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import os
from dotenv import load_dotenv
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition
from langgraph.prebuilt import ToolNode
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.document_loaders import ArxivLoader
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool
# from langchain_openai import ChatOpenAI
from langchain_deepseek import ChatDeepSeek
load_dotenv()
DEEPSEEK_API_KEY = os.getenv("DEEPSEEK_API_KEY")
@tool
def multiply(a: int, b: int) -> int:
"""Multiplies two numbers."""
return a * b
@tool
def add (a: int, b: int) -> int:
"""Adds two numbers."""
return a + b
@tool
def subtract (a: int, b: int) -> int:
"""Subtracts two numbers."""
return a - b
@tool
def divide (a: int, b: int) -> int:
"""Divides two numbers."""
return a / b
@tool
def modulo (a: int, b: int) -> int:
"""Returns the remainder of two numbers."""
return a % b
@tool
def wiki_search(query:str)->str:
"Using Wikipedia, search for a query and return the first result."
search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
formatted_search_docs = "\n\n---\n\n".join(
[
f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
for doc in search_docs
])
return {"wiki_results": formatted_search_docs}
@tool
def arvix_search(query: str) -> str:
"""Search Arxiv for a query and return maximum 3 result.
Args:
query: The search query."""
search_docs = ArxivLoader(query=query, load_max_docs=3).load()
formatted_search_docs = "\n\n---\n\n".join(
[
f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
for doc in search_docs
])
return {"arvix_results": formatted_search_docs}
# load the system prompt from the file
with open("system_prompt.txt", "r", encoding="utf-8") as f:
system_prompt = f.read()
sys_msg = SystemMessage(content=system_prompt)
tools = [
multiply,
add,
subtract,
divide,
modulo,
wiki_search,
arvix_search,
]
def build_graph():
llm = ChatDeepSeek(
model="deepseek-chat",
temperature=0,
max_tokens=None,
timeout=None,
max_retries=2,
api_key=DEEPSEEK_API_KEY,
)
llm_with_tools = llm.bind_tools(tools)
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_edge(START, "assistant")
builder.add_conditional_edges(
"assistant",
tools_condition,
)
builder.add_edge("tools", "assistant")
return builder.compile()
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()
png_data = graph.get_graph().draw_mermaid_png()
with open("graph.png", "wb") as f:
f.write(png_data)
# Run the graph
messages = [HumanMessage(content=question)]
messages = graph.invoke({"messages": messages})
for m in messages["messages"]:
m.pretty_print()
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