import { Injectable, OnModuleInit } from '@nestjs/common';
import { pipeline, FeatureExtractionPipeline } from '@xenova/transformers';
import { MongoCoreService } from 'src/mongo/mongo-core.service';
import { FFBProductionRepository } from 'src/mongo/mongo-ffb-production.repository';
import { FFBProduction } from './ffb-production.schema';

@Injectable()
export class FFBVectorService implements OnModuleInit {
  private embedder: FeatureExtractionPipeline;
  private repo: FFBProductionRepository;
  private readonly VECTOR_DIM = 384; // must match your index

  constructor(private readonly mongoCore: MongoCoreService) { }

  /** Initialize model and repository at module startup */
  async onModuleInit() {
    const modelName = process.env.EMBEDDING_MODEL || 'Xenova/all-MiniLM-L6-v2';
    console.log(`🔹 Loading embedding model: ${modelName}...`);

    this.embedder = (await pipeline('feature-extraction', modelName)) as unknown as FeatureExtractionPipeline;

    const db = await this.mongoCore.getDb();
    this.repo = new FFBProductionRepository(db);
    await this.repo.init();

    console.log(`✅ Embedding model loaded and repository ready.`);
  }

  /** Convert an FFBProduction record into text for embedding */
  private recordToText(record: FFBProduction): string {
    return `Production on ${new Date(record.productionDate).toISOString()} at ${record.site} in ${record.phase} ${record.block} produced ${record.quantity} ${record.quantityUom} with a total weight of ${record.weight} ${record.weightUom}.`;
  }

  /** Generate embedding vector from text */
  private async embedText(text: string): Promise<number[]> {
    const output = await this.embedder(text, { pooling: 'mean', normalize: true });
    const vector = Array.from(output.data);
    if (vector.length !== this.VECTOR_DIM) {
      throw new Error(`Embedding dimension mismatch. Expected ${this.VECTOR_DIM}, got ${vector.length}`);
    }
    return vector;
  }

  /** Insert a single record with embedding vector */
  async insertWithVector(record: FFBProduction) {
    const text = this.recordToText(record);
    const vector = await this.embedText(text);

    // Explicitly tell TypeScript this object matches the repository type
    const data: FFBProduction & { vector: number[] } = { ...record, vector };
    return this.repo.create(data);
  }

  /** Search for top-k similar records using a text query */
  async vectorSearch(query: string, k = 5) {
    if (!query) throw new Error('Query string cannot be empty');

    // Step 1: Embed the query text
    const vector = await this.embedText(query);

    // Step 2: Use repository aggregation for vector search
    const results = await this.repo.vectorSearch(vector, k, 50); // numCandidates = 50
    // Step 3: Return results directly (they now include the full document + score)
    return results.map(r => ({
      ...r,      // all FFBProduction fields
      _id: r._id.toString(), // convert ObjectId to string if needed
      score: r.score           // similarity score
    }));
  }

}