agent.py

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_huggingface import 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, AIMessage
from langchain_core.tools import tool
from langchain.tools.retriever import create_retriever_tool
from langchain_community.vectorstores import FAISS
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()

# Tools:
@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'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
            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'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
            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'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
            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()

# Retrieval
INDEX_DIR = "./faiss_index"
CSV_PATH = "./supabase_docs.csv"
# Use a lightweight 384-dim model to avoid excessive RAM and potential onnxruntime crashes
EMBED_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
_SIMILARITY_THRESHOLD = 0.2  # lower distance means more similar

embeddings = HuggingFaceEmbeddings(model_name=EMBED_MODEL, model_kwargs={"device": "cpu"})

if os.path.exists(INDEX_DIR):
    print(f"Loading existing FAISS index from {INDEX_DIR}")
    vector_store = FAISS.load_local(INDEX_DIR, embeddings, allow_dangerous_deserialization=True)
else:
    print(f"Creating new FAISS index at {INDEX_DIR}, and loading documents from {CSV_PATH}")
    if os.path.exists(INDEX_DIR):
        shutil.rmtree(INDEX_DIR)
    os.makedirs(INDEX_DIR)

    if not os.path.exists(CSV_PATH):
        raise FileNotFoundError(f"CSV file {CSV_PATH} does not exist")
    
    df = pd.read_csv(CSV_PATH)
    documents = []
    for i, row in df.iterrows():
        content = row["content"]
        
        question_part = content.split("Final answer :")[0].strip()
        final_answer_part = content.split("Final answer :")[-1].strip() if "Final answer :" in content else ""
        
        try:
            metadata = json.loads(row["metadata"].replace("'", '"'))
        except json.JSONDecodeError:
            metadata = {}
        
        metadata["final_answer"] = final_answer_part

        documents.append(Document(page_content=question_part, metadata=metadata))

        # Simple progress indicator every 200 docs
        if (i + 1) % 200 == 0:
            print(f"Prepared {i + 1}/{len(df)} documents for embedding…")

    if not documents:
        print("No documents loaded from CSV. FAISS index will be empty.")

        vector_store = FAISS.from_documents(documents=[], embedding=embeddings)
        vector_store.save_local(INDEX_DIR)
    else:
        print("Embedding documents and building FAISS index — this may take a few minutes…")
        vector_store = FAISS.from_documents(documents=documents, embedding=embeddings)
        vector_store.save_local(INDEX_DIR)
        print(f"FAISS index built and saved with {len(documents)} documents from CSV.")
        

# Retriever tool
retriever_tool = create_retriever_tool(
    retriever=vector_store.as_retriever(),
    name="Question_Search",
    description="Retrieve similar questions from FAISS index; metadata includes 'final_answer'."
)

# Agent

tools = [
    multiply,
    add,
    subtract,
    divide,
    modulus,
    wiki_search,
    web_search,
    arvix_search,
    retriever_tool,
]

def build_graph_agent():
    llm = ChatGoogleGenerativeAI(
        model="gemini-1.5-flash",
        temperature=0.0,
    )

    llm_with_tools = llm.bind_tools(tools)

    def assistant(state: MessagesState):
        return {
            "messages": [llm_with_tools.invoke(state["messages"])],
        }
    
    def retriever(state: MessagesState):
        query = state["messages"][-1].content
        similar_docs = vector_store.similarity_search(query, k=3)

        if similar_docs:
            similar_doc = similar_docs[0]
            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()

            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()