import os
import datetime
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_core.documents import Document

FAISS_PATH = "data/faiss_index"

def load_faiss():
    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
    if os.path.exists(FAISS_PATH):
        return FAISS.load_local(
            FAISS_PATH,
            embeddings,
            allow_dangerous_deserialization=True
        )
    return FAISS.from_texts(
        ["Initialisation mémoire Kibali"],
        embeddings
    )

def save_memory(vectordb, user_msg, assistant_msg):
    ts = datetime.datetime.now().isoformat()
    vectordb.add_documents([
        Document(page_content=f"[User {ts}] {user_msg}"),
        Document(page_content=f"[Kibali {ts}] {assistant_msg}")
    ])
    vectordb.save_local(FAISS_PATH)