#!/usr/bin/env python3
"""
Interroge la base vectorielle Qdrant avec une question en langage naturel.
Usage: rag-query "ma question" [--top N]
Retourne les passages de documentation les plus pertinents.
"""
import sys
import json
import os
import urllib.request

QDRANT_URL  = os.environ.get("QDRANT_URL",  "http://127.0.0.1:6333")
EMBED_URL   = os.environ.get("EMBED_URL",   "http://192.168.10.20:1234/v1/embeddings")
EMBED_MODEL = os.environ.get("EMBED_MODEL", "qwen3-8b")
COLLECTION  = os.environ.get("RAG_COLLECTION", "funk-docs")
MIN_SCORE   = 0.60


def _post(url, data):
    req = urllib.request.Request(
        url,
        data=json.dumps(data).encode(),
        headers={"Content-Type": "application/json"},
        method="POST"
    )
    with urllib.request.urlopen(req, timeout=60) as r:
        return json.loads(r.read())


def embed(text):
    result = _post(EMBED_URL, {"model": EMBED_MODEL, "input": text})
    return result["data"][0]["embedding"]


def search(question, top=5):
    vector = embed(question)
    result = _post(f"{QDRANT_URL}/collections/{COLLECTION}/points/search", {
        "vector": vector,
        "limit": top,
        "with_payload": True,
        "score_threshold": MIN_SCORE,
    })
    return result["result"]


if __name__ == "__main__":
    if len(sys.argv) < 2:
        print("Usage: rag-query \"question\" [--top N]", file=sys.stderr)
        sys.exit(1)

    question = sys.argv[1]
    top = 5
    if "--top" in sys.argv:
        idx = sys.argv.index("--top")
        try:
            top = int(sys.argv[idx + 1])
        except (IndexError, ValueError):
            pass

    try:
        results = search(question, top)
    except Exception as e:
        print(f"ERREUR: {e}", file=sys.stderr)
        sys.exit(1)

    if not results:
        print(f"Aucun résultat pertinent pour : {question}")
        sys.exit(0)

    print(f"=== Contexte RAG — {len(results)} résultat(s) pour : {question} ===\n")
    for i, r in enumerate(results, 1):
        p = r["payload"]
        score = r["score"]
        print(f"--- [{i}] {p['file']} § {p['section']}  (score: {score:.3f}) ---")
        print(p["text"][:700])
        print()