feat: separate internal schema dimensions from embedding API request dimensions

index.ts

@@ -84,7 +84,10 @@ interface PluginConfig {
     apiKey: string | string[];
     model?: string;
     baseURL?: string;
+    /** Internal schema/validation dimension (LanceDB + local checks). */
     dimensions?: number;
+    /** Optional provider request dimension (dimensions/output_dimension). */
+    requestDimensions?: number;
     omitDimensions?: boolean;
     taskQuery?: string;
     taskPassage?: string;
@@ -1639,7 +1642,10 @@ const memoryLanceDBProPlugin = {
       apiKey: config.embedding.apiKey,
       model: config.embedding.model || "text-embedding-3-small",
       baseURL: config.embedding.baseURL,
+      // Internal dimension for local schema/validation checks.
       dimensions: config.embedding.dimensions,
+      // Optional request hint sent to providers that support variable dimensions.
+      requestDimensions: config.embedding.requestDimensions,
       omitDimensions: config.embedding.omitDimensions,
       taskQuery: config.embedding.taskQuery,
       taskPassage: config.embedding.taskPassage,
@@ -3776,6 +3782,8 @@ export function parsePluginConfig(value: unknown): PluginConfig {
       // Accept number, numeric string, or env-var string (e.g. "${EMBED_DIM}").
       // Also accept legacy top-level `dimensions` for convenience.
       dimensions: parsePositiveInt(embedding.dimensions ?? cfg.dimensions),
+      // Request dimension is intentionally separate from internal schema sizing.
+      requestDimensions: parsePositiveInt(embedding.requestDimensions),
       omitDimensions:
         typeof embedding.omitDimensions === "boolean"
           ? embedding.omitDimensions

openclaw.plugin.json

@@ -46,6 +46,11 @@
             "type": "integer",
             "minimum": 1
           },
+          "requestDimensions": {
+            "type": "integer",
+            "minimum": 1,
+            "description": "Optional output dimension sent to embedding API requests only (for providers supporting variable dimensions)"
+          },
           "omitDimensions": {
             "type": "boolean",
             "description": "When true, omit the dimensions parameter from embedding requests even if dimensions is configured"
@@ -862,6 +867,12 @@
       "help": "Override vector dimensions for custom models not in the built-in lookup table",
       "advanced": true
     },
+    "embedding.requestDimensions": {
+      "label": "Request Dimensions",
+      "placeholder": "unset (do not send)",
+      "help": "Optional output dimension sent to the embedding API request only (dimensions/output_dimension depending on provider). If unset, no request dimension field is sent.",
+      "advanced": true
+    },
     "embedding.omitDimensions": {
       "label": "Omit Request Dimensions",
       "help": "Do not send the dimensions parameter to the embedding API even if embedding.dimensions is configured. Useful for local models like Qwen3-Embedding that reject the field.",

src/embedder.ts

@@ -90,7 +90,10 @@ export interface EmbeddingConfig {
   apiKey: string | string[];
   model: string;
   baseURL?: string;
+  /** Internal vector dimension for schema/validation. This does NOT imply sending API dimensions. */
   dimensions?: number;
+  /** Optional API request output dimension for providers that support variable dimensions. */
+  requestDimensions?: number;
 
   /** Optional task type for query embeddings (e.g. "retrieval.query") */
   taskQuery?: string;
@@ -428,7 +431,8 @@ export class Embedder {
     this._taskQuery = config.taskQuery;
     this._taskPassage = config.taskPassage;
     this._normalized = config.normalized;
-    this._requestDimensions = config.dimensions;
+    // Request-side dimension hint is isolated from internal schema dimension.
+    this._requestDimensions = config.requestDimensions;
     this._omitDimensions = config.omitDimensions === true;
     // Enable auto-chunking by default for better handling of long documents
     this._autoChunk = config.chunking !== false;
@@ -472,7 +476,12 @@ export class Embedder {
       console.log(`[memory-lancedb-pro] Initialized ${this.clients.length} API keys for round-robin rotation`);
     }
 
-    this.dimensions = getVectorDimensions(config.model, config.dimensions);
+    // Internal dimension用于本地校验，优先使用requestDimensions（如有），否则fallback到dimensions。
+    // 这样可变维度模型（如text-embedding-3-large + requestDimensions: 1024）本地校验与API一致。
+    const effectiveDims = this._requestDimensions && this._requestDimensions > 0
+      ? this._requestDimensions
+      : config.dimensions;
+    this.dimensions = getVectorDimensions(config.model, effectiveDims);
     this._cache = new EmbeddingCache(256, 30); // 256 entries, 30 min TTL
   }
 

test/embedder-error-hints.test.mjs

@@ -130,7 +130,7 @@ async function run() {
   installMockEmbeddingClient(jinaEmbedder, async (payload) => {
     assert.equal(payload.task, "retrieval.passage");
     assert.equal(payload.normalized, true);
-    assert.equal(payload.dimensions, 1024);
+    assert.equal(payload.dimensions, undefined, "jina should not send dimensions unless requestDimensions is set");
     return createEmbeddingResponse(1024);
   });
   await jinaEmbedder.embedPassage("hello");
@@ -144,7 +144,7 @@ async function run() {
   });
   installMockEmbeddingClient(genericEmbedder, async (payload) => {
     assert.equal(payload.encoding_format, "float");
-    assert.equal(payload.dimensions, 384);
+    assert.equal(payload.dimensions, undefined, "generic profile should not send dimensions unless requestDimensions is set");
     return createEmbeddingResponse(384);
   });
   await genericEmbedder.embedPassage("hello");
@@ -189,14 +189,15 @@ async function run() {
   });
   await voyageTaskEmbedder.embedQuery("hello");
 
-  // Voyage: configured dimensions should be sent as output_dimension, not dimensions.
+  // Voyage: requestDimensions should be sent as output_dimension, not dimensions.
   // voyage-4-lite is a recommended Voyage model that supports output_dimension.
   const voyageDimEmbedder = new Embedder({
     provider: "openai-compatible",
     apiKey: "test-key",
     model: "voyage-4-lite",
     baseURL: "https://api.voyageai.com/v1",
     dimensions: 512,
+    requestDimensions: 512,
   });
   installMockEmbeddingClient(voyageDimEmbedder, async (payload) => {
     assert.equal(payload.output_dimension, 512, "voyage should send output_dimension");
@@ -211,7 +212,7 @@ async function run() {
   await withEmbeddingCaptureServer(
     (payload) => {
       assert.equal(payload.encoding_format, "float", "generic profile should send encoding_format");
-      assert.equal(payload.dimensions, 384, "generic profile should send dimensions");
+      assert.equal(payload.dimensions, undefined, "generic profile should not send dimensions by default");
       assert.equal(payload.task, undefined, "generic profile should not send task");
       assert.equal(payload.normalized, undefined, "generic profile should not send normalized");
       return { body: createEmbeddingResponse(384) };
@@ -228,6 +229,25 @@ async function run() {
     },
   );
 
+  await withEmbeddingCaptureServer(
+    (payload) => {
+      assert.equal(payload.encoding_format, "float", "generic profile should send encoding_format");
+      assert.equal(payload.dimensions, 384, "generic profile should send dimensions when requestDimensions is set");
+      return { body: createEmbeddingResponse(384) };
+    },
+    async ({ baseURL }) => {
+      const embedder = new Embedder({
+        provider: "openai-compatible",
+        apiKey: "test-key",
+        model: "custom-embed-model",
+        baseURL,
+        dimensions: 384,
+        requestDimensions: 384,
+      });
+      await embedder.embedPassage("hello world");
+    },
+  );
+
   await withJsonServer(
     403,
     { error: { message: "Invalid API key", code: "invalid_api_key" } },

test/plugin-manifest-regression.mjs

@@ -109,6 +109,11 @@ assert.equal(
   "boolean",
   "embedding.omitDimensions should be declared in the plugin schema",
 );
+assert.equal(
+  manifest.configSchema.properties.embedding.properties.requestDimensions?.type,
+  "integer",
+  "embedding.requestDimensions should be declared in the plugin schema",
+);
 assert.equal(
   manifest.configSchema.properties.sessionMemory.properties.enabled.default,
   false,
@@ -325,14 +330,44 @@ try {
     });
     const requestCountBeforeWithDimensions = embeddingRequests.length;
     await withDimensionsTool.execute("tool-3", {
-      text: "dimensions should be sent by default",
+      text: "dimensions should not be sent by default",
       scope: "global",
     });
     const withDimensionsRequest = embeddingRequests.at(requestCountBeforeWithDimensions);
     assert.equal(
-      withDimensionsRequest?.dimensions,
+      Object.prototype.hasOwnProperty.call(withDimensionsRequest ?? {}, "dimensions"),
+      false,
+      "embedding.dimensions should be used for internal schema sizing, not forwarded by default",
+    );
+
+    const withRequestDimensionsApi = createMockApi({
+      dbPath: path.join(workDir, "db-with-request-dimensions"),
+      autoCapture: false,
+      autoRecall: false,
+      embedding: {
+        provider: "openai-compatible",
+        apiKey: "dummy",
+        model: "text-embedding-3-small",
+        baseURL: embeddingBaseURL,
+        dimensions: 4,
+        requestDimensions: 4,
+      },
+    });
+    plugin.register(withRequestDimensionsApi);
+    const withRequestDimensionsTool = withRequestDimensionsApi.toolFactories.memory_store({
+      agentId: "main",
+      sessionKey: "agent:main:test",
+    });
+    const requestCountBeforeRequestDimensions = embeddingRequests.length;
+    await withRequestDimensionsTool.execute("tool-3b", {
+      text: "requestDimensions should be forwarded",
+      scope: "global",
+    });
+    const withRequestDimensionsRequest = embeddingRequests.at(requestCountBeforeRequestDimensions);
+    assert.equal(
+      withRequestDimensionsRequest?.dimensions,
       4,
-      "embedding.dimensions should be forwarded by default",
+      "embedding.requestDimensions should be forwarded to embedding requests",
     );
 
     const omitDimensionsApi = createMockApi({
@@ -345,6 +380,7 @@ try {
         model: "text-embedding-3-small",
         baseURL: embeddingBaseURL,
         dimensions: 4,
+        requestDimensions: 4,
         omitDimensions: true,
       },
     });
@@ -362,7 +398,7 @@ try {
     assert.equal(
       Object.prototype.hasOwnProperty.call(omitDimensionsRequest, "dimensions"),
       false,
-      "embedding.omitDimensions=true should omit dimensions from embedding requests",
+      "embedding.omitDimensions=true should omit dimensions from embedding requests even when requestDimensions is set",
     );
   } finally {
     await new Promise((resolve) => embeddingServer.close(resolve));