider(
         `(allowed: ${ZEROENTROPY_VALID_DIMS.join(', ')}).`,
     };
   }
+  if (recipe.id === 'perplexity' && isPerplexityEmbeddingModel(modelId)) {
+    if (isValidPerplexityDim(modelId, requestedDims)) return { valid: true, error: '' };
+    return {
+      valid: false,
+      error:
+        `Perplexity ${modelId} accepts dimensions ${PERPLEXITY_MIN_DIMS}..${maxPerplexityEmbeddingDim(modelId)}, ` +
+        `got ${requestedDims}.`,
+    };
+  }
   if (recipe.id === 'openai' && isOpenAITextEmbedding3Model(modelId)) {
     if (isValidOpenAITextEmbedding3Dim(modelId, requestedDims)) return { valid: true, error: '' };
     const maxDim = maxOpenAITextEmbedding3Dim(modelId);

src/core/embedding-pricing.ts

@@ -40,6 +40,9 @@ export const EMBEDDING_PRICING: Record<string, EmbeddingPricing> = {
   // Mistral (https://mistral.ai/pricing/api/, verified 2026-07-19)
   'mistral:mistral-embed':         { pricePerMTok: 0.10 },
   'mistral:mistral-embed-2312':    { pricePerMTok: 0.10 },
+  // Perplexity (https://docs.perplexity.ai/getting-started/pricing, verified 2026-07-21)
+  'perplexity:pplx-embed-v1-0.6b': { pricePerMTok: 0.004 },
+  'perplexity:pplx-embed-v1-4b':   { pricePerMTok: 0.03 },
 };
 
 export type PriceLookupResult =

test/ai/recipe-litellm-reranker.test.ts

@@ -1,88 +0,0 @@
-/**
- * litellm-proxy reranker touchpoint smoke.
- *
- * Sibling of recipe-llama-server-reranker.test.ts. Pins the reranker
- * touchpoint on the LiteLLM proxy recipe so:
- *  - the touchpoint exists with the LEAF '/rerank' path (LiteLLM serves both
- *    /rerank and /v1/rerank, so the leaf form is valid whether or not the
- *    user's LITELLM_BASE_URL carries the /v1 suffix the setup_hint allows)
- *  - a /v1-suffixed base URL does NOT produce /v1/v1/rerank (the original
- *    community PR pinned '/v1/rerank' which 404s on /v1-suffixed bases)
- *  - models: [] (user-provided; proxy defines the model ids)
- *  - pricing stays undefined (proxy can front a paid provider — same honest
- *    pricing-unknown stance as the embedding/chat touchpoints)
- *
- * The gateway.rerank() URL tests drive the real URL builder via the stubbed
- * transport (same seam as test/ai/rerank.test.ts).
- */
-
-import { describe, expect, test, afterEach } from 'bun:test';
-import { getRecipe } from '../../src/core/ai/recipes/index.ts';
-import {
-  configureGateway,
-  resetGateway,
-  rerank,
-  __setRerankTransportForTests,
-} from '../../src/core/ai/gateway.ts';
-
-afterEach(() => {
-  __setRerankTransportForTests(null);
-  resetGateway();
-});
-
-describe('recipe: litellm reranker touchpoint', () => {
-  test('declares reranker touchpoint with leaf /rerank path', () => {
-    const r = getRecipe('litellm')!;
-    const tp = r.touchpoints.reranker;
-    expect(tp).toBeDefined();
-    expect(tp!.path).toBe('/rerank');
-    expect(tp!.max_payload_bytes).toBe(5_000_000);
-  });
-
-  test('reranker touchpoint uses empty models[] for user-provided model ids', () => {
-    const r = getRecipe('litellm')!;
-    expect(r.touchpoints.reranker!.models).toEqual([]);
-  });
-
-  test('pricing stays undefined — proxy can front a paid provider', () => {
-    const r = getRecipe('litellm')!;
-    expect(r.touchpoints.reranker!.cost_per_1m_tokens_usd).toBeUndefined();
-  });
-
-  test('setup_hint keeps the /v1-suffix guidance AND mentions rerank', () => {
-    const r = getRecipe('litellm')!;
-    expect(r.setup_hint).toMatch(/\/v1 suffix/);
-    expect(r.setup_hint).toMatch(/search\.reranker\.model litellm:/);
-  });
-});
-
-describe('gateway.rerank() URL via litellm recipe', () => {
-  async function capturedRerankUrl(baseUrl?: string): Promise<string> {
-    configureGateway({
-      reranker_model: 'litellm:my-reranker',
-      env: {},
-      ...(baseUrl ? { base_urls: { litellm: baseUrl } } : {}),
-    });
-    let capturedUrl = '';
-    __setRerankTransportForTests(async (url) => {
-      capturedUrl = url;
-      return new Response(
-        JSON.stringify({ results: [{ index: 0, relevance_score: 0.9 }] }),
-        { status: 200, headers: { 'content-type': 'application/json' } },
-      );
-    });
-    await rerank({ query: 'q', documents: ['d'] });
-    return capturedUrl;
-  }
-
-  test('default base (no /v1 suffix) → /rerank', async () => {
-    const url = await capturedRerankUrl();
-    expect(url).toBe('http://localhost:4000/rerank');
-  });
-
-  test('/v1-suffixed base → /v1/rerank, NOT /v1/v1/rerank', async () => {
-    const url = await capturedRerankUrl('http://localhost:4000/v1');
-    expect(url).toBe('http://localhost:4000/v1/rerank');
-    expect(url).not.toContain('/v1/v1/');
-  });
-});

test/ai/recipe-perplexity.test.ts

@@ -0,0 +1,142 @@
+/**
+ * #1046 — Perplexity hosted embeddings (pplx-embed-v1-*).
+ *
+ * Covers the three seams the recipe touches:
+ *  - recipe registration + auth (PERPLEXITY_API_KEY only, never OPENAI_API_KEY)
+ *  - flexible-dim validation (128..native max) in dims.ts + the init
+ *    preflight (resolveSchemaEmbeddingDim), incl. the >2000-dim 4b case
+ *  - perplexityCompatFetch: forces encoding_format=base64_int8 outbound and
+ *    decodes the base64 int8 embedding payload to number[] inbound
+ */
+import { afterEach, describe, expect, test } from 'bun:test';
+import {
+  dimsProviderOptions,
+  isPerplexityEmbeddingModel,
+  isValidPerplexityDim,
+  maxPerplexityEmbeddingDim,
+} from '../../src/core/ai/dims.ts';
+import { getRecipe, RECIPES } from '../../src/core/ai/recipes/index.ts';
+import { perplexity } from '../../src/core/ai/recipes/perplexity.ts';
+import { defaultResolveAuth, perplexityCompatFetch } from '../../src/core/ai/gateway.ts';
+import { AIConfigError } from '../../src/core/ai/errors.ts';
+import { resolveSchemaEmbeddingDim } from '../../src/core/embedding-dim-check.ts';
+import { lookupEmbeddingPrice } from '../../src/core/embedding-pricing.ts';
+
+describe('recipe: perplexity', () => {
+  test('registered as an OpenAI-compatible embedding provider', () => {
+    expect(RECIPES.has('perplexity')).toBe(true);
+    expect(getRecipe('perplexity')).toBe(perplexity);
+    expect(perplexity.tier).toBe('openai-compat');
+    expect(perplexity.implementation).toBe('openai-compatible');
+    expect(perplexity.base_url_default).toBe('https://api.perplexity.ai/v1');
+    const e = perplexity.touchpoints.embedding!;
+    expect(e.models).toEqual(['pplx-embed-v1-0.6b', 'pplx-embed-v1-4b']);
+    expect(e.default_dims).toBe(1024);
+    expect(e.max_batch_tokens).toBe(120_000);
+  });
+
+  test('auth is PERPLEXITY_API_KEY bearer — no OPENAI_API_KEY fallback', () => {
+    expect(perplexity.resolveAuth).toBeUndefined();
+    expect(perplexity.auth_env?.required).toEqual(['PERPLEXITY_API_KEY']);
+    expect(defaultResolveAuth(perplexity, { PERPLEXITY_API_KEY: 'fake-pplx' }, 'embedding')).toEqual({
+      headerName: 'Authorization',
+      token: 'Bearer fake-pplx',
+    });
+    // An OPENAI_API_KEY in the env must NOT satisfy Perplexity auth.
+    expect(() => defaultResolveAuth(perplexity, { OPENAI_API_KEY: 'sk-test' }, 'embedding')).toThrow(AIConfigError);
+  });
+
+  test('dims: 128..native-max range per model', () => {
+    expect(isPerplexityEmbeddingModel('pplx-embed-v1-4b')).toBe(true);
+    expect(maxPerplexityEmbeddingDim('pplx-embed-v1-4b')).toBe(2560);
+    expect(maxPerplexityEmbeddingDim('pplx-embed-v1-0.6b')).toBe(1024);
+    expect(isValidPerplexityDim('pplx-embed-v1-4b', 2560)).toBe(true);
+    expect(isValidPerplexityDim('pplx-embed-v1-4b', 128)).toBe(true);
+    expect(isValidPerplexityDim('pplx-embed-v1-4b', 64)).toBe(false);
+    expect(isValidPerplexityDim('pplx-embed-v1-0.6b', 2560)).toBe(false);
+  });
+
+  test('dimsProviderOptions emits native `dimensions`, fails loud out of range', () => {
+    expect(dimsProviderOptions('openai-compatible', 'pplx-embed-v1-4b', 2560)).toEqual({
+      openaiCompatible: { dimensions: 2560 },
+    });
+    // Symmetric provider — inputType never emitted.
+    expect(dimsProviderOptions('openai-compatible', 'pplx-embed-v1-4b', 1024, 'query')).toEqual({
+      openaiCompatible: { dimensions: 1024 },
+    });
+    expect(() => dimsProviderOptions('openai-compatible', 'pplx-embed-v1-0.6b', 2560)).toThrow(AIConfigError);
+  });
+
+  test('init preflight accepts the 4b model at its native 2560 dims (halfvec territory)', () => {
+    const res = resolveSchemaEmbeddingDim({
+      embedding_model: 'perplexity:pplx-embed-v1-4b',
+      embedding_dimensions: 2560,
+    });
+    expect(res).toEqual({
+      ok: true,
+      dim: 2560,
+      model: 'perplexity:pplx-embed-v1-4b',
+      provider: 'perplexity',
+      recipeDefault: 1024,
+    });
+    const bad = resolveSchemaEmbeddingDim({
+      embedding_model: 'perplexity:pplx-embed-v1-4b',
+      embedding_dimensions: 4096,
+    });
+    expect(bad.ok).toBe(false);
+  });
+
+  test('embedding pricing table knows both models', () => {
+    expect(lookupEmbeddingPrice('perplexity:pplx-embed-v1-4b')).toMatchObject({ kind: 'known', pricePerMTok: 0.03 });
+    expect(lookupEmbeddingPrice('perplexity:pplx-embed-v1-0.6b')).toMatchObject({ kind: 'known', pricePerMTok: 0.004 });
+  });
+});
+
+describe('perplexityCompatFetch — int8 wire shim', () => {
+  const realFetch = globalThis.fetch;
+  afterEach(() => {
+    globalThis.fetch = realFetch;
+  });
+
+  test('forces encoding_format=base64_int8 outbound and decodes int8 base64 inbound', async () => {
+    const int8 = new Int8Array([3, -7, 127, -128]);
+    const b64 = Buffer.from(int8.buffer).toString('base64');
+    let sentBody: any;
+    globalThis.fetch = (async (_input: any, init?: RequestInit) => {
+      sentBody = JSON.parse(init!.body as string);
+      return new Response(
+        JSON.stringify({
+          object: 'list',
+          model: 'pplx-embed-v1-4b',
+          data: [{ object: 'embedding', index: 0, embedding: b64 }],
+          usage: { prompt_tokens: 4, total_tokens: 4 },
+        }),
+        { status: 200, headers: { 'content-type': 'application/json' } },
+      );
+    }) as any;
+
+    const resp = await (perplexityCompatFetch as any)('https://api.perplexity.ai/v1/embeddings', {
+      method: 'POST',
+      headers: { 'content-type': 'application/json' },
+      // The AI SDK sends encoding_format:'float' — Perplexity rejects it.
+      body: JSON.stringify({ model: 'pplx-embed-v1-4b', input: ['hi'], encoding_format: 'float', dimensions: 4 }),
+    });
+
+    expect(sentBody.encoding_format).toBe('base64_int8');
+    expect(sentBody.dimensions).toBe(4); // native field, untouched
+    const json = await resp.json();
+    expect(json.data[0].embedding).toEqual([3, -7, 127, -128]);
+    expect(json.usage.prompt_tokens).toBe(4);
+  });
+
+  test('non-JSON and error responses pass through untouched', async () => {
+    globalThis.fetch = (async () =>
+      new Response('nope', { status: 401, headers: { 'content-type': 'text/plain' } })) as any;
+    const resp = await (perplexityCompatFetch as any)('https://api.perplexity.ai/v1/embeddings', {
+      method: 'POST',
+      body: JSON.stringify({ model: 'pplx-embed-v1-4b', input: ['hi'] }),
+    });
+    expect(resp.status).toBe(401);
+    expect(await resp.text()).toBe('nope');
+  });
+});