chore: reduce layout threshold to 0.2, tune vLLM memory, and add test scripts and reports

2026-07-02 15:30:19 +07:00
1 parent bdb3a49742
commit 5c7c64e2f4
@@ -0,0 +1,109 @@
+const fs = require('fs');
+const path = require('path');
+
+const PIPELINE_URL = 'http://localhost:8000/layout-parsing';
+const PROXY_URL = 'http://localhost:8000/api/vllm-proxy/v1/chat/completions';
+const UPLOADS_DIR = path.join(__dirname, '..', 'uploads');
+
+const notFoundImages = [
+  '1782888211457-rotated_1782888204591_rotated_1782888199962_rotated_1782888195402_CAP7202641176939787142.jpg',
+  '1782875064223-CAP2007290974474760139.jpg',
+  '1782884586047-CAP9169719214882332189.jpg',
+  '1782893409879-CAP4330863738757813156.jpg'
+];
+
+async function testImage(filename) {
+  console.log(`\n========================================`);
+  console.log(`TESTING FILE: ${filename}`);
+  console.log(`========================================`);
+
+  const filePath = path.join(UPLOADS_DIR, filename);
+  if (!fs.existsSync(filePath)) {
+    console.error(`File does not exist on disk: ${filePath}`);
+    return;
+  }
+
+  const fileBuffer = fs.readFileSync(filePath);
+  const base64Image = fileBuffer.toString('base64');
+
+  // Test 1: Hit the Layout Parsing Pipeline API
+  console.log('\n--- Test 1: Layout Parsing Pipeline (Standard) ---');
+  try {
+    const res = await fetch(PIPELINE_URL, {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify({
+        file: base64Image,
+        matchHistoryJob: false,
+        useLayoutDetection: true,
+        fileType: 1,
+        useDocUnwarping: false,
+        useDocOrientationClassify: true
+      })
+    });
+
+    if (res.ok) {
+      const data = await res.json();
+      const markdown = data.result?.layoutParsingResults?.[0]?.markdown?.text || data.layoutParsingResults?.[0]?.markdown?.text || '';
+      console.log('Resulting Markdown snippet (first 300 chars):');
+      console.log(markdown.substring(0, 300));
+      console.log(`\nDoes it contain PO, SO, DO or Tanggal?`);
+      console.log(`- "Tanggal": ${/Tanggal/i.test(markdown)}`);
+      console.log(`- "SO": ${/SO/i.test(markdown)}`);
+      console.log(`- "DO": ${/DO/i.test(markdown)}`);
+      console.log(`- "PO": ${/PO/i.test(markdown)}`);
+    } else {
+      console.error(`Pipeline returned status ${res.status}: ${await res.text()}`);
+    }
+  } catch (err) {
+    console.error('Pipeline test failed:', err.message);
+  }
+
+  // Test 2: Direct vLLM completions with simple extraction prompt
+  console.log('\n--- Test 2: Direct vLLM Simple Extraction ---');
+  try {
+    const res = await fetch(PROXY_URL, {
+      method: 'POST',
+      headers: { 'Content-Type': 'application/json' },
+      body: JSON.stringify({
+        model: "PaddleOCR-VL-1.6-0.9B",
+        messages: [
+          {
+            role: "user",
+            content: [
+              {
+                type: "image_url",
+                image_url: { url: `data:image/jpeg;base64,${base64Image}` }
+              },
+              {
+                type: "text",
+                text: "Read the top right section of the document. Extract Tanggal, No. SO, No. DO, and No. PO."
+              }
+            ]
+          }
+        ],
+        temperature: 0.1,
+        max_tokens: 300
+      })
+    });
+
+    if (res.ok) {
+      const data = await res.json();
+      const content = data.choices?.[0]?.message?.content;
+      console.log('vLLM Response:');
+      console.log(content);
+    } else {
+      console.error(`vLLM proxy returned status ${res.status}: ${await res.text()}`);
+    }
+  } catch (err) {
+    console.error('vLLM test failed:', err.message);
+  }
+}
+
+async function runAll() {
+  for (const filename of notFoundImages) {
+    await testImage(filename);
+  }
+}
+
+runAll();