"
],
"text/html": [
"def is_prime(n):\n",
" if n < 2:\n",
" return False\n",
" for i in range(2, int(n**0.5) + 1):\n",
" if n % i == 0:\n",
" return False\n",
" return True\n",
"\n",
"primes = []\n",
"num = 2\n",
"while len(primes) < 50:\n",
" if is_prime(num):\n",
" primes.append(num)\n",
" num += 1\n",
"\n",
"sum_of_primes = sum(primes)\n",
"print(f'The first 50 prime numbers are: {primes}')\n",
"print(f'The sum of the first 50 prime numbers is: {sum_of_primes}')\n",
""
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"text/markdown": "---"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"text/markdown": "The first 50 prime numbers are: [2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229]\nThe sum of the first 50 prime numbers is: 5117\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"text/markdown": "---"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"text/markdown": "The first 50 prime numbers are:\n2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97, 101, 103, 107, 109, 113, 127, 131, 137, 139, 149, 151, 157, 163, 167, 173, 179, 181, 191, 193, 197, 199, 211, 223, 227, 229.\n\nThe sum of these first 50 prime numbers is **5117**."
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"text/markdown": "---"
},
"metadata": {}
}
],
"source": [
"response = client.models.generate_content(\n",
" model=MODEL,\n",
" contents='What is the sum of the first 50 prime numbers? '\n",
" 'Generate and run code for the calculation, and make sure you get all 50.',\n",
" config=types.GenerateContentConfig(\n",
" tools=[types.Tool(\n",
" code_execution=types.ToolCodeExecution\n",
" )]\n",
" )\n",
")\n",
"\n",
"def display_code_execution_result(response):\n",
" for part in response.candidates[0].content.parts:\n",
" if part.text is not None:\n",
" display(Markdown(part.text))\n",
" if part.executable_code is not None:\n",
" code_html = f'{part.executable_code.code}' # Change code color\n",
" display(HTML(code_html))\n",
" if part.code_execution_result is not None:\n",
" display(Markdown(part.code_execution_result.output))\n",
" if part.inline_data is not None:\n",
" display(Image(data=part.inline_data.data, format=\"png\"))\n",
" display(Markdown(\"---\"))\n",
"\n",
"display_code_execution_result(response)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1rEg-U9aL0cJ"
},
"source": [
"#### Prompt Caching\n",
"\n",
"One of the cool new features which has been added by Gemini is prompt caching. If a part of your prompt or instructions is not changing, you can save some tokens by caching that part of the prompt. This is very useful in production where the same prompt might be used for thousands and millions of times and allows us to optimize cost.\n",
"\n",
"In order to use the cache, we create a cache context and then call the generate content endpoint with the additional context.\n",
"\n",
"First we download some data which is reused again and again across context."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "phXF4nVrL0cJ",
"outputId": "5db0733d-9b22-4f50-f580-bfa839dd4bef"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"INTRODUCTION\n",
"\n",
"This is the transcription of the Technical Air-to-Ground Voice Transmission (GOSS NET 1) from the Apollo 11 mission.\n",
"\n",
"Communicators in the text may be identified according to the following list.\n",
"\n",
"Spacecraft:\n",
"CDR\tCommander\tNeil A. Armstrong\n",
"CMP\tCommand module pilot \tMichael Collins\n",
"LMP\tLunar module pilot\tEdwin E. ALdrin, Jr.\n"
]
}
],
"source": [
"!wget -q https://storage.googleapis.com/generativeai-downloads/data/a11.txt\n",
"!head a11.txt"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 389
},
"id": "l_ODeZVxL0cK",
"outputId": "82e8f6ed-c3ef-4de8-f571-d06acf9d5bd5"
},
"outputs": [
{
"output_type": "error",
"ename": "ClientError",
"evalue": "429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'TotalCachedContentStorageTokensPerModelFreeTier limit exceeded for model gemini-2.0-flash: limit=0, requested=322698', 'status': 'RESOURCE_EXHAUSTED'}}",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mClientError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/tmp/ipython-input-30-1881286979.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# Create a cache with a 5 minute TTL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m cache = client.caches.create(\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcache_model\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m config=types.CreateCachedContentConfig(\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/google/genai/caches.py\u001b[0m in \u001b[0;36mcreate\u001b[0;34m(self, model, config)\u001b[0m\n\u001b[1;32m 1535\u001b[0m \u001b[0mrequest_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_common\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode_unserializable_types\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1537\u001b[0;31m response = self._api_client.request(\n\u001b[0m\u001b[1;32m 1538\u001b[0m \u001b[0;34m'post'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhttp_options\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1539\u001b[0m )\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/google/genai/_api_client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, http_method, path, request_dict, http_options)\u001b[0m\n\u001b[1;32m 1075\u001b[0m \u001b[0mhttp_method\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhttp_options\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1076\u001b[0m )\n\u001b[0;32m-> 1077\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp_request\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1078\u001b[0m response_body = (\n\u001b[1;32m 1079\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse_stream\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresponse_stream\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/google/genai/_api_client.py\u001b[0m in \u001b[0;36m_request\u001b[0;34m(self, http_request, stream)\u001b[0m\n\u001b[1;32m 966\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 967\u001b[0m ) -> HttpResponse:\n\u001b[0;32m--> 968\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_retry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_request_once\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhttp_request\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[no-any-return]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 969\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 970\u001b[0m async def _async_request_once(\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/tenacity/__init__.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 473\u001b[0m \u001b[0mretry_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRetryCallState\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretry_object\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 474\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 475\u001b[0;31m \u001b[0mdo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretry_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mretry_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 476\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDoAttempt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 477\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/tenacity/__init__.py\u001b[0m in \u001b[0;36miter\u001b[0;34m(self, retry_state)\u001b[0m\n\u001b[1;32m 374\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 375\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0maction\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mactions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 376\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretry_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 377\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 378\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/tenacity/__init__.py\u001b[0m in \u001b[0;36mexc_check\u001b[0;34m(rs)\u001b[0m\n\u001b[1;32m 416\u001b[0m \u001b[0mretry_exc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretry_error_cls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfut\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 417\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mretry_exc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 419\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mretry_exc\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mfut\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 420\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/tenacity/__init__.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNoReturn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 184\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlast_attempt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfailed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlast_attempt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 186\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.11/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 447\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mCancelledError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 448\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mFINISHED\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 451\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python3.11/concurrent/futures/_base.py\u001b[0m in \u001b[0;36m__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 399\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 400\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 401\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 402\u001b[0m \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 403\u001b[0m \u001b[0;31m# Break a reference cycle with the exception in self._exception\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/tenacity/__init__.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 476\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDoAttempt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 477\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 478\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 479\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# noqa: B902\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 480\u001b[0m \u001b[0mretry_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[arg-type]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/google/genai/_api_client.py\u001b[0m in \u001b[0;36m_request_once\u001b[0;34m(self, http_request, stream)\u001b[0m\n\u001b[1;32m 956\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhttp_request\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 957\u001b[0m )\n\u001b[0;32m--> 958\u001b[0;31m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAPIError\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mraise_for_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 959\u001b[0m return HttpResponse(\n\u001b[1;32m 960\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstream\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/google/genai/errors.py\u001b[0m in \u001b[0;36mraise_for_response\u001b[0;34m(cls, response)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[0mstatus_code\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstatus_code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;36m400\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mstatus_code\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m500\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 105\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mClientError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatus_code\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse_json\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 106\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;36m500\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mstatus_code\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m600\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mServerError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstatus_code\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse_json\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mClientError\u001b[0m: 429 RESOURCE_EXHAUSTED. {'error': {'code': 429, 'message': 'TotalCachedContentStorageTokensPerModelFreeTier limit exceeded for model gemini-2.0-flash: limit=0, requested=322698', 'status': 'RESOURCE_EXHAUSTED'}}"
]
}
],
"source": [
"import datetime\n",
"\n",
"# upload the files.\n",
"document = client.files.upload(file=\"a11.txt\")\n",
"\n",
"cache_model='models/gemini-2.0-flash-001'\n",
"\n",
"# Create a cache with a 5 minute TTL\n",
"cache = client.caches.create(\n",
" model=cache_model,\n",
" config=types.CreateCachedContentConfig(\n",
" display_name='transcript',\n",
" system_instruction=(\n",
" \"You are an expert at analyzing transcripts.\"\n",
" ),\n",
" contents=[document],\n",
" ttl=\"300s\",\n",
" )\n",
")\n",
"\n",
"# Construct a GenerativeModel which uses the created cache.\n",
"response = client.models.generate_content(\n",
" model = cache_model,\n",
" contents= (\"Find a lighthearted moment from this transcript\"),\n",
" config=types.GenerateContentConfig(cached_content=cache.name)\n",
")\n",
"\n",
"\n",
"print(response.text)\n",
"\n",
"\n",
"print(response.usage_metadata)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pBaqTfLJL0cK"
},
"outputs": [],
"source": [
"# Once you have used the cache you can also delete it (or it expires automatically at the given time)\n",
"client.caches.delete(name=cache.name)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5z8KsFjxL0cK"
},
"source": [
"#### URL Context\n",
"\n",
"This is the latest functionality just dropped into the SDK, which allows you to provide Gemini with URLs as additional context for your prompt. The model can then retrieve content from the URLs and use that content to inform and shape its response.\n",
"\n",
"This tool is useful for tasks like the following:\n",
"\n",
"- Extracting key data points or talking points from articles\n",
"- Comparing information across multiple links\n",
"- Synthesizing data from several sources\n",
"- Answering questions based on the content of a specific page or pages\n",
"- Analyzing content for specific purposes (like writing a job description or creating test questions)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "RYomYIK7L0cK",
"outputId": "1f2ac971-2d98-4353-f751-59f844fdf637"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Both Indian Healthy Recipes and Tea for Turmeric provide comprehensive guides for making chicken biryani, though they offer slightly different approaches and ingredient preferences.\n",
"\n",
"**Recipe Methods:**\n",
"* **Indian Healthy Recipes** offers two distinct methods: a \"one-pot\" biryani that can be made in a pressure cooker or regular pot where marinated meat is cooked first, and then uncooked rice is layered and cooked with hot water; and a \"restaurant-style\" or \"pakki dum biryani,\" which involves cooking the chicken gravy separately, par-cooking the rice, and then layering them for dum (steam) cooking.\n",
"* **Tea for Turmeric** focuses on a method similar to the \"pakki dum biryani,\" where the chicken curry is prepared, rice is parboiled, and then both are layered for final steaming. This recipe also provides an oven steaming method as an alternative.\n",
"\n",
"**Key Ingredients and Flavor Profiles:**\n",
"* **Biryani Masala:** Both recipes emphasize the importance of good quality spices. Both provide a recipe for homemade biryani masala and suggest using a good store-bought alternative.\n",
"* **Tomatoes:** Indian Healthy Recipes notes that authentic biryani traditionally does not include tomatoes. In contrast, Tea for Turmeric's recipe incorporates a small amount of finely chopped tomatoes, considering that other sour ingredients like yogurt, dried plums, and lemon are also used.\n",
"* **Unique Additions:** The Tea for Turmeric recipe distinctly includes dried plums (alu bukhara), which impart a sweet and tangy flavor to the biryani. This ingredient is not mentioned in the Indian Healthy Recipes.\n",
"* **Chicken Type:** Both recommend using bone-in chicken pieces (such as thighs and drumsticks) for better flavor and tenderness.\n",
"* **Rice:** Both highly recommend using aged, long-grain basmati rice for optimal fluffy and distinct grains. Indian Healthy Recipes suggests soaking the rice for 30 minutes, while Tea for Turmeric provides detailed guidance on parboiling times to achieve the perfect consistency.\n",
"* **Marination:** Both recipes stress the importance of marinating the chicken in yogurt and spices for tenderness and flavor, with recommended times ranging from a minimum of 30 minutes to overnight.\n",
"\n",
"**Cooking Techniques:**\n",
"* **Dum Cooking:** Both recipes heavily rely on the \"dum\" method, where the layered biryani is sealed and cooked over low heat, allowing flavors to meld and rice to cook perfectly through steam.\n",
"\n",
"In summary, while both recipes aim for an authentic chicken biryani, the Tea for Turmeric recipe leans towards a Pakistani style, incorporating ingredients like dried plums and a small amount of tomatoes, alongside detailed instructions for achieving fluffy rice. Indian Healthy Recipes provides versatility with its two methods, including a quicker one-pot option, while maintaining a traditional Indian approach by omitting tomatoes.\n",
"url_metadata=[UrlMetadata(\n",
" retrieved_url='https://www.indianhealthyrecipes.com/chicken-biryani-recipe/',\n",
" url_retrieval_status=\n",
"), UrlMetadata(\n",
" retrieved_url='https://www.teaforturmeric.com/chicken-biryani/',\n",
" url_retrieval_status=\n",
")]\n"
]
}
],
"source": [
"model_id = \"gemini-2.5-flash-preview-05-20\"\n",
"\n",
"url_context_tool = types.Tool(\n",
" url_context = types.UrlContext\n",
")\n",
"\n",
"response = client.models.generate_content(\n",
" model=model_id,\n",
" contents=\"Compare recipes from https://www.indianhealthyrecipes.com/chicken-biryani-recipe/ and https://www.teaforturmeric.com/chicken-biryani/\",\n",
" config=types.GenerateContentConfig(\n",
" tools=[url_context_tool],\n",
" response_modalities=[\"TEXT\"],\n",
" )\n",
")\n",
"\n",
"for each in response.candidates[0].content.parts:\n",
" print(each.text)\n",
"# get URLs retrieved for context\n",
"print(response.candidates[0].url_context_metadata)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5t6e4j5mL0cK"
},
"source": [
"### Exercise 3: An Assessment Generator for School Teachers.\n",
"\n",
"This is a slightly more complex one, we definitely dont want to single shot this app. I have listed down some prompts/requirements below - you can use these to build up the app step by step. You can choose to change these prompts or add any other functionality you like as well. We want to be able to \"teach at right level\" and think about creating a mixture of seen and unseen questions. We can use the PDF link to understand the scope and extract some relevant questions from there.\n",
"\n",
"As you build up the functionality, think about testing this app, and how you will ensure that nothing is breaking after each iteration.\n",
"\n",
"1. Download the Grade 4 Maths book from here: `https://drive.google.com/file/d/1yc-jZ3r7etKX0JpMW2rSO0uOLlzZ8Ckk/view?usp=sharing`\n",
"2. Extract one topic from Unit 1, e.g. Comparing and Ordering numbers. Pages 13 to 18.\n",
"3. Upload this slice as a cache on Gemini, with expire of one hour.\n",
"4. Use URL Context to add additional context for generating the assessment: `https://teaching.betterlesson.com/lesson/496389/comparing-and-ordering-whole-numbers`\n",
"5. Use the Code Execution to solve the generated questions and build an answer sheet.\n",
"6. Generate a formative assessment with 10 questions, including the ruberic and the answer sheet with it."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "iUhH9JGEL0cL"
},
"outputs": [],
"source": [
"# Exercise 3 code here."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "j_r216pXL0cL"
},
"source": [
"## Thank you.\n",
"\n",
"Thank you for attending this workshop. You can find more details about me on https://karachiwala.dev. I am available over most platforms as @mashhoodr.\n",
"\n",
"You can find many more examples for Gemini on the following repositories.\n",
"\n",
"- https://github.com/google-gemini/cookbook\n",
"- https://github.com/GoogleCloudPlatform/generative-ai\n",
"\n",
"If you have any feedback on this workshop please share it with me using the following link:\n",
"\n",
"https://docs.google.com/forms/d/1iAEO1JSlh6GTLC0uudUxAiTDiNN_iMzfdCwDLZ_78sg/edit\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.6"
},
"colab": {
"provenance": []
}
},
"nbformat": 4,
"nbformat_minor": 0
} | ![](\"https://www.tensorflow.org/images/colab_logo_32px.png\"){kind=logo}
Run in Google Colab![\"The](\"https://storage.googleapis.com/generativeai-downloads/images/secrets.jpg\")
\n",
"\n",
"2. Create a new secret with the name `GOOGLE_API_KEY`.\n",
"3. Copy/paste your API key into the `Value` input box of `GOOGLE_API_KEY`.\n",
"4. Toggle the button on the left to allow notebook access to the secret.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "owLPoaPxL0bz"
},
"source": [
"### Install the Python SDK"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rhUARP34L0b0",
"outputId": "3fd77ce9-f30e-49b2-9847-5d6aede8f23e"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m41.6/41.6 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90mββββββββββββββββββββββββββββββββββββββββ\u001b[0m \u001b[32m226.8/226.8 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
],
"source": [
"!pip install -q -U google-genai"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TnAu3S-vL0b3"
},
"source": [
"### Configure the SDK with your API key.\n",
"\n",
"You'll call `genai.configure` with your API key, but instead of pasting your key into the notebook, you'll read it from Colab Secrets."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "9qhOm4z5L0b4"
},
"outputs": [],
"source": [
"from google import genai\n",
"from google.genai import types\n",
"from google.colab import userdata\n",
"from pydantic import BaseModel\n",
"from IPython.display import Markdown, HTML, Image\n",
"\n",
"GOOGLE_API_KEY=userdata.get('GOOGLE_API_KEY')\n",
"client = genai.Client(api_key=GOOGLE_API_KEY)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vsOY315tL0b4"
},
"source": [
"And that's it! Now you're ready to use the Gemini API.\n",
"\n",
"Now lets set our model we want to use throughout this notebook. You can change this to any available model."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "Hja1Um0PL0b5"
},
"outputs": [],
"source": [
"\n",
"MODEL = \"gemini-2.5-flash\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vXr20H0fL0b5"
},
"source": [
"### Running your first prompt\n",
"\n",
"Use the `generate_content` method to generate responses to your prompts. You can pass text directly to generate_content, and use the `.text` property to get the text content of the response."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ooIksAeTL0b6",
"outputId": "e0d44368-3a77-4dd5-b03f-19c1d205af33"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"AI learns patterns from data to make predictions or decisions.\n"
]
}
],
"source": [
"response = client.models.generate_content(\n",
" model=MODEL, contents=\"Explain how AI works in a few words.\"\n",
")\n",
"\n",
"print(response.text)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vmU52b4UL0b6"
},
"source": [
"> *Do it yourself: Update the above using a different Gemini version available. Did the response change?*"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Wk_ysYx7L0b7"
},
"source": [
"#### Use images in your prompt\n",
"\n",
"Here we download an image from a URL and pass that image in our prompt.\n",
"\n",
"First, we download the image and load it with PIL:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OeR32394L0b7",
"outputId": "175cc3aa-aeb8-457b-9b17-3221d08b5b41"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"\r 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\r100 349k 100 349k 0 0 3061k 0 --:--:-- --:--:-- --:--:-- 3090k\n"
]
}
],
"source": [
"!curl -o image.jpg https://storage.googleapis.com/generativeai-downloads/images/jetpack.jpg"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 923
},
"id": "_KifkGlOL0b7",
"outputId": "69cbf341-7fbd-4eb0-9ed0-8c65f748fbcc"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"