const { QdrantClient } = require('@qdrant/js-client-rest');
const axios = require('axios');
require('dotenv').config();

const QDRANT_URL = process.env.QDRANT_URL;
const QDRANT_API_KEY = process.env.QDRANT_API_KEY;
const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const VECTOR_SIZE = 1536; // OpenAI Ada embeddings size

const COLLECTION_NAME = 'entities';

let client = null;

// Initialize Qdrant client
function initClient() {
  client = new QdrantClient({
    url: QDRANT_URL,
    apiKey: QDRANT_API_KEY
  });
}

// Create or recreate collection
async function createCollection() {
  if (!client) initClient();
  try {
    await client.recreateCollection(COLLECTION_NAME, {
      vectors: {
        size: VECTOR_SIZE,
        distance: 'Cosine'
      }
    });
    console.log(`Collection '${COLLECTION_NAME}' created/recreated.`);
  } catch (err) {
    console.error('Error creating collection:', err.message);
    throw err;
  }
}

// Get embedding from OpenAI
async function getEmbedding(text) {
  try {
    const response = await axios.post(
      'https://api.openai.com/v1/embeddings',
      {
        input: text,
        model: 'text-embedding-ada-002'
      },
      {
        headers: {
          'Content-Type': 'application/json',
          Authorization: `Bearer ${OPENAI_API_KEY}`
        }
      }
    );
    if (response.data && response.data.data && response.data.data[0]) {
      return response.data.data[0].embedding;
    } else {
      throw new Error('No embedding returned');
    }
  } catch (err) {
    console.error('Error getting embedding:', err.message);
    throw err;
  }
}

// Upsert embeddings for an entity
async function upsertEmbeddings(entity, text) {
  if (!client) initClient();
  try {
    const vector = await getEmbedding(text);
    const point = {
      id: entity,
      vector,
      payload: {
        entity,
        text
      }
    };
    await client.upsertPoints(COLLECTION_NAME, {
      points: [point]
    });
    console.log(`Upserted embeddings for entity '${entity}'.`);
  } catch (err) {
    console.error('Error upserting embeddings:', err.message);
    throw err;
  }
}

// Search embeddings
async function searchEmbeddings(query, limit = 3) {
  if (!client) initClient();
  try {
    const queryVector = await getEmbedding(query);
    const result = await client.search(COLLECTION_NAME, {
      vector: queryVector,
      limit,
      with_payload: true,
      with_vector: false
    });
    return result.hits.map((hit) => ({
      id: hit.id,
      score: hit.score,
      payload: hit.payload
    }));
  } catch (err) {
    console.error('Error searching embeddings:', err.message);
    throw err;
  }
}

module.exports = {
  initClient,
  createCollection,
  upsertEmbeddings,
  searchEmbeddings
};