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