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https://github.com/hwchase17/langchain.git
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5c0c5fafb2
Add a notebook showing how to make a chain that composes multiple OpenAPI Endpoint operations to accomplish tasks.
293 lines
8.0 KiB
Plaintext
293 lines
8.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "c7ad998d",
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"metadata": {},
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"source": [
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"# Multi-hop Task Execution with the NLAToolkit\n",
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"\n",
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"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",
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"\n",
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"For a detailed walkthrough of the OpenAPI chains wrapped within the NLAToolkit, see the [OpenAPI Operation Chain](openapi.ipynb) notebook.\n",
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"\n",
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"### First, import dependencies and load the LLM"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6593f793",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from typing import List, Optional\n",
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"from langchain.chains import LLMChain\n",
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"from langchain.llms import OpenAI\n",
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"from langchain.prompts import PromptTemplate\n",
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"from langchain.requests import Requests\n",
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"from langchain.tools import APIOperation, OpenAPISpec\n",
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"from langchain.agents import AgentType, Tool, initialize_agent\n",
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"from langchain.agents.agent_toolkits import NLAToolkit"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dd720860",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Select the LLM to use. Here, we use text-davinci-003\n",
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"llm = OpenAI(temperature=0, max_tokens=700) # You can swap between different core LLM's here."
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]
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},
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{
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"cell_type": "markdown",
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"id": "4cadac9d",
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"metadata": {
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"tags": []
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},
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"source": [
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"### Next, load the Natural Language API Toolkits"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6b208ab0",
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"metadata": {
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"scrolled": true,
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"tags": []
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},
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"outputs": [],
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"source": [
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"speak_toolkit = NLAToolkit.from_llm_and_url(llm, \"https://api.speak.com/openapi.yaml\")\n",
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"klarna_toolkit = NLAToolkit.from_llm_and_url(llm, \"https://www.klarna.com/us/shopping/public/openai/v0/api-docs/\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "16c7336f",
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"metadata": {},
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"source": [
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"### Create the Agent"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "730a0dc2-b4d0-46d5-a1e9-583803220973",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Slightly tweak the instructions from the default agent\n",
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"openapi_format_instructions = \"\"\"Use the following format:\n",
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"\n",
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"Question: the input question you must answer\n",
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"Thought: you should always think about what to do\n",
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"Action: the action to take, should be one of [{tool_names}]\n",
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"Action Input: what to instruct the AI Action representative.\n",
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"Observation: The Agent's response\n",
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"... (this Thought/Action/Action Input/Observation can repeat N times)\n",
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"Thought: I now know the final answer. User can't see any of my observations, API responses, links, or tools.\n",
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"Final Answer: the final answer to the original input question with the right amount of detail\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "40a979c3",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"natural_language_tools = speak_toolkit.get_tools() + klarna_toolkit.get_tools()\n",
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"mrkl = initialize_agent(natural_language_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, \n",
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" verbose=True, agent_kwargs={\"format_instructions\":openapi_format_instructions})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "794380ba",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"mrkl.run(\"I have an end of year party for my Italian class and have to buy some Italian clothes for it\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c61d92a8",
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"metadata": {},
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"source": [
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"### Using Auth + Adding more Endpoints\n",
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"\n",
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"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",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "markdown",
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"id": "f0d132cc",
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"metadata": {},
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"source": [
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"**Adding the Spoonacular endpoints.**\n",
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"\n",
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"1. Go to the [Spoonacular API Console](https://spoonacular.com/food-api/console#Profile) and make a free account.\n",
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"2. Click on `Profile` and copy your API key below."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c2368b9c",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"spoonacular_api_key = \"\" # Copy from the API Console"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fbd97c28-fef6-41b5-9600-a9611a32bfb3",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"requests = Requests(headers={\"x-api-key\": spoonacular_api_key})\n",
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"spoonacular_toolkit = NLAToolkit.from_llm_and_url(\n",
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" llm, \n",
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" \"https://spoonacular.com/application/frontend/downloads/spoonacular-openapi-3.json\",\n",
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" requests=requests,\n",
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" max_text_length=1800, # If you want to truncate the response text\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "81a6edac",
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"metadata": {
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"scrolled": true,
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"tags": []
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},
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"outputs": [],
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"source": [
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"natural_language_api_tools = (speak_toolkit.get_tools() \n",
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" + klarna_toolkit.get_tools() \n",
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" + spoonacular_toolkit.get_tools()[:30]\n",
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" )\n",
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"print(f\"{len(natural_language_api_tools)} tools loaded.\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "831f772d-5cd1-4467-b494-a3172af2ff48",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Create an agent with the new tools\n",
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"mrkl = initialize_agent(natural_language_api_tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, \n",
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" verbose=True, agent_kwargs={\"format_instructions\":openapi_format_instructions})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0385e04b",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Make the query more complex!\n",
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"user_input = (\n",
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" \"I'm learning Italian, and my language class is having an end of year party... \"\n",
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" \" Could you help me find an Italian outfit to wear and\"\n",
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" \" an appropriate recipe to prepare so I can present for the class in Italian?\"\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6ebd3f55",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"mrkl.run(user_input)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a2959462",
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"metadata": {},
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"source": [
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"## Thank you!"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6fcda5f0",
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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"natural_language_api_tools[1].run(\"Tell the LangChain audience to 'enjoy the meal' in Italian, please!\")['output']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ab366dc0",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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