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Harrison Chase
2024-01-15 15:30:35 -08:00
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examples/chat_executor_with_function_calling/force-calling-a-tool-first.ipynb
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@@ -1,87 +1,248 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53",
"metadata": {},
"source": [
"# Force Calling a Tool First\n",
"\n",
"In this example we will build a chat executor that always calls a certain tool first. In this example, we will create an agent with a search tool. However, at the start we will force the agent to call the search tool (and then let it do whatever it wants after). This is useful when you want to force agents to call particular tools, but still want flexibility of what happens after that.\n",
"\n",
"This examples builds off the base chat executor. It is highly recommended you learn about that executor before going through this notebook. You can find documentation for that example [here](./base.ipynb).\n",
"\n",
"Any modifications of that example are called below with **MODIFICATION**, so if you are looking for the differences you can just search for that."
]
},
{
"cell_type": "markdown",
"id": "7cbd446a-808f-4394-be92-d45ab818953c",
"metadata": {},
"source": [
"## Setup\n",
"\n",
"First we need to install the packages required"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "aa9f9110-ea74-43af-aa72-6b45518abd6e",
"id": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833",
"metadata": {},
"outputs": [],
"source": [
"!pip install --quiet -U langchain langchain_openai tavily-python"
]
},
{
"cell_type": "markdown",
"id": "0abe11f4-62ed-4dc4-8875-3db21e260d1d",
"metadata": {},
"source": [
"Next, we need to set API keys for OpenAI (the LLM we will use) and Tavily (the search tool we will use)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import getpass\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n",
"os.environ[\"TAVILY_API_KEY\"] = getpass.getpass(\"Tavily API Key:\")"
]
},
{
"cell_type": "markdown",
"id": "f0ed46a8-effe-4596-b0e1-a6a29ee16f5c",
"metadata": {},
"source": [
"Optionally, we can set API key for [LangSmith tracing](https://smith.langchain.com/), which will give us best-in-class observability."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "95e25aec-7c9f-4a63-b143-225d0e9a79c3",
"metadata": {},
"outputs": [],
"source": [
"os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"LangSmith API Key:\")"
]
},
{
"cell_type": "markdown",
"id": "21ac643b-cb06-4724-a80c-2862ba4773f1",
"metadata": {},
"source": [
"## Set up the tools\n",
"\n",
"We will first define the tools we want to use.\n",
"For this simple example, we will use a built-in search tool via Tavily.\n",
"However, it is really easy to create your own tools - see documentation [here](https://python.langchain.com/docs/modules/agents/tools/custom_tools) on how to do that.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d7ef57dd-5d6e-4ad3-9377-a92201c1310e",
"metadata": {},
"outputs": [],
"source": [
"from langchain import hub\n",
"from langchain_openai import ChatOpenAI\n",
"from langchain_community.tools.tavily_search import TavilySearchResults\n",
"from langgraph.prebuilt.messages_executor import create_messages_executor\n",
"from langchain_core.messages import HumanMessage"
"\n",
"tools = [TavilySearchResults(max_results=1)]"
]
},
{
"cell_type": "markdown",
"id": "01885785-b71a-44d1-b1d6-7b5b14d53b58",
"metadata": {},
"source": [
"We can now wrap these tools in a simple ToolExecutor.\n",
"This is a real simple class that takes in a ToolInvocation and calls that tool, returning the output.\n",
"A ToolInvocation is any class with `tool` and `tool_input` attribute.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0f005b2d-ddc0-4d60-8af9-9eb3dbeb45b7",
"id": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7",
"metadata": {},
"outputs": [],
"source": [
"tools = [TavilySearchResults(max_results=1)]\n",
"model = ChatOpenAI()"
"from langgraph.prebuilt import ToolExecutor\n",
"\n",
"tool_executor = ToolExecutor(tools)"
]
},
{
"cell_type": "markdown",
"id": "5497ed70-fce3-47f1-9cad-46f912bad6a5",
"metadata": {},
"source": [
"## Set up the model\n",
"\n",
"Now we need to load the chat model we want to use.\n",
"Importantly, this should satisfy two criteria:\n",
"\n",
"1. It should work with messages. We will represent all agent state in the form of messages, so it needs to be able to work well with them.\n",
"2. It should work with OpenAI function calling. This means it should either be an OpenAI model or a model that exposes a similar interface.\n",
"\n",
"Note: these model requirements are not requirements for using LangGraph - they are just requirements for this one example.\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "02831581-f1ba-4701-ba33-f0f68468907d",
"id": "892b54b9-75f0-4804-9ed0-88b5e5532989",
"metadata": {},
"outputs": [],
"source": [
"from langchain.tools.render import format_tool_to_openai_function"
"from langchain_openai import ChatOpenAI\n",
"\n",
"# We will set streaming=True so that we can stream tokens\n",
"# See the streaming section for more information on this.\n",
"model = ChatOpenAI(temperature=0, streaming=True)"
]
},
{
"cell_type": "markdown",
"id": "a77995c0-bae2-4cee-a036-8688a90f05b9",
"metadata": {},
"source": [
"\n",
"After we've done this, we should make sure the model knows that it has these tools available to call.\n",
"We can do this by converting the LangChain tools into the format for OpenAI function calling, and then bind them to the model class.\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "a5c0ca12-4922-461c-b740-62ff99f8ae56",
"id": "cd3cbae5-d92c-4559-a4aa-44721b80d107",
"metadata": {},
"outputs": [],
"source": [
"model = model.bind_functions([format_tool_to_openai_function(t) for t in tools])"
"from langchain.tools.render import format_tool_to_openai_function\n",
"\n",
"functions = [format_tool_to_openai_function(t) for t in tools]\n",
"model = model.bind_functions(functions)"
]
},
{
"cell_type": "markdown",
"id": "8e8b9211-93d0-4ad5-aa7a-9c09099c53ff",
"metadata": {},
"source": [
"## Define the agent state\n",
"\n",
"The main type of graph in `langgraph` is the `StatefulGraph`.\n",
"This graph is parameterized by a state object that it passes around to each node.\n",
"Each node then returns operations to update that state.\n",
"These operations can either SET specific attributes on the state (e.g. overwrite the existing values) or ADD to the existing attribute.\n",
"Whether to set or add is denoted by annotating the state object you construct the graph with.\n",
"\n",
"For this example, the state we will track will just be a list of messages.\n",
"We want each node to just add messages to that list.\n",
"Therefore, we will use a `TypedDict` with one key (`messages`) and annotate it so that the `messages` attribute is always added to.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "57547622-ddd8-4179-aa4a-e1b69ca4523c",
"metadata": {},
"outputs": [],
"source": [
"from langgraph.prebuilt.tool_executor import ToolExecutor\n",
"tool_executor = ToolExecutor(tools)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "96141ebd-32af-4c9d-a0b0-f46482b8bb88",
"id": "ea793afa-2eab-4901-910d-6eed90cd6564",
"metadata": {},
"outputs": [],
"source": [
"from typing import TypedDict, Annotated, Sequence\n",
"import operator\n",
"from langchain_core.messages import BaseMessage\n",
"# We create the AgentState that we will pass around\n",
"# This simply involves a list of messages\n",
"# We want steps to return messages to append to the list\n",
"# So we annotate the messages attribute with operator.add\n",
"\n",
"\n",
"class AgentState(TypedDict):\n",
" messages: Annotated[Sequence[BaseMessage], operator.add]"
]
},
{
"cell_type": "markdown",
"id": "e03c5094-9297-4d19-a04e-3eedc75cefb4",
"metadata": {},
"source": [
"## Define the nodes\n",
"\n",
"We now need to define a few different nodes in our graph.\n",
"In `langgraph`, a node can be either a function or a [runnable](https://python.langchain.com/docs/expression_language/).\n",
"There are two main nodes we need for this:\n",
"\n",
"1. The agent: responsible for deciding what (if any) actions to take.\n",
"2. A function to invoke tools: if the agent decides to take an action, this node will then execute that action.\n",
"\n",
"We will also need to define some edges.\n",
"Some of these edges may be conditional.\n",
"The reason they are conditional is that based on the output of a node, one of several paths may be taken.\n",
"The path that is taken is not known until that node is run (the LLM decides).\n",
"\n",
"1. Conditional Edge: after the agent is called, we should either:\n",
" a. If the agent said to take an action, then the function to invoke tools should be called\n",
" b. If the agent said that it was finished, then it should finish\n",
"2. Normal Edge: after the tools are invoked, it should always go back to the agent to decide what to do next\n",
"\n",
"Let's define the nodes, as well as a function to decide how what conditional edge to take.\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "f422dc99-672e-414f-88b3-1a6dd21b6882",
"execution_count": 6,
"id": "3b541bb9-900c-40d0-964d-7b5dfee30667",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.agents import AgentAction\n",
"from langgraph.prebuilt import ToolInvocation\n",
"import json\n",
"from langchain_core.messages import FunctionMessage\n",
"\n",
@@ -109,46 +270,33 @@
" # Based on the continue condition\n",
" # we know the last message involves a function call\n",
" last_message = messages[-1]\n",
" # We construct an AgentAction from the function_call\n",
" action = AgentAction(\n",
" # We construct an ToolInvocation from the function_call\n",
" action = ToolInvocation(\n",
" tool=last_message.additional_kwargs[\"function_call\"][\"name\"],\n",
" tool_input=json.loads(last_message.additional_kwargs[\"function_call\"][\"arguments\"]),\n",
" log=\"\",\n",
" )\n",
" # We call the tool_executor and get back a response\n",
" response = tool_executor.invoke(action)\n",
" # We use the response to create a FunctionMessage\n",
" function_message = FunctionMessage(content=str(response), name=action.tool)\n",
" # We return a list, because this will get added to the existing list\n",
" return {\"messages\": [function_message]}\n",
"\n"
" return {\"messages\": [function_message]}"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "47bf79f1-652c-43dc-aeb3-0f0de4400539",
"cell_type": "markdown",
"id": "7c3e0ac2-0c89-4751-bc2c-f644654841d1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'tavily_search_results_json'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tools[0].name"
"**MODIFICATION**\n",
"\n",
"Here we create a node that returns an AIMessage with a tool call - we will use this at the start to force it call a tool"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "ab62da37-9632-4dc8-833b-feb4c62bab37",
"execution_count": 7,
"id": "1bfd2b22-292a-4f4d-91a0-46bb704f5e38",
"metadata": {},
"outputs": [],
"source": [
@@ -158,22 +306,39 @@
"\n",
"def first_model(state):\n",
" human_input = state['messages'][-1].content\n",
" return {\"messages\": [AIMessage(\n",
" content=\"\", \n",
" additional_kwargs={\n",
" \"function_call\": {\n",
" \"name\": \"tavily_search_results_json\", \n",
" \"arguments\": json.dumps({\"query\": human_input})\n",
" }\n",
" }\n",
" )\n",
" ]}"
" return {\n",
" \"messages\": [\n",
" AIMessage(\n",
" content=\"\", \n",
" additional_kwargs={\n",
" \"function_call\": {\n",
" \"name\": \"tavily_search_results_json\", \n",
" \"arguments\": json.dumps({\"query\": human_input})\n",
" }\n",
" }\n",
" )\n",
" ]\n",
" }"
]
},
{
"cell_type": "markdown",
"id": "ffd6e892-946c-4899-8cc0-7c9291c1f73b",
"metadata": {},
"source": [
"## Define the graph\n",
"\n",
"We can now put it all together and define the graph!\n",
"\n",
"**MODIFICATION**\n",
"\n",
"We will define a `first_agent` node which we will set as the entrypoint."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "1133ec83-7af9-4444-9f88-c793fbdce214",
"execution_count": 8,
"id": "813ae66c-3b58-4283-a02a-36da72a2ab90",
"metadata": {},
"outputs": [],
"source": [
@@ -226,38 +391,71 @@
"app = workflow.compile()"
]
},
{
"cell_type": "markdown",
"id": "547c3931-3dae-4281-ad4e-4b51305594d4",
"metadata": {},
"source": [
"## Use it!\n",
"\n",
"We can now use it!\n",
"This now exposes the [same interface](https://python.langchain.com/docs/expression_language/) as all other LangChain runnables."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "289c648f-bfc6-464f-8df9-50be8b1b9e48",
"execution_count": 9,
"id": "8edb04b9-40b6-46f1-a7a8-4b2d8aba7752",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Output from node 'first_agent':\n",
"---\n",
"{'messages': [AIMessage(content='', additional_kwargs={'function_call': {'name': 'tavily_search_results_json', 'arguments': '{\"query\": \"what is the weather in sf\"}'}})]}\n",
"----\n",
"{'messages': [FunctionMessage(content=\"[{'url': 'https://weatherspark.com/h/m/557/2024/1/Historical-Weather-in-January-2024-in-San-Francisco-California-United-States', 'content': 'January 2024 Weather History in San Francisco California, United States Daily Precipitation in January 2024 in San Francisco Observed Weather in January 2024 in San Francisco San Francisco Temperature History January 2024 Hourly Temperature in January 2024 in San Francisco Hours of Daylight and Twilight in January 2024 in San Francisco80 deg, E Cloud Cover Mostly Cloudy 18,000 ft Mostly Clear 4,000 ft Partly Cloudy 15,000 ft Raw: KSFO 121956Z 08006KT 10SM FEW040 SCT150 BKN180 11/07 A3028 RMK AO2 SLP254 T01110067 This report shows the past weather for San Francisco, providing a weather history for January 2024.'}]\", name='tavily_search_results_json')]}\n",
"----\n",
"{'messages': [AIMessage(content=\"I'm sorry, but I couldn't find the current weather in San Francisco. However, you can visit [this link](https://weatherspark.com/h/m/557/2024/1/Historical-Weather-in-January-2024-in-San-Francisco-California-United-States) to see the historical weather data for January 2024 in San Francisco.\")]}\n",
"----\n",
"{'messages': [HumanMessage(content='what is the weather in sf'), AIMessage(content='', additional_kwargs={'function_call': {'name': 'tavily_search_results_json', 'arguments': '{\"query\": \"what is the weather in sf\"}'}}), FunctionMessage(content=\"[{'url': 'https://weatherspark.com/h/m/557/2024/1/Historical-Weather-in-January-2024-in-San-Francisco-California-United-States', 'content': 'January 2024 Weather History in San Francisco California, United States Daily Precipitation in January 2024 in San Francisco Observed Weather in January 2024 in San Francisco San Francisco Temperature History January 2024 Hourly Temperature in January 2024 in San Francisco Hours of Daylight and Twilight in January 2024 in San Francisco80 deg, E Cloud Cover Mostly Cloudy 18,000 ft Mostly Clear 4,000 ft Partly Cloudy 15,000 ft Raw: KSFO 121956Z 08006KT 10SM FEW040 SCT150 BKN180 11/07 A3028 RMK AO2 SLP254 T01110067 This report shows the past weather for San Francisco, providing a weather history for January 2024.'}]\", name='tavily_search_results_json'), AIMessage(content=\"I'm sorry, but I couldn't find the current weather in San Francisco. However, you can visit [this link](https://weatherspark.com/h/m/557/2024/1/Historical-Weather-in-January-2024-in-San-Francisco-California-United-States) to see the historical weather data for January 2024 in San Francisco.\")]}\n",
"----\n"
"\n",
"---\n",
"\n",
"Output from node 'action':\n",
"---\n",
"{'messages': [FunctionMessage(content=\"[{'url': 'https://weatherspark.com/h/m/557/2024/1/Historical-Weather-in-January-2024-in-San-Francisco-California-United-States', 'content': 'January 2024 Weather History in San Francisco California, United States Daily Precipitation in January 2024 in San Francisco Observed Weather in January 2024 in San Francisco San Francisco Temperature History January 2024 Hourly Temperature in January 2024 in San Francisco Hours of Daylight and Twilight in January 2024 in San FranciscoJanuary 2024 Weather History in San Francisco California, United States. The data for this report comes from the San Francisco International Airport. ... frigid 15°F freezing 32°F very cold 45°F cold 55°F cool 65°F comfortable 75°F warm 85°F hot 95°F sweltering. The hourly reported temperature, color coded into bands. ...'}]\", name='tavily_search_results_json')]}\n",
"\n",
"---\n",
"\n",
"Output from node 'agent':\n",
"---\n",
"{'messages': [AIMessage(content=\"I'm sorry, but I couldn't find the current weather in San Francisco. However, you can check the historical weather data for January 2024 in San Francisco on this [website](https://weatherspark.com/h/m/557/2024/1/Historical-Weather-in-January-2024-in-San-Francisco-California-United-States).\")]}\n",
"\n",
"---\n",
"\n",
"Output from node '__end__':\n",
"---\n",
"{'messages': [HumanMessage(content='what is the weather in sf'), AIMessage(content='', additional_kwargs={'function_call': {'name': 'tavily_search_results_json', 'arguments': '{\"query\": \"what is the weather in sf\"}'}}), FunctionMessage(content=\"[{'url': 'https://weatherspark.com/h/m/557/2024/1/Historical-Weather-in-January-2024-in-San-Francisco-California-United-States', 'content': 'January 2024 Weather History in San Francisco California, United States Daily Precipitation in January 2024 in San Francisco Observed Weather in January 2024 in San Francisco San Francisco Temperature History January 2024 Hourly Temperature in January 2024 in San Francisco Hours of Daylight and Twilight in January 2024 in San FranciscoJanuary 2024 Weather History in San Francisco California, United States. The data for this report comes from the San Francisco International Airport. ... frigid 15°F freezing 32°F very cold 45°F cold 55°F cool 65°F comfortable 75°F warm 85°F hot 95°F sweltering. The hourly reported temperature, color coded into bands. ...'}]\", name='tavily_search_results_json'), AIMessage(content=\"I'm sorry, but I couldn't find the current weather in San Francisco. However, you can check the historical weather data for January 2024 in San Francisco on this [website](https://weatherspark.com/h/m/557/2024/1/Historical-Weather-in-January-2024-in-San-Francisco-California-United-States).\")]}\n",
"\n",
"---\n",
"\n"
]
}
],
"source": [
"from langchain_core.messages import HumanMessage\n",
"\n",
"inputs = {\"messages\": [HumanMessage(content=\"what is the weather in sf\")]}\n",
"for s in app.stream(inputs):\n",
" print(list(s.values())[0])\n",
" print(\"----\")"
"for output in app.stream(inputs):\n",
" # stream() yields dictionaries with output keyed by node name\n",
" for key, value in output.items():\n",
" print(f\"Output from node '{key}':\")\n",
" print(\"---\")\n",
" print(value)\n",
" print(\"\\n---\\n\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4cd4b39f-831a-4818-bf56-99cf301b0555",
"id": "08ae8246-11d5-40e1-8567-361e5bef8917",
"metadata": {},
"outputs": [],
"source": []