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import os
from typing import TypedDict, Annotated, Sequence
import operator
from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode, tools_condition
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
from langchain_core.messages import BaseMessage, HumanMessage, SystemMessage
from langchain.agents import Tool
from langchain_core.tools import tool
# Load environment variables (compatible with Hugging Face Space)
google_api_key = os.getenv("GOOGLE_API_KEY") or os.environ.get("GOOGLE_API_KEY")
if not google_api_key:
raise ValueError("Missing GOOGLE_API_KEY environment variable")
# --- System Prompt ---
with open("System_Prompt.txt", "r", encoding="utf-8") as f:
system_prompt = f.read()
sys_msg = SystemMessage(content=system_prompt)
# --- Tool Definitions ---
@tool
def multiply(a: int, b: int) -> int:
"""Multiply two integers together."""
return a * b
@tool
def add(a: int, b: int) -> int:
"""Add two integers together."""
return a + b
@tool
def subtract(a: int, b: int) -> int:
"""Subtract b from a."""
return a - b
@tool
def divide(a: int, b: int) -> float:
"""Divide a by b. Returns float. Raises error if b is zero."""
if b == 0:
raise ValueError("Cannot divide by zero.")
return a / b
@tool
def wiki_search(query: str) -> str:
"""Search Wikipedia and return up to 2 relevant documents."""
docs = WikipediaLoader(query=query, load_max_docs=2).load()
if not docs:
return "No Wikipedia results found."
return "\n\n".join([d.page_content[:1000] for d in docs])
# Tool inventory with proper categorization
tools = [
Tool(name="Math_Multiply", func=multiply, description="Multiplies two integers"),
Tool(name="Math_Add", func=add, description="Adds two integers"),
Tool(name="Math_Subtract", func=subtract, description="Subtracts two integers"),
Tool(name="Math_Divide", func=divide, description="Divides two numbers"),
Tool(name="Search_Wikipedia", func=wiki_search, description="Searches Wikipedia"),
Tool(
name="Search_Web",
func=DuckDuckGoSearchRun().run,
description="Searches the web using DuckDuckGo"
)
]
# --- Graph Definition ---
class AgentState(TypedDict):
"""State definition for the agent workflow"""
messages: Annotated[Sequence[BaseMessage], operator.add]
def build_graph():
"""Constructs and compiles the LangGraph workflow"""
# Initialize LLM with Gemini 2.0 Flash
llm = ChatGoogleGenerativeAI(
model="gemini-2.0-flash-exp",
temperature=0.3,
google_api_key=google_api_key
)
llm_with_tools = llm.bind_tools(tools)
# Node definitions
def agent_node(state: AgentState):
"""Main agent node that processes messages"""
response = llm_with_tools.invoke(state["messages"])
return {"messages": [response]}
# Graph construction
workflow = StateGraph(AgentState)
# Add nodes to the workflow
workflow.add_node("agent", agent_node)
workflow.add_node("tools", ToolNode(tools))
# Configure graph flow
workflow.set_entry_point("agent")
workflow.add_conditional_edges(
"agent",
tools_condition, # LangGraph's built-in tool detection
{"tools": "tools", "end": END} # Fixed END reference
)
workflow.add_edge("tools", "agent")
return workflow.compile()
# Initialize the agent graph
agent_graph = build_graph() |