"""Supabase retriever utilities shared across agents/nodes."""
from functools import lru_cache
from typing import List, Optional
import os

from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings
from supabase import create_client, Client

from services.vectorstore_service import get_vector_store, PatchedSupabaseVectorStore

from config.settings import settings


@lru_cache(maxsize=4)
def get_retriever(
    doc_type: str,
    k: Optional[int] = None,
    similarity_threshold: Optional[float] = None,
):
    """Return a retriever for a given `doc_type` using Supabase vector store.

    Raises ValueError if Supabase credentials are missing.
    """
    url = settings.supabase_url or os.getenv("SUPABASE_URL")
    key = settings.supabase_key or (
        os.getenv("SUPABASE_KEY")
        or os.getenv("SUPABASE_SERVICE_ROLE_KEY")
        or os.getenv("SUPABASE_ANON_KEY")
        or os.getenv("NEXT_PUBLIC_SUPABASE_ANON_KEY")
    )
    if not url or not key:
        raise ValueError("SUPABASE_URL and a SUPABASE_*KEY env var are required.")

    client: Client = create_client(url, key)
    vector_store = PatchedSupabaseVectorStore(
        embedding=OpenAIEmbeddings(api_key=settings.openai_api_key),
        client=client,
        table_name=settings.supabase_table,
        query_name=settings.supabase_match_fn,
    )
    top_k = int(k or settings.rag_top_k)
    search_kwargs = {"k": top_k, "filter": {"type": doc_type}}
    if similarity_threshold is not None:
        search_kwargs["score_threshold"] = float(similarity_threshold)
        return vector_store.as_retriever(
            search_type="similarity_score_threshold",
            search_kwargs=search_kwargs,
        )
    return vector_store.as_retriever(search_kwargs=search_kwargs)


def get_retriever_for(
    index_name: str,
    k: Optional[int] = None,
    filter: Optional[dict] = None,
    similarity_threshold: Optional[float] = None,
):
    """Return a retriever for a specific logical index (table/query pair)."""
    vector_store = get_vector_store(index_name=index_name)
    top_k = int(k or settings.rag_top_k)
    search_kwargs = {"k": top_k, "filter": filter}
    if similarity_threshold is not None:
        search_kwargs["score_threshold"] = float(similarity_threshold)
        return vector_store.as_retriever(
            search_type="similarity_score_threshold",
            search_kwargs=search_kwargs,
        )
    return vector_store.as_retriever(search_kwargs=search_kwargs)


def format_documents(
    docs: List[Document], doc_type: str, max_chars_per_doc: int = 1200
) -> str:
    """Format documents into compact blocks suitable as system context."""
    blocks: List[str] = []
    for i, doc in enumerate(docs, 1):
        text = (doc.page_content or "")[:max_chars_per_doc]
        meta = doc.metadata or {}
        src = meta.get("source", "N/A")
        page = meta.get("page_number", "N/A")
        kind = meta.get("type", doc_type)
        contact = meta.get("contact", None)
        header = f"[{i}] source={src} page={page} type={kind}"
        if contact:
            header += f" contact={contact}"
        blocks.append(f"<document>\n{header}\n{text}</document>".strip())
    return "\n\n---\n\n".join(blocks)