tool.py

from langchain_core.tools import tool
from langchain_community.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders.wikipedia import WikipediaLoader
from langchain_community.document_loaders.arxiv import ArxivLoader
from langchain_community.document_loaders.pubmed import PubMedLoader
from typing import Optional

import os
import tempfile
import requests
from urllib.parse import urlparse
import pytesseract
from PIL import Image
import pandas as pd
import uuid

## Simple algebra tools
@tool
def add(a: float, b: float) -> float: 
    """Add two numbers.
      
      Args:
          a: first float
          b: second float
    """
    return a + b

@tool
def substract(a: float, b: float) -> float: 
    """Substract two numbers.
      
      Args:
          a: first float
          b: second float
    """
    return a - b

@tool
def multiply(a: float, b: float) -> float: 
    """Multiply two numbers.
      
      Args:
          a: first float
          b: second float
    """
    return a * b

@tool
def divide(a: float, b: float) -> float: 
    """Divide two numbers.
      
      Args:
          a: first float
          b: second float
    """
    if b == 0:
        raise ValueError("Cannot divide any number by zero.")
    return a / b

# Maybe add algebra tools???

## Search Tools
def DuckDuckGoSearchTool(query: str) -> str:
    """Search DuckDuckGo for a query and return maximum 5 results.
    
    Args:
        query: The search query.
    """
    results = DuckDuckGoSearchAPIWrapper().results(query = query, max_results=5)
    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'<Document source="{res["link"]}" title="{res["title"]}">\n{res["snippet"]}\n</Document>'
            for res in results
        ])
    return {"web_results": formatted_search_docs}

@tool
def TavilySearchTool(query: str) -> str:
    """Search Tavily for a query and return maximum 3 results.
    
    Args:
        query: The search query."""
    search_docs = TavilySearchResults(max_results=5).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 WikipediaSearchTool(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=5).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 ArxivSearchTool(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=5).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}

@tool
def PubmedSearchTool(query: str) -> str:
    """Search Arxiv for a query and return maximum 3 result.
    
    Args:
        query: The search query."""
    search_docs = PubMedLoader(query=query, load_max_docs=5).load()
    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'<Document source="{doc.metadata["uid"]}" title="{doc.metadata["Title"]}"/>\n{doc.page_content[:1000]}\n</Document>'
            for doc in search_docs
        ])
    return {"pubmed_results": formatted_search_docs}


@tool
def save_and_read_file(content: str, filename: Optional[str] = None) -> str:
    """Save content to a file and return the path.
    
    Args:
        content (str): the content to save to the file
        filename (str, optional): the name of the file. If not provided, a random name file will be created.
    """
    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 download_file_from_url(url: str, filename: Optional[str] = None) -> str:
    """Download a file from a URL and save it to a temporary location.
    
    Args:
        url (str): the URL of the file to download.
        filename (str, optional): the name of the file. If not provided, a random name file will be created.
    """
    try:
        # Parse URL to get filename if not provided
        if not filename:
            path = urlparse(url).path
            filename = os.path.basename(path)
            if not filename:
                filename = f"downloaded_{uuid.uuid4().hex[:8]}"

        # Create temporary file
        temp_dir = tempfile.gettempdir()
        filepath = os.path.join(temp_dir, filename)

        # Download the file
        response = requests.get(url, stream=True)
        response.raise_for_status()

        # Save the file
        with open(filepath, "wb") as f:
            for chunk in response.iter_content(chunk_size=8192):
                f.write(chunk)

        return f"File downloaded to {filepath}. You can read this file to process its contents."
    except Exception as e:
        return f"Error downloading file: {str(e)}"


@tool
def extract_text_from_image(image_path: str) -> str:
    """Extract text from an image using OCR library pytesseract (if available).
    
    Args:
        image_path (str): the path to the image file.
    """
    try:
        # Open the image
        image = Image.open(image_path)

        # Extract text from the image
        text = pytesseract.image_to_string(image)

        return f"Extracted text from image:\n\n{text}"
    except Exception as e:
        return f"Error extracting text from image: {str(e)}"


@tool
def analyze_csv_file(file_path: str, query: str) -> str:
    """Analyze a CSV file using pandas and answer a question about it.
    
    Args:
        file_path (str): the path to the CSV file.
        query (str): Question about the data
    """
    try:
        # Read the CSV file
        df = pd.read_csv(file_path)

        # Run various analyses based on the query
        result = f"CSV file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
        result += f"Columns: {', '.join(df.columns)}\n\n"

        # Add summary statistics
        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 using pandas and answer a question about it.
    
    Args:
        file_path (str): the path to the Excel file.
        query (str): Question about the data
    """
    try:
        # Read the Excel file
        df = pd.read_excel(file_path)

        # Run various analyses based on the query
        result = (
            f"Excel file loaded with {len(df)} rows and {len(df.columns)} columns.\n"
        )
        result += f"Columns: {', '.join(df.columns)}\n\n"

        # Add summary statistics
        result += "Summary statistics:\n"
        result += str(df.describe())

        return result

    except Exception as e:
        return f"Error analyzing Excel file: {str(e)}"