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"""LangGraph Agent"""
import dotenv
from langgraph.graph import MessagesState
from langgraph.graph import START, StateGraph
from langgraph.prebuilt import tools_condition
from langgraph.prebuilt import ToolNode
from langchain_google_genai import ChatGoogleGenerativeAI
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.tools import tool
from langchain_core.messages import SystemMessage
@tool
def multiply(a: int, b: int) -> int:
"""Multiply two numbers.
Args:
a: first int
b: second int
"""
return a * b
@tool
def add(a: int, b: int) -> int:
"""Add two numbers.
Args:
a: first int
b: second int
"""
return a + b
@tool
def subtract(a: int, b: int) -> int:
"""Subtract two numbers.
Args:
a: first int
b: second int
"""
return a - b
@tool
def divide(a: int, b: int) -> int:
"""Divide two numbers.
Args:
a: first int
b: second int
"""
if b == 0:
raise ValueError("Cannot divide by zero.")
return a / b
@tool
def modulus(a: int, b: int) -> int:
"""Get the modulus of two numbers.
Args:
a: first int
b: second int
"""
return a % b
@tool
def wiki_search(query: str) -> str:
"""Search Wikipedia for a query and return maximum 2 results.
Args:
query: The search query."""
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 web_search(query: str) -> str:
"""Search Tavily for a query and return maximum 3 results.
Args:
query: The search query."""
search_docs = TavilySearchResults(max_results=3).invoke(query=query)
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 {"web_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}
tools = [
multiply,
add,
subtract,
divide,
modulus,
wiki_search,
web_search,
arvix_search,
]
# Load environment variables from .env file
dotenv.load_dotenv()
llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash")
llm_with_tools = llm.bind_tools(tools)
# load the system prompt from the file
with open("system_prompt.txt", "r", encoding="utf-8") as f:
system_prompt = f.read()
# System message
sys_msg = SystemMessage(content=system_prompt)
# Node
def assistant(state: MessagesState):
"""Assistant node"""
return {"messages": [llm_with_tools.invoke([sys_msg] + state["messages"])]}
# Build graph function
def build_graph():
"""Build the graph"""
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")
# Compile graph
return builder.compile()
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