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   "source": [
    "# Google Cloud Platform Vertex AI PaLM \n",
    "\n",
    "Note: This is seperate from the Google PaLM integration, it exposes [Vertex AI PaLM API](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) on Google Cloud. \n",
    "\n",
    "By default, Google Cloud [does not use](https://cloud.google.com/vertex-ai/docs/generative-ai/data-governance#foundation_model_development) Customer Data to train its foundation models as part of Google Cloud`s AI/ML Privacy Commitment. More details about how Google processes data can also be found in [Google's Customer Data Processing Addendum (CDPA)](https://cloud.google.com/terms/data-processing-addendum).\n",
    "\n",
    "To use Vertex AI PaLM you must have the `google-cloud-aiplatform` Python package installed and either:\n",
    "- Have credentials configured for your environment (gcloud, workload identity, etc...)\n",
    "- Store the path to a service account JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable\n",
    "\n",
    "This codebase uses the `google.auth` library which first looks for the application credentials variable mentioned above, and then looks for system-level auth.\n",
    "\n",
    "For more information, see: \n",
    "- https://cloud.google.com/docs/authentication/application-default-credentials#GAC\n",
    "- https://googleapis.dev/python/google-auth/latest/reference/google.auth.html#module-google.auth\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#!pip install google-cloud-aiplatform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.llms import VertexAI\n",
    "from langchain import PromptTemplate, LLMChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "template = \"\"\"Question: {question}\n",
    "\n",
    "Answer: Let's think step by step.\"\"\"\n",
    "\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = VertexAI()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Justin Bieber was born on March 1, 1994. The Super Bowl in 1994 was won by the San Francisco 49ers.\\nThe final answer: San Francisco 49ers.'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
    "\n",
    "llm_chain.run(question)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can now leverage the Codey API for code generation within Vertex AI. The model names are:\n",
    "- code-bison: for code suggestion\n",
    "- code-gecko: for code completion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-17T21:16:53.149438Z",
     "iopub.status.busy": "2023-06-17T21:16:53.149065Z",
     "iopub.status.idle": "2023-06-17T21:16:53.421824Z",
     "shell.execute_reply": "2023-06-17T21:16:53.421136Z",
     "shell.execute_reply.started": "2023-06-17T21:16:53.149415Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "llm = VertexAI(model_name=\"code-bison\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-17T21:17:11.179077Z",
     "iopub.status.busy": "2023-06-17T21:17:11.178686Z",
     "iopub.status.idle": "2023-06-17T21:17:11.182499Z",
     "shell.execute_reply": "2023-06-17T21:17:11.181895Z",
     "shell.execute_reply.started": "2023-06-17T21:17:11.179052Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-06-17T21:18:47.024785Z",
     "iopub.status.busy": "2023-06-17T21:18:47.024230Z",
     "iopub.status.idle": "2023-06-17T21:18:49.352249Z",
     "shell.execute_reply": "2023-06-17T21:18:49.351695Z",
     "shell.execute_reply.started": "2023-06-17T21:18:47.024762Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'```python\\ndef is_prime(n):\\n  \"\"\"\\n  Determines if a number is prime.\\n\\n  Args:\\n    n: The number to be tested.\\n\\n  Returns:\\n    True if the number is prime, False otherwise.\\n  \"\"\"\\n\\n  # Check if the number is 1.\\n  if n == 1:\\n    return False\\n\\n  # Check if the number is 2.\\n  if n == 2:\\n    return True\\n\\n'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question = \"Write a python function that identifies if the number is a prime number?\"\n",
    "\n",
    "llm_chain.run(question)"
   ]
  }
 ],
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