# 09. Delete message (RemoveMessage)
## How to delete messages
One of the general states of graphs is a list of messages. Normally, only add messages to that state. But sometimes **Remove message** You may need to.
for this `RemoveMessage` Modifiers are available. And, `RemoveMessage` Modifier `reducer` Is to have a key.
basic `MessagesState` has a messages key, and the reducer of that key is `RemoveMessage` Allow modifiers.
This reducer `RemoveMessage` Delete the message from the key using
### Settings
First, let's build a simple graph that uses messages. Essential `reducer` Include `MessagesState` Please note that you are using.
```python
# Configuration file for managing API keys as environment variables
from dotenv import load_dotenv
# Load API key information
load_dotenv()
```
```
True
```
```python
# Set up LangSmith tracking. https://smith.langchain.com
# !pip install -qU langchain-teddynote
from langchain_teddynote import logging
# Enter a project name.
logging.langsmith("CH17-LangGraph-Modules")
```
```
Start tracking LangSmith.
[Project name]
CH17-LangGraph-Modules
```
### Build a basic LangGraph for tutorial progress
`RemoveMessage` Build the basic LangGraph required to use modifiers.
```python
from typing import Literal
from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph import MessagesState, StateGraph, START, END
from langgraph.prebuilt import ToolNode, tools_condition
# Initialize memory object for checkpoint storage
memory = MemorySaver()
# Define a tool function that mimics the web search function.
@tool
def search(query: str):
"""Call to surf on the web."""
return "Web search results: LangGraph 한글 튜토리얼은 https://wikidocs.net/233785 You can check it at."
# Create a tool list and initialize the tool node
tools = [search]
tool_node = ToolNode(tools)
# Model Initialization and Tool Binding
model = ChatOpenAI(model_name="gpt-4o-mini")
bound_model = model.bind_tools(tools)
# # Function to determine the next execution node based on the conversation status
def should_continue(state: MessagesState):
last_message = state["messages"][-1]
if not last_message.tool_calls:
return END
return "tool"
# LLM model call and response processing functions
def call_model(state: MessagesState):
response = model.invoke(state["messages"])
return {"messages": response}
# Initializing a state-based workflow graph
workflow = StateGraph(MessagesState)
# Adding Agents and Action Nodes
workflow.add_node("agent", call_model)
workflow.add_node("tool", tool_node)
# Set the starting point to the agent node
workflow.add_edge(START, "agent")
# Conditional Edge Setting: Defining the execution flow after the agent node
workflow.add_conditional_edges("agent", should_continue, {"tool": "tool", END: END})
# Added an edge that returns to the agent after running the tool
workflow.add_edge("tool", "agent")
# Compile the final executable workflow with checkpoints
app = workflow.compile(checkpointer=memory)
```
Visualize the graph.
```python
from langchain_teddynote.graphs import visualize_graph
visualize_graph(app)
```
```python
from langchain_core.messages import HumanMessage
# Initialize default settings object with thread ID 1
config = {"configurable": {"thread_id": "1"}}
# Perform the first question
input_message = HumanMessage(
content="Hello! My name is Teddy. Nice to meet you."
)
# Process messages and output responses in stream mode, display details of the last message
for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
event["messages"][-1].pretty_print()
```
```
================================ Human Message =================================
Hello! My name is Teddy. Take good care of me.
================================== Ai Message ==================================
Hello, Teddy! Nice to meet you. How can I help you?
```
```python
# Ask follow-up questions
input_message = HumanMessage(content="내 이름이 뭐라고요?")
# Process the second message in stream mode and output the response
for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
event["messages"][-1].pretty_print()
```
```
================================ Human Message =================================
What is my name?
================================== Ai Message ==================================
You said Teddy! Is that correct?
```
```python
# Step-by-step status check
messages = app.get_state(config).values["messages"]
for message in messages:
message.pretty_print()
```
```
================================ Human Message =================================
Hello! My name is Teddy. Take good care of me.
================================== Ai Message ==================================
Hello, Teddy! Nice to meet you. What can you help?
================================ Human Message =================================
What is my name?
================================== Ai Message ==================================
You said Teddy! Is that correct?
```
### Delete message using RemoveMessage modifier
First, let's look at how to delete messages manually. Let's check the status of the current thread.
```python
# Extract and save message list from app status
messages = app.get_state(config).values["messages"]
# Return a list of messages
for message in messages:
message.pretty_print()
```
```
================================== Ai Message ==================================
Hello, Teddy! nice to meet you. How can I help you?
================================ Human Message =================================
What is my name?
================================== Ai Message ==================================
You said Teddy! Is that correct?
```
`update_state` When you call and pass id of the first message, that message is deleted.
```python
from langchain_core.messages import RemoveMessage
# Remove the first message from the message array based on ID and update the app state.
app.update_state(config, {"messages": RemoveMessage(id=messages[0].id)})
```
```
{'configurable': {'thread_id': '1','checkpoint_ns':'','checkpoint_id': '1ef99ffd-6687-6200-8005-d84565cbcc69'}}
```
Now if you check the messages, you can confirm that the first message has been deleted.
```python
# Extract message list from app status and view saved conversation history
messages = app.get_state(config).values["messages"]
for message in messages:
message.pretty_print()
```
```
================================== Ai Message ==================================
Hello, Teddy! Nice to meet you. What can you help?
================================ Human Message =================================
What is my name?
================================== Ai Message ==================================
You said Teddy! Is that correct?
```
### Dynamically delete more messages
You can also delete messages programmatically inside the graph.
Let's take a look at how to modify the graph to delete old messages (messages earlier than 3 previous messages) when the graph execution ends.
```python
from langchain_core.messages import RemoveMessage
from langgraph.graph import END
# When the number of messages exceeds 3, delete old messages and keep only the latest messages.
def delete_messages(state):
messages = state["messages"]
if len(messages) > 3:
return {"messages": [RemoveMessage(id=m.id) for m in messages[:-3]]}
# Logic to determine the next execution node based on message status
def should_continue(state: MessagesState) -> Literal["action", "delete_messages"]:
"""Return the next node to execute."""
last_message = state["messages"][-1]
# Execute message deletion function if no function call is made
if not last_message.tool_calls:
return "delete_messages"
# Execute an action when a function call is made
return "action"
# Defining a message state-based workflow graph
workflow = StateGraph(MessagesState)
# Adding Agents and Action Nodes
workflow.add_node("agent", call_model)
workflow.add_node("action", tool_node)
# Add a Delete Message node
workflow.add_node(delete_messages)
# Connecting from the start node to the agent node
workflow.add_edge(START, "agent")
# Controlling flow between nodes by adding conditional edges
workflow.add_conditional_edges(
"agent",
should_continue,
)
# Connecting from action node to agent node
workflow.add_edge("action", "agent")
# Connect from the message delete node to the end node
workflow.add_edge("delete_messages", END)
# Compile workflow using memory checkpointer
app = workflow.compile(checkpointer=memory)
```
Visualize the graph.
```python
from langchain_teddynote.graphs import visualize_graph
visualize_graph(app)
```
Now you can try this. `graph` You can call 2 times and then check the status.
```python
# Import HumanMessage class for LangChain message processing
from langchain_core.messages import HumanMessage
# Initialize a settings object containing a thread ID.
config = {"configurable": {"thread_id": "2"}}
# Perform the first question
input_message = HumanMessage(
content="Hello! My name is Teddy. Nice to meet you."
)
for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
print([(message.type, message.content) for message in event["messages"]])
```
```
[('human','Hello! My name is Teddy. Take good care of me.')]
[('human','Hello! My name is Teddy. Take good care of me.'), ('ai','Hello, Teddy! nice to meet you. How can I help you?')]
```
```python
# Perform the second question
input_message = HumanMessage(content="What is my name?")
for event in app.stream({"messages": [input_message]}, config, stream_mode="values"):
print([(message.type, message.content) for message in event["messages"]])
```
```
[('human','Hello! My name is Teddy. Take good care of me.'), ('ai','Hello, Teddy! nice to meet you. How can I help you?'), ('human','What is my name saying?')]
[('human','Hello! My name is Teddy. Take good care of me.'), ('ai','Hello, Teddy! nice to meet you. How can I help you?'), ('human','What is my name saying?'), ('ai','Teddy said! Is that correct?')]
[('ai','Hello, Teddy! nice to meet you. How can I help you?'), ('human','What is my name saying?'), ('ai','Teddy said! Is that correct?')]
```
When you check the final state, you can see that there are only three messages.
This is because I just deleted the previous messages.
```python
# Extract and save message list from app status
messages = app.get_state(config).values["messages"]
# Return a list of messages
for message in messages:
message.pretty_print()
```
```
================================== Ai Message ==================================
Hello, Teddy! nice to meet you. How can I help you?
================================ Human Message=================================
What is my name?
================================== Ai Message ==================================
You said Teddy! Is that correct?
```