"""Opt-in acceptance against an actual OpenAI-compatible embedding endpoint."""

import os

import pytest

from deerflow.config.llmwiki_config import LocalWikiEmbeddingConfig, LocalWikiIndexConfig
from deerflow.integrations.weknora.local_index.embedding import StrictWikiEmbeddingClient
from deerflow.integrations.weknora.local_index.normalize import normalize_wiki_page
from deerflow.integrations.weknora.local_index.search import WikiVectorSearchService
from deerflow.integrations.weknora.local_index.vector_cache import WikiVectorCache
from deerflow.integrations.weknora.local_index.vector_codec import encode_vector
from deerflow.persistence.llmwiki_index.memory import MemoryLlmWikiIndexStore


@pytest.mark.asyncio
@pytest.mark.skipif(not os.getenv("KNOWLEDGE_TEST_EMBEDDING_KEY"), reason="Live embedding credentials not configured")
async def test_real_embedding_search_preserves_library_scope_and_excludes_drafts():
    client = StrictWikiEmbeddingClient(
        LocalWikiEmbeddingConfig(
            provider="openai_compatible",
            base_url=os.getenv("KNOWLEDGE_TEST_EMBEDDING_URL", "https://api.siliconflow.cn/v1"),
            model=os.getenv("KNOWLEDGE_TEST_EMBEDDING_MODEL", "BAAI/bge-m3"),
            api_key=os.environ["KNOWLEDGE_TEST_EMBEDDING_KEY"],
            dimensions=1024,
        )
    )
    content = "设备过热时应先断电，再检查风道和散热风扇。"
    try:
        vectors = await client.embed_texts([content, "读书交流活动通过分享心得促进阅读。"])
        assert [len(vector) for vector in vectors] == [1024, 1024]
        store = MemoryLlmWikiIndexStore()
        for base_id in ("selected", "other"):
            await store.try_acquire_sync_lease(base_id, owner="test", sync_id="sync", lease_seconds=60, fingerprint=client.fingerprint)
            for slug, status, index in (("cooling", "published", 0), ("books", "published", 1), ("draft", "draft", 0)):
                page = normalize_wiki_page(base_id, {"slug": slug, "title": slug, "content": content if index == 0 else "读书交流活动", "status": status})
                blob, _ = encode_vector(vectors[index], dimensions=1024)
                await store.replace_page_vectors(
                    page=page,
                    vectors=[
                        {
                            "section_index": 0,
                            "heading": slug,
                            "section_content": content if index == 0 else "读书交流活动",
                            "content_hash": slug,
                            "embedding_dimensions": 1024,
                            "vector_blob": blob,
                        }
                    ],
                    fingerprint=client.fingerprint,
                    sync_id="sync",
                )
        service = WikiVectorSearchService(LocalWikiIndexConfig(min_similarity=0, top_k_sections=10, top_k_pages=8), client, WikiVectorCache(store, revision_check_seconds=0))
        result = await service.search("设备太热应该如何处理？", [{"id": "selected", "name": "验收设备库"}])
        assert result["results"][0]["wiki_slug"] == "cooling"
        assert {row["wiki_slug"] for row in result["results"]} == {"cooling", "books"}
        assert all("selected" in row["url"] for row in result["results"])
        overview = await service.search("此知识库都讲了什么？", [{"id": "selected", "name": "验收设备库"}])
        assert overview["mode"] == "overview"
        assert overview["results"]
    finally:
        await client.close()