agent.py

"""LangGraph Agent"""
import os
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
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 langchain_openai import ChatOpenAI

from langchain.tools import Tool
from code_interpreter import CodeInterpreter
#from langchain_chroma import Chroma
from langchain_core.messages import AIMessage

interpreter_instance = CodeInterpreter()


load_dotenv()
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
SUPABASE_URL = os.environ.get("SUPABASE_URL")
SUPABASE_SERVICE_KEY = os.environ.get("SUPABASE_SERVICE_KEY")

### ======================================== MATHEMATICAL TOOLS ======================================== ###

def multiply(a: int, b: int) -> int:
    return a * b

multiply_tool = Tool(
    name="multiply",
    func=multiply,
    description="Multiply two numbers. Args (a: first int, b: second int)"
)

def add(a: int, b: int) -> int:
    return a + b

add_tool = Tool(
    name="add",
    func=add,
    description="Add two numbers. Args (a: first int, b: second int)"
)

def substract(a: int, b: int) -> int:
    return a - b

substract_tool = Tool(
    name="substract",
    func=substract,
    description="Substract two numbers. Args (a: first int, b: second int)"
)

def divide(a: int, b: int) -> int:
    if b == 0:
        raise ValueError("Cannot divide by zero.")
    return a / b

divide_tool = Tool(
    name="divide",
    func=divide,
    description="Divide two numbers. Args (a: first int, b: second int)"
)

def modulus(a: int, b: int) -> int:
    return a % b
    
modulus_tool = Tool(
    name="modulus",
    func=modulus,
    description="Modulus two numbers. Args (a: first int, b: second int)"
)

def power(a: float, b: float) -> float:
    return a**b

power_tool = Tool(
    name="power",
    func=power,
    description="Power two numbers. Args (a: first float, b: second float)"
)

def square_root(a: float) -> float | complex:
    if a >= 0:
        return a**0.5
    return cmath.sqrt(a)

square_root_power = Tool(
    name="square_root",
    func=square_root,
    description="Square two numbers. Args (a: float)"
)


### ======================================== BROWSER TOOLS ======================================== ###

def wiki_search(query: str) -> str:
    search_docs = WikipediaLoader(query=query, 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}

wiki_search_tool = Tool(
    name="wiki_search",
    func=wiki_search,
    description="Search Wikipedia for a query and return maximum 2 results. Args (query: the search query)"
)

def web_search(query: str) -> str:
    search_docs = TavilySearchResults(max_results=3).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}
    
web_search_tool = Tool(
    name="web_search",
    func=web_search,
    description="Search Tavily for a query and return maximum 3 results. Args (query: the search query)"
)

def arvix_search(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=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}

arvix_search_tool = Tool(
    name="arvix_search",
    func=arvix_search,
    description="Search Arxiv for a query and return maximum 3 result. Args (query: the search query)"
)


### ======================================== CODE INTERPRETER TOOLS ======================================== ###

def execute_code_multilang(code: str, language: str = "python") -> str:
    """Execute code in multiple languages (Python, Bash, SQL, C, Java) and return results.
    Args:
        code (str): The source code to execute.
        language (str): The language of the code. Supported: "python", "bash", "sql", "c", "java".
    Returns:
        A string summarizing the execution results (stdout, stderr, errors, plots, dataframes if any).
    """
    supported_languages = ["python", "bash", "sql", "c", "java"]
    language = language.lower()

    if language not in supported_languages:
        return f"❌ Unsupported language: {language}. Supported languages are: {', '.join(supported_languages)}"

    result = interpreter_instance.execute_code(code, language=language)

    response = []

    if result["status"] == "success":
        response.append(f"✅ Code executed successfully in **{language.upper()}**")

        if result.get("stdout"):
            response.append(
                "\n**Standard Output:**\n```\n" + result["stdout"].strip() + "\n```"
            )

        if result.get("stderr"):
            response.append(
                "\n**Standard Error (if any):**\n```\n"
                + result["stderr"].strip()
                + "\n```"
            )

        if result.get("result") is not None:
            response.append(
                "\n**Execution Result:**\n```\n"
                + str(result["result"]).strip()
                + "\n```"
            )

        if result.get("dataframes"):
            for df_info in result["dataframes"]:
                response.append(
                    f"\n**DataFrame `{df_info['name']}` (Shape: {df_info['shape']})**"
                )
                df_preview = pd.DataFrame(df_info["head"])
                response.append("First 5 rows:\n```\n" + str(df_preview) + "\n```")

        if result.get("plots"):
            response.append(
                f"\n**Generated {len(result['plots'])} plot(s)** (Image data returned separately)"
            )

    else:
        response.append(f"❌ Code execution failed in **{language.upper()}**")
        if result.get("stderr"):
            response.append(
                "\n**Error Log:**\n```\n" + result["stderr"].strip() + "\n```"
            )

    return "\n".join(response)

execute_code_multilang_tool = Tool(
    name="execute_code_multilang",
    func=execute_code_multilang,
    description="""Execute code in multiple languages (Python, Bash, SQL, C, Java) and return results.
        Args:
            code (str): The source code to execute.
            language (str): The language of the code. Supported: "python", "bash", "sql", "c", "java".
    """
)


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


### ======================================== RETRIEVER TOOLS ======================================== ###

embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")

"""
Using chroma database

vector_store = Chroma(
    collection_name="gaia_dataset",
    embedding_function=embeddings,
    persist_directory="./data/chroma_langchain_db",  # Where to save data locally, remove if not necessary
)
# It's not going to be used later
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.",
)
"""

# Using supabase database
# build a retriever
supabase: Client = create_client(SUPABASE_URL, SUPABASE_SERVICE_KEY)
vector_store = SupabaseVectorStore(
    embedding=embeddings,
    client=supabase,
    table_name="gaia_dataset",
    query_name="match_documents",
)
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.",
)


# 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)

tools = [
    multiply_tool,
    add_tool,
    substract_tool,
    divide_tool,
    modulus_tool,
    power_tool,
    square_root,
    wiki_search_tool,
    web_search_tool,
    arvix_search_tool,
    execute_code_multilang_tool,
    
]

# Build graph function
def build_graph(provider: str = "huggingface"):
    """Build the graph"""
    # Load environment variables from .env file
    if provider == "google":
        # Google Gemini
        chat = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
    elif provider == "groq":
        # Groq https://console.groq.com/docs/models
        chat = ChatGroq(model="qwen-qwq-32b", temperature=0) # optional : qwen-qwq-32b gemma2-9b-it
    elif provider == "openai":
        # Set the model from openai here
        model_openai = "gpt-4o"
        chat = ChatOpenAI(
            model=model_openai,
            temperature=0,
            api_key=OPENAI_API_KEY
        )
    elif provider == "huggingface":
        # Add huggingface endpoint
        #repo_id = "mistralai/Mistral-7B-Instruct-v0.2"
        #repo_id = "Qwen/Qwen2.5-Coder-32B-Instruct" # -> it doesn't reply well
        #repo_id = "deepseek-ai/DeepSeek-Coder-V2-Instruct" -> it doesn't work (error on StopIteration)
        #repo_id = "meta-llama/CodeLlama-34b-Instruct-hf" -> it doesn't work (error on StopIteration)
        repo_id = "WizardLMTeam/WizardCoder-15B-V1.0"
        chat = ChatHuggingFace(
            #llm=HuggingFaceEndpoint(
            #    endpoint_url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
            #    temperature=0,
            #),
            llm=HuggingFaceEndpoint(
                repo_id=repo_id,
                temperature=0.1
            )
        )
    else:
        raise ValueError("Invalid provider. Choose 'google', 'groq', 'openai' or 'huggingface'.")
    # Bind tools to LLM
    chat_with_tools = chat.bind_tools(tools)

    # Node
    def assistant(state: MessagesState):
        """Assistant node"""
        return {"messages": [chat_with_tools.invoke(state["messages"])]}

    def retriever(state: MessagesState):
        query = state["messages"][-1].content
        similar_doc = vector_store.similarity_search(query, k=1)[0]

        content = similar_doc.page_content
        if "Final answer :" in content:
            answer = content.split("Final answer :")[-1].strip()
        else:
            answer = content.strip()

        return {"messages": [AIMessage(content=answer)]}    

    """
    Graph with retriever and tools
    
    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")
    """

    builder = StateGraph(MessagesState)
    builder.add_node("retriever", retriever)
    # Retriever ist Start und Endpunkt
    builder.set_entry_point("retriever")
    builder.set_finish_point("retriever")
    # Compile graph
    return builder.compile()


    """
    Graph with tools conditions
    
    builder = StateGraph(MessagesState)
    # Define nodes: these do the work
    builder.add_node("assistant", assistant)
    builder.add_node("tools", ToolNode(tools))
    # Define edges: these determine how the control flow moves
    builder.add_edge(START, "assistant")
    builder.add_conditional_edges(
        "assistant",
        # If the latest message requires a tool, route to tools
        # Otherwise, provide a direct response
        tools_condition,
    )
    builder.add_edge("tools", "assistant")
    # Compile graph
    return builder.compile()
    """