Untitled19.ipynb

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    "from marvin import openai\n",
    "\n",
    "\n",
    "def annuity_present_value(p: int, r: float, n: int) -> list[str]:\n",
    "    \"\"\"\n",
    "    Returns the present value of an annuity with principal `p`,\n",
    "    interest rate `r` and number of months `n`.\n",
    "    \"\"\"\n",
    "    return round(p * (1 - (1 + (r / 12)) ** (-n)) / (r / 12), 2)\n",
    "\n",
    "\n",
    "response = openai.ChatCompletion.create(\n",
    "    messages=[\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": \"What if I put it $100 every month for 60 months at 12%?\",\n",
    "        }\n",
    "    ],\n",
    "    functions=[annuity_present_value],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
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   "id": "8c0ec914",
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   "source": [
    "respons2 = openai.ChatCompletion.create(\n",
    "    messages=[\n",
    "        {\n",
    "            \"role\": \"user\",\n",
    "            \"content\": \"What if I put it $100 every month for 60 months at 12%?\",\n",
    "        },\n",
    "        response.call_function(),\n",
    "    ],\n",
    "    functions=[annuity_present_value],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f5dbf0ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<OpenAIObject at 0x1176d5130> JSON: {\n",
       "  \"role\": \"assistant\",\n",
       "  \"content\": \"If you invest $100 every month for 60 months at an interest rate of 12%, the present value of the annuity would be $4,495.50.\"\n",
       "}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "respons2.message"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4d6df9e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'role': 'user', 'content': \"What's the weather like in Boston?\"},\n",
       " <OpenAIObject at 0x11748d5b0> JSON: {\n",
       "   \"role\": \"assistant\",\n",
       "   \"content\": null,\n",
       "   \"function_call\": {\n",
       "     \"name\": \"get_current_weather\",\n",
       "     \"arguments\": \"{\\n  \\\"location\\\": \\\"Boston, MA\\\"\\n}\"\n",
       "   }\n",
       " },\n",
       " {'role': 'function',\n",
       "  'name': 'get_current_weather',\n",
       "  'content': '{\"location\": \"Boston, MA\", \"temperature\": \"72\", \"unit\": null, \"forecast\": [\"sunny\", \"windy\"]}'}]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from marvin import settings\n",
    "import openai\n",
    "import json\n",
    "\n",
    "\n",
    "# Example dummy function hard coded to return the same weather\n",
    "# In production, this could be your backend API or an external API\n",
    "def get_current_weather(location, unit=\"fahrenheit\"):\n",
    "    \"\"\"Get the current weather in a given location\"\"\"\n",
    "    weather_info = {\n",
    "        \"location\": location,\n",
    "        \"temperature\": \"72\",\n",
    "        \"unit\": unit,\n",
    "        \"forecast\": [\"sunny\", \"windy\"],\n",
    "    }\n",
    "    return json.dumps(weather_info)\n",
    "\n",
    "\n",
    "def run_conversation():\n",
    "    # Step 1: send the conversation and available functions to GPT\n",
    "    messages = [{\"role\": \"user\", \"content\": \"What's the weather like in Boston?\"}]\n",
    "    functions = [\n",
    "        {\n",
    "            \"name\": \"get_current_weather\",\n",
    "            \"description\": \"Get the current weather in a given location\",\n",
    "            \"parameters\": {\n",
    "                \"type\": \"object\",\n",
    "                \"properties\": {\n",
    "                    \"location\": {\n",
    "                        \"type\": \"string\",\n",
    "                        \"description\": \"The city and state, e.g. San Francisco, CA\",\n",
    "                    },\n",
    "                    \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"]},\n",
    "                },\n",
    "                \"required\": [\"location\"],\n",
    "            },\n",
    "        }\n",
    "    ]\n",
    "    response = openai.ChatCompletion.create(\n",
    "        model=\"gpt-3.5-turbo-0613\",\n",
    "        api_key=settings.openai.api_key.get_secret_value(),\n",
    "        messages=messages,\n",
    "        functions=functions,\n",
    "        function_call=\"auto\",  # auto is default, but we'll be explicit\n",
    "    )\n",
    "    response_message = response[\"choices\"][0][\"message\"]\n",
    "\n",
    "    # Step 2: check if GPT wanted to call a function\n",
    "    if response_message.get(\"function_call\"):\n",
    "        # Step 3: call the function\n",
    "        # Note: the JSON response may not always be valid; be sure to handle errors\n",
    "        available_functions = {\n",
    "            \"get_current_weather\": get_current_weather,\n",
    "        }  # only one function in this example, but you can have multiple\n",
    "        function_name = response_message[\"function_call\"][\"name\"]\n",
    "        fuction_to_call = available_functions[function_name]\n",
    "        function_args = json.loads(response_message[\"function_call\"][\"arguments\"])\n",
    "        function_response = fuction_to_call(\n",
    "            location=function_args.get(\"location\"),\n",
    "            unit=function_args.get(\"unit\"),\n",
    "        )\n",
    "\n",
    "        # Step 4: send the info on the function call and function response to GPT\n",
    "        messages.append(response_message)  # extend conversation with assistant's reply\n",
    "        messages.append(\n",
    "            {\n",
    "                \"role\": \"function\",\n",
    "                \"name\": function_name,\n",
    "                \"content\": function_response,\n",
    "            }\n",
    "        )  # extend conversation with function response\n",
    "\n",
    "        return messages\n",
    "        second_response = openai.ChatCompletion.create(\n",
    "            model=\"gpt-3.5-turbo-0613\",\n",
    "            messages=messages,\n",
    "            api_key=settings.openai.api_key.get_secret_value(),\n",
    "        )  # get a new response from GPT where it can see the function response\n",
    "        return second_response\n",
    "\n",
    "\n",
    "run_conversation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "817ef578",
   "metadata": {},
   "outputs": [],
   "source": []
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