import Anthropic from "@anthropic-ai/sdk";
import { pipeline } from "node:stream/promises";
import { Transform, Writable } from "node:stream";
import { embedNotes } from "./embedding.service.js";
import { validateStructure, computeCohesionScore } from "./validation.service.js";

const client = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY,
});

const buildPrompt = (notes) => {
  const notesJson = JSON.stringify(notes, null, 2);

  return `You are an expert at analyzing text for semantic similarity and thematic patterns.

Below is a JSON array of sticky notes. Each note has an "id" and a "text" field. Analyze the "text" field of every note and group them into meaningful thematic clusters.

For each cluster, return ONLY a valid JSON array with the below exact structure — no markdown, no explanation, no extra text - where the value for the "label" key is a name you create to describe the cluster's theme and the value for the "noteIds" key is an array containing the Ids of the notes that fit into that cluster theme.

[
  {
    "label": "Short descriptive theme name for cluster",
    "noteIds": ["note_001", "note_002"]
  }
]

Rules:
- Every note must appear in exactly one cluster
- Each cluster must have a concise, descriptive label
- Group by semantic meaning, not by keywords
- Aim for the most natural number of groups given the data

Here are the notes:

${notesJson}`;
};

const textDeltas = () => new Transform({
  objectMode: true,
  transform(event, _encoding, callback) {
    if (event?.type === 'content_block_delta' && event.delta?.type === 'text_delta') {
      callback(null, event.delta.text);
      return;
    }
    callback();
  },
});

// Rejects as soon as the first non-whitespace character proves the response
// is not the JSON array we asked for, rather than after the full generation.
const collectClusterJson = (sink) => new Writable({
  objectMode: true,
  write(text, _encoding, callback) {
    if (!sink.sawOpeningBracket) {
      const leading = (sink.parts.join('') + text).trimStart();
      if (leading.length > 0) {
        if (!leading.startsWith('[')) {
          callback(new Error('LLM API returned non-JSON response'));
          return;
        }
        sink.sawOpeningBracket = true;
      }
    }
    sink.parts.push(text);
    callback();
  },
});

const requestClusters = async (notes, signal) => {
  const sink = { parts: [], sawOpeningBracket: false };

  const options = signal ? [{ signal }] : [];

  const events = client.messages.stream(
    {
      model: "claude-sonnet-5",
      max_tokens: 4096,
      messages: [
        { role: "user", content: buildPrompt(notes) },
      ],
    },
    ...options
  );

  await pipeline(events, textDeltas(), collectClusterJson(sink), ...options);

  const text = sink.parts.join('');

  if (text.trim().length === 0) {
    throw new Error('Unexpected response from LLM API: no text content returned');
  }

  try {
    return JSON.parse(text);
  } catch {
    throw new Error('LLM API returned non-JSON response');
  }
};

export const clusterNotes = async (notes, { signal } = {}) => {
  const [clusters, embeddingMap] = await Promise.all([
    requestClusters(notes, signal),
    embedNotes(notes),
  ]);

  const noteIds = notes.map((n) => n.id);
  const { valid, reasons } = validateStructure(clusters, noteIds);
  if (!valid) {
    throw new Error(`Cluster validation failed: ${reasons.join('; ')}`);
  }

  const score = computeCohesionScore(clusters, embeddingMap);

  return { clusters, score: Math.round(score * 100) / 100 };
};