Updated agent.py

agent.py

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+"""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()