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import os
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition, ToolNode
from langchain_openai import ChatOpenAI
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool
@tool
def multiply(a: int, b: int) -> int:
"""Multiply two integers."""
return a * b
@tool
def add(a: int, b: int) -> int:
"""Add two integers."""
return a + b
@tool
def subtract(a: int, b: int) -> int:
"""Subtract the second integer from the first."""
return a - b
@tool
def divide(a: int, b: int) -> float:
"""Divide first integer by second; error if divisor is zero."""
if b == 0:
raise ValueError("Cannot divide by zero.")
return a / b
@tool
def modulus(a: int, b: int) -> int:
"""Return the remainder of dividing first integer by second."""
return a % b
@tool
def wiki_search(query: str) -> dict:
"""Search Wikipedia for a query and return up to 2 documents."""
docs = WikipediaLoader(query=query, load_max_docs=2).load()
formatted = "\n\n---\n\n".join(
f'<Document source="{d.metadata["source"]}"/>\n{d.page_content}'
for d in docs
)
return {"wiki_results": formatted}
@tool
def web_search(query: str) -> dict:
"""Perform a web search (via Tavily) and return up to 3 results."""
docs = TavilySearchResults(max_results=3).invoke(query=query)
formatted = "\n\n---\n\n".join(
f'<Document source="{d.metadata["source"]}"/>\n{d.page_content}'
for d in docs
)
return {"web_results": formatted}
@tool
def arvix_search(query: str) -> dict:
"""Search arXiv for a query and return up to 3 paper excerpts."""
docs = ArxivLoader(query=query, load_max_docs=3).load()
formatted = "\n\n---\n\n".join(
f'<Document source="{d.metadata["source"]}"/>\n{d.page_content[:1000]}'
for d in docs
)
return {"arvix_results": formatted}
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
HF_SPACE_TOKEN = os.getenv("HF_SPACE_TOKEN")
tools = [
multiply, add, subtract, divide, modulus,
wiki_search, web_search, arvix_search,
]
with open("prompt.txt", "r", encoding="utf-8") as f:
system_prompt = f.read()
sys_msg = SystemMessage(content=system_prompt)
def build_graph(provider: str = "openai"):
"""Build the LangGraph agent with chosen LLM (default: OpenAI)."""
if provider == "openai":
llm = ChatOpenAI(
model_name="o4-mini-2025-04-16",
openai_api_key=OPENAI_API_KEY,
# no temperature override here
)
elif provider == "huggingface":
llm = ChatHuggingFace(
llm=HuggingFaceEndpoint(
url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
),
temperature=0,
)
else:
raise ValueError("Invalid provider. Choose 'openai' or 'huggingface'.")
llm_with_tools = llm.bind_tools(tools)
def assistant(state: MessagesState):
return {"messages": [llm_with_tools.invoke(state["messages"])]}
builder = StateGraph(MessagesState)
builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(tools))
builder.add_edge(START, "assistant")
builder.add_conditional_edges("assistant", tools_condition)
builder.add_edge("tools", "assistant")
return builder.compile()
if __name__ == "__main__":
graph = build_graph()
msgs = graph.invoke({"messages":[ HumanMessage(content="What’s the capital of France?") ]})
for m in msgs["messages"]:
m.pretty_print() |