Duriin-API/intelligence/augorWorker.js

const https = require("https");
const http = require("http");

const { findMatchedCompaniesByEmbedding } = require("./embeddings");

async function runAugorWorker(archiveDb, intelligenceDb, config) {
  const loopDelay = config.workers?.augorLoopDelayMs ?? 1500;
  const llmConfig = config.openRouter || {};

  const getPending = intelligenceDb.prepare(`
    SELECT * FROM article_queue WHERE status = 'pending' LIMIT 1
  `);

  const setStatus = intelligenceDb.prepare(`
    UPDATE article_queue SET status = ?, updated_at = CURRENT_TIMESTAMP WHERE article_id = ?
  `);

  const getEventArticleIds = archiveDb.prepare(
    "SELECT id FROM articles WHERE event_id = ?"
  );

  const setStatusByArticleId = intelligenceDb.prepare(`
    UPDATE article_queue SET status = 'processed', updated_at = CURRENT_TIMESTAMP
    WHERE article_id = ? AND status = 'pending'
  `);

  const deleteKnowledge = intelligenceDb.prepare(
    "DELETE FROM event_knowledge WHERE event_id = ?"
  );
  const deletePredictions = intelligenceDb.prepare(
    "DELETE FROM event_predictions WHERE event_id = ?"
  );

  const insertKnowledge = intelligenceDb.prepare(`
    INSERT INTO event_knowledge (event_id, company_id, type, data, event_date)
    VALUES (?, ?, ?, ?, ?)
  `);
  const insertPrediction = intelligenceDb.prepare(`
    INSERT INTO event_predictions (event_id, company_id, type, direction, magnitude, timeframe, rationale, event_date)
    VALUES (?, ?, ?, ?, ?, ?, ?, ?)
  `);

  const getEventDate = archiveDb.prepare(`
    SELECT pub_date_effective FROM articles
    WHERE event_id = ? AND pub_date_effective IS NOT NULL
    ORDER BY pub_date_effective ASC LIMIT 1
  `);

  while (true) {
    try {
      const queueRow = getPending.get();

      if (!queueRow) {
        await sleep(loopDelay);
        continue;
      }

      const article = archiveDb.prepare(`
        SELECT id, event_id, content, has_embedding
        FROM articles WHERE id = ?
      `).get(queueRow.article_id);

      if (!article || !article.content || !article.has_embedding || !article.event_id) {
        setStatus.run("skipped", queueRow.article_id);
        continue;
      }

      const eventId = article.event_id;

      const event = archiveDb.prepare("SELECT * FROM events WHERE id = ?").get(eventId);
      if (!event) {
        setStatus.run("skipped", queueRow.article_id);
        continue;
      }

      const eventArticles = archiveDb.prepare(`
        SELECT id, title, description, content
        FROM articles
        WHERE event_id = ? AND content IS NOT NULL AND content != ''
        ORDER BY id ASC
        LIMIT 25
      `).all(eventId);

      const eventArticleIds = eventArticles.map(a => a.id);

      const matchedCompanies = findMatchedCompaniesByEmbedding(
        eventArticleIds, archiveDb, intelligenceDb, config
      );

      if (matchedCompanies.length === 0) {
        for (const r of getEventArticleIds.all(eventId)) setStatusByArticleId.run(r.id);
        console.log(`[augor] event ${eventId} — no company match, skipped`);
        continue;
      }


      deleteKnowledge.run(eventId);
      deletePredictions.run(eventId);

      const eventDateRow = getEventDate.get(eventId);
      const eventDate = eventDateRow ? eventDateRow.pub_date_effective : null;

      const articleText = eventArticles.map((a, i) => {
        const body = (a.content || a.description || "").slice(0, 2000);
        return `[Article ${i + 1}] ${a.title}\n${body}`;
      }).join("\n\n---\n\n");

      for (const company of matchedCompanies) {
        try {
          const result = await callLlm(llmConfig, buildPrompt(company.name, event.title, articleText));

          if (result) {
            const writeAll = intelligenceDb.transaction(() => {
              for (const r of (result.knowledge?.relationships || [])) {
                insertKnowledge.run(eventId, company.id, "relationship", JSON.stringify(r), eventDate);
              }
              for (const t of (result.knowledge?.themes || [])) {
                insertKnowledge.run(eventId, company.id, "theme", JSON.stringify(t), eventDate);
              }
              for (const f of (result.knowledge?.factors || [])) {
                insertKnowledge.run(eventId, company.id, "factor", JSON.stringify(f), eventDate);
              }

              for (const p of (result.predictions || [])) {
                insertPrediction.run(eventId, company.id, p.type, p.direction, p.magnitude, p.timeframe, p.rationale, eventDate);
              }
            });

            writeAll();
          }

        } catch (llmErr) {
          console.error(`[augor] LLM error for ${company.name} on event ${eventId}:`, llmErr.message);
        }
      }

      for (const r of getEventArticleIds.all(eventId)) setStatusByArticleId.run(r.id);
      console.log(`[augor] processed event ${eventId} (${matchedCompanies.length} companies, ${eventArticles.length} articles)`);

    } catch (err) {
      console.error("[augor] error:", err.message);
      await sleep(loopDelay);
    }
  }
}

function buildPrompt(companyName, eventTitle, articleText) {
  return `You are a financial intelligence analyst. Extract structured knowledge about ${companyName} from these news articles.

Event: ${eventTitle}

${articleText}

Return JSON only — no explanation. Shape:
{
  "knowledge": {
    "relationships": [
      { "type": "supplier|customer|competitor", "entity": "string", "confidence": "high|medium|low", "evidence": "string" }
    ],
    "themes": [
      { "theme": "string", "direction": "increasing|stable|decreasing", "evidence": "string" }
    ],
    "factors": [
      { "factor": "string", "relationship": "string", "evidence": "string" }
    ]
  },
  "predictions": [
    { "type": "market_share|stock_price|competitive_position|other", "direction": "positive|negative|neutral", "magnitude": "high|medium|low", "timeframe": "short|medium|long", "rationale": "string" }
  ]
}

Only include claims directly supported by the articles. Use empty arrays if nothing applies.`;
}

async function callLlm(llmConfig, prompt) {
  const body = JSON.stringify({
    model: llmConfig.llmModel || llmConfig.model,
    messages: [{ role: "user", content: prompt }],
    temperature: 0.1,
  });

  const url = new URL("https://openrouter.ai/api/v1/chat/completions");

  const responseText = await httpPost(url, body, {
    "Content-Type": "application/json",
    "Authorization": `Bearer ${llmConfig.apiKey || ""}`,
  });

  let parsed;
  try {
    parsed = JSON.parse(responseText);
  } catch (e) {
    throw new Error(`LLM response not JSON: ${responseText.slice(0, 300)}`);
  }
  const content = parsed.choices?.[0]?.message?.content;
  if (!content) return null;

  const stripped = content.replace(/^```(?:json)?\s*/i, '').replace(/\s*```$/, '').trim();
  return JSON.parse(stripped);
}

function httpPost(url, body, headers) {
  return new Promise((resolve, reject) => {
    const lib = url.protocol === "https:" ? https : http;
    const req = lib.request({
      hostname: url.hostname,
      port: url.port || (url.protocol === "https:" ? 443 : 80),
      path: url.pathname + url.search,
      method: "POST",
      headers: { ...headers, "Content-Length": Buffer.byteLength(body) },
    }, (res) => {
      let data = "";
      res.on("data", chunk => data += chunk);
      res.on("end", () => {
        if (res.statusCode >= 200 && res.statusCode < 300) {
          resolve(data);
        } else {
          reject(new Error(`LLM ${res.statusCode}: ${data.slice(0, 300)}`));
        }
      });
    });

    req.on("error", reject);
    req.write(body);
    req.end();
  });
}

function sleep(ms) {
  return new Promise(r => setTimeout(r, ms));
}

module.exports = { runAugorWorker };