How to build a mock RAG pipeline in Python

def simple_rag_pipeline(question, documents):
    """
    A minimal mock RAG pipeline: retrieve relevant context, then generate an answer.
    """
    # Step 1: Retrieve — mock retrieval by simple keyword scoring
    scores = []
    for doc in documents:
        doc_words = set(doc.lower().split())
        question_words = set(question.lower().split())
        overlap = len(doc_words & question_words)
        scores.append(overlap)
    
    # Get the most relevant document(s)
    best_idx = max(range(len(scores)), key=lambda i: scores[i])
    retrieved_context = documents[best_idx] if scores[best_idx] > 0 else "No relevant documents found."

    # Step 2: Generate — mock generation using template
    answer = (
        f"Based on the retrieved context: \"{retrieved_context}\"\n"
        f"Answer to '{question}': "
        f"The best matching document has {scores[best_idx]} keyword overlaps."
    )
    return answer


if __name__ == "__main__":
    docs = [
        "Python is a high-level programming language.",
        "RAG stands for Retrieval-Augmented Generation.",
        "Mock pipelines simulate real system behavior without expensive components."
    ]
    question = "What is RAG?"
    result = simple_rag_pipeline(question, docs)
    print(result)