langchain/docs/modules/chains/examples/multihop_openapi.ipynb

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  {
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   "source": [
    "# Multi-hop Task Execution with the NLAToolkit\n",
    "\n",
    "Natural Language API Toolkits (NLAToolkits) permit LangChain Agents to efficiently plan and combine calls across endpoints. This notebook demonstrates a sample composition of the Speak, Klarna, and Spoonacluar APIs.\n",
    "\n",
    "For a detailed walkthrough of the OpenAPI chains wrapped within the NLAToolkit, see the [OpenAPI Operation Chain](openapi.ipynb) notebook.\n",
    "\n",
    "### First, import dependencies and load the LLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6593f793",
   "metadata": {
    "tags": []
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   "source": [
    "from typing import List, Optional\n",
    "from langchain.chains import LLMChain\n",
    "from langchain.llms import OpenAI\n",
    "from langchain.prompts import PromptTemplate\n",
    "from langchain.requests import Requests\n",
    "from langchain.tools import APIOperation, OpenAPISpec\n",
    "from langchain.agents import AgentType, Tool, initialize_agent\n",
    "from langchain.agents.agent_toolkits import NLAToolkit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd720860",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Select the LLM to use. Here, we use text-davinci-003\n",
    "llm = OpenAI(temperature=0, max_tokens=700) # You can swap between different core LLM's here."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cadac9d",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Next, load the Natural Language API Toolkits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b208ab0",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "speak_toolkit = NLAToolkit.from_llm_and_url(llm, \"https://api.speak.com/openapi.yaml\")\n",
    "klarna_toolkit = NLAToolkit.from_llm_and_url(llm, \"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16c7336f",
   "metadata": {},
   "source": [
    "### Create the Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "730a0dc2-b4d0-46d5-a1e9-583803220973",
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   "source": [
    "# Slightly tweak the instructions from the default agent\n",
    "openapi_format_instructions = \"\"\"Use the following format:\n",
    "\n",
    "Question: the input question you must answer\n",
    "Thought: you should always think about what to do\n",
    "Action: the action to take, should be one of [{tool_names}]\n",
    "Action Input: what to instruct the AI Action representative.\n",
    "Observation: The Agent's response\n",
    "... (this Thought/Action/Action Input/Observation can repeat N times)\n",
    "Thought: I now know the final answer. User can't see any of my observations, API responses, links, or tools.\n",
    "Final Answer: the final answer to the original input question with the right amount of detail\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40a979c3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "natural_language_tools = speak_toolkit.get_tools() + klarna_toolkit.get_tools()\n",
    "mrkl = initialize_agent(natural_language_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, \n",
    "                        verbose=True, agent_kwargs={\"format_instructions\":openapi_format_instructions})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "794380ba",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mrkl.run(\"I have an end of year party for my Italian class and have to buy some Italian clothes for it\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c61d92a8",
   "metadata": {},
   "source": [
    "### Using Auth + Adding more Endpoints\n",
    "\n",
    "Some endpoints may require user authentication via things like access tokens. Here we show how to pass in the authentication information via the `Requests` wrapper object.\n",
    "\n",
    "Since each NLATool exposes a concisee natural language interface to its wrapped API, the top level conversational agent has an easier job incorporating each endpoint to satisfy a user's request."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0d132cc",
   "metadata": {},
   "source": [
    "**Adding the Spoonacular endpoints.**\n",
    "\n",
    "1. Go to the [Spoonacular API Console](https://spoonacular.com/food-api/console#Profile) and make a free account.\n",
    "2. Click on `Profile` and copy your API key below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2368b9c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "spoonacular_api_key = \"\" # Copy from the API Console"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbd97c28-fef6-41b5-9600-a9611a32bfb3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "requests = Requests(headers={\"x-api-key\": spoonacular_api_key})\n",
    "spoonacular_toolkit = NLAToolkit.from_llm_and_url(\n",
    "    llm, \n",
    "    \"https://spoonacular.com/application/frontend/downloads/spoonacular-openapi-3.json\",\n",
    "    requests=requests,\n",
    "    max_text_length=1800, # If you want to truncate the response text\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81a6edac",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "natural_language_api_tools = (speak_toolkit.get_tools() \n",
    "                              + klarna_toolkit.get_tools() \n",
    "                              + spoonacular_toolkit.get_tools()[:30]\n",
    "                             )\n",
    "print(f\"{len(natural_language_api_tools)} tools loaded.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "831f772d-5cd1-4467-b494-a3172af2ff48",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create an agent with the new tools\n",
    "mrkl = initialize_agent(natural_language_api_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, \n",
    "                        verbose=True, agent_kwargs={\"format_instructions\":openapi_format_instructions})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0385e04b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Make the query more complex!\n",
    "user_input = (\n",
    "    \"I'm learning Italian, and my language class is having an end of year party... \"\n",
    "    \" Could you help me find an Italian outfit to wear and\"\n",
    "    \" an appropriate recipe to prepare so I can present for the class in Italian?\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ebd3f55",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mrkl.run(user_input)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2959462",
   "metadata": {},
   "source": [
    "## Thank you!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fcda5f0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "natural_language_api_tools[1].run(\"Tell the LangChain audience to 'enjoy the meal' in Italian, please!\")['output']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab366dc0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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