fix: parallelize text + media LLM analysis instead of sequential

- text-only and media analysis now run concurrently via Promise.all
- text no longer blocks on media download + vision analysis
- each path independently saves to DB when its own results are ready
- same batch still uses single context fetch + attachment lookup

services/discord-gateway/src/modules/ai-moderation/aiAnalysisWorker.ts

@@ -180,65 +180,75 @@ async function processBatch(job: {
 
   const allRows: MessageRecord[] = [];
 
-  // Phase 1: Text-only → save immediately (fast)
-  if (textOnly.length > 0) {
-    const textResult = await runModerationAnalysis({
-      targets: textOnly,
-      contextText: contextLines.join("\n"),
-      attachments,
-    });
-    const textUpdates = textResult.results.map((analysisResult) => ({
-      messageId: analysisResult.messageId,
-      result: {
-        status: analysisResult.status,
-        flags: JSON.stringify(analysisResult.flags),
-        score: analysisResult.score,
-        analysis: analysisResult.analysis,
-        categories: analysisResult.categories,
-        severity: analysisResult.severity,
-        confidence: analysisResult.confidence,
-        recommendedAction: analysisResult.recommendedAction,
-        analyzedAt: Date.now(),
-        error: null,
-      },
-    }));
-    if (textUpdates.length > 0) {
-      const rows = await updateMessagesAIAnalysisBulk(textUpdates);
-      allRows.push(...rows);
-      logger.info(
-        { count: textUpdates.length, conversationKey },
-        "Text-only batch saved — media analysis still in progress",
-      );
-    }
-  }
+  // ── Parallel: text-only + media analysis run concurrently ──────────
+  // Text-only → fast LLM call. Media → download + vision + LLM.
+  // Running both in parallel means media downloads overlap with text LLM call.
+  // Each path saves to DB as soon as its own results are ready.
+  // ────────────────────────────────────────────────────────────────────
+  const textPromise = textOnly.length > 0
+    ? runModerationAnalysis({
+        targets: textOnly,
+        contextText: contextLines.join("\n"),
+        attachments,
+      }).then((result) => {
+        const updates = result.results.map((analysisResult) => ({
+          messageId: analysisResult.messageId,
+          result: {
+            status: analysisResult.status,
+            flags: JSON.stringify(analysisResult.flags),
+            score: analysisResult.score,
+            analysis: analysisResult.analysis,
+            categories: analysisResult.categories,
+            severity: analysisResult.severity,
+            confidence: analysisResult.confidence,
+            recommendedAction: analysisResult.recommendedAction,
+            analyzedAt: Date.now(),
+            error: null,
+          },
+        }));
+        if (updates.length > 0) {
+          return updateMessagesAIAnalysisBulk(updates).then((rows) => {
+            allRows.push(...rows);
+            logger.info(
+              { count: updates.length, conversationKey },
+              "Text-only batch saved — media analysis still in progress",
+            );
+          });
+        }
+      })
+    : Promise.resolve();
 
-  // Phase 2: Media → save when done (slow: download + vision)
-  if (media.length > 0) {
-    const mediaResult = await runModerationAnalysis({
-      targets: media,
-      contextText: contextLines.join("\n"),
-      attachments,
-    });
-    const mediaUpdates = mediaResult.results.map((analysisResult) => ({
-      messageId: analysisResult.messageId,
-      result: {
-        status: analysisResult.status,
-        flags: JSON.stringify(analysisResult.flags),
-        score: analysisResult.score,
-        analysis: analysisResult.analysis,
-        categories: analysisResult.categories,
-        severity: analysisResult.severity,
-        confidence: analysisResult.confidence,
-        recommendedAction: analysisResult.recommendedAction,
-        analyzedAt: Date.now(),
-        error: null,
-      },
-    }));
-    if (mediaUpdates.length > 0) {
-      const rows = await updateMessagesAIAnalysisBulk(mediaUpdates);
-      allRows.push(...rows);
-    }
-  }
+  const mediaPromise = media.length > 0
+    ? runModerationAnalysis({
+        targets: media,
+        contextText: contextLines.join("\n"),
+        attachments,
+      }).then((result) => {
+        const updates = result.results.map((analysisResult) => ({
+          messageId: analysisResult.messageId,
+          result: {
+            status: analysisResult.status,
+            flags: JSON.stringify(analysisResult.flags),
+            score: analysisResult.score,
+            analysis: analysisResult.analysis,
+            categories: analysisResult.categories,
+            severity: analysisResult.severity,
+            confidence: analysisResult.confidence,
+            recommendedAction: analysisResult.recommendedAction,
+            analyzedAt: Date.now(),
+            error: null,
+          },
+        }));
+        if (updates.length > 0) {
+          return updateMessagesAIAnalysisBulk(updates).then((rows) => {
+            allRows.push(...rows);
+          });
+        }
+      })
+    : Promise.resolve();
+
+  // Wait for both to complete
+  await Promise.all([textPromise, mediaPromise]);
 
   logger.info(
     { total: messages.length, textOnly: textOnly.length, media: media.length, saved: allRows.length },

services/discord-gateway/src/modules/ai-moderation/llmModerationClient.ts

@@ -707,12 +707,13 @@ async function runTextOnlyBatch(
   const maxBatchSize = config.AI_LLM_TEXT_BATCH_SIZE ?? 20;
   const timeoutMs = config.AI_LLM_MEDIA_ANALYSIS_TIMEOUT_MS ?? 60000;
 
+  // ── Phase A: Prepare context in parallel ──────────────────
+  // URL fetching (web_content) and SearXNG (web_searches) are independent —
+  // both just enrich the LLM prompt. Run them concurrently so SearXNG
+  // doesn't block on slow URLs (or vice versa).
+  //
   // ── Fetch web content from URLs in text-only messages ──
-  // Prevents LLM from guessing based on domain name alone (e.g., false "scam" flags).
-  // The fetched text content is injected into the message XML so the LLM can
-  // analyze the actual page rather than pattern-match the URL string.
-  const urlFetchMap = new Map<string, string>(); // url → fetched text content
-  {
+  const urlFetchPromise = (async () => {
     const allUrls = new Set<string>();
     for (const msg of targets) {
       const content = msg.edited_content ?? msg.content;
@@ -720,7 +721,7 @@ async function runTextOnlyBatch(
         allUrls.add(url);
       }
     }
-    const urlArr = Array.from(allUrls).slice(0, 10); // cap to 10 fetches per batch
+    const urlArr = Array.from(allUrls).slice(0, 10);
     if (urlArr.length > 0) {
       log.debug(
         { urlCount: urlArr.length },
@@ -729,6 +730,7 @@ async function runTextOnlyBatch(
       const results = await Promise.allSettled(
         urlArr.map((url) => fetchUrlSafely(url)),
       );
+      const map = new Map<string, string>();
       for (let i = 0; i < urlArr.length; i++) {
         const r = results[i];
         if (
@@ -736,19 +738,16 @@ async function runTextOnlyBatch(
           r.value.type === "text" &&
           r.value.textContent
         ) {
-          urlFetchMap.set(urlArr[i], r.value.textContent);
+          map.set(urlArr[i], r.value.textContent);
         }
       }
+      return map;
     }
-  }
+    return new Map<string, string>();
+  })();
 
-  // ── SearXNG enrichment for suspicious/ambiguous content ──────────
-  // Uses SearXNG to look up references mentioned in messages (e.g. anime
-  // titles, drug names). Results are injected as <web_search> XML tags
-  // so the LLM can make informed decisions instead of guessing.
-  // All messages are searched — Redis cache prevents redundant lookups.
-  const searxngResults = new Map<string, string>(); // query → formatted XML
-  {
+  // ── SearXNG enrichment ──
+  const searxngPromise = (async () => {
     const queries = new Set<string>();
     for (const msg of targets) {
       const content = msg.edited_content ?? msg.content;
@@ -757,7 +756,7 @@ async function runTextOnlyBatch(
       }
     }
     if (queries.size > 0) {
-      const queryArr = Array.from(queries).slice(0, 3); // cap to 3 searches per batch
+      const queryArr = Array.from(queries).slice(0, 3);
       log.debug(
         { searchQueries: queryArr },
         "Running SearXNG enrichment for batch",
@@ -765,14 +764,23 @@ async function runTextOnlyBatch(
       const results = await Promise.allSettled(
         queryArr.map((q) => searchSearxng(q)),
       );
+      const map = new Map<string, string>();
       for (let i = 0; i < queryArr.length; i++) {
         const r = results[i];
         if (r.status === "fulfilled" && r.value.length > 0) {
-          searxngResults.set(queryArr[i], formatSearchResults(r.value));
+          map.set(queryArr[i], formatSearchResults(r.value));
         }
       }
+      return map;
     }
-  }
+    return new Map<string, string>();
+  })();
+
+  // Wait for BOTH concurrently
+  const [urlFetchMap, searxngResults] = await Promise.all([
+    urlFetchPromise,
+    searxngPromise,
+  ]);
 
   // ── Group identical short messages (< 20 chars) to reduce redundant analysis ──
   // Messages with identical normalized content share a single representative.