diff --git a/ui/litellm-dashboard/src/components/chat_ui/llm_calls/embeddings_api.test.tsx b/ui/litellm-dashboard/src/components/chat_ui/llm_calls/embeddings_api.test.tsx
index c8d4d49cbe..f24a50074c 100644
--- a/ui/litellm-dashboard/src/components/chat_ui/llm_calls/embeddings_api.test.tsx
+++ b/ui/litellm-dashboard/src/components/chat_ui/llm_calls/embeddings_api.test.tsx
@@ -1,44 +1,40 @@
 import { afterEach, beforeEach, describe, expect, it, vi } from "vitest";
 import { makeOpenAIEmbeddingsRequest } from "./embeddings_api";
-import OpenAI from "openai";
 
-vi.mock("openai");
+vi.mock("@/components/networking", () => ({
+  getProxyBaseUrl: vi.fn(() => "https://example.com"),
+}));
 
 describe("embeddings_api", () => {
-  const mockCreate = vi.fn();
   const mockUpdateEmbeddingsUI = vi.fn();
+  const mockFetch = vi.fn();
 
   beforeEach(() => {
-    // Mock the response structure from OpenAI embeddings API
-    mockCreate.mockResolvedValue({
-      data: [
-        {
-          embedding: [0.1, 0.2, 0.3, 0.4, 0.5],
-          index: 0,
-          object: "embedding",
-        },
-      ],
-      model: "text-embedding-3-small",
-      object: "list",
-      usage: {
-        prompt_tokens: 5,
-        total_tokens: 5,
-      },
-    });
+    mockFetch.mockResolvedValue({
+      ok: true,
+      json: async () => ({
+        data: [
+          {
+            embedding: [0.1, 0.2, 0.3, 0.4, 0.5],
+            index: 0,
+            object: "embedding",
+          },
+        ],
+        model: "text-embedding-3-small",
+        object: "list",
+      }),
+      text: async () => "",
+    } as Response);
 
-    // Mock the OpenAI constructor and its methods
-    (OpenAI as any).mockImplementation(() => ({
-      embeddings: {
-        create: mockCreate,
-      },
-    }));
+    // @ts-ignore - assigning to global for test environment
+    global.fetch = mockFetch;
   });
 
   afterEach(() => {
     vi.clearAllMocks();
   });
 
-  it("should make a request to the embeddings API", async () => {
+  it("should make a request to the embeddings endpoint", async () => {
     await makeOpenAIEmbeddingsRequest(
       "Hello, world!",
       mockUpdateEmbeddingsUI,
@@ -47,13 +43,36 @@ describe("embeddings_api", () => {
       [],
     );
 
-    expect(mockCreate).toHaveBeenCalledWith({
-      model: "text-embedding-3-small",
-      input: "Hello, world!",
+    expect(mockFetch).toHaveBeenCalledTimes(1);
+    expect(mockFetch).toHaveBeenCalledWith("https://example.com/embeddings", {
+      method: "POST",
+      headers: {
+        "Content-Type": "application/json",
+        Authorization: "Bearer 1234567890",
+      },
+      body: JSON.stringify({
+        model: "text-embedding-3-small",
+        input: "Hello, world!",
+      }),
     });
     expect(mockUpdateEmbeddingsUI).toHaveBeenCalledWith(
       JSON.stringify([0.1, 0.2, 0.3, 0.4, 0.5]),
       "text-embedding-3-small",
     );
   });
+
+  it("should not include encoding_format when making the request", async () => {
+    await makeOpenAIEmbeddingsRequest("Sample text", mockUpdateEmbeddingsUI, "text-embedding-3-small", "abcdef", []);
+
+    const fetchCall = mockFetch.mock.calls[0];
+    const options = fetchCall[1] as RequestInit;
+    const body = options.body as string;
+    const parsedBody = JSON.parse(body);
+
+    expect(parsedBody).not.toHaveProperty("encoding_format");
+    expect(parsedBody).toEqual({
+      model: "text-embedding-3-small",
+      input: "Sample text",
+    });
+  });
 });
diff --git a/ui/litellm-dashboard/src/components/chat_ui/llm_calls/embeddings_api.tsx b/ui/litellm-dashboard/src/components/chat_ui/llm_calls/embeddings_api.tsx
index 7a5ab21d9f..832d29bb85 100644
--- a/ui/litellm-dashboard/src/components/chat_ui/llm_calls/embeddings_api.tsx
+++ b/ui/litellm-dashboard/src/components/chat_ui/llm_calls/embeddings_api.tsx
@@ -1,6 +1,5 @@
 import NotificationManager from "@/components/molecules/notifications_manager";
 import { getProxyBaseUrl } from "@/components/networking";
-import openai from "openai";
 
 export async function makeOpenAIEmbeddingsRequest(
   input: string,
@@ -26,20 +25,36 @@ export async function makeOpenAIEmbeddingsRequest(
     headers["x-litellm-tags"] = tags.join(",");
   }
 
-  const client = new openai.OpenAI({
-    apiKey: accessToken,
-    baseURL: proxyBaseUrl,
-    dangerouslyAllowBrowser: true,
-    defaultHeaders: headers,
-  });
-
   try {
-    const response = await client.embeddings.create({
-      model: selectedModel,
-      input: input,
+    const normalizedBaseUrl = proxyBaseUrl.endsWith("/") ? proxyBaseUrl.slice(0, -1) : proxyBaseUrl;
+    const requestUrl = `${normalizedBaseUrl}/embeddings`;
+
+    const response = await fetch(requestUrl, {
+      method: "POST",
+      headers: {
+        "Content-Type": "application/json",
+        Authorization: `Bearer ${accessToken}`,
+        ...headers,
+      },
+      body: JSON.stringify({
+        model: selectedModel,
+        input,
+      }),
     });
 
-    updateEmbeddingsUI(JSON.stringify(response.data[0].embedding), selectedModel);
+    if (!response.ok) {
+      const errorText = await response.text();
+      throw new Error(errorText || `Request failed with status ${response.status}`);
+    }
+
+    const responseData = await response.json();
+    const embedding = responseData?.data?.[0]?.embedding;
+
+    if (!embedding) {
+      throw new Error("No embedding returned from server");
+    }
+
+    updateEmbeddingsUI(JSON.stringify(embedding), responseData?.model ?? selectedModel);
   } catch (error: unknown) {
     NotificationManager.fromBackend(
       `Error occurred while making embeddings request. Please try again. Error: ${error}`,