diff --git a/src/core/ai/recipes/index.ts b/src/core/ai/recipes/index.ts
index 3ebe56361..d4810f2e8 100644
--- a/src/core/ai/recipes/index.ts
+++ b/src/core/ai/recipes/index.ts
@@ -18,6 +18,7 @@ import { together } from './together.ts';
 import { llamaServer } from './llama-server.ts';
 import { minimax } from './minimax.ts';
 import { dashscope } from './dashscope.ts';
+import { zhipu } from './zhipu.ts';
 
 const ALL: Recipe[] = [
   openai,
@@ -32,6 +33,7 @@ const ALL: Recipe[] = [
   llamaServer,
   minimax,
   dashscope,
+  zhipu,
 ];
 
 /** Map from `provider:id` key to recipe. */
diff --git a/src/core/ai/recipes/zhipu.ts b/src/core/ai/recipes/zhipu.ts
new file mode 100644
index 000000000..758c5c9cb
--- /dev/null
+++ b/src/core/ai/recipes/zhipu.ts
@@ -0,0 +1,40 @@
+import type { Recipe } from '../types.ts';
+
+/**
+ * Zhipu AI (智谱AI) BigModel Open Platform. OpenAI-compatible /embeddings
+ * endpoint at open.bigmodel.cn. Hosts embedding-2 (1024d) and embedding-3
+ * (Matryoshka up to 2048d).
+ *
+ * embedding-3 at 2048 dims exceeds pgvector's HNSW cap of 2000 — those
+ * brains fall back to exact vector scans (see
+ * src/core/ai/vector-index.ts:PGVECTOR_HNSW_VECTOR_MAX_DIMS). v0.32 ships
+ * with `default_dims: 1024` (HNSW-compatible) and exposes 2048 via
+ * dims_options for users who want the full embedding fidelity at the
+ * cost of slower retrieval.
+ *
+ * Reference: https://open.bigmodel.cn/
+ */
+export const zhipu: Recipe = {
+  id: 'zhipu',
+  name: 'Zhipu AI (智谱AI BigModel)',
+  tier: 'openai-compat',
+  implementation: 'openai-compatible',
+  base_url_default: 'https://open.bigmodel.cn/api/paas/v4',
+  auth_env: {
+    required: ['ZHIPUAI_API_KEY'],
+    setup_url: 'https://open.bigmodel.cn/',
+  },
+  touchpoints: {
+    embedding: {
+      models: ['embedding-3', 'embedding-2'],
+      default_dims: 1024,
+      // 2048 exposed but breaks HNSW (exact-scan fallback). 1024/512/256
+      // stay HNSW-compatible.
+      dims_options: [256, 512, 1024, 2048],
+      max_batch_tokens: 8192,
+      chars_per_token: 2,
+    },
+  },
+  setup_hint:
+    'Get an API key at https://open.bigmodel.cn/, then `export ZHIPUAI_API_KEY=...`',
+};
diff --git a/test/ai/recipe-zhipu.test.ts b/test/ai/recipe-zhipu.test.ts
new file mode 100644
index 000000000..65cce385c
--- /dev/null
+++ b/test/ai/recipe-zhipu.test.ts
@@ -0,0 +1,71 @@
+/**
+ * Zhipu AI (BigModel) recipe smoke (Commit 7 of the v0.32 wave).
+ *
+ * Coverage:
+ *  - Recipe registered with expected shape
+ *  - default auth: ZHIPUAI_API_KEY → "Bearer <key>"; missing → AIConfigError
+ *  - dims_options exposes [256, 512, 1024, 2048]; default 1024 (HNSW-compatible)
+ *  - 2048-dim path falls into exact-scan branch via chunkEmbeddingIndexSql
+ *    from src/core/vector-index.ts
+ */
+
+import { describe, expect, test } from 'bun:test';
+import { getRecipe } from '../../src/core/ai/recipes/index.ts';
+import { defaultResolveAuth } from '../../src/core/ai/gateway.ts';
+import { AIConfigError } from '../../src/core/ai/errors.ts';
+import {
+  PGVECTOR_HNSW_VECTOR_MAX_DIMS,
+  chunkEmbeddingIndexSql,
+} from '../../src/core/vector-index.ts';
+
+describe('recipe: zhipu', () => {
+  test('registered with expected shape', () => {
+    const r = getRecipe('zhipu');
+    expect(r).toBeDefined();
+    expect(r!.id).toBe('zhipu');
+    expect(r!.tier).toBe('openai-compat');
+    expect(r!.implementation).toBe('openai-compatible');
+    expect(r!.base_url_default).toBe('https://open.bigmodel.cn/api/paas/v4');
+    expect(r!.auth_env?.required).toEqual(['ZHIPUAI_API_KEY']);
+  });
+
+  test('embedding touchpoint declares embedding-3 first + 1024 dims (HNSW-compatible default)', () => {
+    const r = getRecipe('zhipu')!;
+    expect(r.touchpoints.embedding).toBeDefined();
+    expect(r.touchpoints.embedding!.models[0]).toBe('embedding-3');
+    expect(r.touchpoints.embedding!.models).toContain('embedding-2');
+    expect(r.touchpoints.embedding!.default_dims).toBe(1024);
+    expect(r.touchpoints.embedding!.dims_options).toEqual([256, 512, 1024, 2048]);
+    // The default must stay HNSW-compatible.
+    expect(r.touchpoints.embedding!.default_dims).toBeLessThanOrEqual(
+      PGVECTOR_HNSW_VECTOR_MAX_DIMS,
+    );
+  });
+
+  test('default auth: ZHIPUAI_API_KEY set → "Bearer <key>"', () => {
+    const r = getRecipe('zhipu')!;
+    const auth = defaultResolveAuth(r, { ZHIPUAI_API_KEY: 'fake-zhipu-key' }, 'embedding');
+    expect(auth.headerName).toBe('Authorization');
+    expect(auth.token).toBe('Bearer fake-zhipu-key');
+  });
+
+  test('default auth: missing ZHIPUAI_API_KEY → AIConfigError', () => {
+    const r = getRecipe('zhipu')!;
+    expect(() => defaultResolveAuth(r, {}, 'embedding')).toThrow(AIConfigError);
+  });
+
+  test('2048-dim option from dims_options falls into exact-scan branch', () => {
+    // 2048d exceeds the HNSW cap, so chunkEmbeddingIndexSql returns the
+    // exact-scan-skip-index path. Users picking 2048 trade ANN speed for
+    // full embedding fidelity.
+    const sql = chunkEmbeddingIndexSql(2048);
+    expect(sql.toLowerCase()).toContain('skipped');
+    expect(sql.toLowerCase()).toContain('hnsw');
+  });
+
+  test('1024-dim default returns the HNSW index SQL (fast path)', () => {
+    const sql = chunkEmbeddingIndexSql(1024);
+    expect(sql.toLowerCase()).toContain('create index');
+    expect(sql.toLowerCase()).toContain('hnsw');
+  });
+});