from dotenv import load_dotenv load_dotenv() from langgraph.graph import START, StateGraph, MessagesState from langgraph.prebuilt import tools_condition from langgraph.prebuilt import ToolNode from langchain_core.messages import SystemMessage, HumanMessage from tools.searchtools import wiki_search, web_search, arxiv_search, vector_store from tools.mathtools import multiply, add, subtract, divide, modulus,power,square_root from tools.codetools import execute_code_multilang from tools.documenttools import save_and_read_file,download_file_from_url, extract_text_from_image, analyze_csv_file, analyze_excel_file from tools.imagetools import analyze_image, transform_image, draw_on_image, generate_simple_image, combine_images from langchain_groq import ChatGroq from langchain_huggingface import ChatHuggingFace, HuggingFaceEndpoint # 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 = [ 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_multilang, analyze_image, transform_image, draw_on_image, generate_simple_image, combine_images, ] # Build graph function def build_graph(): """Build the graph""" # Load environment variables from .env file llm = ChatGroq(model="qwen-qwq-32b", temperature=0) # 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) if similar_question: # Check if the list is not empty 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: # Handle the case when no similar questions are found 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") # Compile graph return builder.compile()