import os from dotenv import load_dotenv from langchain_community.vectorstores import Chroma from langchain_core.messages import HumanMessage, SystemMessage, ToolMessage from langchain_huggingface import (ChatHuggingFace, HuggingFaceEmbeddings, HuggingFaceEndpoint) from langgraph.graph import START, MessagesState, StateGraph from langgraph.prebuilt import ToolNode, tools_condition from tools import (absolute, add, analyze_csv_file, analyze_excel_file, arvix_search, audio_transcription, compound_interest, convert_temperature, divide, exponential, extract_text, factorial, floor_divide, get_current_time_in_timezone, greatest_common_divisor, is_prime, least_common_multiple, logarithm, modulus, multiply, percentage_calculator, power, python_code_parser, reverse_sentence, roman_calculator_converter, square_root, subtract, web_search, wiki_search) # Load Constants load_dotenv() HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN") GEMINI_API_KEY = os.getenv("GEMINI_API_KEY") tools = [ multiply, add, subtract, power, divide, modulus, square_root, floor_divide, absolute, logarithm, exponential, web_search, roman_calculator_converter, get_current_time_in_timezone, compound_interest, convert_temperature, factorial, greatest_common_divisor, is_prime, least_common_multiple, percentage_calculator, wiki_search, analyze_excel_file, arvix_search, audio_transcription, python_code_parser, analyze_csv_file, extract_text, reverse_sentence ] # Load system prompt system_prompt = """ You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string. """ # System message sys_msg = SystemMessage(content=system_prompt) def get_vector_store(persist_directory="chroma_db"): """ Initializes and returns a Chroma vector store. If the database exists, it loads it. If not, it creates it, adds some initial documents, and persists them. """ embedding_function = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") if os.path.exists(persist_directory) and os.listdir(persist_directory): print("Loading existing vector store...") vector_store = Chroma( persist_directory=persist_directory, embedding_function=embedding_function ) else: print("Creating new vector store...") os.makedirs(persist_directory, exist_ok=True) # Example documents to add initial_documents = [ "The Principle of Double Effect is an ethical theory that distinguishes between the intended and foreseen consequences of an action.", "St. Thomas Aquinas is often associated with the development of the Principle of Double Effect.", "LangGraph is a library for building stateful, multi-actor applications with LLMs.", "Chroma is a vector database used for storing and retrieving embeddings." ] vector_store = Chroma.from_texts( texts=initial_documents, embedding=embedding_function, persist_directory=persist_directory ) # No need to call persist() when using from_texts with a persist_directory return vector_store # --- Initialize Vector Store and Retriever --- vector_store = get_vector_store() retriever_component = vector_store.as_retriever( search_type="mmr", # Use Maximum Marginal Relevance for diverse results search_kwargs={'k': 2, 'lambda_mult': 0.5} # Retrieve 2 documents ) def build_graph(): """Build the graph""" # First create the HuggingFaceEndpoint llm_endpoint = HuggingFaceEndpoint( repo_id="Qwen/Qwen2.5-Coder-32B-Instruct", huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN, #api_key=GEMINI_API_KEY, temperature=0.3, max_new_tokens=512, timeout=60, ) # Then wrap it with ChatHuggingFace to get chat model functionality llm = ChatHuggingFace(llm=llm_endpoint) # Bind tools to LLM llm_with_tools = llm.bind_tools(tools) # --- Nodes --- def assistant(state: MessagesState): """Assistant node""" # Prepend the system message to the state messages_with_system_prompt = [sys_msg] + state["messages"] return {"messages": [llm_with_tools.invoke(messages_with_system_prompt)]} def retriever_node(state: MessagesState): """ Retrieves relevant documents from the vector store based on the latest human message. """ last_human_message = state["messages"][-1].content retrieved_docs = retriever_component.invoke(last_human_message) if retrieved_docs: retrieved_context = "\n\n".join([doc.page_content for doc in retrieved_docs]) # Create a ToolMessage to hold the retrieved context context_message = ToolMessage( content=f"Retrieved context from vector store:\n\n{retrieved_context}", tool_call_id="retriever" # A descriptive ID ) return {"messages": [context_message]} return {"messages": []} # --- Graph Definition --- builder = StateGraph(MessagesState) builder.add_node("retriever", retriever_node) 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?" # Build the graph graph = build_graph() # Run the graph messages = [HumanMessage(content=question)] # The initial state for the graph initial_state = {"messages": messages} # Invoke the graph stream to see the steps for s in graph.stream(initial_state, stream_mode="values"): message = s["messages"][-1] if isinstance(message, ToolMessage): print("---RETRIEVED CONTEXT---") print(message.content) print("-----------------------") else: message.pretty_print()