



Copied!







%%capture --no-stderr
%pip install --quiet -U langgraph-checkpoint-sqlite langchain_core langgraph langchain_openai

%%capture --no-stderr
%pip install --quiet -U langgraph-checkpoint-sqlite langchain_core langgraph langchain_openai





Copied!







%%capture --no-stderr
%pip install --quiet -U langgraph-checkpoint-sqlite langchain_core langgraph langchain_openai

%%capture --no-stderr
%pip install --quiet -U langgraph-checkpoint-sqlite langchain_core langgraph langchain_openai





Copied!







import os, getpass

def _set_env(var: str):
    if not os.environ.get(var):
        os.environ[var] = getpass.getpass(f"{var}: ")

_set_env("OPENAI_API_KEY")

import os, getpass

def _set_env(var: str):
    if not os.environ.get(var):
        os.environ[var] = getpass.getpass(f"{var}: ")

_set_env("OPENAI_API_KEY")





Copied!







import os, getpass

def _set_env(var: str):
    if not os.environ.get(var):
        os.environ[var] = getpass.getpass(f"{var}: ")

_set_env("OPENAI_API_KEY")

import os, getpass

def _set_env(var: str):
    if not os.environ.get(var):
        os.environ[var] = getpass.getpass(f"{var}: ")

_set_env("OPENAI_API_KEY")





Copied!







import sqlite3
# In memory
conn = sqlite3.connect(":memory:", check_same_thread = False)

import sqlite3
# In memory
conn = sqlite3.connect(":memory:", check_same_thread = False)





Copied!







import sqlite3
# In memory
conn = sqlite3.connect(":memory:", check_same_thread = False)

import sqlite3
# In memory
conn = sqlite3.connect(":memory:", check_same_thread = False)





Copied!







# pull file if it doesn't exist and connect to local db
!mkdir -p state_db && [ ! -f state_db/example.db ] && wget -P state_db https://github.com/langchain-ai/langchain-academy/raw/main/module-2/state_db/example.db

db_path = "state_db/example.db"
conn = sqlite3.connect(db_path, check_same_thread=False)

# pull file if it doesn't exist and connect to local db
!mkdir -p state_db && [ ! -f state_db/example.db ] && wget -P state_db https://github.com/langchain-ai/langchain-academy/raw/main/module-2/state_db/example.db

db_path = "state_db/example.db"
conn = sqlite3.connect(db_path, check_same_thread=False)





Copied!







# pull file if it doesn't exist and connect to local db
!mkdir -p state_db && [ ! -f state_db/example.db ] && wget -P state_db https://github.com/langchain-ai/langchain-academy/raw/main/module-2/state_db/example.db

db_path = "state_db/example.db"
conn = sqlite3.connect(db_path, check_same_thread=False)

# pull file if it doesn't exist and connect to local db
!mkdir -p state_db && [ ! -f state_db/example.db ] && wget -P state_db https://github.com/langchain-ai/langchain-academy/raw/main/module-2/state_db/example.db

db_path = "state_db/example.db"
conn = sqlite3.connect(db_path, check_same_thread=False)





Copied!







# Here is our checkpointer 
from langgraph.checkpoint.sqlite import SqliteSaver
memory = SqliteSaver(conn)

# Here is our checkpointer 
from langgraph.checkpoint.sqlite import SqliteSaver
memory = SqliteSaver(conn)





Copied!







# Here is our checkpointer 
from langgraph.checkpoint.sqlite import SqliteSaver
memory = SqliteSaver(conn)

# Here is our checkpointer 
from langgraph.checkpoint.sqlite import SqliteSaver
memory = SqliteSaver(conn)





Copied!







from langchain_openai import ChatOpenAI
from langchain_core.messages import SystemMessage, HumanMessage, RemoveMessage

from langgraph.graph import END
from langgraph.graph import MessagesState

model = ChatOpenAI(model="gpt-4o",temperature=0)

class State(MessagesState):
    summary: str

# Define the logic to call the model
def call_model(state: State):
    
    # Get summary if it exists
    summary = state.get("summary", "")

    # If there is summary, then we add it
    if summary:
        
        # Add summary to system message
        system_message = f"Summary of conversation earlier: {summary}"

        # Append summary to any newer messages
        messages = [SystemMessage(content=system_message)] + state["messages"]
    
    else:
        messages = state["messages"]
    
    response = model.invoke(messages)
    return {"messages": response}

def summarize_conversation(state: State):
    
    # First, we get any existing summary
    summary = state.get("summary", "")

    # Create our summarization prompt 
    if summary:
        
        # A summary already exists
        summary_message = (
            f"This is summary of the conversation to date: {summary}\n\n"
            "Extend the summary by taking into account the new messages above:"
        )
        
    else:
        summary_message = "Create a summary of the conversation above:"

    # Add prompt to our history
    messages = state["messages"] + [HumanMessage(content=summary_message)]
    response = model.invoke(messages)
    
    # Delete all but the 2 most recent messages
    delete_messages = [RemoveMessage(id=m.id) for m in state["messages"][:-2]]
    return {"summary": response.content, "messages": delete_messages}

# Determine whether to end or summarize the conversation
def should_continue(state: State):
    
    """Return the next node to execute."""
    
    messages = state["messages"]
    
    # If there are more than six messages, then we summarize the conversation
    if len(messages) > 6:
        return "summarize_conversation"
    
    # Otherwise we can just end
    return END

from langchain_openai import ChatOpenAI
from langchain_core.messages import SystemMessage, HumanMessage, RemoveMessage

from langgraph.graph import END
from langgraph.graph import MessagesState

model = ChatOpenAI(model="gpt-4o",temperature=0)

class State(MessagesState):
    summary: str

# Define the logic to call the model
def call_model(state: State):
    
    # Get summary if it exists
    summary = state.get("summary", "")

    # If there is summary, then we add it
    if summary:
        
        # Add summary to system message
        system_message = f"Summary of conversation earlier: {summary}"

        # Append summary to any newer messages
        messages = [SystemMessage(content=system_message)] + state["messages"]
    
    else:
        messages = state["messages"]
    
    response = model.invoke(messages)
    return {"messages": response}

def summarize_conversation(state: State):
    
    # First, we get any existing summary
    summary = state.get("summary", "")

    # Create our summarization prompt 
    if summary:
        
        # A summary already exists
        summary_message = (
            f"This is summary of the conversation to date: {summary}\n\n"
            "Extend the summary by taking into account the new messages above:"
        )
        
    else:
        summary_message = "Create a summary of the conversation above:"

    # Add prompt to our history
    messages = state["messages"] + [HumanMessage(content=summary_message)]
    response = model.invoke(messages)
    
    # Delete all but the 2 most recent messages
    delete_messages = [RemoveMessage(id=m.id) for m in state["messages"][:-2]]
    return {"summary": response.content, "messages": delete_messages}

# Determine whether to end or summarize the conversation
def should_continue(state: State):
    
    """Return the next node to execute."""
    
    messages = state["messages"]
    
    # If there are more than six messages, then we summarize the conversation
    if len(messages) > 6:
        return "summarize_conversation"
    
    # Otherwise we can just end
    return END





Copied!







from langchain_openai import ChatOpenAI
from langchain_core.messages import SystemMessage, HumanMessage, RemoveMessage

from langgraph.graph import END
from langgraph.graph import MessagesState

model = ChatOpenAI(model="gpt-4o",temperature=0)

class State(MessagesState):
    summary: str

# Define the logic to call the model
def call_model(state: State):
    
    # Get summary if it exists
    summary = state.get("summary", "")

    # If there is summary, then we add it
    if summary:
        
        # Add summary to system message
        system_message = f"Summary of conversation earlier: {summary}"

        # Append summary to any newer messages
        messages = [SystemMessage(content=system_message)] + state["messages"]
    
    else:
        messages = state["messages"]
    
    response = model.invoke(messages)
    return {"messages": response}

def summarize_conversation(state: State):
    
    # First, we get any existing summary
    summary = state.get("summary", "")

    # Create our summarization prompt 
    if summary:
        
        # A summary already exists
        summary_message = (
            f"This is summary of the conversation to date: {summary}\n\n"
            "Extend the summary by taking into account the new messages above:"
        )
        
    else:
        summary_message = "Create a summary of the conversation above:"

    # Add prompt to our history
    messages = state["messages"] + [HumanMessage(content=summary_message)]
    response = model.invoke(messages)
    
    # Delete all but the 2 most recent messages
    delete_messages = [RemoveMessage(id=m.id) for m in state["messages"][:-2]]
    return {"summary": response.content, "messages": delete_messages}

# Determine whether to end or summarize the conversation
def should_continue(state: State):
    
    """Return the next node to execute."""
    
    messages = state["messages"]
    
    # If there are more than six messages, then we summarize the conversation
    if len(messages) > 6:
        return "summarize_conversation"
    
    # Otherwise we can just end
    return END

from langchain_openai import ChatOpenAI
from langchain_core.messages import SystemMessage, HumanMessage, RemoveMessage

from langgraph.graph import END
from langgraph.graph import MessagesState

model = ChatOpenAI(model="gpt-4o",temperature=0)

class State(MessagesState):
    summary: str

# Define the logic to call the model
def call_model(state: State):
    
    # Get summary if it exists
    summary = state.get("summary", "")

    # If there is summary, then we add it
    if summary:
        
        # Add summary to system message
        system_message = f"Summary of conversation earlier: {summary}"

        # Append summary to any newer messages
        messages = [SystemMessage(content=system_message)] + state["messages"]
    
    else:
        messages = state["messages"]
    
    response = model.invoke(messages)
    return {"messages": response}

def summarize_conversation(state: State):
    
    # First, we get any existing summary
    summary = state.get("summary", "")

    # Create our summarization prompt 
    if summary:
        
        # A summary already exists
        summary_message = (
            f"This is summary of the conversation to date: {summary}\n\n"
            "Extend the summary by taking into account the new messages above:"
        )
        
    else:
        summary_message = "Create a summary of the conversation above:"

    # Add prompt to our history
    messages = state["messages"] + [HumanMessage(content=summary_message)]
    response = model.invoke(messages)
    
    # Delete all but the 2 most recent messages
    delete_messages = [RemoveMessage(id=m.id) for m in state["messages"][:-2]]
    return {"summary": response.content, "messages": delete_messages}

# Determine whether to end or summarize the conversation
def should_continue(state: State):
    
    """Return the next node to execute."""
    
    messages = state["messages"]
    
    # If there are more than six messages, then we summarize the conversation
    if len(messages) > 6:
        return "summarize_conversation"
    
    # Otherwise we can just end
    return END





Copied!







from IPython.display import Image, display
from langgraph.graph import StateGraph, START

# Define a new graph
workflow = StateGraph(State)
workflow.add_node("conversation", call_model)
workflow.add_node(summarize_conversation)

# Set the entrypoint as conversation
workflow.add_edge(START, "conversation")
workflow.add_conditional_edges("conversation", should_continue)
workflow.add_edge("summarize_conversation", END)

# Compile
graph = workflow.compile(checkpointer=memory)
display(Image(graph.get_graph().draw_mermaid_png()))

from IPython.display import Image, display
from langgraph.graph import StateGraph, START

# Define a new graph
workflow = StateGraph(State)
workflow.add_node("conversation", call_model)
workflow.add_node(summarize_conversation)

# Set the entrypoint as conversation
workflow.add_edge(START, "conversation")
workflow.add_conditional_edges("conversation", should_continue)
workflow.add_edge("summarize_conversation", END)

# Compile
graph = workflow.compile(checkpointer=memory)
display(Image(graph.get_graph().draw_mermaid_png()))





Copied!







from IPython.display import Image, display
from langgraph.graph import StateGraph, START

# Define a new graph
workflow = StateGraph(State)
workflow.add_node("conversation", call_model)
workflow.add_node(summarize_conversation)

# Set the entrypoint as conversation
workflow.add_edge(START, "conversation")
workflow.add_conditional_edges("conversation", should_continue)
workflow.add_edge("summarize_conversation", END)

# Compile
graph = workflow.compile(checkpointer=memory)
display(Image(graph.get_graph().draw_mermaid_png()))

from IPython.display import Image, display
from langgraph.graph import StateGraph, START

# Define a new graph
workflow = StateGraph(State)
workflow.add_node("conversation", call_model)
workflow.add_node(summarize_conversation)

# Set the entrypoint as conversation
workflow.add_edge(START, "conversation")
workflow.add_conditional_edges("conversation", should_continue)
workflow.add_edge("summarize_conversation", END)

# Compile
graph = workflow.compile(checkpointer=memory)
display(Image(graph.get_graph().draw_mermaid_png()))





Copied!







# Create a thread
config = {"configurable": {"thread_id": "1"}}

# Start conversation
input_message = HumanMessage(content="hi! I'm Lance")
output = graph.invoke({"messages": [input_message]}, config) 
for m in output['messages'][-1:]:
    m.pretty_print()

input_message = HumanMessage(content="what's my name?")
output = graph.invoke({"messages": [input_message]}, config) 
for m in output['messages'][-1:]:
    m.pretty_print()

input_message = HumanMessage(content="i like the 49ers!")
output = graph.invoke({"messages": [input_message]}, config) 
for m in output['messages'][-1:]:
    m.pretty_print()

# Create a thread
config = {"configurable": {"thread_id": "1"}}

# Start conversation
input_message = HumanMessage(content="hi! I'm Lance")
output = graph.invoke({"messages": [input_message]}, config) 
for m in output['messages'][-1:]:
    m.pretty_print()

input_message = HumanMessage(content="what's my name?")
output = graph.invoke({"messages": [input_message]}, config) 
for m in output['messages'][-1:]:
    m.pretty_print()

input_message = HumanMessage(content="i like the 49ers!")
output = graph.invoke({"messages": [input_message]}, config) 
for m in output['messages'][-1:]:
    m.pretty_print()

/var/folders/l9/bpjxdmfx7lvd1fbdjn38y5dh0000gn/T/ipykernel_18873/2173919996.py:55: LangChainBetaWarning: The class `RemoveMessage` is in beta. It is actively being worked on, so the API may change.
  delete_messages = [RemoveMessage(id=m.id) for m in state["messages"][:-2]]

================================== Ai Message ==================================

Hello again, Lance! It's great to hear from you. Since you like the 49ers, is there a particular player or moment in their history that stands out to you? Or perhaps you'd like to discuss their current season? Let me know!
================================== Ai Message ==================================

Your name is Lance! How can I assist you today? Would you like to talk more about the San Francisco 49ers or something else?
================================== Ai Message ==================================

That's awesome, Lance! The San Francisco 49ers have a rich history and a passionate fan base. Is there a specific aspect of the team you'd like to discuss? For example, we could talk about:

- Their legendary players like Joe Montana and Jerry Rice
- Memorable games and Super Bowl victories
- The current roster and season prospects
- Rivalries, like the one with the Seattle Seahawks
- Levi's Stadium and the fan experience

Let me know what interests you!





Copied!







# Create a thread
config = {"configurable": {"thread_id": "1"}}

# Start conversation
input_message = HumanMessage(content="hi! I'm Lance")
output = graph.invoke({"messages": [input_message]}, config) 
for m in output['messages'][-1:]:
    m.pretty_print()

input_message = HumanMessage(content="what's my name?")
output = graph.invoke({"messages": [input_message]}, config) 
for m in output['messages'][-1:]:
    m.pretty_print()

input_message = HumanMessage(content="i like the 49ers!")
output = graph.invoke({"messages": [input_message]}, config) 
for m in output['messages'][-1:]:
    m.pretty_print()

# Create a thread
config = {"configurable": {"thread_id": "1"}}

# Start conversation
input_message = HumanMessage(content="hi! I'm Lance")
output = graph.invoke({"messages": [input_message]}, config) 
for m in output['messages'][-1:]:
    m.pretty_print()

input_message = HumanMessage(content="what's my name?")
output = graph.invoke({"messages": [input_message]}, config) 
for m in output['messages'][-1:]:
    m.pretty_print()

input_message = HumanMessage(content="i like the 49ers!")
output = graph.invoke({"messages": [input_message]}, config) 
for m in output['messages'][-1:]:
    m.pretty_print()

/var/folders/l9/bpjxdmfx7lvd1fbdjn38y5dh0000gn/T/ipykernel_18873/2173919996.py:55: LangChainBetaWarning: The class `RemoveMessage` is in beta. It is actively being worked on, so the API may change.
  delete_messages = [RemoveMessage(id=m.id) for m in state["messages"][:-2]]

================================== Ai Message ==================================

Hello again, Lance! It's great to hear from you. Since you like the 49ers, is there a particular player or moment in their history that stands out to you? Or perhaps you'd like to discuss their current season? Let me know!
================================== Ai Message ==================================

Your name is Lance! How can I assist you today? Would you like to talk more about the San Francisco 49ers or something else?
================================== Ai Message ==================================

That's awesome, Lance! The San Francisco 49ers have a rich history and a passionate fan base. Is there a specific aspect of the team you'd like to discuss? For example, we could talk about:

- Their legendary players like Joe Montana and Jerry Rice
- Memorable games and Super Bowl victories
- The current roster and season prospects
- Rivalries, like the one with the Seattle Seahawks
- Levi's Stadium and the fan experience

Let me know what interests you!





Copied!







config = {"configurable": {"thread_id": "1"}}
graph_state = graph.get_state(config)
graph_state

config = {"configurable": {"thread_id": "1"}}
graph_state = graph.get_state(config)
graph_state

StateSnapshot(values={'messages': [HumanMessage(content="hi! I'm Lance", id='d5bb4b3f-b1e9-4f61-8c75-7a7210b30253'), AIMessage(content="Hello again, Lance! It's great to hear from you. Since you like the 49ers, is there a particular player or moment in their history that stands out to you? Or perhaps you'd like to discuss their current season? Let me know!", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 50, 'prompt_tokens': 337, 'total_tokens': 387}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_157b3831f5', 'finish_reason': 'stop', 'logprobs': None}, id='run-dde04d51-d305-4a9e-8ad5-6bdf5583196e-0', usage_metadata={'input_tokens': 337, 'output_tokens': 50, 'total_tokens': 387}), HumanMessage(content="what's my name?", id='d7530770-f130-4a05-a602-a96fd87859c6'), AIMessage(content='Your name is Lance! How can I assist you today? Would you like to talk more about the San Francisco 49ers or something else?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 29, 'prompt_tokens': 243, 'total_tokens': 272}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_fde2829a40', 'finish_reason': 'stop', 'logprobs': None}, id='run-763b6387-c4c9-4658-9d01-7c018dde7a62-0', usage_metadata={'input_tokens': 243, 'output_tokens': 29, 'total_tokens': 272}), HumanMessage(content='i like the 49ers!', id='235dcec4-b656-4bee-b741-e330e1a026e2'), AIMessage(content="That's awesome, Lance! The San Francisco 49ers have a rich history and a passionate fan base. Is there a specific aspect of the team you'd like to discuss? For example, we could talk about:\n\n- Their legendary players like Joe Montana and Jerry Rice\n- Memorable games and Super Bowl victories\n- The current roster and season prospects\n- Rivalries, like the one with the Seattle Seahawks\n- Levi's Stadium and the fan experience\n\nLet me know what interests you!", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 98, 'prompt_tokens': 287, 'total_tokens': 385}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_fde2829a40', 'finish_reason': 'stop', 'logprobs': None}, id='run-729860b2-16c3-46ff-ad1e-0a5475565b20-0', usage_metadata={'input_tokens': 287, 'output_tokens': 98, 'total_tokens': 385})], 'summary': 'Here\'s an extended summary of the conversation:\n\nLance introduced himself multiple times during the conversation, each time stating, "Hi! I\'m Lance." He expressed his fondness for the San Francisco 49ers football team. The AI assistant acknowledged Lance\'s name each time and showed willingness to discuss the 49ers, offering to talk about various aspects of the team such as their history, current roster, memorable games, prospects for the upcoming season, rivalries, and their home stadium, Levi\'s Stadium. Despite the AI\'s attempts to engage in a more detailed discussion about the 49ers, Lance reintroduced himself again without directly responding to the AI\'s questions or prompts about the team. The conversation remained brief and somewhat repetitive, focusing mainly on Lance\'s introductions and his interest in the 49ers.'}, next=(), config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef6a36d-ca9c-6144-801b-6d0cf97adc73'}}, metadata={'source': 'loop', 'writes': {'conversation': {'messages': AIMessage(content="That's awesome, Lance! The San Francisco 49ers have a rich history and a passionate fan base. Is there a specific aspect of the team you'd like to discuss? For example, we could talk about:\n\n- Their legendary players like Joe Montana and Jerry Rice\n- Memorable games and Super Bowl victories\n- The current roster and season prospects\n- Rivalries, like the one with the Seattle Seahawks\n- Levi's Stadium and the fan experience\n\nLet me know what interests you!", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 98, 'prompt_tokens': 287, 'total_tokens': 385}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_fde2829a40', 'finish_reason': 'stop', 'logprobs': None}, id='run-729860b2-16c3-46ff-ad1e-0a5475565b20-0', usage_metadata={'input_tokens': 287, 'output_tokens': 98, 'total_tokens': 385})}}, 'step': 27, 'parents': {}}, created_at='2024-09-03T20:55:33.466540+00:00', parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef6a36d-b8f3-6b40-801a-494776d2e9e0'}}, tasks=())





Copied!







config = {"configurable": {"thread_id": "1"}}
graph_state = graph.get_state(config)
graph_state

config = {"configurable": {"thread_id": "1"}}
graph_state = graph.get_state(config)
graph_state

StateSnapshot(values={'messages': [HumanMessage(content="hi! I'm Lance", id='d5bb4b3f-b1e9-4f61-8c75-7a7210b30253'), AIMessage(content="Hello again, Lance! It's great to hear from you. Since you like the 49ers, is there a particular player or moment in their history that stands out to you? Or perhaps you'd like to discuss their current season? Let me know!", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 50, 'prompt_tokens': 337, 'total_tokens': 387}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_157b3831f5', 'finish_reason': 'stop', 'logprobs': None}, id='run-dde04d51-d305-4a9e-8ad5-6bdf5583196e-0', usage_metadata={'input_tokens': 337, 'output_tokens': 50, 'total_tokens': 387}), HumanMessage(content="what's my name?", id='d7530770-f130-4a05-a602-a96fd87859c6'), AIMessage(content='Your name is Lance! How can I assist you today? Would you like to talk more about the San Francisco 49ers or something else?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 29, 'prompt_tokens': 243, 'total_tokens': 272}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_fde2829a40', 'finish_reason': 'stop', 'logprobs': None}, id='run-763b6387-c4c9-4658-9d01-7c018dde7a62-0', usage_metadata={'input_tokens': 243, 'output_tokens': 29, 'total_tokens': 272}), HumanMessage(content='i like the 49ers!', id='235dcec4-b656-4bee-b741-e330e1a026e2'), AIMessage(content="That's awesome, Lance! The San Francisco 49ers have a rich history and a passionate fan base. Is there a specific aspect of the team you'd like to discuss? For example, we could talk about:\n\n- Their legendary players like Joe Montana and Jerry Rice\n- Memorable games and Super Bowl victories\n- The current roster and season prospects\n- Rivalries, like the one with the Seattle Seahawks\n- Levi's Stadium and the fan experience\n\nLet me know what interests you!", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 98, 'prompt_tokens': 287, 'total_tokens': 385}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_fde2829a40', 'finish_reason': 'stop', 'logprobs': None}, id='run-729860b2-16c3-46ff-ad1e-0a5475565b20-0', usage_metadata={'input_tokens': 287, 'output_tokens': 98, 'total_tokens': 385})], 'summary': 'Here\'s an extended summary of the conversation:\n\nLance introduced himself multiple times during the conversation, each time stating, "Hi! I\'m Lance." He expressed his fondness for the San Francisco 49ers football team. The AI assistant acknowledged Lance\'s name each time and showed willingness to discuss the 49ers, offering to talk about various aspects of the team such as their history, current roster, memorable games, prospects for the upcoming season, rivalries, and their home stadium, Levi\'s Stadium. Despite the AI\'s attempts to engage in a more detailed discussion about the 49ers, Lance reintroduced himself again without directly responding to the AI\'s questions or prompts about the team. The conversation remained brief and somewhat repetitive, focusing mainly on Lance\'s introductions and his interest in the 49ers.'}, next=(), config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef6a36d-ca9c-6144-801b-6d0cf97adc73'}}, metadata={'source': 'loop', 'writes': {'conversation': {'messages': AIMessage(content="That's awesome, Lance! The San Francisco 49ers have a rich history and a passionate fan base. Is there a specific aspect of the team you'd like to discuss? For example, we could talk about:\n\n- Their legendary players like Joe Montana and Jerry Rice\n- Memorable games and Super Bowl victories\n- The current roster and season prospects\n- Rivalries, like the one with the Seattle Seahawks\n- Levi's Stadium and the fan experience\n\nLet me know what interests you!", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 98, 'prompt_tokens': 287, 'total_tokens': 385}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_fde2829a40', 'finish_reason': 'stop', 'logprobs': None}, id='run-729860b2-16c3-46ff-ad1e-0a5475565b20-0', usage_metadata={'input_tokens': 287, 'output_tokens': 98, 'total_tokens': 385})}}, 'step': 27, 'parents': {}}, created_at='2024-09-03T20:55:33.466540+00:00', parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef6a36d-b8f3-6b40-801a-494776d2e9e0'}}, tasks=())





Copied!







# Create a thread
config = {"configurable": {"thread_id": "1"}}
graph_state = graph.get_state(config)
graph_state

# Create a thread
config = {"configurable": {"thread_id": "1"}}
graph_state = graph.get_state(config)
graph_state

StateSnapshot(values={'messages': [HumanMessage(content="hi! I'm Lance", id='d5bb4b3f-b1e9-4f61-8c75-7a7210b30253'), AIMessage(content="Hello again, Lance! It's great to hear from you. Since you like the 49ers, is there a particular player or moment in their history that stands out to you? Or perhaps you'd like to discuss their current season? Let me know!", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 50, 'prompt_tokens': 337, 'total_tokens': 387}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_157b3831f5', 'finish_reason': 'stop', 'logprobs': None}, id='run-dde04d51-d305-4a9e-8ad5-6bdf5583196e-0', usage_metadata={'input_tokens': 337, 'output_tokens': 50, 'total_tokens': 387}), HumanMessage(content="what's my name?", id='d7530770-f130-4a05-a602-a96fd87859c6'), AIMessage(content='Your name is Lance! How can I assist you today? Would you like to talk more about the San Francisco 49ers or something else?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 29, 'prompt_tokens': 243, 'total_tokens': 272}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_fde2829a40', 'finish_reason': 'stop', 'logprobs': None}, id='run-763b6387-c4c9-4658-9d01-7c018dde7a62-0', usage_metadata={'input_tokens': 243, 'output_tokens': 29, 'total_tokens': 272}), HumanMessage(content='i like the 49ers!', id='235dcec4-b656-4bee-b741-e330e1a026e2'), AIMessage(content="That's awesome, Lance! The San Francisco 49ers have a rich history and a passionate fan base. Is there a specific aspect of the team you'd like to discuss? For example, we could talk about:\n\n- Their legendary players like Joe Montana and Jerry Rice\n- Memorable games and Super Bowl victories\n- The current roster and season prospects\n- Rivalries, like the one with the Seattle Seahawks\n- Levi's Stadium and the fan experience\n\nLet me know what interests you!", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 98, 'prompt_tokens': 287, 'total_tokens': 385}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_fde2829a40', 'finish_reason': 'stop', 'logprobs': None}, id='run-729860b2-16c3-46ff-ad1e-0a5475565b20-0', usage_metadata={'input_tokens': 287, 'output_tokens': 98, 'total_tokens': 385})], 'summary': 'Here\'s an extended summary of the conversation:\n\nLance introduced himself multiple times during the conversation, each time stating, "Hi! I\'m Lance." He expressed his fondness for the San Francisco 49ers football team. The AI assistant acknowledged Lance\'s name each time and showed willingness to discuss the 49ers, offering to talk about various aspects of the team such as their history, current roster, memorable games, prospects for the upcoming season, rivalries, and their home stadium, Levi\'s Stadium. Despite the AI\'s attempts to engage in a more detailed discussion about the 49ers, Lance reintroduced himself again without directly responding to the AI\'s questions or prompts about the team. The conversation remained brief and somewhat repetitive, focusing mainly on Lance\'s introductions and his interest in the 49ers.'}, next=(), config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef6a36d-ca9c-6144-801b-6d0cf97adc73'}}, metadata={'source': 'loop', 'writes': {'conversation': {'messages': AIMessage(content="That's awesome, Lance! The San Francisco 49ers have a rich history and a passionate fan base. Is there a specific aspect of the team you'd like to discuss? For example, we could talk about:\n\n- Their legendary players like Joe Montana and Jerry Rice\n- Memorable games and Super Bowl victories\n- The current roster and season prospects\n- Rivalries, like the one with the Seattle Seahawks\n- Levi's Stadium and the fan experience\n\nLet me know what interests you!", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 98, 'prompt_tokens': 287, 'total_tokens': 385}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_fde2829a40', 'finish_reason': 'stop', 'logprobs': None}, id='run-729860b2-16c3-46ff-ad1e-0a5475565b20-0', usage_metadata={'input_tokens': 287, 'output_tokens': 98, 'total_tokens': 385})}}, 'step': 27, 'parents': {}}, created_at='2024-09-03T20:55:33.466540+00:00', parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef6a36d-b8f3-6b40-801a-494776d2e9e0'}}, tasks=())





Copied!







# Create a thread
config = {"configurable": {"thread_id": "1"}}
graph_state = graph.get_state(config)
graph_state

# Create a thread
config = {"configurable": {"thread_id": "1"}}
graph_state = graph.get_state(config)
graph_state

StateSnapshot(values={'messages': [HumanMessage(content="hi! I'm Lance", id='d5bb4b3f-b1e9-4f61-8c75-7a7210b30253'), AIMessage(content="Hello again, Lance! It's great to hear from you. Since you like the 49ers, is there a particular player or moment in their history that stands out to you? Or perhaps you'd like to discuss their current season? Let me know!", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 50, 'prompt_tokens': 337, 'total_tokens': 387}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_157b3831f5', 'finish_reason': 'stop', 'logprobs': None}, id='run-dde04d51-d305-4a9e-8ad5-6bdf5583196e-0', usage_metadata={'input_tokens': 337, 'output_tokens': 50, 'total_tokens': 387}), HumanMessage(content="what's my name?", id='d7530770-f130-4a05-a602-a96fd87859c6'), AIMessage(content='Your name is Lance! How can I assist you today? Would you like to talk more about the San Francisco 49ers or something else?', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 29, 'prompt_tokens': 243, 'total_tokens': 272}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_fde2829a40', 'finish_reason': 'stop', 'logprobs': None}, id='run-763b6387-c4c9-4658-9d01-7c018dde7a62-0', usage_metadata={'input_tokens': 243, 'output_tokens': 29, 'total_tokens': 272}), HumanMessage(content='i like the 49ers!', id='235dcec4-b656-4bee-b741-e330e1a026e2'), AIMessage(content="That's awesome, Lance! The San Francisco 49ers have a rich history and a passionate fan base. Is there a specific aspect of the team you'd like to discuss? For example, we could talk about:\n\n- Their legendary players like Joe Montana and Jerry Rice\n- Memorable games and Super Bowl victories\n- The current roster and season prospects\n- Rivalries, like the one with the Seattle Seahawks\n- Levi's Stadium and the fan experience\n\nLet me know what interests you!", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 98, 'prompt_tokens': 287, 'total_tokens': 385}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_fde2829a40', 'finish_reason': 'stop', 'logprobs': None}, id='run-729860b2-16c3-46ff-ad1e-0a5475565b20-0', usage_metadata={'input_tokens': 287, 'output_tokens': 98, 'total_tokens': 385})], 'summary': 'Here\'s an extended summary of the conversation:\n\nLance introduced himself multiple times during the conversation, each time stating, "Hi! I\'m Lance." He expressed his fondness for the San Francisco 49ers football team. The AI assistant acknowledged Lance\'s name each time and showed willingness to discuss the 49ers, offering to talk about various aspects of the team such as their history, current roster, memorable games, prospects for the upcoming season, rivalries, and their home stadium, Levi\'s Stadium. Despite the AI\'s attempts to engage in a more detailed discussion about the 49ers, Lance reintroduced himself again without directly responding to the AI\'s questions or prompts about the team. The conversation remained brief and somewhat repetitive, focusing mainly on Lance\'s introductions and his interest in the 49ers.'}, next=(), config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef6a36d-ca9c-6144-801b-6d0cf97adc73'}}, metadata={'source': 'loop', 'writes': {'conversation': {'messages': AIMessage(content="That's awesome, Lance! The San Francisco 49ers have a rich history and a passionate fan base. Is there a specific aspect of the team you'd like to discuss? For example, we could talk about:\n\n- Their legendary players like Joe Montana and Jerry Rice\n- Memorable games and Super Bowl victories\n- The current roster and season prospects\n- Rivalries, like the one with the Seattle Seahawks\n- Levi's Stadium and the fan experience\n\nLet me know what interests you!", additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 98, 'prompt_tokens': 287, 'total_tokens': 385}, 'model_name': 'gpt-4o-2024-05-13', 'system_fingerprint': 'fp_fde2829a40', 'finish_reason': 'stop', 'logprobs': None}, id='run-729860b2-16c3-46ff-ad1e-0a5475565b20-0', usage_metadata={'input_tokens': 287, 'output_tokens': 98, 'total_tokens': 385})}}, 'step': 27, 'parents': {}}, created_at='2024-09-03T20:55:33.466540+00:00', parent_config={'configurable': {'thread_id': '1', 'checkpoint_ns': '', 'checkpoint_id': '1ef6a36d-b8f3-6b40-801a-494776d2e9e0'}}, tasks=())

# Getting Started with React

## Prerequisites

Before you start working with React, make sure you have the following installed:

- Node.js (version 14 or later)
- npm (version 6 or later) or yarn
- A code editor (VS Code recommended)

## Creating a New React App

The easiest way to create a new React application is by using Create React App:

```bash
npx create-react-app my-app
cd my-app
npm start

Keys	Action
`?`	Open this help
`n`	Next page
`p`	Previous page
`s`	Search

支持消息摘要与外部数据库记忆的聊天机器人¶

回顾¶

目标¶

Sqlite¶

持久化状态¶

LangGraph Studio¶

Key Concepts¶

Components¶

JSX¶

State and Props¶

Next Steps¶

核心概念¶

组件¶

JSX¶

状态与属性¶

后续学习¶