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Add human in the loop notebook

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Nuno Campos
2024-02-07 20:05:05 -08:00
parent c4f87cdccd
commit 37bad4e30a
4 changed files with 529 additions and 10 deletions
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README.md
+8 -3
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@@ -135,7 +135,7 @@ The path that is taken is not known until that node is run (the LLM decides).
1. Conditional Edge: after the agent is called, we should either:
a. If the agent said to take an action, then the function to invoke tools should be called
b. If the agent said that it was finished, then it should finish
2. Normal Edge: after the tools are invoked, it should always go back to the agent to decide what to do next
@@ -469,7 +469,7 @@ It can often be tough to evaluation chat bots in multi-turn situations. One way
### Async
If you are running LangGraph in async workflows, you may want to create the nodes to be async by default.
In order for a walkthrough on how to do that, see [this documentation](https://github.com/langchain-ai/langgraph/blob/main/examples/async.ipynb)
For a walkthrough on how to do that, see [this documentation](https://github.com/langchain-ai/langgraph/blob/main/examples/async.ipynb)
### Streaming Tokens
@@ -479,7 +479,12 @@ For a guide on how to do this, see [this documentation](https://github.com/langc
### Persistence
LangGraph comes with built-in persistence, allowing you to save the state of the graph at point and resume from there.
In order for a walkthrough on how to do that, see [this documentation](https://github.com/langchain-ai/langgraph/blob/main/examples/persistence.ipynb)
For a walkthrough on how to do that, see [this documentation](https://github.com/langchain-ai/langgraph/blob/main/examples/persistence.ipynb)
### Human-in-the-loop
LangGraph comes with built-in support for human-in-the-loop workflows. This is useful when you want to have a human review the current state before proceeding to a particular node.
For a walkthrough on how to do that, see [this documentation](https://github.com/langchain-ai/langgraph/blob/main/examples/human-in-the-loop.ipynb)
## Documentation
examples/human-in-the-loop.ipynb
+495
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@@ -0,0 +1,495 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "51466c8d-8ce4-4b3d-be4e-18fdbeda5f53",
"metadata": {},
"source": [
"# Persistence\n",
"\n",
"When creating LangGraph agents, you can also set them up so that they persist their state. This allows you to do things like interact with an agent multiple times and have it remember previous interactions."
]
},
{
"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": "af4ce0ba-7596-4e5f-8bf8-0b0bd6e62833",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
]
}
],
"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": 2,
"id": "c903a1cf-2977-4e2d-ad7d-8b3946821d89",
"metadata": {},
"outputs": [
{
"name": "stdin",
"output_type": "stream",
"text": [
"OpenAI API Key: ········\n",
"Tavily API Key: ········\n"
]
}
],
"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_community.tools.tavily_search import TavilySearchResults\n",
"\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": "5cf3331e-ccb3-41c8-aeb9-a840a94d41e7",
"metadata": {},
"outputs": [],
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "892b54b9-75f0-4804-9ed0-88b5e5532989",
"metadata": {},
"outputs": [],
"source": [
"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": "cd3cbae5-d92c-4559-a4aa-44721b80d107",
"metadata": {},
"outputs": [],
"source": [
"from langchain_core.utils.function_calling import convert_to_openai_function\n",
"\n",
"functions = [convert_to_openai_function(t) for t in tools]\n",
"model = model.bind_functions(functions)"
]
},
{
"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."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "3b541bb9-900c-40d0-964d-7b5dfee30667",
"metadata": {},
"outputs": [],
"source": [
"from langgraph.prebuilt import ToolInvocation\n",
"import json\n",
"from langchain_core.messages import FunctionMessage\n",
"\n",
"# Define the function that determines whether to continue or not\n",
"def should_continue(messages):\n",
" last_message = messages[-1]\n",
" # If there is no function call, then we finish\n",
" if \"function_call\" not in last_message.additional_kwargs:\n",
" return \"end\"\n",
" # Otherwise if there is, we continue\n",
" else:\n",
" return \"continue\"\n",
"\n",
"# Define the function that calls the model\n",
"def call_model(messages):\n",
" response = model.invoke(messages)\n",
" # We return a list, because this will get added to the existing list\n",
" return response\n",
"\n",
"# Define the function to execute tools\n",
"def call_tool(messages):\n",
" # Based on the continue condition\n",
" # we know the last message involves a function call\n",
" last_message = messages[-1]\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",
" )\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 function_message"
]
},
{
"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!"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "812b4e70-4956-4415-8880-db48b3dcbad2",
"metadata": {},
"outputs": [],
"source": [
"from langgraph.graph import MessageGraph, END\n",
"# Define a new graph\n",
"workflow = MessageGraph()\n",
"\n",
"# Define the two nodes we will cycle between\n",
"workflow.add_node(\"agent\", call_model)\n",
"workflow.add_node(\"action\", call_tool)\n",
"\n",
"# Set the entrypoint as `agent`\n",
"# This means that this node is the first one called\n",
"workflow.set_entry_point(\"agent\")\n",
"\n",
"# We now add a conditional edge\n",
"workflow.add_conditional_edges(\n",
" # First, we define the start node. We use `agent`.\n",
" # This means these are the edges taken after the `agent` node is called.\n",
" \"agent\",\n",
" # Next, we pass in the function that will determine which node is called next.\n",
" should_continue,\n",
" # Finally we pass in a mapping.\n",
" # The keys are strings, and the values are other nodes.\n",
" # END is a special node marking that the graph should finish.\n",
" # What will happen is we will call `should_continue`, and then the output of that\n",
" # will be matched against the keys in this mapping.\n",
" # Based on which one it matches, that node will then be called.\n",
" {\n",
" # If `tools`, then we call the tool node.\n",
" \"continue\": \"action\",\n",
" # Otherwise we finish.\n",
" \"end\": END\n",
" }\n",
")\n",
"\n",
"# We now add a normal edge from `tools` to `agent`.\n",
"# This means that after `tools` is called, `agent` node is called next.\n",
"workflow.add_edge('action', 'agent')"
]
},
{
"cell_type": "markdown",
"id": "bc9c8536-f90b-44fa-958d-5df016c66d8f",
"metadata": {},
"source": [
"**Persistence**\n",
"\n",
"To add in persistence, we pass in a checkpoint when compiling the graph"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "6845ed6a-d155-4105-9160-28849877248b",
"metadata": {},
"outputs": [],
"source": [
"from langgraph.checkpoint.sqlite import SqliteSaver\n",
"\n",
"memory = SqliteSaver.from_conn_string(\":memory:\")"
]
},
{
"cell_type": "markdown",
"id": "cc7fa795-b3f8-4731-b37e-db7a802558ac",
"metadata": {},
"source": [
"**Interrupt**\n",
"\n",
"To always interrupt before a particular node, pass the name of the node to compile."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "79d29875-8aa8-434c-9f20-1c58346a6249",
"metadata": {},
"outputs": [],
"source": [
"# Finally, we compile it!\n",
"# This compiles it into a LangChain Runnable,\n",
"# meaning you can use it as you would any other runnable\n",
"app = workflow.compile(checkpointer=memory, interrupt_before=['action'])"
]
},
{
"cell_type": "markdown",
"id": "2a1b56c5-bd61-4192-8bdb-458a1e9f0159",
"metadata": {},
"source": [
"## Interacting with the Agent\n",
"\n",
"We can now interact with the agent and see that it stops before calling a tool.\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "cfd140f0-a5a6-4697-8115-322242f197b5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"content='Hello Bob! How can I assist you today?'\n"
]
}
],
"source": [
"from langchain_core.messages import HumanMessage\n",
"inputs = [HumanMessage(content=\"hi! I'm bob\")]\n",
"for event in app.stream(inputs, {\"configurable\": {\"thread_id\": \"2\"}}):\n",
" for k, v in event.items():\n",
" if k != \"__end__\":\n",
" print(v)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "08ae8246-11d5-40e1-8567-361e5bef8917",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"content='Your name is Bob.'\n"
]
}
],
"source": [
"inputs = [HumanMessage(content=\"what is my name?\")]\n",
"for event in app.stream(inputs, {\"configurable\": {\"thread_id\": \"2\"}}):\n",
" for k, v in event.items():\n",
" if k != \"__end__\":\n",
" print(v)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "273d56a8-f40f-4a51-a27f-7c6bb2bda0ba",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"content='' additional_kwargs={'function_call': {'arguments': '{\\n \"query\": \"weather in San Francisco now\"\\n}', 'name': 'tavily_search_results_json'}}\n"
]
}
],
"source": [
"inputs = [HumanMessage(content=\"what's the weather in sf now?\")]\n",
"for event in app.stream(inputs, {\"configurable\": {\"thread_id\": \"2\"}}):\n",
" for k, v in event.items():\n",
" if k != \"__end__\":\n",
" print(v)"
]
},
{
"cell_type": "markdown",
"id": "1bca3814-db08-4b0b-8c0c-95b6c5440c81",
"metadata": {},
"source": [
"**Resume**\n",
"\n",
"We can now call the agent again with no inputs to continue, ie. run the tool as requested."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "51923913-20f7-4ee1-b9ba-d01f5fb2869b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"content='[{\\'url\\': \\'https://www.sfexaminer.com/news/climate/san-francisco-weather-forecast-calls-for-strongest-2024-rain/article_75347810-bfc3-11ee-abf6-e74c528e0583.html\\', \\'content\\': \"San Francisco is projected to receive 2.5 and 3 inches of rain, Clouser said, as well as gusts of wind up to 45 mph. Bay Area starting Wednesday at 4 a.m. and a 24-hour wind advisory in San Francisco starting at the same time. One of winter\\'s \\'stronger\\' storms to douse San Francisco On the heels of record-breaking heat to open the week, heavy rain is slated to pound the Bay Area on Wednesday.A series of historic storms last winter led to one of the wettest water years (Oct. 1 to Sept. 30) in The City\\'s history, highlighted by a 10-day stretch last January in which San Francisco...\"}]' name='tavily_search_results_json'\n",
"content=\"Currently, I couldn't retrieve the exact weather information for San Francisco. However, there is a forecast of heavy rain and gusts of wind up to 45 mph in San Francisco starting Wednesday at 4 a.m. You may want to check a reliable weather website or app for the most up-to-date weather conditions.\"\n"
]
}
],
"source": [
"for event in app.stream(None, {\"configurable\": {\"thread_id\": \"2\"}}):\n",
" for k, v in event.items():\n",
" if k != \"__end__\":\n",
" print(v)"
]
}
],
"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": "3.11.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
langgraph/graph/graph.py
+16 -4
View File
@@ -1,6 +1,6 @@
from asyncio import iscoroutinefunction
from collections import defaultdict
from typing import Any, Callable, Dict, NamedTuple, Optional
from typing import Any, Callable, Dict, NamedTuple, Optional, Sequence
from langchain_core.runnables import Runnable
from langchain_core.runnables.base import (
@@ -88,7 +88,7 @@ class Graph:
def set_finish_point(self, key: str) -> None:
return self.add_edge(key, END)
def validate(self) -> None:
def validate(self, interrupt: Optional[Sequence[str]] = None) -> None:
all_starts = {src for src, _ in self.edges} | {src for src in self.branches}
for node in self.nodes:
if node not in all_starts:
@@ -114,8 +114,17 @@ class Graph:
if node not in all_ends:
raise ValueError(f"Node `{node}` is not reachable")
def compile(self, checkpointer: Optional[BaseCheckpointSaver] = None) -> Pregel:
self.validate()
if interrupt:
for node in interrupt:
if node not in self.nodes:
raise ValueError(f"Node `{node}` is not present")
def compile(
self,
checkpointer: Optional[BaseCheckpointSaver] = None,
interrupt_before: Optional[Sequence[str]] = None,
) -> Pregel:
self.validate(interrupt=interrupt_before)
outgoing_edges = defaultdict(list)
for start, end in self.edges:
@@ -145,4 +154,7 @@ class Graph:
output=END,
hidden=[f"{node}:inbox" for node in self.nodes],
checkpointer=checkpointer,
interrupt=[f"{node}:inbox" for node in interrupt_before]
if interrupt_before
else [],
)
langgraph/graph/state.py
+10 -3
View File
@@ -1,7 +1,7 @@
from collections import defaultdict
from functools import partial
from inspect import signature
from typing import Any, Optional, Type
from typing import Any, Optional, Sequence, Type
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
from langchain_core.runnables.base import RunnableLike
@@ -34,8 +34,12 @@ class StateGraph(Graph):
)
return super().add_node(key, action)
def compile(self, checkpointer: Optional[BaseCheckpointSaver] = None) -> Pregel:
self.validate()
def compile(
self,
checkpointer: Optional[BaseCheckpointSaver] = None,
interrupt_before: Optional[Sequence[str]] = None,
) -> Pregel:
self.validate(interrupt=interrupt_before)
state_keys = list(self.channels)
state_keys_read = state_keys[0] if state_keys == ["__root__"] else state_keys
@@ -98,6 +102,9 @@ class StateGraph(Graph):
output=END,
hidden=[f"{node}:inbox" for node in self.nodes] + [START] + state_keys,
checkpointer=checkpointer,
interrupt=[f"{node}:inbox" for node in interrupt_before]
if interrupt_before
else [],
)