agent.py

"""LangGraph Agent"""
import os
import tempfile
import cmath
import pandas as pd
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, GoogleGenerativeAIEmbeddings
from langchain_groq import ChatGroq
from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint, HuggingFaceEmbeddings
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.document_loaders import ArxivLoader
from langchain_community.vectorstores import SupabaseVectorStore
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool
from langchain.tools.retriever import create_retriever_tool
from supabase.client import Client, create_client
from typing import List, Dict, Any, Optional

load_dotenv()

@tool
def multiply(a: int, b: int) -> int:
    """
    Multiply two integers.
    
    Args:
        a (int): The first integer.
        b (int): The second integer.
    
    Returns:
        int: The product of a and b.
    """
    return a * b

@tool
def add(a: int, b: int) -> int:
    """
    Add two integers.
    
    Args:
        a (int): The first integer.
        b (int): The second integer.
    
    Returns:
        int: The sum of a and b.
    """
    return a + b

@tool
def subtract(a: int, b: int) -> int:
    """
    Subtract one integer from another.
    
    Args:
        a (int): The integer to subtract from.
        b (int): The integer to subtract.
    
    Returns:
        int: The result of a minus b.
    """
    return a - b

@tool
def divide(a: int, b: int) -> float:
    """
    Divide one integer by another.
    
    Args:
        a (int): The numerator.
        b (int): The denominator. Must not be zero.
    
    Returns:
        float: The result of a divided by b.
    
    Raises:
        ValueError: If b is zero.
    """
    if b == 0:
        raise ValueError("Cannot divide by zero.")
    return a / b

@tool
def modulus(a: int, b: int) -> int:
    """
    Compute the modulus (remainder) of two integers.
    
    Args:
        a (int): The dividend.
        b (int): The divisor.
    
    Returns:
        int: The remainder after dividing a by b.
    """
    return a % b

@tool
def power(a: float, b: float) -> float:
    """
    Raise a number to the power of another number.
    
    Args:
        a (float): The base number.
        b (float): The exponent.
    
    Returns:
        float: The result of a raised to the power of b.
    """
    return a**b

@tool
def square_root(a: float) -> float | complex:
    """
    Compute the square root of a number. Returns a complex number if input is negative.
    
    Args:
        a (float): The number to compute the square root of.
    
    Returns:
        float or complex: The square root of a. Complex if a < 0.
    """
    if a >= 0:
        return a**0.5
    return cmath.sqrt(a)

### =============== DOCUMENT PROCESSING TOOLS =============== ###

@tool
def save_and_read_file(content: str, filename: Optional[str] = None) -> str:
    """
    Save text content to a file and return the file path.
    
    Args:
        content (str): The text content to save.
        filename (str, optional): The name of the file. If not provided, a random name is generated.
    
    Returns:
        str: The file path where the content was saved.
    """
    temp_dir = tempfile.gettempdir()
    if filename is None:
        temp_file = tempfile.NamedTemporaryFile(delete=False, dir=temp_dir)
        filepath = temp_file.name
    else:
        filepath = os.path.join(temp_dir, filename)

    with open(filepath, "w") as f:
        f.write(content)

    return f"File saved to {filepath}. You can read this file to process its contents."

@tool
def analyze_csv_file(file_path: str, query: str) -> str:
    """
    Analyze a CSV file and answer a question about its data.
    
    Args:
        file_path (str): The path to the CSV file.
        query (str): The question to answer about the data.
    
    Returns:
        str: The analysis result or error message.
    """
    try:
        df = pd.read_csv(file_path)
        result = f"CSV file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
        result += f"Columns: {', '.join(df.columns)}\n\n"
        result += "Summary statistics:\n"
        result += str(df.describe())
        return result
    except Exception as e:
        return f"Error analyzing CSV file: {str(e)}"

@tool
def analyze_excel_file(file_path: str, query: str) -> str:
    """
    Analyze an Excel file and answer a question about its data.
    
    Args:
        file_path (str): The path to the Excel file.
        query (str): The question to answer about the data.
    
    Returns:
        str: The analysis result or error message.
    """
    try:
        df = pd.read_excel(file_path)
        result = (
            f"Excel file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
        )
        result += f"Columns: {', '.join(df.columns)}\n\n"
        result += "Summary statistics:\n"
        result += str(df.describe())
        return result
    except Exception as e:
        return f"Error analyzing Excel file: {str(e)}"

@tool
def wiki_search(input: str) -> str:
    """
    Search Wikipedia for a query and return up to 2 results.
    
    Args:
        input (str): The search query string.
    
    Returns:
        str: A formatted string containing up to 2 Wikipedia search results.
    """
    search_docs = WikipediaLoader(query=input, 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(input: str) -> str:
    """
    Search the web using Tavily and return up to 5 results.
    
    Args:
        input (str): The search query string.
    
    Returns:
        str: A formatted string containing up to 5 web search results.
    """
    search_docs = TavilySearchResults(max_results=5).invoke(input)
    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>'
                if hasattr(doc, "metadata") and hasattr(doc, "page_content")
                else
                f'<Document source="{doc.get("source", "")}" page="{doc.get("page", "")}"/>\n{doc.get("content", doc.get("page_content", ""))}\n</Document>'
            )
            for doc in search_docs
        ]
    )
    return {"web_results": formatted_search_docs}

@tool
def arvix_search(input: str) -> str:
    """
    Search Arxiv for a query and return up to 3 results.
    
    Args:
        input (str): The search query string.
    
    Returns:
        str: A formatted string containing up to 3 Arxiv search results.
    """
    search_docs = ArxivLoader(query=input, 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()

# System message
sys_msg = SystemMessage(content=system_prompt)

# build a retriever
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") #  dim=768
#embeddings = GoogleGenerativeAIEmbeddings(model="models/gemini-embedding-exp-03-07")
supabase: Client = create_client(
    os.environ.get("SUPABASE_URL"), 
    os.environ.get("SUPABASE_SERVICE_KEY"))
vector_store = SupabaseVectorStore(
    client=supabase,
    embedding= embeddings,
    table_name="documents",
    query_name="match_documents_langchain",
)
create_retriever_tool = create_retriever_tool(
    retriever=vector_store.as_retriever(),
    name="Question Search",
    description="A tool to retrieve similar questions from a vector store.",
)

tools = [
    multiply,
    add,
    subtract,
    divide,
    modulus,
    power,
    square_root,
    wiki_search,
    web_search,
    arvix_search,
    save_and_read_file,
    analyze_csv_file,
    analyze_excel_file,
    # create_retriever_tool
]

# 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="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
    elif provider == "huggingface":
        # TODO: Add huggingface endpoint
        llm = ChatHuggingFace(
            llm=HuggingFaceEndpoint(
                url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
                temperature=0,
            ),
        )
    else:
        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")
    # Bind tools to LLM
    llm_with_tools = llm.bind_tools(tools)

    # Node
    def assistant(state: MessagesState):
        """Assistant node"""
        return {"messages": [llm_with_tools.invoke(state["messages"])]}
    
    def retriever(state: MessagesState):
        """Retriever node"""
        similar_question = vector_store.similarity_search(state["messages"][0].content)
        # similar_question = "What is the surname of the equine veterinarian mentioned in 1.E Exercises from the chemistry materials licensed by Marisa Alviar-Agnew & Henry Agnew under the CK-12 license in LibreText's Introductory Chemistry materials as compiled 08/21/2023?"
        if similar_question:
            example_msg = HumanMessage(
                content=f"Here I provide a similar question and answer for reference: \n\n{similar_question[0].page_content}",
            )
        else:
            example_msg = HumanMessage(
                content="No similar questions found in the database.",
            )
        return {"messages": [sys_msg] + state["messages"] + [example_msg]}

    builder = StateGraph(MessagesState)
    builder.add_node("retriever", retriever)
    builder.add_node("assistant", assistant)
    builder.add_node("tools", ToolNode(tools))
    builder.add_edge(START, "retriever")
    builder.add_edge("retriever", "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?"
    question = "What is the surname of the equine veterinarian mentioned in 1.E Exercises from the chemistry materials licensed by Marisa Alviar-Agnew & Henry Agnew under the CK-12 license in LibreText's Introductory Chemistry materials as compiled 08/21/2023?"
    # Build the graph
    graph = build_graph(provider="google")
    # Run the graph
    messages = [HumanMessage(content=question)]
    messages = graph.invoke({"messages": messages})
    for m in messages["messages"]:
        m.pretty_print()