import { createEmbeddingClientFromEnvironment, type EmbeddingEnvironment } from './embedding-client';
import { createQdrantRegulatoryIndexFromEnvironment, type QdrantEnvironment } from './qdrant-regulatory-index';
import { RegulatoryFindingRetrievalService } from './regulatory-finding-retrieval';
import {
  DenseRegulatoryCandidateRetriever,
  HybridRegulatoryCandidateRetriever,
  RegulatoryIngestionService,
  RegulatoryRetrievalPipeline,
  SparseRegulatoryCandidateRetriever
} from './regulatory-rag-service';

export interface RegulatoryRagEnvironment extends EmbeddingEnvironment, QdrantEnvironment {
  ASSESSOR_RAG_CHUNK_CHARACTERS?: string;
  ASSESSOR_RAG_EMBEDDING_BATCH_SIZE?: string;
}

export function createRegulatoryRagRuntime(
  environment: RegulatoryRagEnvironment = {
    ASSESSOR_QDRANT_URL: process.env.ASSESSOR_QDRANT_URL,
    ASSESSOR_QDRANT_API_KEY: process.env.ASSESSOR_QDRANT_API_KEY,
    ASSESSOR_QDRANT_TIMEOUT_MS: process.env.ASSESSOR_QDRANT_TIMEOUT_MS,
    ASSESSOR_RAG_EMBEDDING_BASE_URL: process.env.ASSESSOR_RAG_EMBEDDING_BASE_URL,
    ASSESSOR_RAG_EMBEDDING_MODEL: process.env.ASSESSOR_RAG_EMBEDDING_MODEL,
    ASSESSOR_RAG_EMBEDDING_API_KEY: process.env.ASSESSOR_RAG_EMBEDDING_API_KEY,
    ASSESSOR_RAG_EMBEDDING_DIMENSIONS: process.env.ASSESSOR_RAG_EMBEDDING_DIMENSIONS,
    ASSESSOR_RAG_EMBEDDING_REQUEST_DIMENSIONS: process.env.ASSESSOR_RAG_EMBEDDING_REQUEST_DIMENSIONS,
    ASSESSOR_RAG_EMBEDDING_TIMEOUT_MS: process.env.ASSESSOR_RAG_EMBEDDING_TIMEOUT_MS,
    ASSESSOR_RAG_CHUNK_CHARACTERS: process.env.ASSESSOR_RAG_CHUNK_CHARACTERS,
    ASSESSOR_RAG_EMBEDDING_BATCH_SIZE: process.env.ASSESSOR_RAG_EMBEDDING_BATCH_SIZE
  }
): {
  ingestion: RegulatoryIngestionService;
  retrieval: RegulatoryRetrievalPipeline;
  findingRetrieval: RegulatoryFindingRetrievalService;
  index: ReturnType<typeof createQdrantRegulatoryIndexFromEnvironment>;
  embeddingModel: string;
} {
  const embeddings = createEmbeddingClientFromEnvironment(environment);
  const index = createQdrantRegulatoryIndexFromEnvironment(embeddings.dimensions, embeddings.model, environment);
  const dense = new DenseRegulatoryCandidateRetriever(embeddings, index);
  const sparse = new SparseRegulatoryCandidateRetriever(index);
  const retrieval = new RegulatoryRetrievalPipeline(new HybridRegulatoryCandidateRetriever(dense, sparse));
  return {
    ingestion: new RegulatoryIngestionService(embeddings, index, {
      maximumChunkCharacters: boundedInteger(environment.ASSESSOR_RAG_CHUNK_CHARACTERS, 1_600, 200, 20_000, 'ASSESSOR_RAG_CHUNK_CHARACTERS'),
      embeddingBatchSize: boundedInteger(environment.ASSESSOR_RAG_EMBEDDING_BATCH_SIZE, 32, 1, 128, 'ASSESSOR_RAG_EMBEDDING_BATCH_SIZE')
    }),
    retrieval,
    findingRetrieval: new RegulatoryFindingRetrievalService(retrieval),
    index,
    embeddingModel: embeddings.model
  };
}

function boundedInteger(value: string | undefined, fallback: number, minimum: number, maximum: number, name: string): number {
  if (!value?.trim()) return fallback;
  const parsed = Number(value);
  if (!Number.isInteger(parsed) || parsed < minimum || parsed > maximum) throw new Error(`${name} must be an integer between ${minimum} and ${maximum}.`);
  return parsed;
}