agent.py

import os
from dotenv import load_dotenv
from tools.python_interpreter import CodeInterpreter

interpreter_instance = CodeInterpreter()


from tools.image import *

"""Langraph"""
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import ToolNode, tools_condition
from langchain_groq import ChatGroq
from langchain_huggingface import (
    ChatHuggingFace,
    HuggingFaceEndpoint,
    HuggingFaceEmbeddings,
)
from langchain_community.vectorstores import SupabaseVectorStore
from langchain_core.messages import SystemMessage, HumanMessage
from langchain.tools import create_retriever_tool
from supabase.client import Client, create_client
# ------- Tools
from tools.browse import web_search, wiki_search, arxiv_search
from tools.document_process import save_and_read_file, analyze_csv_file, analyze_excel_file, extract_text_from_image, download_file_from_url
from tools.image_tools import analyze_image, generate_simple_image , transform_image, draw_on_image, combine_images
from tools.simple_math import multiply, add, subtract, divide, modulus, power, square_root
from tools.python_interpreter import execute_code_lang

load_dotenv()

with open("system_prompt.txt", "r", encoding="utf-8") as f:
    system_prompt = f.read()
print(system_prompt)

# System message
sys_msg = SystemMessage(content=system_prompt)

# build a retriever
embeddings = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-mpnet-base-v2",
)  #  dim=768
supabase: Client = create_client(
    os.environ.get("SUPABASE_URL_HUGGING_FACE"), os.environ.get("SUPABASE_SERVICE_ROLE_HUGGING_FACE")
)
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 = [
    web_search,
    wiki_search,
    arxiv_search,
    multiply,
    add,
    subtract,
    divide,
    modulus,
    power,
    square_root,
    save_and_read_file,
    download_file_from_url,
    extract_text_from_image,
    analyze_csv_file,
    analyze_excel_file,
    execute_code_lang,
    analyze_image,
    transform_image,
    draw_on_image,
    generate_simple_image,
    combine_images,
]

def build_graph(provider: str = "groq"):
    if provider == "groq":
        # Groq https://console.groq.com/docs/models
        llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
        # llm = ChatGroq(model="deepseek-r1-distill-llama-70b", temperature=0)
    elif provider == "huggingface":
        llm = ChatHuggingFace(
            llm=HuggingFaceEndpoint(
                repo_id="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
                task="text-generation",  # for chat‐style use “text-generation”
                max_new_tokens=1024,
                do_sample=False,
                repetition_penalty=1.03,
                temperature=0,
            ),
            verbose=True,
        )
    else:
        raise ValueError("Invalid provider. Choose 'groq' or 'huggingface'.")

    llm_with_tools = llm.bind_tools(tools)

    def assistant(state: MessagesState):
        """Assistant Node"""
        return {"messages": [llm_with_tools.invoke(state['messages'])]}

    def retriever(state: MessagesState):
        """Retriever Node"""
        # Extract the latest message content
        query = state['messages'][-1].content
        similar_question = vector_store.similarity_search(query, k = 2)
        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}",
            )
            return {"messages": [sys_msg] + state["messages"] + [example_msg]}
        else:
            return {"messages": [sys_msg] + state["messages"]}


    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")
    return builder.compile()

if __name__ == "__main__":
    question = "How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)? You can use the latest 2022 version of english wikipedia."
    # question = """Q is Examine the video at https://www.youtube.com/watch?v=1htKBjuUWec. What does Teal'c say in response to the question "Isn't that hot?"""
    graph = build_graph(provider="groq")
    messages = [HumanMessage(content=question)]
    messages = graph.invoke({"messages": messages})
    for m in messages["messages"]:
        m.pretty_print()