diff --git a/src/providers/embedding/local.ts b/src/providers/embedding/local.ts
index ad6c2d214..9f74f33c7 100644
--- a/src/providers/embedding/local.ts
+++ b/src/providers/embedding/local.ts
@@ -1,4 +1,5 @@
 import type { EmbeddingProvider } from "../../types.js";
+import { getEnvVar } from "../../config.js";
 
 type Pipeline = (
   task: string,
@@ -10,11 +11,57 @@ type Pipeline = (
   ) => Promise<{ tolist: () => number[][] }>
 >;
 
+/** 已知模型的嵌入维度映射表（未列出的模型需通过 OPENAI_EMBEDDING_DIMENSIONS 指定） */
+const KNOWN_DIMS: Record<string, number> = {
+  // MiniLM 系列（英文）
+  "Xenova/all-MiniLM-L6-v2": 384,
+  // BGE 中文系列
+  "Xenova/bge-large-zh-v1.5": 1024,
+  "Xenova/bge-base-zh-v1.5": 768,
+  "Xenova/bge-small-zh-v1.5": 512,
+  // BGE 多语言系列
+  "Xenova/bge-m3": 1024,
+  // 多语言 MiniLM
+  "Xenova/paraphrase-multilingual-MiniLM-L12-v2": 384,
+  // E5 多语言系列
+  "Xenova/multilingual-e5-large": 1024,
+  "Xenova/multilingual-e5-base": 768,
+  "Xenova/multilingual-e5-small": 384,
+};
+
+const DEFAULT_MODEL = "Xenova/all-MiniLM-L6-v2";
+const DEFAULT_DIMS = 384;
+
+function resolveDimensions(
+  modelName: string,
+  override: string | undefined,
+): number {
+  if (override !== undefined && override.trim().length > 0) {
+    const parsed = parseInt(override, 10);
+    if (!Number.isFinite(parsed) || parsed <= 0) {
+      throw new Error(
+        `OPENAI_EMBEDDING_DIMENSIONS must be a positive integer, got: ${override}`,
+      );
+    }
+    return parsed;
+  }
+  return KNOWN_DIMS[modelName] ?? DEFAULT_DIMS;
+}
+
 export class LocalEmbeddingProvider implements EmbeddingProvider {
   readonly name = "local";
-  readonly dimensions = 384;
+  readonly dimensions: number;
+  private modelName: string;
   private extractor: Awaited<ReturnType<Pipeline>> | null = null;
 
+  constructor() {
+    this.modelName = getEnvVar("EMBEDDING_MODEL") || DEFAULT_MODEL;
+    this.dimensions = resolveDimensions(
+      this.modelName,
+      getEnvVar("OPENAI_EMBEDDING_DIMENSIONS"),
+    );
+  }
+
   async embed(text: string): Promise<Float32Array> {
     const [result] = await this.embedBatch([text]);
     return result;
@@ -45,7 +92,8 @@ export class LocalEmbeddingProvider implements EmbeddingProvider {
 
     this.extractor = await transformers.pipeline(
       "feature-extraction",
-      "Xenova/all-MiniLM-L6-v2",
+      this.modelName,
+      { local_files_only: true, quantized: false },
     );
     return this.extractor;
   }