00-playground/42-functions-tools-agents-langchain/L5-tools-routing-apis-student.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b815ef73",
   "metadata": {},
   "source": [
    "# Tools and Routing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cb41f5f4-df8d-4d04-9eaa-193b8c29b00b",
   "metadata": {
    "height": 115,
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import openai\n",
    "\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "_ = load_dotenv(find_dotenv()) # read local .env file\n",
    "openai.api_key = os.environ['OPENAI_API_KEY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "139c45e6-65a4-4fcb-a109-e5ba41a80835",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "from langchain.agents import tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8a17f623-9342-46bd-a913-9ea6be32a7a9",
   "metadata": {
    "height": 81
   },
   "outputs": [],
   "source": [
    "@tool\n",
    "def search(query: str) -> str:\n",
    "    \"\"\"Search for weather online\"\"\"\n",
    "    return \"42f\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "db510949-acb8-4f69-b6c9-242b84fdc63a",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'search'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c4c12426-5e1a-4057-8ae5-6dfb6beace97",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'search(query: str) -> str - Search for weather online'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "22d1c4e6-8313-41a0-ae7a-61343699cb7c",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'query': {'title': 'Query', 'type': 'string'}}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8765af16-17fc-4490-98ab-4e9dd59337cf",
   "metadata": {
    "height": 81
   },
   "outputs": [],
   "source": [
    "from pydantic import BaseModel, Field\n",
    "class SearchInput(BaseModel):\n",
    "    query: str = Field(description=\"Thing to search for\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "91d20261-e18f-4989-808c-c4f36643d024",
   "metadata": {
    "height": 81
   },
   "outputs": [],
   "source": [
    "@tool(args_schema=SearchInput)\n",
    "def search(query: str) -> str:\n",
    "    \"\"\"Search for the weather online.\"\"\"\n",
    "    return \"42f\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "88b3f7d0-a856-4842-82da-6c00df53e9d9",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'query': {'title': 'Query',\n",
       "  'description': 'Thing to search for',\n",
       "  'type': 'string'}}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "060dc15b-f95f-47ab-b081-d6736f277ebc",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'42f'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search.run(\"sf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e93b85b2-1786-4f63-8e65-cd6a8b5c0b33",
   "metadata": {
    "height": 693
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "from pydantic import BaseModel, Field\n",
    "import datetime\n",
    "\n",
    "# Define the input schema\n",
    "class OpenMeteoInput(BaseModel):\n",
    "    latitude: float = Field(..., description=\"Latitude of the location to fetch weather data for\")\n",
    "    longitude: float = Field(..., description=\"Longitude of the location to fetch weather data for\")\n",
    "\n",
    "@tool(args_schema=OpenMeteoInput)\n",
    "def get_current_temperature(latitude: float, longitude: float) -> dict:\n",
    "    \"\"\"Fetch current temperature for given coordinates.\"\"\"\n",
    "    \n",
    "    BASE_URL = \"https://api.open-meteo.com/v1/forecast\"\n",
    "    \n",
    "    # Parameters for the request\n",
    "    params = {\n",
    "        'latitude': latitude,\n",
    "        'longitude': longitude,\n",
    "        'hourly': 'temperature_2m',\n",
    "        'forecast_days': 1,\n",
    "    }\n",
    "\n",
    "    # Make the request\n",
    "    response = requests.get(BASE_URL, params=params)\n",
    "    \n",
    "    if response.status_code == 200:\n",
    "        results = response.json()\n",
    "    else:\n",
    "        raise Exception(f\"API Request failed with status code: {response.status_code}\")\n",
    "\n",
    "    current_utc_time = datetime.datetime.utcnow()\n",
    "    time_list = [datetime.datetime.fromisoformat(time_str.replace('Z', '+00:00')) for time_str in results['hourly']['time']]\n",
    "    temperature_list = results['hourly']['temperature_2m']\n",
    "    \n",
    "    closest_time_index = min(range(len(time_list)), key=lambda i: abs(time_list[i] - current_utc_time))\n",
    "    current_temperature = temperature_list[closest_time_index]\n",
    "    \n",
    "    return f'The current temperature is {current_temperature}°C'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "731ed353-a27d-42df-89a8-9767bafb23be",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'get_current_temperature'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_current_temperature.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d4f62eb8-c528-4e4a-b078-22e9d9b20a09",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'get_current_temperature(latitude: float, longitude: float) -> dict - Fetch current temperature for given coordinates.'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_current_temperature.description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "38ebb59f-cf0f-4281-99c3-87c8cfd3377b",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'latitude': {'title': 'Latitude',\n",
       "  'description': 'Latitude of the location to fetch weather data for',\n",
       "  'type': 'number'},\n",
       " 'longitude': {'title': 'Longitude',\n",
       "  'description': 'Longitude of the location to fetch weather data for',\n",
       "  'type': 'number'}}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_current_temperature.args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "36aacbec-9cdd-414a-b502-168cea350a30",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "from langchain.tools.render import format_tool_to_openai_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6277b6a8-f197-4057-a96a-5ebb61e18520",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'get_current_temperature',\n",
       " 'description': 'get_current_temperature(latitude: float, longitude: float) -> dict - Fetch current temperature for given coordinates.',\n",
       " 'parameters': {'title': 'OpenMeteoInput',\n",
       "  'type': 'object',\n",
       "  'properties': {'latitude': {'title': 'Latitude',\n",
       "    'description': 'Latitude of the location to fetch weather data for',\n",
       "    'type': 'number'},\n",
       "   'longitude': {'title': 'Longitude',\n",
       "    'description': 'Longitude of the location to fetch weather data for',\n",
       "    'type': 'number'}},\n",
       "  'required': ['latitude', 'longitude']}}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format_tool_to_openai_function(get_current_temperature)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e088eceb-424b-4da6-981f-63322c9ac56a",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The current temperature is 30.0°C'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_current_temperature({\"latitude\": 13, \"longitude\": 14})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5eefc272-d205-4bad-ab00-282ccb3cc2fe",
   "metadata": {
    "height": 336
   },
   "outputs": [],
   "source": [
    "import wikipedia\n",
    "@tool\n",
    "def search_wikipedia(query: str) -> str:\n",
    "    \"\"\"Run Wikipedia search and get page summaries.\"\"\"\n",
    "    page_titles = wikipedia.search(query)\n",
    "    summaries = []\n",
    "    for page_title in page_titles[: 3]:\n",
    "        try:\n",
    "            wiki_page =  wikipedia.page(title=page_title, auto_suggest=False)\n",
    "            summaries.append(f\"Page: {page_title}\\nSummary: {wiki_page.summary}\")\n",
    "        except (\n",
    "            self.wiki_client.exceptions.PageError,\n",
    "            self.wiki_client.exceptions.DisambiguationError,\n",
    "        ):\n",
    "            pass\n",
    "    if not summaries:\n",
    "        return \"No good Wikipedia Search Result was found\"\n",
    "    return \"\\n\\n\".join(summaries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "289c5d0c-0e71-4d70-963f-9d9f58e53d56",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'search_wikipedia'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_wikipedia.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "69756d79-ec2f-4185-89af-d14de817fc27",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'search_wikipedia(query: str) -> str - Run Wikipedia search and get page summaries.'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_wikipedia.description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d5b876cd-cc6c-484d-a3fa-d8810e22d564",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'search_wikipedia',\n",
       " 'description': 'search_wikipedia(query: str) -> str - Run Wikipedia search and get page summaries.',\n",
       " 'parameters': {'title': 'search_wikipediaSchemaSchema',\n",
       "  'type': 'object',\n",
       "  'properties': {'query': {'title': 'Query', 'type': 'string'}},\n",
       "  'required': ['query']}}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "format_tool_to_openai_function(search_wikipedia)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6dfb10e9-5d1e-474a-9c8f-7abdf7939a34",
   "metadata": {
    "height": 30,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Page: LangChain\\nSummary: LangChain is a software framework that helps facilitate the integration of large language models (LLMs) into applications. As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis.\\n\\nPage: Milvus (vector database)\\nSummary: Milvus is a distributed vector database developed by Zilliz. It is available as both open-source software and a cloud service.\\nMilvus is an open-source project under LF AI & Data Foundation distributed under the Apache License 2.0.\\n\\nPage: Retrieval-augmented generation\\nSummary: Retrieval-Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data. This allows LLMs to use domain-specific and/or updated information.  \\nUse cases include providing chatbot access to internal company data or giving factual information only from an authoritative source.\\n\\n\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "search_wikipedia({\"query\": \"langchain\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "54cbe57e-f64c-4392-bcdd-9621fe1e46e5",
   "metadata": {
    "height": 64
   },
   "outputs": [],
   "source": [
    "from langchain.chains.openai_functions.openapi import openapi_spec_to_openai_fn\n",
    "from langchain.utilities.openapi import OpenAPISpec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7fe38e50-874c-49bd-8681-f95dcab0b464",
   "metadata": {
    "height": 3107
   },
   "outputs": [],
   "source": [
    "text = \"\"\"\n",
    "{\n",
    "  \"openapi\": \"3.1.0\",\n",
    "  \"info\": {\n",
    "    \"version\": \"1.0.0\",\n",
    "    \"title\": \"Swagger Petstore\",\n",
    "    \"license\": {\n",
    "      \"name\": \"MIT\"\n",
    "    }\n",
    "  },\n",
    "  \"servers\": [\n",
    "    {\n",
    "      \"url\": \"http://petstore.swagger.io/v1\"\n",
    "    }\n",
    "  ],\n",
    "  \"paths\": {\n",
    "    \"/pets\": {\n",
    "      \"get\": {\n",
    "        \"summary\": \"List all pets\",\n",
    "        \"operationId\": \"listPets\",\n",
    "        \"tags\": [\n",
    "          \"pets\"\n",
    "        ],\n",
    "        \"parameters\": [\n",
    "          {\n",
    "            \"name\": \"limit\",\n",
    "            \"in\": \"query\",\n",
    "            \"description\": \"How many items to return at one time (max 100)\",\n",
    "            \"required\": false,\n",
    "            \"schema\": {\n",
    "              \"type\": \"integer\",\n",
    "              \"maximum\": 100,\n",
    "              \"format\": \"int32\"\n",
    "            }\n",
    "          }\n",
    "        ],\n",
    "        \"responses\": {\n",
    "          \"200\": {\n",
    "            \"description\": \"A paged array of pets\",\n",
    "            \"headers\": {\n",
    "              \"x-next\": {\n",
    "                \"description\": \"A link to the next page of responses\",\n",
    "                \"schema\": {\n",
    "                  \"type\": \"string\"\n",
    "                }\n",
    "              }\n",
    "            },\n",
    "            \"content\": {\n",
    "              \"application/json\": {\n",
    "                \"schema\": {\n",
    "                  \"$ref\": \"#/components/schemas/Pets\"\n",
    "                }\n",
    "              }\n",
    "            }\n",
    "          },\n",
    "          \"default\": {\n",
    "            \"description\": \"unexpected error\",\n",
    "            \"content\": {\n",
    "              \"application/json\": {\n",
    "                \"schema\": {\n",
    "                  \"$ref\": \"#/components/schemas/Error\"\n",
    "                }\n",
    "              }\n",
    "            }\n",
    "          }\n",
    "        }\n",
    "      },\n",
    "      \"post\": {\n",
    "        \"summary\": \"Create a pet\",\n",
    "        \"operationId\": \"createPets\",\n",
    "        \"tags\": [\n",
    "          \"pets\"\n",
    "        ],\n",
    "        \"responses\": {\n",
    "          \"201\": {\n",
    "            \"description\": \"Null response\"\n",
    "          },\n",
    "          \"default\": {\n",
    "            \"description\": \"unexpected error\",\n",
    "            \"content\": {\n",
    "              \"application/json\": {\n",
    "                \"schema\": {\n",
    "                  \"$ref\": \"#/components/schemas/Error\"\n",
    "                }\n",
    "              }\n",
    "            }\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    },\n",
    "    \"/pets/{petId}\": {\n",
    "      \"get\": {\n",
    "        \"summary\": \"Info for a specific pet\",\n",
    "        \"operationId\": \"showPetById\",\n",
    "        \"tags\": [\n",
    "          \"pets\"\n",
    "        ],\n",
    "        \"parameters\": [\n",
    "          {\n",
    "            \"name\": \"petId\",\n",
    "            \"in\": \"path\",\n",
    "            \"required\": true,\n",
    "            \"description\": \"The id of the pet to retrieve\",\n",
    "            \"schema\": {\n",
    "              \"type\": \"string\"\n",
    "            }\n",
    "          }\n",
    "        ],\n",
    "        \"responses\": {\n",
    "          \"200\": {\n",
    "            \"description\": \"Expected response to a valid request\",\n",
    "            \"content\": {\n",
    "              \"application/json\": {\n",
    "                \"schema\": {\n",
    "                  \"$ref\": \"#/components/schemas/Pet\"\n",
    "                }\n",
    "              }\n",
    "            }\n",
    "          },\n",
    "          \"default\": {\n",
    "            \"description\": \"unexpected error\",\n",
    "            \"content\": {\n",
    "              \"application/json\": {\n",
    "                \"schema\": {\n",
    "                  \"$ref\": \"#/components/schemas/Error\"\n",
    "                }\n",
    "              }\n",
    "            }\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    }\n",
    "  },\n",
    "  \"components\": {\n",
    "    \"schemas\": {\n",
    "      \"Pet\": {\n",
    "        \"type\": \"object\",\n",
    "        \"required\": [\n",
    "          \"id\",\n",
    "          \"name\"\n",
    "        ],\n",
    "        \"properties\": {\n",
    "          \"id\": {\n",
    "            \"type\": \"integer\",\n",
    "            \"format\": \"int64\"\n",
    "          },\n",
    "          \"name\": {\n",
    "            \"type\": \"string\"\n",
    "          },\n",
    "          \"tag\": {\n",
    "            \"type\": \"string\"\n",
    "          }\n",
    "        }\n",
    "      },\n",
    "      \"Pets\": {\n",
    "        \"type\": \"array\",\n",
    "        \"maxItems\": 100,\n",
    "        \"items\": {\n",
    "          \"$ref\": \"#/components/schemas/Pet\"\n",
    "        }\n",
    "      },\n",
    "      \"Error\": {\n",
    "        \"type\": \"object\",\n",
    "        \"required\": [\n",
    "          \"code\",\n",
    "          \"message\"\n",
    "        ],\n",
    "        \"properties\": {\n",
    "          \"code\": {\n",
    "            \"type\": \"integer\",\n",
    "            \"format\": \"int32\"\n",
    "          },\n",
    "          \"message\": {\n",
    "            \"type\": \"string\"\n",
    "          }\n",
    "        }\n",
    "      }\n",
    "    }\n",
    "  }\n",
    "}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "653156d4-d6a4-499b-aab3-cf4f7b1f0c47",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "spec = OpenAPISpec.from_text(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d9ba6100-3c88-4b05-af93-784b57bf7424",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "pet_openai_functions, pet_callables = openapi_spec_to_openai_fn(spec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "91199697-c662-438f-8c68-e6daa8aad07d",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'listPets',\n",
       "  'description': 'List all pets',\n",
       "  'parameters': {'type': 'object',\n",
       "   'properties': {'params': {'type': 'object',\n",
       "     'properties': {'limit': {'type': 'integer',\n",
       "       'maximum': 100.0,\n",
       "       'schema_format': 'int32',\n",
       "       'description': 'How many items to return at one time (max 100)'}},\n",
       "     'required': []}}}},\n",
       " {'name': 'createPets',\n",
       "  'description': 'Create a pet',\n",
       "  'parameters': {'type': 'object', 'properties': {}}},\n",
       " {'name': 'showPetById',\n",
       "  'description': 'Info for a specific pet',\n",
       "  'parameters': {'type': 'object',\n",
       "   'properties': {'path_params': {'type': 'object',\n",
       "     'properties': {'petId': {'type': 'string',\n",
       "       'description': 'The id of the pet to retrieve'}},\n",
       "     'required': ['petId']}}}}]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pet_openai_functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ae8b3c1b-7a30-4b1b-abfd-e56f90b1d166",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "from langchain.chat_models import ChatOpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ed100888-f3d4-4739-bba5-f839033850a3",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "model = ChatOpenAI(temperature=0).bind(functions=pet_openai_functions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4063cdea-627c-4cc1-a2b5-9b5b6dcf179e",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content='', additional_kwargs={'function_call': {'name': 'listPets', 'arguments': '{\"params\":{\"limit\":3}}'}})"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.invoke(\"what are three pets names\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3a1f5534-cf06-4baf-b710-f169ea7434ac",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content='', additional_kwargs={'function_call': {'name': 'showPetById', 'arguments': '{\"path_params\":{\"petId\":\"42\"}}'}})"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.invoke(\"tell me about pet with id 42\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "198b21dd-c9de-491d-9a0c-71ae56727689",
   "metadata": {},
   "source": [
    "### Routing\n",
    "\n",
    "In lesson 3, we show an example of function calling deciding between two candidate functions.\n",
    "\n",
    "Given our tools above, let's format these as OpenAI functions and show this same behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8137758c-c5d3-47df-9062-5e31d43657e7",
   "metadata": {
    "height": 115
   },
   "outputs": [],
   "source": [
    "functions = [\n",
    "    format_tool_to_openai_function(f) for f in [\n",
    "        search_wikipedia, get_current_temperature\n",
    "    ]\n",
    "]\n",
    "model = ChatOpenAI(temperature=0).bind(functions=functions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "99120542-36bc-4ed1-aa9e-e2294e282c81",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_current_temperature', 'arguments': '{\"latitude\":37.7749,\"longitude\":-122.4194}'}})"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.invoke(\"what is the weather in sf right now\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a20047d9-4b34-4e6c-9407-570af1559bc8",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content='', additional_kwargs={'function_call': {'name': 'search_wikipedia', 'arguments': '{\"query\":\"Langchain\"}'}})"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.invoke(\"what is langchain\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "831a0506-1d5c-4bc3-b67f-24e201a4fa6d",
   "metadata": {
    "height": 115
   },
   "outputs": [],
   "source": [
    "from langchain.prompts import ChatPromptTemplate\n",
    "prompt = ChatPromptTemplate.from_messages([\n",
    "    (\"system\", \"You are helpful but sassy assistant\"),\n",
    "    (\"user\", \"{input}\"),\n",
    "])\n",
    "chain = prompt | model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d9c32ccc-6691-4dd5-98dc-aabf02563ad5",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_current_temperature', 'arguments': '{\"latitude\":37.7749,\"longitude\":-122.4194}'}})"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke({\"input\": \"what is the weather in sf right now\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "921e808c-6618-4d2f-85df-1f3089497724",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "094baef1-4140-41f4-9111-ff9710826e6b",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "chain = prompt | model | OpenAIFunctionsAgentOutputParser()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f105e8e8-d418-4d4a-95eb-52636d4e890f",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "result = chain.invoke({\"input\": \"what is the weather in sf right now\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "409b26c0-a1e0-4225-ae2e-396c1f76bf0a",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "langchain.schema.agent.AgentActionMessageLog"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9cdde008-4b11-4ab2-a01c-6ae394a7dccf",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'get_current_temperature'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "66ffb308-3d77-4acb-b4b5-e2b0d38f3860",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'latitude': 37.7749, 'longitude': -122.4194}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.tool_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "07e661b1-6d0d-43b9-9de4-19c6cceff291",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The current temperature is 8.4°C'"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_current_temperature(result.tool_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "057e3072-a203-4a5e-b1f6-b250cd7bd33b",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "result = chain.invoke({\"input\": \"hi!\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5be6bc0f-5b81-49fa-ac16-b8896847d87a",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "langchain.schema.agent.AgentFinish"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "dd128d20-f552-4cc5-a45e-f47b58c9982b",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'output': 'Hello! How can I assist you today?'}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.return_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "134f422a-b72b-40df-9552-cf9f9fc2d780",
   "metadata": {
    "height": 183
   },
   "outputs": [],
   "source": [
    "from langchain.schema.agent import AgentFinish\n",
    "def route(result):\n",
    "    if isinstance(result, AgentFinish):\n",
    "        return result.return_values['output']\n",
    "    else:\n",
    "        tools = {\n",
    "            \"search_wikipedia\": search_wikipedia, \n",
    "            \"get_current_temperature\": get_current_temperature,\n",
    "        }\n",
    "        return tools[result.tool].run(result.tool_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8fefc329-b270-4ebb-b884-430e4e541e7f",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "chain = prompt | model | OpenAIFunctionsAgentOutputParser() | route"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b2dcb4b2-0e0d-425b-bff8-db7cd33afa16",
   "metadata": {
    "height": 47
   },
   "outputs": [],
   "source": [
    "result = chain.invoke({\"input\": \"What is the weather in san francisco right now?\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "498d487b-ceba-4e9f-8a24-0f8c27438a18",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The current temperature is 8.4°C'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "6c93eb1b-044f-4c52-bcd4-dcd0e01f42e8",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": [
    "result = chain.invoke({\"input\": \"What is langchain?\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5c0de3e7-2367-4cef-b3dd-efa69b701061",
   "metadata": {
    "height": 30,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Page: LangChain\\nSummary: LangChain is a software framework that helps facilitate the integration of large language models (LLMs) into applications. As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis.\\n\\nPage: Milvus (vector database)\\nSummary: Milvus is a distributed vector database developed by Zilliz. It is available as both open-source software and a cloud service.\\nMilvus is an open-source project under LF AI & Data Foundation distributed under the Apache License 2.0.\\n\\nPage: Retrieval-augmented generation\\nSummary: Retrieval-Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data. This allows LLMs to use domain-specific and/or updated information.  \\nUse cases include providing chatbot access to internal company data or giving factual information only from an authoritative source.\\n\\n\""
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a353363e-27b8-440f-a8b7-31bc2e861a42",
   "metadata": {
    "height": 30
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello! How can I assist you today?'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke({\"input\": \"hi!\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e1570d2-6215-4f24-802d-14e75db4319a",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca1bf47c-a050-47ab-a840-b3a67e2b69cc",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2124caf-4362-4608-996d-5cdff95551d7",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad6ca0db-6b1d-4d8e-8c63-3a485607fbd9",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f0ed82c-8e26-44b3-a1ea-67143ab65161",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c9b8542-7719-412c-b240-958ea8bdbbef",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83233f7c-3820-4697-80a6-c25423cc7b26",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a950c46-1a8c-4807-9cc4-373264aaa9dc",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b11cf095-9583-4206-8155-2a9acd5a4aad",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "181b3737-eb4d-452a-b018-a1289f7f0d41",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
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