# https://python.langchain.com/en/latest/use_cases/agent_simulations/characters.html#create-a-generative-character
def create_new_memory_retriever(ctx: Context):
    """Create a new vector store retriever unique to the agent."""
    # Define your embedding model
    embeddings_model = load_embedding_from_config(ctx.settings.model.embedding)
    # Initialize the vectorstore as empty
    embedding_size = 1536
    index = faiss.IndexFlatL2(embedding_size)
    vectorstore = FAISS(
        embeddings_model.embed_query,
        index,
        InMemoryDocstore({}),
        {},
        relevance_score_fn=relevance_score_fn,
    )
    return TimeWeightedVectorStoreRetriever(
        vectorstore=vectorstore, other_score_keys=["importance"], k=15
    )