"""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_core.messages import SystemMessage, HumanMessage from langchain_core.tools import tool from langchain.tools.retriever import create_retriever_tool from langchain_community.vectorstores import Chroma from langchain_core.documents import Document import shutil import pandas as pd # Ny import för pandas import json # För att parsa metadata-kolumnen load_dotenv() @tool def multiply(a: int, b: int) -> int: """Multiply two numbers. Args: a: first int b: second int """ return a * b @tool def add(a: int, b: int) -> int: """Add two numbers. Args: a: first int b: second int """ return a + b @tool def subtract(a: int, b: int) -> int: """Subtract two numbers. Args: a: first int b: second int """ return a - b @tool def divide(a: int, b: int) -> int: """Divide two numbers. Args: a: first int b: second int """ if b == 0: raise ValueError("Cannot divide by zero.") return a / b @tool def modulus(a: int, b: int) -> int: """Get the modulus of two numbers. Args: a: first int b: second int """ return a % b @tool def wiki_search(query: str) -> str: """Search Wikipedia for a query and return maximum 2 results. Args: query: The search query.""" search_docs = WikipediaLoader(query=query, load_max_docs=2).load() formatted_search_docs = "\n\n---\n\n".join( [ f'\n{doc.page_content}\n' for doc in search_docs ]) return {"wiki_results": formatted_search_docs} @tool def web_search(query: str) -> str: """Search Tavily for a query and return maximum 3 results. Args: query: The search query.""" search_docs = TavilySearchResults(max_results=3).invoke(query=query) formatted_search_docs = "\n\n---\n\n".join( [ f'\n{doc.page_content}\n' for doc in search_docs ]) return {"web_results": formatted_search_docs} @tool 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'\n{doc.page_content[:1000]}\n' for doc in search_docs ]) return {"arvix_results": formatted_search_docs} # 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) # --- Start ChromaDB Setup --- # Define the directory for ChromaDB persistence CHROMA_DB_DIR = "./chroma_db" CSV_FILE_PATH = "./supabase_docs.csv" # Path to your CSV file # Build embeddings (this remains the same) embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") # dim=768 # Initialize ChromaDB # If the directory exists and contains data, load the existing vector store. # Otherwise, create a new one and add documents from the CSV file. if os.path.exists(CHROMA_DB_DIR) and os.listdir(CHROMA_DB_DIR): print(f"Loading existing ChromaDB from {CHROMA_DB_DIR}") vector_store = Chroma( persist_directory=CHROMA_DB_DIR, embedding_function=embeddings ) else: print(f"Creating new ChromaDB at {CHROMA_DB_DIR} and loading documents from {CSV_FILE_PATH}.") # Ensure the directory is clean before creating new if os.path.exists(CHROMA_DB_DIR): shutil.rmtree(CHROMA_DB_DIR) os.makedirs(CHROMA_DB_DIR) # Load data from the CSV file if not os.path.exists(CSV_FILE_PATH): raise FileNotFoundError(f"CSV file not found at {CSV_FILE_PATH}. Please ensure it's in the root directory.") df = pd.read_csv(CSV_FILE_PATH) documents = [] for index, row in df.iterrows(): content = row["content"] # Extract the question part from the content # Assuming the question is everything before "Final answer :" question_part = content.split("Final answer :")[0].strip() # Extract the final answer part from the content final_answer_part = content.split("Final answer :")[-1].strip() if "Final answer :" in content else "" # Parse the metadata string into a dictionary # The metadata column might be stored as a string representation of a dictionary try: metadata = json.loads(row["metadata"].replace("'", "\"")) # Replace single quotes for valid JSON except json.JSONDecodeError: metadata = {} # Fallback if parsing fails # Add the extracted final answer to the metadata for easy retrieval metadata["final_answer"] = final_answer_part # Create a Document object. The page_content should be the question for similarity search. # The answer will be in metadata. documents.append(Document(page_content=question_part, metadata=metadata)) if not documents: print("No documents loaded from CSV. ChromaDB will be empty.") # Create an empty ChromaDB if no documents are found vector_store = Chroma( persist_directory=CHROMA_DB_DIR, embedding_function=embeddings ) else: vector_store = Chroma.from_documents( documents=documents, embedding=embeddings, persist_directory=CHROMA_DB_DIR ) vector_store.persist() # Save the new vector store to disk print(f"ChromaDB initialized and persisted with {len(documents)} documents from CSV.") # Create retriever tool using the Chroma vector store retriever_tool = create_retriever_tool( retriever=vector_store.as_retriever(), name="Question_Search", description="A tool to retrieve similar questions from a vector store. The retrieved document's metadata contains the 'final_answer' to the question.", ) # Add the new retriever tool to your list of tools tools = [ multiply, add, subtract, divide, modulus, wiki_search, web_search, arvix_search, retriever_tool, ] # Build graph function def build_graph(provider: str = "google"): """Build the graph""" if provider == "google": llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0) elif provider == "groq": llm = ChatGroq(model="qwen-qwq-32b", temperature=0) elif provider == "huggingface": llm = ChatHuggingFace( llm=HuggingFaceEndpoint( url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf", temperature=0, ), ) else: raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.") llm_with_tools = llm.bind_tools(tools) def assistant(state: MessagesState): """Assistant node""" return {"messages": [llm_with_tools.invoke(state["messages"])]} from langchain_core.messages import AIMessage def retriever(state: MessagesState): query = state["messages"][-1].content # Use the vector_store directly for similarity search to get the full Document object similar_docs = vector_store.similarity_search(query, k=1) if similar_docs: similar_doc = similar_docs[0] # Prioritize 'final_answer' from metadata, then check page_content if "final_answer" in similar_doc.metadata and similar_doc.metadata["final_answer"]: answer = similar_doc.metadata["final_answer"] elif "Final answer :" in similar_doc.page_content: answer = similar_doc.page_content.split("Final answer :")[-1].strip() else: answer = similar_doc.page_content.strip() # Fallback to page_content if no explicit answer # The system prompt expects "FINAL ANSWER: [ANSWER]". # We should return the extracted answer directly, as the prompt handles the formatting. return {"messages": [AIMessage(content=answer)]} else: return {"messages": [AIMessage(content="No similar questions found in the knowledge base.")]} builder = StateGraph(MessagesState) builder.add_node("retriever", retriever) builder.set_entry_point("retriever") builder.set_finish_point("retriever") return builder.compile()