agent.py

"""LangGraph Agent"""
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_google_genai import ChatGoogleGenerativeAI
from langchain_groq import ChatGroq
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool

# Optional imports - will be used if available
try:
    from langchain_community.tools.tavily_search import TavilySearchResults
    TAVILY_AVAILABLE = True
except ImportError:
    TAVILY_AVAILABLE = False

try:
    from langchain_community.document_loaders import WikipediaLoader
    WIKIPEDIA_AVAILABLE = True
except ImportError:
    WIKIPEDIA_AVAILABLE = False

load_dotenv()

@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 sqrt(a: float) -> float:
    """Calculate the square root of a number.

    Args:
        a: number to find square root of
    """
    import math
    if a < 0:
        raise ValueError("Cannot calculate square root of negative number.")
    return math.sqrt(a)

@tool
def power(a: float, b: float) -> float:
    """Calculate a number raised to a power (a^b).

    Args:
        a: base number
        b: exponent
    """
    return a ** b

@tool
def absolute(a: float) -> float:
    """Get the absolute value of a number.

    Args:
        a: number to get absolute value of
    """
    return abs(a)

@tool
def wiki_search(query: str) -> str:
    """Search Wikipedia for a query and return maximum 2 results.

    Args:
        query: The search query."""
    if not WIKIPEDIA_AVAILABLE:
        return {"wiki_results": "Wikipedia search is not available. Please install langchain-community to enable this feature."}

    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."""
    if not TAVILY_AVAILABLE:
        return {"web_results": "Tavily search is not available. Please install langchain-community to enable this feature."}

    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."""
    if not WIKIPEDIA_AVAILABLE:  # Using same check since ArxivLoader is also in community
        return {"arvix_results": "Arxiv search is not available. Please install langchain-community to enable this feature."}

    try:
        from langchain_community.document_loaders import ArxivLoader
        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}
    except ImportError:
        return {"arvix_results": "Arxiv search is not available. Please install langchain-community to enable this feature."}

# load the system prompt from the file
try:
    with open("system_prompt.txt", "r", encoding="utf-8") as f:
        system_prompt = f.read()
except FileNotFoundError:
    system_prompt = """You are RobotPai, a helpful AI assistant. You can help with calculations, answer questions, and search for information when needed. You have access to various tools including:
- Basic math operations (add, subtract, multiply, divide, modulus)
- Web search (if configured)
- Wikipedia search (if configured)
- Arxiv search (if configured)

Please be helpful and provide accurate information."""

# System message
sys_msg = SystemMessage(content=system_prompt)



tools = [
    multiply,
    add,
    subtract,
    divide,
    modulus,
    sqrt,
    power,
    absolute,
    wiki_search,
    web_search,
    arvix_search,
]

# Build graph function
def build_graph(provider: str = "groq"):
    """Build the graph"""
    # Load environment variables from .env file
    if provider == "google":
        # Google Gemini
        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
    elif provider == "groq":
        # Groq https://console.groq.com/docs/models
        llm = ChatGroq(model="llama3-8b-8192", temperature=0) # Current stable model
    else:
        raise ValueError("Invalid provider. Choose 'google' or 'groq'.")

    # Bind tools to LLM
    llm_with_tools = llm.bind_tools(tools)

    # Node
    def assistant(state: MessagesState):
        """Assistant node"""
        return {"messages": [llm_with_tools.invoke([sys_msg] + 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")

    # Compile graph
    return builder.compile()

# test
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(provider="groq")
    # Run the graph
    messages = [HumanMessage(content=question)]
    messages = graph.invoke({"messages": messages})
    for m in messages["messages"]:
        m.pretty_print()