# -*- coding: utf-8 -*-
import argparse, json, os, glob
from pathlib import Path
import faiss
from sentence_transformers import SentenceTransformer
import numpy as np

def gather_docs():
    items=[]
    # JSON examples/snippets
    for fp in glob.glob("kb/examples/*.json")+glob.glob("kb/snippets/json/*.json"):
        try:
            txt = Path(fp).read_text(encoding="utf-8")
            items.append(("json", fp, txt))
        except: pass
    # GLM snippets as text
    for fp in glob.glob("kb/snippets/glm/*"):
        try:
            if Path(fp).suffix.lower() in {".glm",".md",""}:
                txt = Path(fp).read_text(encoding="utf-8")
                items.append(("glm", fp, txt))
        except: pass
    # cheatsheet + semantics
    for fp in ["kb/cheatsheets/property_glossary.md","kb/specs/point_semantics.json"]:
        if Path(fp).exists():
            items.append(("text", fp, Path(fp).read_text(encoding="utf-8")))
    return items

def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--model", default="sentence-transformers/all-MiniLM-L6-v2")  # small + fast
    ap.add_argument("--outdir", default="rag_index")
    args = ap.parse_args()

    os.makedirs(args.outdir, exist_ok=True)
    docs = gather_docs()
    if not docs: raise SystemExit("No docs to index.")
    model = SentenceTransformer(args.model)
    corpus = [f"[{k}] {p}\n{t}" for (k,p,t) in docs]
    emb = model.encode(corpus, normalize_embeddings=True, convert_to_numpy=True, show_progress_bar=True)
    dim = emb.shape[1]
    index = faiss.IndexFlatIP(dim)
    index.add(emb)
    faiss.write_index(index, str(Path(args.outdir, "kb.faiss")))
    Path(args.outdir, "kb_meta.json").write_text(json.dumps(
        {"paths":[p for (_,p,_) in docs], "model": args.model}, indent=2), encoding="utf-8")
    print("[ok] indexed", len(docs), "docs to", Path(args.outdir,"kb.faiss").resolve())

if __name__ == "__main__":
    main()