Delete agent.py

agent.py

@@ -1,140 +0,0 @@
-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_community.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()