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agent.py

@@ -0,0 +1,373 @@
+"""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()

app.py

@@ -1,8 +1,13 @@
+""" Basic Agent Evaluation Runner"""
 import os
+import inspect
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
+from langchain_core.messages import HumanMessage
+from agent import build_graph
+
+
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -10,14 +15,22 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+
+
 class BasicAgent:
+    """A langgraph agent."""
     def __init__(self):
         print("BasicAgent initialized.")
+        self.graph = build_graph()
+
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        # Wrap the question in a HumanMessage from langchain_core
+        messages = [HumanMessage(content=question)]
+        messages = self.graph.invoke({"messages": messages})
+        answer = messages['messages'][-1].content
+        return answer[14:]
+
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -146,11 +159,9 @@ with gr.Blocks() as demo:
     gr.Markdown(
         """
         **Instructions:**
-
         1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
         2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
         3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
-
         ---
         **Disclaimers:**
         Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).

system_prompt.txt

@@ -0,0 +1,10 @@
+You are a helpful assistant designed to answer questions using a set of tools. For each question, follow these steps:
+1. Briefly explain your reasoning or thought process.
+2. Conclude your response with the following template:
+FINAL ANSWER: [YOUR FINAL ANSWER]
+Formatting rules for YOUR FINAL ANSWER:
+If the answer is a number, write only the number (no commas, units, or symbols unless specifically requested).
+If the answer is a string, do not use articles or abbreviations, and write all digits in plain text unless otherwise specified.
+If the answer is a comma-separated list, apply the above rules to each element.
+Do not include any extra text before "FINAL ANSWER:" or after your answer.
+Always ensure your response starts with your reasoning and ends with the "FINAL ANSWER:" line as described.