Refactor Transformers

src/ts/process/embedding/transformers.ts

@@ -1,14 +1,7 @@
-import { AutoTokenizer, type Pipeline, type PretrainedOptions } from '@xenova/transformers';
+import transformers, { AutoTokenizer, Pipeline, pipeline, type DataArray, type SummarizationOutput } from '@xenova/transformers';
 
-let pipeline: (task: string, model?: string, { quantized, progress_callback, config, cache_dir, local_files_only, revision, }?: PretrainedOptions) => Promise<Pipeline> = null
+transformers.env.localModelPath = "https://sv.risuai.xyz/transformers/"
 
-async function loadTransformer() {
-    if(!pipeline){
-        const transformersLib = await import('@xenova/transformers')
-        transformersLib.env.localModelPath = "https://sv.risuai.xyz/transformers/"
-        pipeline = transformersLib.pipeline
-    }
-}
 type TransformersBodyType = {
     max_new_tokens: number,
     do_sample: boolean,
@@ -33,23 +26,21 @@ type TransformersBodyType = {
 
 
 export const runTransformers = async (baseText:string, model:string,bodyTemplate:TransformersBodyType) => {
-    await loadTransformer()
     let text = baseText
     let generator = await pipeline('text-generation', model);
-    let output:{generated_text:string}[] = await generator(text);
-    return output
+    let output = await generator(text) as transformers.TextGenerationOutput
+    const outputOne = output[0]
+    return outputOne
 }
 
 export const runSummarizer = async (text: string) => {
-    await loadTransformer()
     let classifier = await pipeline("summarization", "Xenova/distilbart-cnn-6-6")
-    const v:{summary_text:string}[] = await classifier(text)
-    return v
+    const v = await classifier(text) as SummarizationOutput
+    return v[0].summary_text
 }
 
-let extractor:Pipeline = null
+let extractor:transformers.FeatureExtractionPipeline = null
 export const runEmbedding = async (text: string):Promise<Float32Array> => {
-    await loadTransformer()
     if(!extractor){
         extractor = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
     }
@@ -69,7 +60,11 @@ export const runEmbedding = async (text: string):Promise<Float32Array> => {
         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)
+            const res:Float32Array = result?.data as Float32Array
+
+            if(res){
+                results.push(res)
+            }
         }
         //set result, as average of all chunks
         let result:Float32Array = new Float32Array(results[0].length)
@@ -84,7 +79,7 @@ export const runEmbedding = async (text: string):Promise<Float32Array> => {
         return result
     }
     let result = await extractor(text, { pooling: 'mean', normalize: true });
-    return result?.data ?? null;
+    return (result?.data as Float32Array) ?? null;
 }
 
 export const runTTS = async (text: string) => {