From 48ae981d69681d3f5848ce47591093ed483fe7ab Mon Sep 17 00:00:00 2001
From: Harrison Chase <hw.chase.17@gmail.com>
Date: Sun, 25 Dec 2022 09:52:48 -0500
Subject: [PATCH] Harrison/multi input tools (#421)

add documentation on how to use tools that require multiple inputs
---
 docs/examples/agents.rst                    |   2 +
 docs/examples/agents/multi_input_tool.ipynb | 136 ++++++++++++++++++++
 2 files changed, 138 insertions(+)
 create mode 100644 docs/examples/agents/multi_input_tool.ipynb

diff --git a/docs/examples/agents.rst b/docs/examples/agents.rst
index b8e8077fe54..80bcc4500d6 100644
--- a/docs/examples/agents.rst
+++ b/docs/examples/agents.rst
@@ -9,6 +9,8 @@ The first category of how-to guides here cover specific parts of working with ag
 
 `Custom Agent <agents/custom_agent.ipynb>`_: How to create a custom agent (specifically, a custom LLM + prompt to drive that agent).
 
+`Multi Input Tools <agents/multi_input_tool.ipynb>`_: How to use a tool that requires multiple inputs with an agent.
+
 
 The next set of examples are all end-to-end agents for specific applications.
 In all examples there is an Agent with a particular set of tools.
diff --git a/docs/examples/agents/multi_input_tool.ipynb b/docs/examples/agents/multi_input_tool.ipynb
new file mode 100644
index 00000000000..d92915619c1
--- /dev/null
+++ b/docs/examples/agents/multi_input_tool.ipynb
@@ -0,0 +1,136 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "87455ddb",
+   "metadata": {},
+   "source": [
+    "# Multi Input Tools\n",
+    "\n",
+    "This notebook shows how to use a tool that requires multiple inputs with an agent.\n",
+    "\n",
+    "The difficulty in doing so comes from the fact that an agent decides it's next step from a language model, which outputs a string. So if that step requires multiple inputs, they need to be parsed from that. Therefor, the currently supported way to do this is write a smaller wrapper function that parses that a string into multiple inputs.\n",
+    "\n",
+    "For a concrete example, let's work on giving an agent access to a multiplication function, which takes as input two integers. In order to use this, we will tell the agent to generate the \"Action Input\" as a comma separated list of length two. We will then write a thin wrapper that takes a string, splits it into two around a comma, and passes both parsed sides as integers to the multiplication function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "291149b6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "from langchain.agents import initialize_agent, Tool"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71b6bead",
+   "metadata": {},
+   "source": [
+    "Here is the multiplication function, as well as a wrapper to parse a string as input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "f0b82020",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def multiplier(a, b):\n",
+    "    return a * b\n",
+    "\n",
+    "def parsing_multiplier(string):\n",
+    "    a, b = string.split(\",\")\n",
+    "    return multiplier(int(a), int(b))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6db1d43f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "llm = OpenAI(temperature=0)\n",
+    "tools = [\n",
+    "    Tool(\n",
+    "        name = \"Multiplier\",\n",
+    "        func=parsing_multiplier,\n",
+    "        description=\"useful for when you need to multiply two numbers together. The input to this tool should be a comma separated list of numbers of length two, representing the two numbers you want to multiply together. For example, `1,2` would be the input if you wanted to multiply 1 by 2.\"\n",
+    "    )\n",
+    "]\n",
+    "mrkl = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "aa25d0ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
+      "\u001b[32;1m\u001b[1;3m I need to multiply two numbers\n",
+      "Action: Multiplier\n",
+      "Action Input: 3,4\u001b[0m\n",
+      "Observation: \u001b[36;1m\u001b[1;3m12\u001b[0m\n",
+      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
+      "Final Answer: 3 times 4 is 12\u001b[0m\n",
+      "\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'3 times 4 is 12'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mrkl.run(\"What is 3 times 4\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ea340c0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}