import axios from 'axios';
import { configDotenv } from 'dotenv';
import OpenAI from "openai";

configDotenv();

const openai = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

async function getEmbedding(text: string) {
  const embedding = await openai.embeddings.create({
    model: "text-embedding-ada-002",
    input: text,
    encoding_format: "float",
  });

  return embedding.data[0].embedding;
}

const cosineSimilarity = (vec1: number[], vec2: number[]): number => {
  const dotProduct = vec1.reduce((sum, val, i) => sum + val * vec2[i], 0);
  const magnitude1 = Math.sqrt(
    vec1.reduce((sum, val) => sum + val * val, 0)
  );
  const magnitude2 = Math.sqrt(
    vec2.reduce((sum, val) => sum + val * val, 0)
  );
  if (magnitude1 === 0 || magnitude2 === 0) return 0;
  return dotProduct / (magnitude1 * magnitude2);
};

// Function to convert text to vector
const textToVector = (searchQuery: string, text: string): number[] => {
  const words = searchQuery.toLowerCase().split(/\W+/);
  return words.map((word) => {
    const count = (text.toLowerCase().match(new RegExp(word, "g")) || [])
      .length;
    return count / text.length;
  });
};

async function performRanking(linksWithContext: string[], links: string[], searchQuery: string) {
  try {
    // Handle invalid inputs
    if (!searchQuery || !linksWithContext.length || !links.length) {
      return [];
    }

    // Sanitize search query by removing null characters
    const sanitizedQuery = searchQuery;

    // Generate embeddings for the search query
    const queryEmbedding = await getEmbedding(sanitizedQuery);

    // Generate embeddings for each link and calculate similarity
    const linksAndScores = await Promise.all(linksWithContext.map(async (linkWithContext, index) => {
      try {
        const linkEmbedding = await getEmbedding(linkWithContext);
        const score = cosineSimilarity(queryEmbedding, linkEmbedding);
        
        return { 
          link: links[index],
          linkWithContext,
          score,
          originalIndex: index
        };
      } catch (err) {
        // If embedding fails for a link, return with score 0
        return {
          link: links[index],
          linkWithContext,
          score: 0,
          originalIndex: index
        };
      }
    }));

    // Sort links based on similarity scores while preserving original order for equal scores
    linksAndScores.sort((a, b) => {
      const scoreDiff = b.score - a.score;
      return scoreDiff === 0 ? a.originalIndex - b.originalIndex : scoreDiff;
    });

    return linksAndScores;
  } catch (error) {
    console.error(`Error performing semantic search: ${error}`);
    return [];
  }
}

export { performRanking };