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from typing import TypedDict, Annotated
from langgraph.graph.message import add_messages
from langchain_core.messages import AnyMessage, HumanMessage, AIMessage
from langgraph.prebuilt import ToolNode
from langgraph.graph import START, StateGraph
from langgraph.prebuilt import tools_condition
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
# added
import os
from retriever import guest_info_tool
# Initialize the Hugging Face model
# added token
HF_TOKEN = os.environ['HF_TOKEN']
llm = HuggingFaceEndpoint(
repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
huggingfacehub_api_token=HF_TOKEN,
)
chat = ChatHuggingFace(llm=llm, verbose=True)
tools = [guest_info_tool]
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()
messages = [HumanMessage(content="Tell me about our guest named 'Lady Ada Lovelace'.")]
response = alfred.invoke({"messages": messages})
print("🎩 Alfred's Response:")
print(response['messages'][-1].content)
if __name__ == "__main__":
GradioUI(alfred).launch() |