"""Lettura del pgvector dietro un'unica interfaccia `.search(query_vec, top_n, kinds)`, così
`search.combined_search` resta agnostico al transport. Due implementazioni:

- `RestSearcher`  → produzione: similarity search via REST (`search_similar`).
- `DirectSearcher` → dev/test: connessione diretta a Postgres/pgvector.

Entrambe mappano i `kind` sulle tabelle per-dominio dello schema `vectors`.
"""

from sqlalchemy import Engine

from tht.adapters.vector.legacy_direct import LegacyDirectVectorStore
from tht.adapters.vector.thoth_http import ThothHttpVectorStore
from tht.vectorstore.rest_client import VectorRestClient
from tht.vectorstore.store import VectorHit

# kind Thoth → tabella dello schema `vectors`.
KIND_TO_TABLE = {
    "schema_table": "schema_records",
    "schema_column": "schema_records",
    "evidence": "evidence",
    "memory": "memory",
    "solved_question": "memory",  # coppie domanda->SQL: stessa tabella, kind dedicato
}
ALL_TABLES = ["schema_records", "evidence", "memory"]


def tables_for_kinds(kinds: list[str] | None) -> list[str]:
    """Tabelle da interrogare per i kind richiesti (tutte se kinds è vuoto/None)."""
    if not kinds:
        return list(ALL_TABLES)
    return sorted({KIND_TO_TABLE[k] for k in kinds if k in KIND_TO_TABLE})


class RestSearcher:
    """Similarity search via REST: una chiamata `search_similar` per tabella, poi fusione."""

    def __init__(self, client: VectorRestClient):
        self.client = client
        self._store = ThothHttpVectorStore(reader=client, writer=None)

    def search(
        self, query_vec: list[float], top_n: int = 10, kinds: list[str] | None = None
    ) -> list[VectorHit]:
        return self._store.search(
            tables_for_kinds(kinds), query_vec, limit=top_n, kinds=kinds
        )


class DirectSearcher:
    """Similarity search diretta su Postgres/pgvector, interrogando le tabelle per-dominio."""

    def __init__(self, engine: Engine, schema: str = "vectors", dim: int = 768):
        self.engine = engine
        self.schema = schema
        self.dim = dim
        self._store = LegacyDirectVectorStore(engine, schema=schema, dim=dim)

    def search(
        self, query_vec: list[float], top_n: int = 10, kinds: list[str] | None = None
    ) -> list[VectorHit]:
        return self._store.search(
            tables_for_kinds(kinds), query_vec, limit=top_n, kinds=kinds
        )