Update examples with OpenAI's o3

examples/o1-and-gpt-4-js/app.ts

@@ -116,7 +116,8 @@ async function chat(
   // First, get the plan from o1-mini
   try {
     const responseO1 = await openai.chat.completions.create({
-      model: "o1-mini", // Assuming this model is accessible
+      model: "o3-mini", // Choose different model by uncommenting
+      //model: "o1-mini",
       messages: [
         { role: "user", content: O1_PROMPT },
         { role: "user", content: userMessage },

examples/o1-js/app.ts → examples/openai-js/app.ts

@@ -6,7 +6,8 @@ import * as dotenv from 'dotenv'
 
 dotenv.config()
 
-const MODEL_NAME = 'o1'
+const MODEL_NAME = 'o3-mini' // Choose different model by uncommenting. You can choose from models with function-calling support, such as o1 or o3-mini.
+// const MODEL_NAME = 'o1'
 
 const SYSTEM_PROMPT = `
 ## your job & context

examples/o1-python/o1.ipynb → examples/openai-python/openai.ipynb

@@ -4,8 +4,8 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Code Interpreting with o1 by OpenAI\n",
-        "This example uses the E2B's [Code Interpreter](https://github.com/e2b-dev/code-interpreter) as a tool for o1. "
+        "# Code Interpreting with OpenAi models\n",
+        "This example uses the E2B's [Code Interpreter](https://github.com/e2b-dev/code-interpreter) as a tool for OpenAI's model. You can choose from models with function-calling support, such as o1 or o3-mini."
       ]
     },
     {
@@ -167,7 +167,8 @@
         "    \n",
         "    print('Waiting for the LLM to respond...')\n",
         "    completion = client.chat.completions.create(\n",
-        "        model=\"o1\",\n",
+        "        model=\"o3\", #Choose different model by uncommenting\n",
+        "        # model=\"o1\",\n",
         "        messages=[\n",
         "            {\"role\": \"system\", \"content\": SYSTEM_PROMPT},\n",
         "            {\"role\": \"user\", \"content\": user_message}\n",