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import os
from dotenv import load_dotenv
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_core.tools import tool
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
from langgraph.graph import START, MessagesState, StateGraph
from langgraph.prebuilt import ToolNode, tools_condition
from tools import (absolute, add, analyze_excel_file, arvix_search,
audio_transcription, compound_interest, convert_temperature,
divide, exponential, factorial, floor_divide,
get_current_time_in_timezone, greatest_common_divisor,
is_prime, least_common_multiple, logarithm, modulus,
multiply, percentage_calculator, power,
roman_calculator_converter, square_root, subtract,
web_search, wiki_search)
# Load Constants
load_dotenv()
HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
tools = [
multiply, add, subtract, power, divide, modulus,
square_root, floor_divide, absolute, logarithm,
exponential, web_search, roman_calculator_converter,
get_current_time_in_timezone, compound_interest,
convert_temperature, factorial, greatest_common_divisor,
is_prime, least_common_multiple, percentage_calculator,
wiki_search, analyze_excel_file, arvix_search, audio_transcription
]
def build_graph():
"""Build the graph"""
# First create the HuggingFaceEndpoint
llm_endpoint = HuggingFaceEndpoint(
repo_id="Qwen/Qwen2.5-Coder-32B-Instruct",
huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
temperature=0.1, # Lower temperature for more consistent responses
max_new_tokens=1024,
timeout=30,
)
# Then wrap it with ChatHuggingFace to get chat model functionality
llm = ChatHuggingFace(llm=llm_endpoint)
# Bind tools to LLM
llm_with_tools = llm.bind_tools(tools)
# Node
def assistant(state: MessagesState):
"""Assistant node"""
return {"messages": [llm_with_tools.invoke(state["messages"])]}
builder = StateGraph(MessagesState)
builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(tools))
builder.add_conditional_edges(
"assistant",
tools_condition,
)
builder.add_edge(START, "assistant")
builder.add_edge("tools", "assistant")
# Compile graph
return builder.compile()
# test
if __name__ == "__main__":
question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
# Build the graph
graph = build_graph()
# Run the graph
messages = [HumanMessage(content=question)]
messages = graph.invoke({"messages": messages})
for m in messages["messages"]:
m.pretty_print() |