
# rag.py
def generate_embeddings(text: str):
    """
    Generates embeddings for the given text.
    """
    return f"Embedding for '{text}'"

def rag_pipeline(query: str, documents: list):
    """
    Implements a Retrieval-Augmented Generation (RAG) pipeline.
    """
    retrieved_docs = documents[:2]  # Simulate retrieval
    response = f"Generated response for '{query}' based on {retrieved_docs}"
    return response
