rag/pipeline.py

from rag.llm import generate
from evaluation.metrics import evaluate_and_log


def run_rag(question, vectorstore):
    docs = vectorstore.similarity_search(question, k=3)
    context = "\n\n".join(d.page_content for d in docs)

    prompt = (
        "Use the context below to answer the question clearly and simply.\n\n"
        f"Context:\n{context}\n\n"
        f"Question: {question}\n\n"
        "Answer:"
    )

    answer = generate(prompt)
    evaluation = evaluate_and_log(question, context, answer)

    sources = f"Retrieved {len(docs)} relevant passages from document"

    return answer, sources, evaluation