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import os
import logging
from typing import TypedDict, Annotated
from langgraph.graph.message import add_messages
from langchain_core.messages import AnyMessage, HumanMessage, AIMessage
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import ToolNode
from langgraph.graph import START, StateGraph
from langgraph.prebuilt import tools_condition
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from smolagents import gradio_ui
from dotenv import load_dotenv
from retriever import guest_info_tool
from tools import weather_info_tool, hub_stats_tool
import gradio_ui_langgraph
# logging.basicConfig(level=logging.DEBUG)
load_dotenv()
# Generate the chat interface, and append the tools
tools = [guest_info_tool, weather_info_tool, hub_stats_tool]
llm = ChatOpenAI(model="gpt-4o")
llm_with_tools = llm.bind_tools(tools, parallel_tool_calls=False)
"""
llm = HuggingFaceEndpoint(
repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
huggingfacehub_api_token=os.environ["HF_TOKEN"], # HUGGINGFACEHUB_API_TOKEN
)
chat = ChatHuggingFace(llm=llm, verbose=True)
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": [llm_with_tools.invoke(state["messages"])], # or chat_with_tools
}
## 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()
gradio_ui_langgraph.GradioUI(alfred).launch() |