# ruff: noqa: BLE001
from pathlib import Path

import typer

from tht.cli._guards import require_server_profile, require_vector_write_allowed
from tht.cli.config_cmd import CONFIG_OPT
from tht.cli.schema_cmd import _load_config_or_exit, annotations_path, physical_path
from tht.ports.vector import VectorWriteRecord
from tht.vectorstore.store import SyncStats, content_hash

vector_app = typer.Typer(help="Indice semantico Qdrant (derivato, rigenerabile)")


def make_embedder(embeddings_cfg):
    """Factory del client embeddings (monkeypatchabile nei test)."""
    from tht.vectorstore.embeddings import OllamaEmbeddings

    return OllamaEmbeddings(embeddings_cfg)


def require_vector_cfg(cfg):
    missing = []
    if cfg.embeddings is None:
        missing.append("embeddings")
    if cfg.vectors is None:
        missing.append("vectors")
    if missing:
        typer.secho(
            f"ERRORE: sezioni mancanti nel workspace yaml: {', '.join(missing)}.",
            fg=typer.colors.RED, err=True,
        )
        raise typer.Exit(code=1)


def open_searcher(cfg):
    """Searcher per la lettura semantic search sul runtime vettoriale attivo."""
    from tht.adapters.factory import build_vector_store
    from tht.vectorstore.reader import tables_for_kinds

    store = build_vector_store(cfg)

    class AdapterSearcher:
        def search(self, query_vec, top_n=10, kinds=None, metadata_filter=None):
            return store.search(
                tables_for_kinds(kinds), query_vec, limit=top_n, kinds=kinds,
                metadata_filter=metadata_filter,
            )

    return AdapterSearcher()


def sync_canonical_records(collection, records, *, store, embedder):
    kinds = sorted({record.kind for record in records})
    existing = store.existing_hashes(collection, kinds)
    pending = []
    stats = SyncStats()
    changed = []
    for record in records:
        hashed = content_hash(record.content)
        current = existing.get(record.id)
        if current == hashed:
            stats.unchanged += 1
            continue
        changed.append((record, hashed, current is None))
    if changed:
        embeddings = embedder.embed_documents([record.content for record, *_ in changed])
        for (record, hashed, is_added), embedding in zip(changed, embeddings, strict=True):
            pending.append(VectorWriteRecord(record=record, embedding=embedding, content_hash=hashed))
            if is_added:
                stats.added += 1
            else:
                stats.updated += 1
        store.upsert(collection, pending)
    return stats


def _print_stats(stats) -> None:
    typer.secho(
        f"OK: {stats.added} nuovi, {stats.updated} aggiornati, "
        f"{stats.deleted} rimossi, {stats.unchanged} invariati",
        fg=typer.colors.GREEN,
    )


@vector_app.command("init")
def init_cmd(
    config: Path = CONFIG_OPT,
    skip_ollama_check: bool = typer.Option(
        False, "--skip-ollama-check", help="Non verificare la raggiungibilita' di Ollama."
    ),
) -> None:
    """Verifica il runtime Qdrant e la raggiungibilita' dell'embedder configurato."""
    from tht.adapters.factory import build_vector_store
    from tht.vectorstore.embeddings import EmbeddingsError

    cfg = _load_config_or_exit(config)
    require_server_profile(cfg, "vector init")
    require_vector_cfg(cfg)
    health = build_vector_store(cfg, require_write=True).health()
    if not health.ok:
        typer.secho(
            f"ERRORE runtime vettoriale: {health.detail or 'Qdrant non raggiungibile o incompatibile'}",
            fg=typer.colors.RED,
            err=True,
        )
        raise typer.Exit(code=1)
    if not skip_ollama_check:
        try:
            make_embedder(cfg.embeddings).embed_query("ping")
        except EmbeddingsError as e:
            typer.secho(f"ERRORE: {e}", fg=typer.colors.RED, err=True)
            raise typer.Exit(code=1)
    typer.secho(
        f"OK: runtime Qdrant pronto per la collezione {cfg.vectors.collection}",
        fg=typer.colors.GREEN,
    )


def index_schema_data(config: Path) -> dict:
    """Synchronize schema records and return a bounded machine result."""
    from tht.cli.schema_cmd import _machine_config
    from tht.mschema.models import Annotations, PhysicalSchema
    from tht.vectorstore.records import schema_records

    cfg = _machine_config(config)
    require_vector_write_allowed(cfg, "vector index-schema")
    require_vector_cfg(cfg)
    phys_file = physical_path(cfg)
    if not phys_file.exists():
        raise ValueError("physical schema missing")
    physical = PhysicalSchema.from_yaml(phys_file)
    annotations = Annotations.from_yaml(annotations_path(cfg))
    records = schema_records(physical, annotations)
    from tht.adapters.factory import build_vector_store

    stats = sync_canonical_records(
        "schema_records",
        records,
        store=build_vector_store(cfg, require_write=True),
        embedder=make_embedder(cfg.embeddings),
    )
    return {
        "status": "succeeded",
        "code": "ok",
        "counts": {
            "added": stats.added,
            "updated": stats.updated,
            "deleted": stats.deleted,
            "unchanged": stats.unchanged,
        },
    }


@vector_app.command("index-schema")
def index_schema_cmd(
    config: Path = CONFIG_OPT,
    json_output: bool = typer.Option(False, "--json", help="Emetti JSON puro su stdout."),
) -> None:
    """Embedda e sincronizza i record schema (tabelle e colonne) nel semantic store."""
    if json_output:
        try:
            payload = index_schema_data(config)
        except Exception:
            payload = {"status": "failed", "code": "schema_index_failed"}
            typer.echo(__import__("json").dumps(payload, sort_keys=True, separators=(",", ":")))
            raise typer.Exit(code=1) from None
        typer.echo(__import__("json").dumps(payload, sort_keys=True, separators=(",", ":")))
        return
    payload = index_schema_data(config)
    _print_stats(type("Stats", (), payload["counts"])())