[fix] embedding over 512 tokens

src/ts/process/embedding/transformers.ts

@@ -1,4 +1,4 @@
-import type { Pipeline, PretrainedOptions } from '@xenova/transformers';
+import { AutoTokenizer, type Pipeline, type PretrainedOptions } from '@xenova/transformers';
 
 let pipeline: (task: string, model?: string, { quantized, progress_callback, config, cache_dir, local_files_only, revision, }?: PretrainedOptions) => Promise<Pipeline> = null
 
@@ -50,6 +50,36 @@ export const runSummarizer = async (text: string) => {
 export const runEmbedding = async (text: string):Promise<Float32Array> => {
     await loadTransformer()
     let extractor = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
+    const tokenizer = await AutoTokenizer.from_pretrained('Xenova/all-MiniLM-L6-v2');
+    const tokens = tokenizer.encode(text)
+    if (tokens.length > 256) {
+        let chunks:string[] = []
+        let chunk:number[] = []
+        for (let i = 0; i < tokens.length; i++) {
+            if (chunk.length > 256) {
+                chunks.push(tokenizer.decode(chunk))
+                chunk = []
+            }
+            chunk.push(tokens[i])
+        }
+        chunks.push(tokenizer.decode(chunk))
+        let results:Float32Array[] = []
+        for (let i = 0; i < chunks.length; i++) {
+            let result = await extractor(chunks[i], { pooling: 'mean', normalize: true });
+            results.push(result?.data ?? null)
+        }
+        //set result, as average of all chunks
+        let result:Float32Array = new Float32Array(results[0].length)
+        for (let i = 0; i < results.length; i++) {
+            for (let j = 0; j < result.length; j++) {
+                result[j] += results[i][j]
+            }
+        }
+        for (let i = 0; i < result.length; i++) {
+            result[i] = Math.round(result[i] / results.length)
+        }
+        return result
+    }
     let result = await extractor(text, { pooling: 'mean', normalize: true });
     return result?.data ?? null;
 }