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docs: Remove duplicated content from how to tools (#21821)

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Content is duplicated, and is covered in how to use chat models.
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Eugene Yurtsev 2024-05-17 13:30:43 -04:00 committed by GitHub
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8
docs/docs/how_to/index.mdx
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@ -166,15 +166,13 @@ Indexing is the process of keeping your vectorstore in-sync with the underlying
LangChain Tools contain a description of the tool (to pass to the language model) as well as the implementation of the function to call).
- [How to: use LangChain tools](/docs/how_to/tools)
- [How to: create tools](/docs/how_to/custom_tools)
- [How to: use a chat model to call tools](/docs/how_to/tool_calling/)
- [How to: use built-in LangChain tools](/docs/how_to/tools)
- [How to: use LangChain toolkits](/docs/how_to/toolkits)
- [How to: define a custom tool](/docs/how_to/custom_tools)
- [How to: use tools with LLMs that do not support tool calling natively](/docs/how_to/tools_prompting)
- [How to: convert LangChain tools to OpenAI functions](/docs/how_to/tools_as_openai_functions)
- [How to: use tools without function calling](/docs/how_to/tools_prompting)
- [How to: let the LLM choose between multiple tools](/docs/how_to/tools_multiple)
- [How to: add a human in the loop to tool usage](/docs/how_to/tools_human)
- [How to: do parallel tool use](/docs/how_to/tools_parallel)
- [How to: handle errors when calling tools](/docs/how_to/tools_error)
- [How to: call tools using multi-modal data](/docs/how_to/tool_calls_multi_modal)

19
docs/docs/how_to/tools_chain.ipynb
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@ -335,7 +335,7 @@
"id": "616f9714-5b18-4eed-b88a-d38e4cb1de99",
"metadata": {},
"source": [
"Agents are also great because they make it easy to use multiple tools. To learn how to build Chains that use multiple tools, check out the [Chains with multiple tools](/docs/how_to/tools_multiple) page."
"Agents are also great because they make it easy to use multiple tools."
]
},
{
@ -457,21 +457,6 @@
"source": [
"Check out the [LangSmith trace here](https://smith.langchain.com/public/eeeb27a4-a2f8-4f06-a3af-9c983f76146c/r)."
]
},
{
"cell_type": "markdown",
"id": "b0e4b7f4-58ce-4ca0-a986-d05a436a7ccf",
"metadata": {},
"source": [
"## Next steps\n",
"\n",
"Here we've gone over the basic ways to use Tools with Chains and Agents. We recommend the following sections to explore next:\n",
"\n",
"- [Agents](/docs/tutorials/agents): Everything related to Agents.\n",
"- [Choosing between multiple tools](/docs/how_to/tools_multiple): How to make tool chains that select from multiple tools.\n",
"- [Prompting for tool use](/docs/how_to/tools_prompting): How to make tool chains that prompt models directly, without using function-calling APIs.\n",
"- [Parallel tool use](/docs/how_to/tools_parallel): How to make tool chains that invoke multiple tools at once."
]
}
],
"metadata": {
@ -490,7 +475,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.4"
"version": "3.11.4"
}
},
"nbformat": 4,

273
docs/docs/how_to/tools_multiple.ipynb
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@ -1,273 +0,0 @@
{
"cells": [
{
"cell_type": "raw",
"id": "1ea1fe24-fe1e-463b-a52c-79f0ef02328e",
"metadata": {},
"source": [
"---\n",
"sidebar_position: 2\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "95982bf1-7d9d-4dd6-a4ad-9de0719fe17f",
"metadata": {},
"source": [
"# How to use an LLM to choose between multiple tools\n",
"\n",
"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."
]
},
{
"cell_type": "markdown",
"id": "3fafec38-443a-42ad-a913-5be7667e3734",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"We'll need to install the following packages for this guide:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "78411bf1-0117-4f33-a3d7-f3d77a97bb78",
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet langchain-core"
]
},
{
"cell_type": "markdown",
"id": "59d08fd0-ddd9-4c74-bcea-a5ca3a86e542",
"metadata": {},
"source": [
"If you'd like to trace your runs in [LangSmith](/docs/langsmith/) uncomment and set the following environment variables:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "4185e74b-0500-4cad-ace0-bac37de466ac",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "markdown",
"id": "d28159f5-b7d0-4385-aa44-4cd1b64507bb",
"metadata": {},
"source": [
"## Tools\n",
"\n",
"Recall we already had a `multiply` tool:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "e13ec98c-8521-4d63-b521-caf92da87b70",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.tools import tool\n",
"\n",
"\n",
"@tool\n",
"def multiply(first_int: int, second_int: int) -> int:\n",
" \"\"\"Multiply two integers together.\"\"\"\n",
" return first_int * second_int"
]
},
{
"cell_type": "markdown",
"id": "3de233af-b3bd-4f0c-8b1a-83527143a8db",
"metadata": {},
"source": [
"And now we can add to it an `exponentiate` and `add` tool:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e93661cd-a2ba-4ada-91ad-baf1b60879ec",
"metadata": {},
"outputs": [],
"source": [
"@tool\n",
"def add(first_int: int, second_int: int) -> int:\n",
" \"Add two integers.\"\n",
" return first_int + second_int\n",
"\n",
"\n",
"@tool\n",
"def exponentiate(base: int, exponent: int) -> int:\n",
" \"Exponentiate the base to the exponent power.\"\n",
" return base**exponent"
]
},
{
"cell_type": "markdown",
"id": "bbea4555-ed10-4a18-b802-e9a3071f132b",
"metadata": {},
"source": [
"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",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\"/>\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "f00f0f3f-8530-4c1d-a26c-d20824e31faf",
"metadata": {},
"outputs": [],
"source": [
"from langchain_anthropic import ChatAnthropic\n",
"\n",
"llm = ChatAnthropic(model=\"claude-3-sonnet-20240229\", temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "c35359ae-a740-48c5-b5e7-1a377fb25aa2",
"metadata": {},
"outputs": [],
"source": [
"from operator import itemgetter\n",
"from typing import Dict, List, Union\n",
"\n",
"from langchain_core.messages import AIMessage\n",
"from langchain_core.runnables import (\n",
" Runnable,\n",
" RunnableLambda,\n",
" RunnableMap,\n",
" RunnablePassthrough,\n",
")\n",
"\n",
"tools = [multiply, exponentiate, add]\n",
"llm_with_tools = llm.bind_tools(tools)\n",
"tool_map = {tool.name: tool for tool in tools}\n",
"\n",
"\n",
"def call_tools(msg: AIMessage) -> Runnable:\n",
" \"\"\"Simple sequential tool calling helper.\"\"\"\n",
" tool_map = {tool.name: tool for tool in tools}\n",
" tool_calls = msg.tool_calls.copy()\n",
" for tool_call in tool_calls:\n",
" tool_call[\"output\"] = tool_map[tool_call[\"name\"]].invoke(tool_call[\"args\"])\n",
" return tool_calls\n",
"\n",
"\n",
"chain = llm_with_tools | call_tools"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ea6dbb32-ec9b-4c70-a90f-a2db93978cf1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'name': 'multiply',\n",
" 'args': {'first_int': 23, 'second_int': 7},\n",
" 'id': 'toolu_01Wf8kUs36kxRKLDL8vs7G8q',\n",
" 'output': 161}]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\"What's 23 times 7\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "b1c6c0f8-6d04-40d4-a40e-8719ca7b27c2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'name': 'add',\n",
" 'args': {'first_int': 1000000, 'second_int': 1000000000},\n",
" 'id': 'toolu_012aK4xZBQg2sXARsFZnqxHh',\n",
" 'output': 1001000000}]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\"add a million plus a billion\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "ce76f299-1a4d-421c-afa4-a6346e34285c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'name': 'exponentiate',\n",
" 'args': {'base': 37, 'exponent': 3},\n",
" 'id': 'toolu_01VDU6X3ugDb9cpnnmCZFPbC',\n",
" 'output': 50653}]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"chain.invoke(\"cube thirty-seven\")"
]
}
],
"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
}

215
docs/docs/how_to/tools_parallel.ipynb
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@ -1,215 +0,0 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "95982bf1-7d9d-4dd6-a4ad-9de0719fe17f",
"metadata": {},
"source": [
"# How to call tools in parallel\n",
"\n",
"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."
]
},
{
"cell_type": "markdown",
"id": "3fafec38-443a-42ad-a913-5be7667e3734",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"We'll need to install the following packages for this guide:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "78411bf1-0117-4f33-a3d7-f3d77a97bb78",
"metadata": {},
"outputs": [],
"source": [
"%pip install --upgrade --quiet langchain-core"
]
},
{
"cell_type": "markdown",
"id": "59d08fd0-ddd9-4c74-bcea-a5ca3a86e542",
"metadata": {},
"source": [
"If you'd like to trace your runs in [LangSmith](/docs/langsmith/) uncomment and set the following environment variables:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4185e74b-0500-4cad-ace0-bac37de466ac",
"metadata": {},
"outputs": [],
"source": [
"import getpass\n",
"import os\n",
"\n",
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
]
},
{
"cell_type": "markdown",
"id": "d28159f5-b7d0-4385-aa44-4cd1b64507bb",
"metadata": {},
"source": [
"## Tools"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e13ec98c-8521-4d63-b521-caf92da87b70",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.tools import tool\n",
"\n",
"\n",
"@tool\n",
"def multiply(first_int: int, second_int: int) -> int:\n",
" \"\"\"Multiply two integers together.\"\"\"\n",
" return first_int * second_int\n",
"\n",
"\n",
"@tool\n",
"def add(first_int: int, second_int: int) -> int:\n",
" \"Add two integers.\"\n",
" return first_int + second_int\n",
"\n",
"\n",
"@tool\n",
"def exponentiate(base: int, exponent: int) -> int:\n",
" \"Exponentiate the base to the exponent power.\"\n",
" return base**exponent"
]
},
{
"cell_type": "markdown",
"id": "119d419c-1c61-4e0d-834a-5dabb72f5514",
"metadata": {},
"source": [
"# Chain\n",
"\n",
"```{=mdx}\n",
"import ChatModelTabs from \"@theme/ChatModelTabs\";\n",
"\n",
"<ChatModelTabs customVarName=\"llm\" hideGoogle=\"true\"/>\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "f67d91d8-cc38-4065-8f80-901e079954dd",
"metadata": {},
"outputs": [],
"source": [
"# | echo: false\n",
"# | output: false\n",
"\n",
"from langchain_openai import ChatOpenAI\n",
"\n",
"llm = ChatOpenAI(model=\"gpt-3.5-turbo-0125\", temperature=0)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "c35359ae-a740-48c5-b5e7-1a377fb25aa2",
"metadata": {},
"outputs": [],
"source": [
"from operator import itemgetter\n",
"from typing import Dict, List, Union\n",
"\n",
"from langchain_core.messages import AIMessage\n",
"from langchain_core.runnables import (\n",
" Runnable,\n",
" RunnableLambda,\n",
" RunnableMap,\n",
" RunnablePassthrough,\n",
")\n",
"\n",
"tools = [multiply, exponentiate, add]\n",
"llm_with_tools = llm.bind_tools(tools)\n",
"tool_map = {tool.name: tool for tool in tools}\n",
"\n",
"\n",
"def call_tools(msg: AIMessage) -> Runnable:\n",
" \"\"\"Simple sequential tool calling helper.\"\"\"\n",
" tool_map = {tool.name: tool for tool in tools}\n",
" tool_calls = msg.tool_calls.copy()\n",
" for tool_call in tool_calls:\n",
" tool_call[\"output\"] = tool_map[tool_call[\"name\"]].invoke(tool_call[\"args\"])\n",
" return tool_calls\n",
"\n",
"\n",
"chain = llm_with_tools | call_tools"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ea6dbb32-ec9b-4c70-a90f-a2db93978cf1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[{'name': 'multiply',\n",
" 'args': {'first_int': 23, 'second_int': 7},\n",
" 'id': 'call_22tgOrsVLyLMsl2RLbUhtycw',\n",
" 'output': 161},\n",
" {'name': 'multiply',\n",
" 'args': {'first_int': 5, 'second_int': 18},\n",
" 'id': 'call_EbKHEG3TjqBhEwb7aoxUtgzf',\n",
" 'output': 90},\n",
" {'name': 'add',\n",
" 'args': {'first_int': 1000000, 'second_int': 1000000000},\n",
" 'id': 'call_LUhu2IT3vINxlTc5fCVY6Nhi',\n",
" 'output': 1001000000},\n",
" {'name': 'exponentiate',\n",
" '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
}