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"""
from typing import Annotated, TypedDict
from langchain_community.chat_models import ChatHuggingFace
from langchain_community.llms import HuggingFaceEndpoint
from langchain_core.messages import AIMessage, AnyMessage, HumanMessage
from langgraph.graph import START, StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition
from retriever import guest_info_tool
from tools import (absolute, add, divide, exponential, floor_divide,
get_current_time_in_timezone, logarithm, modulus, multiply,
power, roman_calculator_converter, square_root, subtract,
web_search)
# Generate the chat interface, including the tools
llm = HuggingFaceEndpoint(
repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
)
chat = ChatHuggingFace(llm=llm, verbose=True)
tools = [
multiply,
add,
subtract,
power,
divide,
modulus,
square_root,
floor_divide,
absolute,
logarithm,
exponential,
web_search,
roman_calculator_converter,
get_current_time_in_timezone,
]
chat_with_tools = chat.bind_tools(tools)
# Generate the AgentState and Agent graph
class AgentState(TypedDict):
messages: Annotated[list[AnyMessage], add_messages]
def assistant(state: AgentState):
return {
"messages": [chat_with_tools.invoke(state["messages"])],
}
## The graph
builder = StateGraph(AgentState)
# Define nodes: these do the work
builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(tools))
# Define edges: these determine how the control flow moves
builder.add_edge(START, "assistant")
builder.add_conditional_edges(
"assistant",
# If the latest message requires a tool, route to tools
# Otherwise, provide a direct response
tools_condition,
)
builder.add_edge("tools", "assistant")
alfred = builder.compile()
"""
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