import { describe, expect, it } from 'vitest';
import { createQdrantRegulatoryIndexFromEnvironment, REGULATORY_QDRANT_COLLECTION } from '../../server/regulatory/qdrant-regulatory-index';
import { DenseRegulatoryCandidateRetriever, RegulatoryIngestionService } from '../../server/regulatory/regulatory-rag-service';
import type { RegulatoryControlDocument } from '../../src/domain/regulatory-rag';
import type { TextEmbeddingClient } from '../../server/regulatory/embedding-client';

const runIntegration = process.env.ASSESSOR_QDRANT_INTEGRATION === '1';
const describeQdrant = runIntegration ? describe : describe.skip;

describeQdrant('Qdrant regulatory index integration', () => {
  it('creates, replaces, filters, and retrieves a synthetic control', async () => {
    const dimensions = Number(process.env.ASSESSOR_RAG_EMBEDDING_DIMENSIONS);
    const model = process.env.ASSESSOR_RAG_EMBEDDING_MODEL ?? '';
    if (!Number.isInteger(dimensions) || dimensions < 64 || !model) throw new Error('Configured embedding dimensions and model are required.');
    const vector = Array.from({ length: dimensions }, (_, index) => index === 0 ? 1 : 0);
    const embeddings: TextEmbeddingClient = { model, dimensions, embed: async texts => texts.map(() => vector) };
    const index = createQdrantRegulatoryIndexFromEnvironment(dimensions, model);
    const ingestion = new RegulatoryIngestionService(embeddings, index);
    const document: RegulatoryControlDocument = {
      schemaVersion: 1,
      corpusId: 'assessor-qdrant-integration',
      corpusVersion: '1',
      documentId: 'synthetic-live-control-de',
      framework: 'ASSESSOR Synthetic Controls',
      frameworkVersion: '1',
      controlId: 'SYN-LIVE',
      language: 'de',
      title: 'Synthetische Livekontrolle',
      text: 'Dieser synthetische Text prueft ausschliesslich den lokalen rebuildbaren Qdrant-Index.'
    };

    try {
      await expect(ingestion.ingest([document])).resolves.toMatchObject({ documentCount: 1, chunkCount: 1, embeddingModel: model });
      const retrieval = new DenseRegulatoryCandidateRetriever(embeddings, index);
      const candidates = await retrieval.retrieveCandidates({
        query: 'synthetic local index check',
        limit: 3,
        filters: { corpusId: document.corpusId, framework: document.framework, language: 'de' }
      });

      expect(candidates).toHaveLength(1);
      expect(candidates[0]).toMatchObject({ chunk: {
        corpusId: document.corpusId,
        documentId: document.documentId,
        controlId: document.controlId,
        language: 'de'
      } });
    } finally {
      await deleteSyntheticCorpus(document.corpusId);
    }
  });
});

async function deleteSyntheticCorpus(corpusId: string): Promise<void> {
  const baseUrl = process.env.ASSESSOR_QDRANT_URL;
  const apiKey = process.env.ASSESSOR_QDRANT_API_KEY;
  if (!baseUrl) return;
  const response = await fetch(new URL(`/collections/${REGULATORY_QDRANT_COLLECTION}/points/delete?wait=true`, baseUrl), {
    method: 'POST',
    headers: { 'Content-Type': 'application/json', ...(apiKey ? { 'api-key': apiKey } : {}) },
    body: JSON.stringify({ filter: { must: [{ key: 'corpus_id', match: { value: corpusId } }] } })
  });
  if (!response.ok) throw new Error(`Synthetic Qdrant integration cleanup failed with HTTP ${response.status}.`);
}