fix: add examples for batch creation with azure-blob storage

Author: b4s36t4

integrations/llms/azure-openai/batches.mdx

@@ -74,7 +74,7 @@ curl --location 'https://api.portkey.ai/v1/batches' \
     "input_file_id": "<file_id>",
     "endpoint": "<endpoint>",
     "completion_window": "<completion_window>",
-    "metadata": {},
+    "metadata": {}
 }'
 ```
 
@@ -130,6 +130,133 @@ print(start_batch_response)
 
 </CodeGroup>
 
+### Create Batch Job with Blob Storage
+<CodeGroup>
+```python Python
+from portkey_ai import Portkey
+
+# Initialize the Portkey client
+portkey = Portkey(
+    api_key="PORTKEY_API_KEY",  # Replace with your Portkey API key
+    provider="@PROVIDER"   
+)
+
+start_batch_response = portkey.batches.create(
+  endpoint="endpoint",  # ex: /v1/chat/completions
+  completion_window="completion_window",  # ex: 24h
+  metadata={},  # metadata for the batch
+  input_blob="<blob_url>",
+  output_folder={
+    "url": "<output_blob_folder>" # both error file and output file will be saved in this folder
+  }
+)
+
+print(start_batch_response)
+```
+
+```javascript Typescript
+import { Portkey } from 'portkey-ai';
+
+// Initialize the Portkey client
+const portkey = new Portkey({
+    apiKey: "PORTKEY_API_KEY",  // Replace with your Portkey API key
+    provider:"@PROVIDER"   
+});
+
+const startBatch = async () => {
+  const startBatchResponse = await portkey.batches.create({
+    endpoint: "endpoint", // ex: /v1/chat/completions
+    completion_window: "completion_window", // ex: 24h
+    metadata: {}, // metadata for the batch
+    input_blob: "<blob_url>",
+    output_folder: {
+      url: "<output_blob_folder>" // both error file and output file will be saved in this folder
+    }
+  });
+
+  console.log(startBatchResponse);
+}
+
+await startBatch();
+```
+
+```bash curl
+curl --location 'https://api.portkey.ai/v1/batches' \
+--header 'x-portkey-api-key: <portkey_api_key>' \
+--header 'x-portkey-provider: @provider' \
+--header 'Content-Type: application/json' \
+--data '{
+    "endpoint": "<endpoint>",
+    "completion_window": "<completion_window>",
+    "metadata": {},
+    "input_blob": "<blob_url>",
+    "output_folder": {
+      "url": "<output_blob_folder>" # both error file and output file will be saved in this folder
+    }
+}'
+```
+
+```javascript OpenAI NodeJS
+import OpenAI from 'openai'; // We're using the v4 SDK
+import { PORTKEY_GATEWAY_URL, createHeaders } from 'portkey-ai'
+
+const openai = new OpenAI({
+  apiKey: 'OPENAI_API_KEY', // defaults to process.env["OPENAI_API_KEY"],
+  baseURL: PORTKEY_GATEWAY_URL,
+  defaultHeaders: createHeaders({
+    provider: "openai",
+    apiKey: "PORTKEY_API_KEY" // defaults to process.env["PORTKEY_API_KEY"]
+  })
+});
+
+const startBatch = async () => {
+  const startBatchResponse = await openai.batches.create({
+    endpoint: "endpoint", // ex: /v1/chat/completions
+    completion_window: "completion_window", // ex: 24h
+    metadata: {}, // metadata for the batch
+    extra_body: {
+      input_blob: "<blob_url>",
+      output_folder: {
+        url: "<output_blob_folder>" // both error file and output file will be saved in this folder
+      }
+    }
+  });
+
+  console.log(startBatchResponse);
+}
+
+await startBatch();
+```
+
+```python OpenAI Python
+from openai import OpenAI
+from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders
+
+openai = OpenAI(
+    api_key='OPENAI_API_KEY',
+    base_url=PORTKEY_GATEWAY_URL,
+    default_headers=createHeaders(
+        provider="openai",
+        api_key="PORTKEY_API_KEY"
+    )
+)
+
+start_batch_response = openai.batches.create(
+  endpoint="endpoint",  # ex: /v1/chat/completions
+  completion_window="completion_window",  # ex: 24h
+  metadata={},  # metadata for the batch
+  extra_body={
+    "input_blob": "<blob_url>",
+    "output_folder": {
+      "url": "<output_blob_folder>"  # both error file and output file will be saved in this folder
+    }
+  }
+)
+
+print(start_batch_response)
+```
+</CodeGroup>
+
 ### List Batch Jobs
 <CodeGroup>
 ```python Python