#!/usr/bin/env python3
"""(Re)construieste rag_index.json din toate documentele din depozit (store.py),
cu embeddings Ollama. Rulat manual sau declansat din dashboard (`/api/reindex`)."""

from __future__ import annotations

import json
import re
import sys

import requests

import config
import store


def chunk_text(text: str) -> list[str]:
    # imparte pe linii goale in paragrafe, uneste bucatile mici cu urmatoarea
    raw_parts = re.split(r"\n\s*\n", text.strip())
    chunks: list[str] = []
    buffer = ""
    for part in raw_parts:
        part = part.strip()
        if not part:
            continue
        buffer = f"{buffer}\n\n{part}" if buffer else part
        if len(buffer) >= 200:
            chunks.append(buffer)
            buffer = ""
    if buffer:
        chunks.append(buffer)
    return chunks


def embed(text: str) -> list[float]:
    resp = requests.post(
        f"{config.get('OLLAMA_URL')}/api/embeddings",
        json={"model": config.get("EMBED_MODEL"), "prompt": text},
        timeout=60,
    )
    resp.raise_for_status()
    return resp.json()["embedding"]


def build() -> dict:
    entries = []
    docs = store.list_documents()
    for doc in docs:
        text = store.read_document(doc["name"])
        for i, chunk in enumerate(chunk_text(text)):
            vec = embed(chunk)
            entries.append({"source": doc["name"], "chunk": i, "text": chunk, "embedding": vec})
    config.STATE_DIR.mkdir(parents=True, exist_ok=True)
    config.INDEX_FILE.write_text(json.dumps(entries, ensure_ascii=False), encoding="utf-8")
    return {"documents": len(docs), "chunks": len(entries)}


if __name__ == "__main__":
    result = build()
    print(
        f"[indexer] {result['documents']} documente, {result['chunks']} chunk-uri -> {config.INDEX_FILE}",
        file=sys.stderr,
    )