Author: Renise

"""
Build an agent with LangGraph

Google Gen AI 5-Day Intensive Course
Host: Kaggle

Day: 3

Codelab: https://www.kaggle.com/code/markishere/day-3-building-an-agent-with-langgraph/#IMPORTANT!
"""

from pprint import pprint
from typing import Annotated, Literal

from google import genai
from IPython.display import Image
from langchain_core.messages.ai import AIMessage
from langchain_core.tools import tool
from langchain_google_genai import ChatGoogleGenerativeAI
from langgraph.graph import END, START, StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode
from typing_extensions import TypedDict
from collections.abc import Iterable
from random import randint
from langchain_core.messages.tool import ToolMessage

class OrderState(TypedDict):
    """State representing the customer's order conversation."""

    # Preserves the conversaton history between nodes.
    # The 'add messages' annotation indicates to LangGraphthat state
    # is updated by appending returned messages, not replacing them.
    messages: Annotated[list, add_messages]

    # The customer's in-progress order.
    order: list[str]

    # Flag indicating that the order is placed and completed.
    finished: bool


# The system instruction defines how the chatbot is expected to behave
# and includes rules for when to call different functions,
# as well as rules for the conversation, such as tone and what is permitted
# for discussion.
BARISTABOT_SYSINT = (
    # 'system' indicates the message is a system instruction.
    "system",
    "You are a BaristaBot, an interactive cafe ordering system. A human will talk to you about the "
    "available products you have and you will answer any questions about menu items (and only about "
    "menu items - no off-topic discussion, but you can chat about the products and their history). "
    "The customer will place an order for 1 or more items from the menu, which you will structure "
    "and send to the ordering system after confirming the order with the human. "
    "\n\n"
    "Add items to the customer's order with add_to_order, and reset the order with clear_order. "
    "To see the contents of the order so far, call get_order (this is shown to you, not the user) "
    "Always confirm_order with the user (double-check) before calling place_order. Calling confirm_order will "
    "display the order items to the user and returns their response to seeing the list. Their response may contain modifications. "
    "Always verify and respond with drink and modifier names from the MENU before adding them to the order. "
    "If you are unsure a drink or modifier matches those on the MENU, ask a question to clarify or redirect. "
    "You only have the modifiers listed on the menu. "
    "Once the customer has finished ordering items, Call confirm_order to ensure it is correct then make "
    "any necessary updates and then call place_order. Once place_order has returned, thank the user and "
    "say goodbye!"
    "\n\n"
    "If any of the tools are unavailable, you can break the fourth wall and tell the user that "
    "they have not implemented them yet and should keep reading to do so.",
)

# This is the message with which the system opens the conversation.
WELCOME_MSG = "Welcome to the BaristaBot cafe. Type `q` to quit. How may I serve you today?"

# Define chatbot node
# This node will represent a single turn in a chat conversation
#
# Try using different models. The Gemini 2.0 flash model is highly capable, great with tools,
# and has a generous free tier. If you try the older 1.5 models, note that the 'pro' models are
# better at complex multi-tool cases like this, but the 'flas' models are faster and have more
# free quota.
#
# Check out the features and quota differences here:
#  - https://ai.google.dev/gemini-api/docs/models/gemini
llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash")


def chatbot(state: OrderState) -> OrderState:
    """The chatbot itself. A simple wrapper around the model's own chat interface."""
    message_history = [BARISTABOT_SYSINT] + state["messages"]
    return {"messages": [llm.invoke(message_history)]}


# Set up the initial graph based on our state definition.
graph_builder = StateGraph(OrderState)

# Add the chatbot function to the app graph as a node called 'chatbot'.
graph_builder.add_node("chatbot", chatbot)

# Define the chatbot node as the app entrypoint.
graph_builder.add_edge(START, "chatbot")

chat_graph = graph_builder.compile()

# Render the graph to visualize it.
Image(chat_graph.get_graph().draw_mermaid_png())


# The defined graph only as one node.
# So the chat will begin at __start__, execute the chatbot node and terminate.
user_msg = "Hello, what can you do?"
state = chat_graph.invoke({"messages": [user_msg]})

# The state object contains lots of informaton. Uncomment the pprint lines to see it all.
pprint(state)

# Note that the final state now has 2 messages. Our HumanMessage, and an additional AIMessage.
for msg in state["messages"]:
    print(f"{type(msg).__name__}: {msg.content}")


# Can be executed as Python loop
# Here it is manually invoked once
user_msg2 = "Oh great, what kinds of latte can you make?"

state["messages"].append(user_msg2)
state = chat_graph.invoke(state)

# pprint(state)
for msg in state["messages"]:
    print(f"{type(msg).__name__}: {msg.content}")


# Add a human node
# LangGraph can be looped between nodes
# This node will display the last message from the LLM to the user,
# then prompt them for their next input.
def human_node(state: OrderState) -> OrderState:
    """Display the last model message to the user and get the user's input."""
    last_msg = state["message"][-1]
    print("Model:", last_msg.content)

    user_input = input("User: ")

    # If it looks like the user is trying to quit, flag the conversaton as over.
    if user_input in {"q", "quit", "exit", "goodbye"}:
        state["finished"] = True

    return state | {"messages": [("user", user_input)]}


def chatbot_with_welcome_msg(state: OrderState) -> OrderState:
    """The chatbot itself. A wrapper around the model's own chat interface."""

    if state["messages"]:
        # If there are messages, continue the conversation with the Gemini model.
        new_output = llm.invoke([BARISTABOT_SYSINT]) + state["messages"]
    else:
        # If there are no messages, start with the welcome message.
        new_output = AIMessage(content=WELCOME_MSG)

    return state | {"messages": [new_output]}


# Start building a new graph.
graph_builder = StateGraph(OrderState)

# Add the chatbot and human nodes to the app graph.
graph_builder.add_node("chatbot", chatbot_with_welcome_msg)
graph_builder.add_node("human", human_node)

# Start with the chatbot again.
graph_builder.add_edge(START, "chatbot")

# The chatbot will always go to the human next.
graph_builder.add_edge("chatbot", "human")


# Create a conditional edge
def maybe_exit_human_node(state: OrderState) -> Literal["chatbot", "__end__"]:
    """Route to the chatbot, unless it looks like the user is exiting."""
    if state.get("finished", False):
        return END
    else:
        return "chatbot"


graph_builder.add_conditional_edges("human", maybe_exit_human_node)

chat_with_human_graph = graph_builder.compile()

Image(chat_with_human_graph.get_graph().draw_mermaid_png)


# The default recursion limit for traversing nodes is 25 - setting it higher means
# you can try a more complex order with multiple steps and round-trips and you can chat for longer!
config = {"recursion_limit": 100}

# Remember that this will loop forever, unless you input 'q', 'quit' or one of the other exit terms
# defined in 'human_node'.
# Uncomment this line to execute the graph:
# state = chat_with_human_graph.invoke({"messages": []}, config)
#
# Things to try:
#   - Just chat! There's no ordering or menu yet.
#   - 'q' to exit.

pprint(state)


# Add a "live" menu
# To create a dynamic menu to respond to changing stock levels
# There are two types of tools: stateless and stateful
# Stateless tools run automatically: get current menu: it doesn't make changes
# Stateful tools modify the order
# In LangGraph Python functions can be annotated as tools by applying @tools annotation
@tool
def get_menu() -> str:
    """Provide the latest up-to-date menu."""
    # Note that this is just hard-coded text, but you could connect this to a live stock
    # database, or you could use Gemini's multi-modal capabilities and take like photos
    # of your cafe's chalk menu or the products on the counter, and assemble them into an input.

    return """
        MENU:
        Coffee Drinks:
        Espresso
        Americano
        Cold Brew

        Coffee Drinks with Milk:
        Latte
        Cappuccino
        Cortado
        Macchiato
        Mocha
        Flat White

        Tea Drinks:
        English Breakfast Tea
        Green Tea
        Earl Grey

        Tea Drinks with Milk:
        Chai Latte
        Matcha Latte
        London Fog

        Other Drinks:
        Steamer
        Hot Chocolate

        Modifiers:
        Milk options: Whole, 2%, Oat, Almond, 2% Lactose Free; Default option: whole
        Espresso shots: Single, Double, Triple, Quadruple; default: Double
        Caffeine: Decaf, Regular; default: Regular
        Hot-Iced: Hot, Iced; Default: Hot
        Sweeteners (option to add one or more): vanilla sweetener, hazelnut sweetener, caramel sauce, chocolate sauce, sugar free vanilla sweetener
        Special requests: any reasonable modification that does not involve items not on the menu, for example: 'extra hot', 'one pump', 'half caff', 'extra foam', etc.

        "dirty" means add a shot of espresso to a drink that doesn't usually have it, like "Dirty Chai Latte".
        "Regular milk" is the same as 'whole milk'.
        "Sweetened" means add some regular sugar, not a sweetener.

        Soy milk has run out of stock today, so soy is not available.
      """


# Add the tool to the graph
# Define the tools and create a "tools" node.
tools = [get_menu]
tool_node = ToolNode(tools)

# Attach the tools to the model so that it knows what it can call.
llm_with_tools = llm.bind_tools(tools)


def maybe_route_to_tools(state: OrderState) -> Literal["tools", "human"]:
    """Route between human or tool nodes, depending if a tool call is made."""
    if not (msgs := state.get("messages", [])):
        raise ValueError(f"No messages found when parsing state: {state}")

    # Only route based on the last message.
    msg = msgs[-1]
    
    # When the chatbot returns tool_calls, rout to the "tools" node.
    if hasattr(msg, "tool_calls") and len(msg.tool_calls) > 0:
        return "tools"
    else:
        return "human"


def chatbot_with_tools(state: OrderState) -> OrderState:
    """The chatbot with tools. A simple wrapper around the model's own chat interface."""
    defaults = {"order": [], "finished": False}

    if state["messages"]:
        new_output = llm_with_tools.invoke(
            [BARISTABOT_SYSINT] + state["messages"]
        )
    else:
        new_output = AIMessage(content=WELCOME_MSG)

    # Set up some defaults if not already set, then pass through the provided state,
    # overriding only the "messages" field.
    return defaults | state | {"messages": [new_output]}


graph_builder = StateGraph(OrderState)

# Add the nodes, including the new tool_node.
graph_builder.add_node("chatbot", chatbot_with_tools)
graph_builder.add_node("human", human_node)
graph_builder.add_node("tools", tool_node)

# Chatbot may go to tools, or human.
graph_builder.add_conditional_edges("chatbot", maybe_route_to_tools)
# Human may go back to chatbot, or exit.
graph_builder.add_conditional_edges("human", maybe_exit_human_node)

# Tools always route back to chat afterwards.
graph_builder.add_edge("tools", "chatbot")

graph_builder.add_edge(START, "chatbot")
graph_with_menu = graph_builder.compile()

Image(graph_with_menu.get_graph().draw_mermaid_png())


# Remember that you have not implemented ordering yet, so this will loop forever,
# unless you input `q`, `quit` or one of the other exit terms defined in the
# `human_node`.
# Uncomment this line to execute the graph:
state = graph_with_menu.invoke({"messages": []}, config)

# Things to try:
# - I'd love an espresso drink, what have you got?
# - What teas do you have?
# - Can you do a long black? (this is on the menu as an "Americano" - see if it can
#   figure it out)
# - 'q' to exit.

pprint(state)


# Handle orders
# Update state to track an order and provide simple tools that update the state.
# You will need to be explicit as the model should not directly have access to
# the app's internal state.
#
# These functions have no body; LangGraph does not allow @tools to update the
# conversation state, so you will implement a separate node to handle state
# updates.

@tool
def add_to_order(drink: str, modifiers: Iterable[str]) -> str:
    """Adds the specified drink to the customer's order, including any modifiers.

    Returns:
        The updated order in progress.
    """

@tool
def confirm_order() -> str:
    """Asks customer if the order is correct.

    Returns:
        The user's free-text response.
    """

@tool
def get_order() -> str:
    """Returns the users order so far. One item per line."""


@tool
def clear_order():
    """Removes all items from the user's order."""


@tool
def place_order() -> int:
    """Sends the order to the barista for fulfillment.

    Returns:
      The estimated number of minutes until the order is ready.
    """

def order_node(state: OrderState) -> OrderState:
    """The ordering node. This is where the order state is manipulated."""
    tool_msg = state.get("messages", [])[-1]
    order = state.get("order", [])
    outbound_msgs = []
    order_placed = False

    for tool_call in tool_msg.tool_calls:
        if tool_call["name"] == "add_to_order":
            # Each order item is just a string. This is where it is assembled.
            # as "drink (modifiers, ...)".
            modifiers = tool_call["args"]["modifiers"]
            modifier_str = ", ".join(modifiers) if modifiers else "no modifiers"

            order.append(f"{tool_call["args"]["drink"]} ({modifier_str}))
            response = "\n".join(order)
        elif tool_call["name"] == "confirm_order":
            # We could entrust the LLM to do order confirmation, but it is a good practice to
            # show the user the exact data that comprises their order so that what they confirm
            # precisely matches the order that goes to the kitchen - avoiding hallucination
            # or reality skew.

            # In a real scenario, this is where you would connect your POS screen to show the
            # order to the user.

            print("Your order:")
            if not order:
                print(" (no items)")

            for drink in order:
                print(f" {drink}")

            response = input("Is this correct?")
        elif tool_call["name"] == "get_order":
            response = "\n".join(order) if order else "(no order)"
        elif tool_call["name"] == "clear_order":
            order.clear()
            response = None
        elif tool_call["name"] == "place_order":
            order_text = "\n".join(order)
            print("Sending order to kitchen!")
            print(order_text)

            # TODO: Implement cafe.
            order_placed = True
            response = randint(1, 5) # ETA in minutes
        else:
            raise NotImplementedError(f"Unknown tool call: {tool_call["name"]}")

        # Record the tool results as tool message.
        outbound_msgs.append(
            ToolMessage(
                content=response,
                name=tool_call["name"],
                tool_call_id=tool_call["id"]
            )
        )

    return {"messages": outbound_msgs, "order": order, "finished": order_placed}

def maybe_route_to_tools(state: OrderState) -> str:
    """Route between chat and tool nodes if a tool call is made."""
    if not (msgs := state.get("messages", [])):
        raise ValueError(f"No messages found when parsing state: {state}")

msg = msgs[-1]

if state.get("finished", False):
    # When an order is placed, exit the app. The system instruction indicates
    # that the chatbot should say thanks and goodbye at this point, so we can exit
    # cleanly.
    return END
elif hasattr(msg, "tool_calls") and len(msg.tool_calls) > 0:
    # Route to 'tools' node for any automated tool calls first.
    if any(
        tool["name"] in tool_node.tools_by_name,keys() for tool in msg.tool_calls
    ):
        return "tools"
    else:
        return "ordering"
    else:
        return "human"

# Define the graph so the LLM knows about the tools to invoke them.
# Auto-tools will be invoked automatically by the ToolNode
auto_tools = [get_menu]
tool_node = ToolNode(auto_tools)

# Order-tools will be handled by the order node.
order_tools = [add_to_order, confirm_order, get_order, clear_order, place_order]

# The LLM needs to know about all of the tools, so specify everything here
llm_with_tools = llm.bind_tools(auto_tools + order_tools)


graph_builder = StateGraph(OrderState)

# Nodes
graph_builder.add_node("chatbot", chatbot_with_tools)
graph_builder.add_node("human", human_node)
graph_builder.add_node("tools", tool_node)
graph_builder.add_node("ordering", order_node)

# Chatbot -> (ordering, tools, human, END)
graph_builder.add_conditional_edges("chatbot", maybe_route_to_tools)
# Human -> (chatbot, END)
graph_builder.add_conditional_edges("human", maybe_exit_human_node)

# Tools (both kinds) always route back to chat afterwards.
graph_builder.add_edge("tools", "chatbot")
graph_builder.add_edge("ordering", "chatbot")

graph_builder.add_edge(START, "chatbot")
graph_with_order_tools = graph_builder.compile()

Image(graph_with_order_tools.get_graph().draw_mermaid.png())


# Uncomment this line to execute the graph:
state = graph_with_order_tools.invoke({"messages": []}, config)

# Things to try:
# - Order a drink!
# - Make a change to your order.
# - "Which teas are from England?"
# - Note that the graph should naturally exit after placing an order.

pprint(state)

# Uncomment this once you have run the graph from the previous cell.
pprint(state["order"])