"""Vector store service wrapping SupabaseVectorStore and embeddings.

Centralizes initialization to keep routes/services clean and consistent.
"""
from __future__ import annotations

import json
import warnings
from functools import lru_cache
from typing import Any, Dict, List, Optional, Tuple
import os

from langchain_core.documents import Document
from langchain_mistralai import MistralAIEmbeddings
from langchain_community.vectorstores import SupabaseVectorStore
from supabase import create_client, Client

from config.settings import settings

_DEBUG_LOG = r"c:\Users\cd\Documents\CAPL\ROUTEUR\dev\routeur_ia_api\.cursor\debug.log"


class VectorStoreServiceError(Exception):
    pass


class PatchedSupabaseVectorStore(SupabaseVectorStore):
    """Fixes postgrest 2.28+ incompatibility where .params moved to .request.params."""

    def similarity_search_by_vector_with_relevance_scores(
        self,
        query: List[float],
        k: int,
        filter: Optional[Dict[str, Any]] = None,
        postgrest_filter: Optional[str] = None,
        score_threshold: Optional[float] = None,
    ) -> List[Tuple[Document, float]]:
        if filter:
            for key, value in filter.items():
                if isinstance(value, dict) and "$in" in value:
                    in_values = value["$in"]
                    values_str = ",".join(f"'{str(v)}'" for v in in_values)
                    new_filter = f"metadata->>{key} IN ({values_str})"
                    if postgrest_filter:
                        postgrest_filter = f"({postgrest_filter}) and ({new_filter})"
                    else:
                        postgrest_filter = new_filter

        match_documents_params = self.match_args(query, filter)
        query_builder = self._client.rpc(self.query_name, match_documents_params)

        # #region agent log
        import time as _t
        with open(_DEBUG_LOG, "a", encoding="utf-8") as _f:
            _f.write(json.dumps({"id": "log_patch_1", "timestamp": int(_t.time()*1000), "location": "vectorstore_service.py:PatchedSupabaseVectorStore", "message": "Using patched similarity_search", "data": {"k": k, "has_postgrest_filter": bool(postgrest_filter), "query_builder_type": type(query_builder).__name__, "has_request_attr": hasattr(query_builder, "request")}, "hypothesisId": "A"}) + "\n")
        # #endregion

        if postgrest_filter:
            query_builder.request.params = query_builder.request.params.set(
                "and", f"({postgrest_filter})"
            )

        query_builder.request.params = query_builder.request.params.set("limit", k)

        res = query_builder.execute()

        # #region agent log
        with open(_DEBUG_LOG, "a", encoding="utf-8") as _f:
            _f.write(json.dumps({"id": "log_patch_2", "timestamp": int(_t.time()*1000), "location": "vectorstore_service.py:PatchedSupabaseVectorStore", "message": "RPC execute success", "data": {"result_count": len(res.data) if res.data else 0}, "hypothesisId": "A"}) + "\n")
        # #endregion

        match_result = [
            (
                Document(
                    metadata=search.get("metadata", {}),
                    page_content=search.get("content", ""),
                ),
                search.get("similarity", 0.0),
            )
            for search in res.data
            if search.get("content")
        ]

        if score_threshold is not None:
            match_result = [
                (doc, similarity)
                for doc, similarity in match_result
                if similarity >= score_threshold
            ]
            if len(match_result) == 0:
                warnings.warn(
                    "No relevant docs were retrieved using the relevance score"
                    f" threshold {score_threshold}"
                )

        return match_result


@lru_cache(maxsize=1)
def _get_supabase_client() -> Client:
    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 VectorStoreServiceError("SUPABASE_URL and a SUPABASE_*KEY env var are required.")
    return create_client(url, key)


@lru_cache(maxsize=1)
def _get_embeddings() -> MistralAIEmbeddings:
    return MistralAIEmbeddings(model="mistral-embed", api_key=settings.mistralai_api_key)


def _resolve_table_and_query(
    index_name: Optional[str],
    table_name: Optional[str],
    query_name: Optional[str],
) -> Tuple[str, str]:
    # Explicit beats implicit
    if table_name and query_name:
        return table_name, query_name

    # Named logical index
    if index_name:
        idx = settings.vector_indexes.get(index_name)
        if not idx:
            raise VectorStoreServiceError(f"Unknown vector index '{index_name}'. Configure settings.vector_indexes.")
        return idx["table"], idx["query_name"]

    # Backward-compatible default
    return settings.supabase_table, settings.supabase_match_fn


@lru_cache(maxsize=16)
def get_vector_store(
    index_name: Optional[str] = None,
    *,
    table_name: Optional[str] = None,
    query_name: Optional[str] = None,
) -> PatchedSupabaseVectorStore:
    client = _get_supabase_client()
    emb = _get_embeddings()
    table, query = _resolve_table_and_query(index_name, table_name, query_name)
    return PatchedSupabaseVectorStore(
        embedding=emb,
        client=client,
        table_name=table,
        query_name=query,
    )


def add_documents(
    documents: List[Document],
    index_name: Optional[str] = None,
    *,
    table_name: Optional[str] = None,
    query_name: Optional[str] = None,
) -> List[str]:
    if not documents:
        return []
    vs = get_vector_store(index_name, table_name=table_name, query_name=query_name)
    return vs.add_documents(documents)