export const querySimilarTechnologies = async (
  inputSkill: string,
  topK: number = 10000,
) => {
  try {
    console.log(
      `[1] Starting search for similar technologies to: "${inputSkill}"`,
    );

    // Step 1: Generate embedding for the input skill
    const embedding = await getEmbedding(inputSkill);
    console.log(`[2] Embedding generated for: "${inputSkill}"`);

    // Step 2: Perform similarity search directly in PostgreSQL
    const similarity = sql<number>`1 - (${cosineDistance(skills.vector, embedding)})`;

    const similarSkills = await db
      .select({
        technology: skills.skill,
        similarity,
      })
      .from(skills)
      .orderBy(cosineDistance(skills.vector, embedding))
      .limit(topK);

    console.log(
      `[3] Retrieved ${similarSkills.length} similar technologies after similarity search.`,
    );

    // Optional: Filter based on a threshold if necessary
    /*
    const threshold = 0.7;
    const filteredSimilarities = similarSkills.filter(s => s.similarity >= threshold);
    console.log(`[4] Found ${filteredSimilarities.length} similar technologies after filtering.`);
    */

    // Return the similar technologies with similarity scores
    const result = similarSkills.map((s) => ({
      technology: s.technology,
      score: parseFloat(s.similarity.toFixed(6)),
    }));

    console.log(`[5] Returning ${result.length} similar technologies.`);
    return result;
  } catch (error) {
    console.error("Error querying similar technologies:", error);
    return [];
  }
};

export const querySimilarTechnologies = async (
  inputSkill: string,
  topK: number = 10000,
) => {
  try {
    console.log(
      `[1] Starting search for similar technologies to: "${inputSkill}"`,
    );

    // Step 1: Generate embedding for the input skill
    const embedding = await getEmbedding(inputSkill);
    console.log(`[2] Embedding generated for: "${inputSkill}"`);

    // Step 2: Perform similarity search directly in PostgreSQL
    const similarity = sql<number>`1 - (${cosineDistance(skills.vector, embedding)})`;

    const similarSkills = await db
      .select({
        technology: skills.skill,
        similarity,
      })
      .from(skills)
      .orderBy(cosineDistance(skills.vector, embedding))
      .limit(topK);

    console.log(
      `[3] Retrieved ${similarSkills.length} similar technologies after similarity search.`,
    );

    // Optional: Filter based on a threshold if necessary
    /*
    const threshold = 0.7;
    const filteredSimilarities = similarSkills.filter(s => s.similarity >= threshold);
    console.log(`[4] Found ${filteredSimilarities.length} similar technologies after filtering.`);
    */

    // Return the similar technologies with similarity scores
    const result = similarSkills.map((s) => ({
      technology: s.technology,
      score: parseFloat(s.similarity.toFixed(6)),
    }));

    console.log(`[5] Returning ${result.length} similar technologies.`);
    return result;
  } catch (error) {
    console.error("Error querying similar technologies:", error);
    return [];
  }
};

const news = await ctx.db.query.newsTable.findMany({
        limit: 20,
        offset: 20 * input.page,
        orderBy: (newsTable, { desc }) => [desc(newsTable.createdAt)],
        with: {
          tweets: {
            with: { pundit: true },
          },
        },
      });

const news = await ctx.db.query.newsTable.findMany({
        limit: 20,
        offset: 20 * input.page,
        orderBy: (newsTable, { desc }) => [desc(newsTable.createdAt)],
        with: {
          tweets: {
            with: { pundit: true },
          },
        },
      });