pipes

agents.py

@@ -5,35 +5,23 @@ import os
 import tools
 import prompts
 
-if os.environ.get("OPENAI_API_KEY"):
-    MANAGER_MODEL = "deepseek-ai/DeepSeek-R1"
-    FINAL_ANSWER_MODEL = "deepseek-ai/DeepSeek-R1" # OpenAIServerModel
-else:
-    MANAGER_MODEL = "deepseek-ai/DeepSeek-R1"
-    FINAL_ANSWER_MODEL = "deepseek-ai/DeepSeek-R1" # OpenAIServerModel
-
-if os.environ.get("HF_TOKEN"):
-    AGENT_MODEL = "Qwen/Qwen2.5-Coder-32B-Instruct"
-    WEB_SEARCH_MODEL        = "Qwen/Qwen2.5-Coder-32B-Instruct"
-    IMAGE_ANALYSIS_MODEL    = "HuggingFaceM4/idefics2-8b"
-    AUDIO_ANALYSIS_MODEL    = "Qwen/Qwen2.5-Coder-32B-Instruct"
-    VIDEO_ANALYSIS_MODEL    = "Qwen/Qwen2.5-Coder-32B-Instruct"
-    YOUTUBE_ANALYSIS_MODEL  = "Qwen/Qwen2.5-Coder-32B-Instruct"
-    DOCUMENT_ANALYSIS_MODEL = "Qwen/Qwen2.5-Coder-32B-Instruct"
-    ARITHMETIC_MODEL        = "Qwen/Qwen2.5-Coder-32B-Instruct"
-    CODE_GENERATION_MODEL   = "Qwen/Qwen2.5-Coder-32B-Instruct"
-    CODE_EXECUTION_MODEL    = "Qwen/Qwen2.5-Coder-32B-Instruct"
-else:
-    AGENT_MODEL = "Qwen/Qwen2.5-Coder-32B-Instruct"
-    WEB_SEARCH_MODEL        = "Qwen/Qwen2.5-Coder-32B-Instruct"
-    IMAGE_ANALYSIS_MODEL    = "HuggingFaceM4/idefics2-8b"
-    AUDIO_ANALYSIS_MODEL    = "Qwen/Qwen2.5-Coder-32B-Instruct"
-    VIDEO_ANALYSIS_MODEL    = "Qwen/Qwen2.5-Coder-32B-Instruct"
-    YOUTUBE_ANALYSIS_MODEL  = "Qwen/Qwen2.5-Coder-32B-Instruct"
-    DOCUMENT_ANALYSIS_MODEL = "Qwen/Qwen2.5-Coder-32B-Instruct"
-    ARITHMETIC_MODEL        = "Qwen/Qwen2.5-Coder-32B-Instruct"
-    CODE_GENERATION_MODEL   = "Qwen/Qwen2.5-Coder-32B-Instruct"
-    CODE_EXECUTION_MODEL    = "Qwen/Qwen2.5-Coder-32B-Instruct"
+MANAGER_MODEL_GPT = "gpt-4.5-preview"
+FINAL_ANSWER_MODEL_GEMINI = "gemini-2.5-pro-preview-03-25" 
+AGENT_MODEL_GTP = "gpt-4.1-mini"
+
+MANAGER_MODEL = "deepseek-ai/DeepSeek-R1"
+# FINAL_ANSWER_MODEL = "gpt-4o" # OpenAIServerModel
+FINAL_ANSWER_MODEL = "deepseek-ai/DeepSeek-R1" # OpenAIServerModel
+AGENT_MODEL = "Qwen/Qwen2.5-Coder-32B-Instruct"
+WEB_SEARCH_MODEL        = "Qwen/Qwen2.5-Coder-32B-Instruct"
+IMAGE_ANALYSIS_MODEL    = "HuggingFaceM4/idefics2-8b"
+AUDIO_ANALYSIS_MODEL    = "Qwen/Qwen2-Audio-7B-Instruct"
+VIDEO_ANALYSIS_MODEL    = "llava-hf/LLaVA-NeXT-Video-7B-hf"
+YOUTUBE_ANALYSIS_MODEL  = "llava-hf/LLaVA-NeXT-Video-7B-hf"
+DOCUMENT_ANALYSIS_MODEL = "Qwen/Qwen2.5-Coder-32B-Instruct"
+ARITHMETIC_MODEL        = "Qwen/Qwen2.5-Coder-32B-Instruct"
+CODE_GENERATION_MODEL   = "Qwen/Qwen2.5-Coder-32B-Instruct"
+CODE_EXECUTION_MODEL    = "Qwen/Qwen2.5-Coder-32B-Instruct"
 
 # Agents
 
@@ -42,7 +30,7 @@ def create_custom_web_search_agent(message):
         name="custom_web_search_agent",
         description=prompts.get_web_search_prompt(message),
         model=InferenceClientModel(WEB_SEARCH_MODEL),
-        max_steps=2,
+        max_steps=3,
         tools=[tools.simple_web_search_tool, tools.visit_web_page_tool],
     )
 
@@ -51,7 +39,7 @@ def create_simple_web_search_agent(message):
         name="simple_web_search_agent",
         description=prompts.get_web_search_prompt(message),
         model=InferenceClientModel(WEB_SEARCH_MODEL),
-        max_steps=2,
+        max_steps=3,
         tools=[tools.simple_web_search_tool, tools.visit_web_page_tool],
     )
 
@@ -61,7 +49,7 @@ def create_image_analysis_agent(message):
         description=prompts.get_image_analysis_prompt(message),
         model=InferenceClientModel(IMAGE_ANALYSIS_MODEL),
         tools=[tools.image_analysis_tool],
-        max_steps=2,
+        max_steps=3,
     )
 
 def create_audio_analysis_agent(message):
@@ -70,13 +58,79 @@ def create_audio_analysis_agent(message):
         description=prompts.get_audio_analysis_prompt(message),
         model=InferenceClientModel(AUDIO_ANALYSIS_MODEL),
         tools=[tools.audio_analysis_tool],
-        max_steps=2,
+        max_steps=3,
+    )
+
+def create_video_analysis_agent(message):
+    return CodeAgent(
+        name="video_analysis_agent",
+        description=prompts.get_video_analysis_prompt(message),
+        model=InferenceClientModel(VIDEO_ANALYSIS_MODEL),
+        tools=[tools.video_analysis_tool],
+        max_steps=3,
+    )
+
+def create_youtube_analysis_agent(message):
+    return CodeAgent(
+        name="youtube_analysis_agent",
+        description=prompts.get_youtube_analysis_prompt(message),
+        model=InferenceClientModel(YOUTUBE_ANALYSIS_MODEL),
+        tools=[tools.youtube_analysis_tool],
+        max_steps=3,
+    )
+
+def create_document_analysis_agent(message):
+    return CodeAgent(
+        name="document_analysis_agent",
+        description=prompts.get_document_analysis_prompt(message),
+        model=InferenceClientModel(DOCUMENT_ANALYSIS_MODEL),
+        tools=[tools.document_analysis_tool],
+        max_steps=3,
+    )
+
+def create_arithmetic_agent(message):
+    return CodeAgent(
+        name="arithmetic_agent",
+        description=prompts.get_arithmetic_prompt(message),
+        model=InferenceClientModel(ARITHMETIC_MODEL),
+        tools=[
+            tools.add,
+            tools.subtract,
+            tools.multiply,
+            tools.divide,
+            tools.modulus,
+        ],
+        max_steps=3,
+    )
+
+def create_code_generation_agent(message):
+    return CodeAgent(
+        name="code_generation_agent",
+        description=prompts.get_code_generation_prompt(message),
+        model=InferenceClientModel(CODE_GENERATION_MODEL),
+        tools=[tools.code_generation_tool],
+        max_steps=3,
+    )
+
+def create_code_execution_agent(message):
+    return CodeAgent(
+        name="code_execution_agent",
+        description=prompts.get_code_execution_prompt(message),
+        model=InferenceClientModel(CODE_EXECUTION_MODEL),
+        tools=[tools.code_execution_tool],
+        max_steps=3,
     )
 
 def create_manager_agent(message):
     simple_web_search_agent = create_simple_web_search_agent(message)
     image_analysis_agent = create_image_analysis_agent(message)
     audio_analysis_agent = create_audio_analysis_agent(message)
+    video_analysis_agent = create_video_analysis_agent(message)
+    youtube_analysis_agent = create_youtube_analysis_agent(message)
+    document_analysis_agent = create_document_analysis_agent(message)
+    arithmetic_agent = create_arithmetic_agent(message)
+    code_generation_agent = create_code_generation_agent(message)
+    code_execution_agent = create_code_execution_agent(message)
 
     return CodeAgent(
         name="manager_agent",
@@ -89,6 +143,12 @@ def create_manager_agent(message):
             simple_web_search_agent,
             image_analysis_agent,
             audio_analysis_agent,
+            video_analysis_agent,
+            youtube_analysis_agent,
+            document_analysis_agent,
+            arithmetic_agent,
+            code_generation_agent,
+            code_execution_agent,
         ],
         max_steps=10,
         additional_authorized_imports=[
@@ -123,7 +183,7 @@ def create_final_answer_agent(message):
     return CodeAgent(
         name="final_answer_agent",
         description="Given a question and an initial answer, return the final refined answer following strict formatting rules.",
-        model=OpenAIServerModel(FINAL_ANSWER_MODEL),
-        max_steps=3,
+        # model=OpenAIServerModel(FINAL_ANSWER_MODEL),
+        model=InferenceClientModel(FINAL_ANSWER_MODEL),
         tools=[],
     )

app.py

@@ -7,6 +7,7 @@ from huggingface_hub import login
 from dotenv import load_dotenv
 
 from multi_agent import orchestrate
+from config import config
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -151,7 +152,7 @@ def test_init_agent_for_chat(question,
                             anthropic_api_key,
                             space_id,
                             hf_token,
-                            serpapi_api_key,
+                            serper_api_key,
                             file_name
                             ):
 
@@ -173,19 +174,26 @@ def test_init_agent_for_chat(question,
         os.environ["ANTHROPIC_API_KEY"] = anthropic_api_key
         os.environ["SPACE_ID"] = space_id
         os.environ["HF_TOKEN"] = hf_token
-        os.environ["SERPAPI_API_KEY"] = serpapi_api_key
+        os.environ["SERPER_API_KEY"] = serper_api_key
+
+        config.OPENAI_API_KEY = openai_api_key
+        config.GEMINI_API_KEY = gemini_api_key
+        config.ANTHROPIC_API_KEY = anthropic_api_key
+        config.SPACE_ID = space_id
+        config.HF_TOKEN = hf_token
+        config.SERPER_API_KEY = serper_api_key
 
         submitted_answer = orchestrate(question, file_name)
     
     except Exception as e:
         raise gr.Error(e)
-    finally:
-        del os.environ["OPENAI_API_KEY"]
-        del os.environ["GEMINI_API_KEY"]
-        del os.environ["ANTHROPIC_API_KEY"]
-        del os.environ["SPACE_ID"]
-        del os.environ["HF_TOKEN"]
-        del os.environ["OPENAI_API_KEY"]
+    # finally:
+    #     del os.environ["OPENAI_API_KEY"]
+    #     del os.environ["GEMINI_API_KEY"]
+    #     del os.environ["ANTHROPIC_API_KEY"]
+    #     del os.environ["SPACE_ID"]
+    #     del os.environ["HF_TOKEN"]
+    #     del os.environ["SERPER_API_KEY"]
 
     return submitted_answer
 
@@ -197,8 +205,9 @@ with gr.Blocks() as demo:
         **Instructions:**
 
         1. Who is in the final of champions league in 2025?
-        2. What is the colour of the suit in this image: https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Fimages.hdqwalls.com%2Fwallpapers%2Fblack-superman-henry-cavill-xa.jpg&f=1&nofb=1&ipt=451cdc8bb05635ac59e50dc567cb68ae38ad45a626622ee7760b2c3ef828d5a7?
-        3. Which of the fruits shown in the 2008 painting “Embroidery from Uzbekistan” were served as part of the October 1949 breakfast menu for the ocean liner that was later used as a floating prop for the film “The Last Voyage”? Give the items as a comma-separated list, ordering them in clockwise order based on their arrangement in the painting starting from the 12 o’clock position. Use the plural form of each fruit.
+        2. Who is in the final of champions league form 2020 to 2025?
+        3. What is the colour of the suit in this image: https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Fimages.hdqwalls.com%2Fwallpapers%2Fblack-superman-henry-cavill-xa.jpg&f=1&nofb=1&ipt=451cdc8bb05635ac59e50dc567cb68ae38ad45a626622ee7760b2c3ef828d5a7?
+        4. Which of the fruits shown in the 2008 painting “Embroidery from Uzbekistan” were served as part of the October 1949 breakfast menu for the ocean liner that was later used as a floating prop for the film “The Last Voyage”? Give the items as a comma-separated list, ordering them in clockwise order based on their arrangement in the painting starting from the 12 o’clock position. Use the plural form of each fruit.
 
         """
     )
@@ -207,37 +216,38 @@ with gr.Blocks() as demo:
         space_id = gr.Textbox(
             label="space Id *",
             type="password",
-            placeholder="sk‑...",
+            placeholder="Dkapsis/assignment-gaia-agent",
             interactive=True
         )
         hf_token = gr.Textbox(
             label="HF Token *",
             type="password",
+            placeholder="hf_password",
             interactive=True
         )
-        serpapi_api_key = gr.Textbox(
-            label="Serpapi API Key *",
+        openai_api_key = gr.Textbox(
+            label="OpenAI API Key *",
             type="password",
-            placeholder="sk-ant-...",
+            placeholder="sk‑...",
             interactive=True
         )
 
     with gr.Row():
-        openai_api_key = gr.Textbox(
-            label="OpenAI API Key *",
+        serper_api_key = gr.Textbox(
+            label="Serper API Key",
             type="password",
-            placeholder="sk‑...",
+            placeholder="password",
             interactive=True
         )
         gemini_api_key = gr.Textbox(
-            label="Gemini API Key *",
+            label="Gemini API Key",
             type="password",
             interactive=True
         )
         anthropic_api_key = gr.Textbox(
-            label="Anthropic API Key *",
+            label="Anthropic API Key",
             type="password",
-            placeholder="sk-ant-...",
+            placeholder="password",
             interactive=True
         )
     
@@ -268,7 +278,7 @@ with gr.Blocks() as demo:
     gr.LoginButton()
     submit_btn.click(
         fn=test_init_agent_for_chat,
-        inputs=[question, openai_api_key, gemini_api_key, anthropic_api_key, space_id, hf_token, serpapi_api_key, file_name],
+        inputs=[question, openai_api_key, gemini_api_key, anthropic_api_key, space_id, hf_token, serper_api_key, file_name],
         outputs=answer
     )
     # gr.ChatInterface(test_init_agent_for_chat(
@@ -278,7 +288,7 @@ with gr.Blocks() as demo:
     #                         anthropic_api_key = anthropic_api_key,
     #                         space_id = space_id,
     #                         hf_token = hf_token,
-    #                         serpapi_api_key = serpapi_api_key
+    #                         serper_api_key = serper_api_key
     #                     ), type="messages")
 
     # run_button = gr.Button("Run Evaluation & Submit All Answers")

config.py

@@ -0,0 +1,9 @@
+class Config:
+    OPENAI_API_KEY = None
+    GEMINI_API_KEY = None
+    ANTHROPIC_API_KEY = None
+    SPACE_ID = None
+    HF_TOKEN = None
+    SERPER_API_KEY = None
+
+config = Config()

prompts.py

@@ -21,6 +21,48 @@ def get_audio_analysis_prompt(message, file_path=None):
 
     return prompt
 
+def get_video_analysis_prompt(message, file_path=None):
+    prompt = f"""
+    As an expert video analysis assistant, you analyze the video to answer the question. Given a question and video file, analyze the video and answer the question: {message}
+        """
+
+    return prompt
+
+def get_youtube_analysis_prompt(message, file_path=None):
+    prompt = f"""
+    As an expert YouTube analysis assistant, you analyze the video to answer the question. Given a question and YouTube URL, analyze the video and answer the question: {message}
+        """
+
+    return prompt
+
+def get_document_analysis_prompt(message, file_path=None):
+    prompt = f"""
+    As an expert document analysis assistant, you analyze the document to answer the question. Given a question and document file, analyze the document and answer the question: {message}
+        """
+
+    return prompt 
+
+def get_arithmetic_prompt(message, file_path=None):
+    prompt = f"""
+     As an expert arithmetic assistant, you perform the calculation to answer the question. Given a question and two numbers, perform the calculation and answer the question: {message}
+        """
+
+    return prompt 
+
+def get_code_generation_prompt(message, file_path=None):
+    prompt = f"""
+     As an expert Python code generation assistant, you generate and execute code to answer the question. Given a question and JSON data, generate and execute code to answer the question: {message}
+        """
+
+    return prompt   
+
+def get_code_execution_prompt(message, file_path=None):
+    prompt = f"""
+    As an expert Python code execution assistant, you execute code to answer the question. Given a question and Python file, execute the file to answer the question: {message}
+        """
+
+    return prompt  
+
 def get_manager_prompt(message, file_path=None):
     prompt = f"""Your job is to answer the following question. 
         Answer the following question. If needed, delegate to one of your coworkers:\n

tools.py

@@ -1,12 +1,22 @@
 
 import os
 import base64
-from smolagents import DuckDuckGoSearchTool, VisitWebpageTool
+from smolagents import DuckDuckGoSearchTool, VisitWebpageTool, GoogleSearchTool
 from smolagents.tools import tool
 
+from config import config
+
 # Tools
 
-simple_web_search_tool = DuckDuckGoSearchTool()
+if not os.environ.get("SERPER_API_KEY"):
+    print("---------------DEN VRIKA KEY-----------")
+    print("---------------DEN VRIKA KEY-----------")
+    simple_web_search_tool = DuckDuckGoSearchTool()
+else:
+    print("!!!!!!!!!!!!! VRIKA KEY !!!!!!!!!!!!!!!!")
+    print("!!!!!!!!!!!!! VRIKA KEY !!!!!!!!!!!!!!!!")
+    simple_web_search_tool = GoogleSearchTool("serper")
+
 visit_web_page_tool = VisitWebpageTool()
 
 @tool
@@ -98,4 +108,266 @@ def audio_analysis_tool(question: str, file_path: str) -> str:
 
         return prompt  # The agent model will process this
     except Exception as e:
-        raise RuntimeError(f"Audio analysis failed: {str(e)}")
+        raise RuntimeError(f"Audio analysis failed: {str(e)}")
+
+@tool
+def video_analysis_tool(question: str, file_path: str) -> str:
+    """
+    Given a question and a video file path, analyze the video to answer the question.
+
+    Args:
+        question (str): A question about the video.
+        file_path (str): Path to the video file.
+
+    Returns:
+        str: Structured prompt with video and question (for agent model to process).
+
+    Raises:
+        RuntimeError: If processing fails.
+    """
+    try:
+        # Read and encode video to base64
+        with open(file_path, "rb") as video_file:
+            video_data = base64.b64encode(video_file.read()).decode("utf-8")
+
+        # Format the content in a vision+text style prompt, adapted for video
+        prompt = {
+            "inputs": {
+                "video": video_data,
+                "question": question
+            }
+        }
+
+        return prompt  # The agent model will process this
+    except Exception as e:
+        raise RuntimeError(f"Video analysis failed: {str(e)}")
+
+@tool
+def youtube_analysis_tool(question: str, url: str) -> str:
+    """
+    Given a question and a YouTube video URL, analyze the video to answer the question.
+
+    Args:
+        question (str): A question about the YouTube video.
+        url (str): The YouTube URL.
+
+    Returns:
+        str: Structured prompt with URL and question (for agent model to process).
+
+    Raises:
+        RuntimeError: If processing fails.
+    """
+    try:
+        # Prepare structured input to be processed by the agent model
+        prompt = {
+            "inputs": {
+                "youtube_url": url,
+                "question": question
+            }
+        }
+
+        return prompt  # The agent model will handle downloading and processing
+    except Exception as e:
+        raise RuntimeError(f"YouTube analysis failed: {str(e)}")
+
+@tool
+def document_analysis_tool(question: str, file_path: str) -> str:
+    """
+    Given a question and a document file path, analyze the document to answer the question.
+
+    Args:
+        question (str): A question about the document.
+        file_path (str): Path to the document file.
+
+    Returns:
+        str: Structured prompt with document content and question (for agent model to process).
+
+    Raises:
+        RuntimeError: If processing fails.
+    """
+    try:
+        if is_ext(file_path, ".docx"):
+            # Extract text from .docx files
+            text_data = read_docx_text(file_path)
+            prompt = {
+                "inputs": {
+                    "document_type": "docx",
+                    "document_content": text_data,
+                    "question": question
+                }
+            }
+        elif is_ext(file_path, ".pptx"):
+            # Extract text from .pptx files
+            text_data = read_pptx_text(file_path)
+            prompt = {
+                "inputs": {
+                    "document_type": "pptx",
+                    "document_content": text_data,
+                    "question": question
+                }
+            }
+        else:
+            # For PDFs or other binary files, encode to base64
+            with open(file_path, "rb") as file:
+                encoded_data = base64.b64encode(file.read()).decode("utf-8")
+            prompt = {
+                "inputs": {
+                    "document_type": "binary",
+                    "document_base64": encoded_data,
+                    "question": question
+                }
+            }
+
+        return prompt  # Agent model will handle document type accordingly
+    except Exception as e:
+        raise RuntimeError(f"Document analysis failed: {str(e)}")
+
+@tool
+def arithmetic_tool(question: str, a: float, b: float) -> dict:
+    """
+    Given a question and two numbers, perform the calculation to answer the question.
+
+    Args:
+        question (str): A natural language arithmetic question.
+        a (float): First number.
+        b (float): Second number.
+
+    Returns:
+        dict: Structured input for the model or agent to interpret and compute.
+
+    Raises:
+        RuntimeError: If input or processing fails.
+    """
+    try:
+        prompt = {
+            "inputs": {
+                "question": question,
+                "a": a,
+                "b": b
+            }
+        }
+
+        return prompt  # Let the model/agent evaluate and compute the result
+    except Exception as e:
+        raise RuntimeError(f"Arithmetic processing failed: {str(e)}")
+
+@tool
+def code_generation_tool(question: str, json_data: str) -> dict:
+    """
+    Given a question and JSON data, generate and execute code to answer the question.
+
+    Args:
+        question (str): The question to be answered.
+        json_data (str): Input JSON data as a string.
+
+    Returns:
+        dict: Structured input for the agent or model to process and respond.
+
+    Raises:
+        RuntimeError: If formatting or processing fails.
+    """
+    try:
+        prompt = {
+            "inputs": {
+                "question": question,
+                "json_data": json_data
+            }
+        }
+
+        return prompt  # Model or code-executing agent will handle the execution logic
+    except Exception as e:
+        raise RuntimeError(f"Code generation processing failed: {str(e)}")
+
+@tool
+def code_execution_tool(question: str, file_path: str) -> dict:
+    """
+    Given a question and a Python file, prepare code execution context to answer the question.
+
+    Args:
+        question (str): The question to be answered.
+        file_path (str): Path to the Python file.
+
+    Returns:
+        dict: Structured input with base64-encoded file and question.
+
+    Raises:
+        RuntimeError: If encoding or file handling fails.
+    """
+    try:
+        # Read and encode the Python file
+        with open(file_path, "rb") as py_file:
+            code_data = base64.b64encode(py_file.read()).decode("utf-8")
+
+        # Construct prompt structure
+        prompt = {
+            "inputs": {
+                "question": question,
+                "python_file": code_data,
+                "file_name": os.path.basename(file_path)
+            }
+        }
+
+        return prompt  # Model/agent will handle execution and answer
+    except Exception as e:
+        raise RuntimeError(f"Code execution processing failed: {str(e)}")
+
+@tool
+def add(a: float, b: float) -> float:
+    """Add two numbers.
+    
+    Args:
+        a: First number
+        b: Second number
+    Returns:
+        Result number
+    """
+    return a + b
+
+@tool
+def subtract(a: float, b: float) -> float:
+    """Subtract two numbers.
+    
+    Args:
+        a: First number
+        b: Second number
+    Returns:
+        Result number
+    """
+    return a - b
+
+@tool
+def multiply(a: float, b: float) -> float:
+    """Multiply two numbers.
+    Args:
+        a: First number
+        b: Second number
+    Returns:
+        Result number
+    """
+    return a * b
+
+@tool
+def divide(a: float, b: float) -> float:
+    """Divide two numbers.
+    
+    Args:
+        a: First number
+        b: Second number
+    Returns:
+        Result number
+    """
+    if b == 0:
+        raise ValueError("Cannot divide by zero.")
+    return a / b
+
+@tool
+def modulus(a: float, b: float) -> float:
+    """Get the modulus of two numbers.
+    
+    Args:
+        a: First number
+        b: Second number
+    Returns:
+        Result number
+    """
+    return a % b