from langchain.chainsimport LLMChain
from langchain.memoryimport ConversationBufferWindowMemory
from langchain_core.messagesimport SystemMessage
from langchain_core.promptsimport ChatPromptTemplate
from langchain_core.prompts.chatimport(
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    MessagesPlaceholder,
)
from langchain_openaiimport ChatOpenAI

prompt= ChatPromptTemplate(
[
        SystemMessage(content="You are a helpful assistant."),
        MessagesPlaceholder(variable_name="chat_history"),
        HumanMessagePromptTemplate.from_template("{text}"),
]
)

memory= ConversationBufferWindowMemory(memory_key="chat_history", return_messages=True)

legacy_chain= LLMChain(
    llm=ChatOpenAI(),
    prompt=prompt,
    memory=memory,
)

legacy_result= legacy_chain.invoke({"text":"my name is bob"})
print(legacy_result)

legacy_result= legacy_chain.invoke({"text":"what was my name"})
print(legacy_result)

{'text': 'Nice to meet you, Bob! How can I assist you today?', 'chat_history': []}
{'text': 'Your name is Bob. How can I assist you further, Bob?', 'chat_history': [HumanMessage(content='my name is bob', additional_kwargs={}, response_metadata={}), AIMessage(content='Nice to meet you, Bob! How can I assist you today?', additional_kwargs={}, response_metadata={})]}

import uuid

from IPython.displayimport Image, display
from langchain_core.messagesimport HumanMessage
from langgraph.checkpoint.memoryimport MemorySaver
from langgraph.graphimport START, MessagesState, StateGraph

# Define a new graph
workflow= StateGraph(state_schema=MessagesState)

# Define a chat model
model= ChatOpenAI()


# Define the function that calls the model
defcall_model(state: MessagesState):
    selected_messages= trim_messages(
        state["messages"],
        token_counter=len,# <-- len will simply count the number of messages rather than tokens
        max_tokens=5,# <-- allow up to 5 messages.
        strategy="last",
# Most chat models expect that chat history starts with either:
# (1) a HumanMessage or
# (2) a SystemMessage followed by a HumanMessage
# start_on="human" makes sure we produce a valid chat history
        start_on="human",
# Usually, we want to keep the SystemMessage
# if it's present in the original history.
# The SystemMessage has special instructions for the model.
        include_system=True,
        allow_partial=False,
)

    response= model.invoke(selected_messages)
# We return a list, because this will get added to the existing list
return{"messages": response}


# Define the two nodes we will cycle between
workflow.add_edge(START,"model")
workflow.add_node("model", call_model)


# Adding memory is straight forward in langgraph!
memory= MemorySaver()

app= workflow.compile(
    checkpointer=memory
)


# The thread id is a unique key that identifies
# this particular conversation.
# We'll just generate a random uuid here.
thread_id= uuid.uuid4()
config={"configurable":{"thread_id": thread_id}}

input_message= HumanMessage(content="hi! I'm bob")
for eventin app.stream({"messages":[input_message]}, config, stream_mode="values"):
    event["messages"][-1].pretty_print()

# Here, let's confirm that the AI remembers our name!
config={"configurable":{"thread_id": thread_id}}
input_message= HumanMessage(content="what was my name?")
for eventin app.stream({"messages":[input_message]}, config, stream_mode="values"):
    event["messages"][-1].pretty_print()

================================ Human Message =================================

hi! I'm bob
================================== Ai Message ==================================

Hello Bob! How can I assist you today?
================================ Human Message =================================

what was my name?
================================== Ai Message ==================================

Your name is Bob. How can I help you, Bob?

import uuid

from langchain_core.messagesimport(
    AIMessage,
    BaseMessage,
    HumanMessage,
    SystemMessage,
    trim_messages,
)
from langchain_core.toolsimport tool
from langchain_openaiimport ChatOpenAI
from langgraph.checkpoint.memoryimport MemorySaver
from langgraph.prebuiltimport create_react_agent


@tool
defget_user_age(name:str)->str:
"""Use this tool to find the user's age."""
# This is a placeholder for the actual implementation
if"bob"in name.lower():
return"42 years old"
return"41 years old"


memory= MemorySaver()
model= ChatOpenAI()


defprompt(state)->list[BaseMessage]:
"""Given the agent state, return a list of messages for the chat model."""
# We're using the message processor defined above.
return trim_messages(
        state["messages"],
        token_counter=len,# <-- len will simply count the number of messages rather than tokens
        max_tokens=5,# <-- allow up to 5 messages.
        strategy="last",
# Most chat models expect that chat history starts with either:
# (1) a HumanMessage or
# (2) a SystemMessage followed by a HumanMessage
# start_on="human" makes sure we produce a valid chat history
        start_on="human",
# Usually, we want to keep the SystemMessage
# if it's present in the original history.
# The SystemMessage has special instructions for the model.
        include_system=True,
        allow_partial=False,
)



app= create_react_agent(
    model,
    tools=[get_user_age],
    checkpointer=memory,
    prompt=prompt,
)

# The thread id is a unique key that identifies
# this particular conversation.
# We'll just generate a random uuid here.
thread_id= uuid.uuid4()
config={"configurable":{"thread_id": thread_id}}

# Tell the AI that our name is Bob, and ask it to use a tool to confirm
# that it's capable of working like an agent.
input_message= HumanMessage(content="hi! I'm bob. What is my age?")

for eventin app.stream({"messages":[input_message]}, config, stream_mode="values"):
    event["messages"][-1].pretty_print()

# Confirm that the chat bot has access to previous conversation
# and can respond to the user saying that the user's name is Bob.
input_message= HumanMessage(content="do you remember my name?")

for eventin app.stream({"messages":[input_message]}, config, stream_mode="values"):
    event["messages"][-1].pretty_print()

================================ Human Message =================================

hi! I'm bob. What is my age?
================================== Ai Message ==================================
Tool Calls:
  get_user_age (call_jsMvoIFv970DhqqLCJDzPKsp)
 Call ID: call_jsMvoIFv970DhqqLCJDzPKsp
  Args:
    name: bob
================================= Tool Message =================================
Name: get_user_age

42 years old
================================== Ai Message ==================================

Bob, you are 42 years old.
================================ Human Message =================================

do you remember my name?
================================== Ai Message ==================================

Yes, your name is Bob.

from langchain_core.messagesimport(
    AIMessage,
    BaseMessage,
    HumanMessage,
    SystemMessage,
    trim_messages,
)
from langchain_core.toolsimport tool
from langchain_openaiimport ChatOpenAI

model= ChatOpenAI()


@tool
defwhat_did_the_cow_say()->str:
"""Check to see what the cow said."""
return"foo"


message_processor= trim_messages(# Returns a Runnable if no messages are provided
    token_counter=len,# <-- len will simply count the number of messages rather than tokens
    max_tokens=5,# <-- allow up to 5 messages.
    strategy="last",
# The start_on is specified
# to make sure we do not generate a sequence where
# a ToolMessage that contains the result of a tool invocation
# appears before the AIMessage that requested a tool invocation
# as this will cause some chat models to raise an error.
    start_on=("human","ai"),
    include_system=True,# <-- Keep the system message
    allow_partial=False,
)

# Note that we bind tools to the model first!
model_with_tools= model.bind_tools([what_did_the_cow_say])

model_with_preprocessor= message_processor| model_with_tools

full_history=[
    SystemMessage("you're a good assistant, you always respond with a joke."),
    HumanMessage("i wonder why it's called langchain"),
    AIMessage(
'Well, I guess they thought "WordRope" and "SentenceString" just didn\'t have the same ring to it!'
),
    HumanMessage("and who is harrison chasing anyways"),
    AIMessage(
"Hmmm let me think.\n\nWhy, he's probably chasing after the last cup of coffee in the office!"
),
    HumanMessage("why is 42 always the answer?"),
    AIMessage(
"Because it’s the only number that’s constantly right, even when it doesn’t add up!"
),
    HumanMessage("What did the cow say?"),
]


# We pass it explicity to the model_with_preprocesor for illustrative purposes.
# If you're using `RunnableWithMessageHistory` the history will be automatically
# read from the source the you configure.
model_with_preprocessor.invoke(full_history).pretty_print()

================================== Ai Message ==================================
Tool Calls:
  what_did_the_cow_say (call_urHTB5CShhcKz37QiVzNBlIS)
 Call ID: call_urHTB5CShhcKz37QiVzNBlIS
  Args: