mirror of https://github.com/hwchase17/langchain
docs: Remove duplicated content from how to tools (#21821)
Content is duplicated, and is covered in how to use chat models.pull/21450/head^2
Eugene Yurtsev
2 weeks ago
committed by
GitHub
parent
e59afe292d
commit
4ca2149b70
@ -1,273 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "raw",
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"id": "1ea1fe24-fe1e-463b-a52c-79f0ef02328e",
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"metadata": {},
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"source": [
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"---\n",
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"sidebar_position: 2\n",
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"---"
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]
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},
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{
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"cell_type": "markdown",
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"id": "95982bf1-7d9d-4dd6-a4ad-9de0719fe17f",
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"metadata": {},
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"source": [
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"# How to use an LLM to choose between multiple tools\n",
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"\n",
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"In our [Quickstart](/docs/how_to/tool_calling) we went over how to build a Chain that calls a single `multiply` tool. Now let's take a look at how we might augment this chain so that it can pick from a number of tools to call. We'll focus on Chains since [Agents](/docs/tutorials/agents) can route between multiple tools by default."
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]
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},
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{
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"cell_type": "markdown",
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"id": "3fafec38-443a-42ad-a913-5be7667e3734",
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"metadata": {},
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"source": [
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"## Setup\n",
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"\n",
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"We'll need to install the following packages for this guide:"
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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": "78411bf1-0117-4f33-a3d7-f3d77a97bb78",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install --upgrade --quiet langchain-core"
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]
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},
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{
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"cell_type": "markdown",
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"id": "59d08fd0-ddd9-4c74-bcea-a5ca3a86e542",
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"metadata": {},
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"source": [
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"If you'd like to trace your runs in [LangSmith](/docs/langsmith/) uncomment and set the following environment variables:"
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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": 1,
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"id": "4185e74b-0500-4cad-ace0-bac37de466ac",
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"metadata": {},
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"outputs": [],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d28159f5-b7d0-4385-aa44-4cd1b64507bb",
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"metadata": {},
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"source": [
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"## Tools\n",
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"\n",
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"Recall we already had a `multiply` tool:"
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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": 5,
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"id": "e13ec98c-8521-4d63-b521-caf92da87b70",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.tools import tool\n",
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"\n",
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"\n",
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"@tool\n",
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"def multiply(first_int: int, second_int: int) -> int:\n",
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" \"\"\"Multiply two integers together.\"\"\"\n",
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" return first_int * second_int"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3de233af-b3bd-4f0c-8b1a-83527143a8db",
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"metadata": {},
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"source": [
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"And now we can add to it an `exponentiate` and `add` tool:"
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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": 6,
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"id": "e93661cd-a2ba-4ada-91ad-baf1b60879ec",
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"metadata": {},
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"outputs": [],
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"source": [
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"@tool\n",
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"def add(first_int: int, second_int: int) -> int:\n",
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" \"Add two integers.\"\n",
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" return first_int + second_int\n",
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"\n",
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"\n",
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"@tool\n",
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"def exponentiate(base: int, exponent: int) -> int:\n",
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" \"Exponentiate the base to the exponent power.\"\n",
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" return base**exponent"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bbea4555-ed10-4a18-b802-e9a3071f132b",
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"metadata": {},
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"source": [
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"The main difference between using one Tool and many is that we can't be sure which Tool the model will invoke upfront, so we cannot hardcode, like we did in the [Quickstart](/docs/how_to/tool_calling), a specific tool into our chain. Instead we'll add `call_tools`, a `RunnableLambda` that takes the output AI message with tools calls and routes to the correct tools.\n",
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"\n",
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"```{=mdx}\n",
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"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
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"\n",
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"<ChatModelTabs customVarName=\"llm\"/>\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": 7,
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"id": "f00f0f3f-8530-4c1d-a26c-d20824e31faf",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_anthropic import ChatAnthropic\n",
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"\n",
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"llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)"
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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": 11,
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"id": "c35359ae-a740-48c5-b5e7-1a377fb25aa2",
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"metadata": {},
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"outputs": [],
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"source": [
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"from operator import itemgetter\n",
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"from typing import Dict, List, Union\n",
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"\n",
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"from langchain_core.messages import AIMessage\n",
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"from langchain_core.runnables import (\n",
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" Runnable,\n",
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" RunnableLambda,\n",
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" RunnableMap,\n",
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" RunnablePassthrough,\n",
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")\n",
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"\n",
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"tools = [multiply, exponentiate, add]\n",
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"llm_with_tools = llm.bind_tools(tools)\n",
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"tool_map = {tool.name: tool for tool in tools}\n",
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"\n",
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"\n",
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"def call_tools(msg: AIMessage) -> Runnable:\n",
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" \"\"\"Simple sequential tool calling helper.\"\"\"\n",
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" tool_map = {tool.name: tool for tool in tools}\n",
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" tool_calls = msg.tool_calls.copy()\n",
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" for tool_call in tool_calls:\n",
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" tool_call[\"output\"] = tool_map[tool_call[\"name\"]].invoke(tool_call[\"args\"])\n",
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" return tool_calls\n",
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"\n",
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"\n",
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"chain = llm_with_tools | call_tools"
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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": 12,
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"id": "ea6dbb32-ec9b-4c70-a90f-a2db93978cf1",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[{'name': 'multiply',\n",
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" 'args': {'first_int': 23, 'second_int': 7},\n",
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" 'id': 'toolu_01Wf8kUs36kxRKLDL8vs7G8q',\n",
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" 'output': 161}]"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain.invoke(\"What's 23 times 7\")"
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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": 13,
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"id": "b1c6c0f8-6d04-40d4-a40e-8719ca7b27c2",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[{'name': 'add',\n",
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" 'args': {'first_int': 1000000, 'second_int': 1000000000},\n",
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" 'id': 'toolu_012aK4xZBQg2sXARsFZnqxHh',\n",
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" 'output': 1001000000}]"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain.invoke(\"add a million plus a billion\")"
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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": 14,
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"id": "ce76f299-1a4d-421c-afa4-a6346e34285c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[{'name': 'exponentiate',\n",
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" 'args': {'base': 37, 'exponent': 3},\n",
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" 'id': 'toolu_01VDU6X3ugDb9cpnnmCZFPbC',\n",
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" 'output': 50653}]"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"chain.invoke(\"cube thirty-seven\")"
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]
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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": "3.10.1"
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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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@ -1,215 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "95982bf1-7d9d-4dd6-a4ad-9de0719fe17f",
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"metadata": {},
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"source": [
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"# How to call tools in parallel\n",
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"\n",
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"In the [Chains with multiple tools](/docs/how_to/tools_multiple) guide we saw how to build function-calling chains that select between multiple tools. Some models, like the OpenAI models released in Fall 2023, also support parallel function calling, which allows you to invoke multiple functions (or the same function multiple times) in a single model call. Our previous chain from the multiple tools guides actually already supports this."
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]
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},
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{
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"cell_type": "markdown",
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"id": "3fafec38-443a-42ad-a913-5be7667e3734",
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"metadata": {},
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"source": [
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"## Setup\n",
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"\n",
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"We'll need to install the following packages for this guide:"
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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": "78411bf1-0117-4f33-a3d7-f3d77a97bb78",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install --upgrade --quiet langchain-core"
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]
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},
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{
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"cell_type": "markdown",
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"id": "59d08fd0-ddd9-4c74-bcea-a5ca3a86e542",
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"metadata": {},
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"source": [
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"If you'd like to trace your runs in [LangSmith](/docs/langsmith/) uncomment and set the following environment variables:"
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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": "4185e74b-0500-4cad-ace0-bac37de466ac",
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"metadata": {},
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"outputs": [],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d28159f5-b7d0-4385-aa44-4cd1b64507bb",
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"metadata": {},
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"source": [
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"## Tools"
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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": 3,
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"id": "e13ec98c-8521-4d63-b521-caf92da87b70",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.tools import tool\n",
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"\n",
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"\n",
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"@tool\n",
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"def multiply(first_int: int, second_int: int) -> int:\n",
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" \"\"\"Multiply two integers together.\"\"\"\n",
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" return first_int * second_int\n",
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"\n",
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"\n",
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"@tool\n",
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"def add(first_int: int, second_int: int) -> int:\n",
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" \"Add two integers.\"\n",
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" return first_int + second_int\n",
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"\n",
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"\n",
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"@tool\n",
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"def exponentiate(base: int, exponent: int) -> int:\n",
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" \"Exponentiate the base to the exponent power.\"\n",
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" return base**exponent"
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]
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},
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{
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"cell_type": "markdown",
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"id": "119d419c-1c61-4e0d-834a-5dabb72f5514",
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"metadata": {},
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"source": [
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"# Chain\n",
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"\n",
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"```{=mdx}\n",
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"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
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"\n",
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"<ChatModelTabs customVarName=\"llm\" hideGoogle=\"true\"/>\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": 7,
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"id": "f67d91d8-cc38-4065-8f80-901e079954dd",
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"metadata": {},
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"outputs": [],
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"source": [
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"# | echo: false\n",
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"# | output: false\n",
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"\n",
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"from langchain_openai import ChatOpenAI\n",
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"\n",
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"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
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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": 8,
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"id": "c35359ae-a740-48c5-b5e7-1a377fb25aa2",
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"metadata": {},
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"outputs": [],
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"source": [
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"from operator import itemgetter\n",
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"from typing import Dict, List, Union\n",
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"\n",
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"from langchain_core.messages import AIMessage\n",
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"from langchain_core.runnables import (\n",
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" Runnable,\n",
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" RunnableLambda,\n",
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" RunnableMap,\n",
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" RunnablePassthrough,\n",
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")\n",
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"\n",
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"tools = [multiply, exponentiate, add]\n",
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"llm_with_tools = llm.bind_tools(tools)\n",
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"tool_map = {tool.name: tool for tool in tools}\n",
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"\n",
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"\n",
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"def call_tools(msg: AIMessage) -> Runnable:\n",
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" \"\"\"Simple sequential tool calling helper.\"\"\"\n",
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" tool_map = {tool.name: tool for tool in tools}\n",
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" tool_calls = msg.tool_calls.copy()\n",
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" for tool_call in tool_calls:\n",
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" tool_call[\"output\"] = tool_map[tool_call[\"name\"]].invoke(tool_call[\"args\"])\n",
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" return tool_calls\n",
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"\n",
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"\n",
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"chain = llm_with_tools | call_tools"
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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": 9,
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"id": "ea6dbb32-ec9b-4c70-a90f-a2db93978cf1",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
|
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"[{'name': 'multiply',\n",
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" 'args': {'first_int': 23, 'second_int': 7},\n",
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" 'id': 'call_22tgOrsVLyLMsl2RLbUhtycw',\n",
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" 'output': 161},\n",
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" {'name': 'multiply',\n",
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" 'args': {'first_int': 5, 'second_int': 18},\n",
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" 'id': 'call_EbKHEG3TjqBhEwb7aoxUtgzf',\n",
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" 'output': 90},\n",
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" {'name': 'add',\n",
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" 'args': {'first_int': 1000000, 'second_int': 1000000000},\n",
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" 'id': 'call_LUhu2IT3vINxlTc5fCVY6Nhi',\n",
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" 'output': 1001000000},\n",
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" {'name': 'exponentiate',\n",
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" 'args': {'base': 37, 'exponent': 3},\n",
|
||||
" 'id': 'call_bnCZIXelOKkmcyd4uGXId9Ct',\n",
|
||||
" 'output': 50653}]"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"chain.invoke(\n",
|
||||
" \"What's 23 times 7, and what's five times 18 and add a million plus a billion and cube thirty-seven\"\n",
|
||||
")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": "3.10.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Loading…
Reference in New Issue