# File Objective  : CLI for the RAG pipeline — ask a question, receive a cited answer.
# Scope           : Dev script — verify end-to-end grounded generation.
# What it does    : Calls domain/rag.py answer() and prints the answer + numbered sources.
# What it does not: Call the agent or expose an HTTP endpoint.

from __future__ import annotations

import sys

from vantage_core.adapters.embedder_st import STEmbedder
from vantage_core.adapters.llm_openai import OpenAILLM
from vantage_core.adapters.reranker_cross_encoder import CrossEncoderReranker
from vantage_core.adapters.vector_qdrant import QdrantVectorRepo
from vantage_core.domain.rag import answer


def main() -> None:
    question = " ".join(sys.argv[1:]).strip() or input("Question: ").strip()
    if not question:
        print("No question provided.")
        sys.exit(1)

    print(f"\n── Question: {question!r} ────────────────────────────────────────")

    result = answer(
        question = question,
        embedder = STEmbedder(),
        repo     = QdrantVectorRepo(),
        reranker = CrossEncoderReranker(),
        llm      = OpenAILLM(),
    )

    print(f"\n── Answer {'(no coverage)' if not result.has_coverage else ''} ───────────────────────────────────────────────")
    print(result.answer)

    if result.sources:
        print(f"\n── Sources ──────────────────────────────────────────────────────")
        for i, sc in enumerate(result.sources, start=1):
            c = sc.chunk
            print(f"  [{i}] {c.category} | {c.title}")
            print(f"       {c.chunk_text[:120]}")
    print()


if __name__ == "__main__":
    main()