From 5a5e2dd05623aad71d8343a5de0480cd8756f85b Mon Sep 17 00:00:00 2001
From: dan <me@danrh.co.uk>
Date: Wed, 7 Jun 2023 21:19:21 -0400
Subject: proto: working sem search with embedding done by workers

---
 semanticsearchscratchpad/create-embeddings.js | 84 ++++++++++++++++++++-------
 1 file changed, 63 insertions(+), 21 deletions(-)

(limited to 'semanticsearchscratchpad/create-embeddings.js')

diff --git a/semanticsearchscratchpad/create-embeddings.js b/semanticsearchscratchpad/create-embeddings.js
index bbb3a81..2d2c321 100644
--- a/semanticsearchscratchpad/create-embeddings.js
+++ b/semanticsearchscratchpad/create-embeddings.js
@@ -1,30 +1,72 @@
-onmessage = msg => {
-  const lines = msg.data;
-
-  const request = indexedDB.open("embeddings");
-
-  request.onupgradeneeded = (event) => {
-    console.log('onupgradeneeded')
-    const db = event.target.result;
-    db.createObjectStore('embeddings', {autoIncrement: true});
-    };
-  
-  let embeddings;
-  request.onsuccess = (event) => {
-    const db = event.target.result;
-    embeddings = db.transaction('embeddings', 'readwrite').objectStore('embeddings').getAll();
+
+//const yemb = (await emb('I really like curry.')).data;
+//
+//
+//console.log('out', xsembs.map(x => ({value:x.value, similarity: cosineSimilarity(x.embeddings, yemb)})));
+//
+//import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.1.1';
+
+//importScripts('./embeddings-lib.js');
+
+import('https://cdn.jsdelivr.net/npm/@xenova/transformers@2.1.1')
+  .then(({pipeline, env}) => {
+
+env.allowLocalModels = false;
+
+//const extractor = await pipeline('embeddings', 'Xenova/all-MiniLM-L6-v2');
+//const emb = x => {postMessage(x) ; return extractor(x, {pooling:'mean', normalize:'true'})};
+return pipeline('embeddings', 'Xenova/all-MiniLM-L6-v2')
+  .then(extractor => {
+
+const emb = x => extractor(x, {pooling:'mean', normalize:'true'});
+
+async function createEmbeddings(xs) {
+    return (await Promise.all(xs.map(emb))).map((x,i) => ({value: xs[i], embeddings: x.data}));
+}
+
+function generateEmbeddings({data: {group, dataset}}) {
+const request = indexedDB.open("embeddings");
+request.onerror = () => {
+  console.error("Why didn't you allow my web app to use IndexedDB?!");
+};
+
+request.onupgradeneeded = (event) => {
+  const db = event.target.result;
+  const objectStore = db.createObjectStore('embeddings', {autoIncrement: true});
+  objectStore.createIndex("value", "value", { unique: false });
+  };
+
+request.onsuccess = (event) => {
+  const db = event.target.result;
+  let t = db.transaction('embeddings', 'readwrite').objectStore('embeddings').getAll();
+  t.onerror = () => console.error('transaction failed');
+  t.onsuccess = e => {
+    const embeddings = e.target.result.filter(x => x.group === group);
     if (!embeddings || !embeddings.length) {
-    createEmbeddings(lines)
+        createEmbeddings(dataset)
       .then(xsembs => {
-        embeddings = xsembs;
         const st = db
             .transaction('embeddings', 'readwrite')
             .objectStore('embeddings');
         xsembs.forEach(emb => {
-            console.log(emb);
-            st.add(emb);
+            st.add({...emb, group});
         });
       });
-  }
-  };
+    }
+}
+}
+
 }
+
+self.onmessage = generateEmbeddings;
+postMessage({loaded:true});
+return generateEmbeddings;
+  });
+  });
+
+
+//   const generateEmbeddings = self.onmessage;
+//
+//export default generateEmbeddings;
+
+
-- 
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